Patent Publication Number: US-11048712-B2

Title: Real-time and adaptive data mining

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a continuation-in-part of U.S. patent application Ser. No. 14/960,316, entitled “REAL-TIME AND ADAPTIVE DATA MINING,” filed on Dec. 4, 2015, which is a continuation-in-part of U.S. patent application Ser. No. 13/928,313, entitled “REAL-TIME AND ADAPTIVE DATA MINING,” filed on Jun. 26, 2013, now U.S. Pat. No. 9,229,977 which claims the benefit under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No. 61/679,024 entitled “LOSS PREVENTION SERVICES PLATFORM BASED ON MACHINE LEARNING,” filed on Aug. 2, 2012, the disclosures of which are expressly incorporated by reference herein in their entireties. 
    
    
     BACKGROUND 
     Field 
     Aspects of the present disclosure relate generally to data mining, and more particularly to providing a real-time data mining platform and interactive feedback platform. 
     Background 
     The Internet provides and generates large amounts of data on a daily basis. The data may be monitored from data sources, such as, but not limited to, social media, web feeds, customer feedback, internal company databases, and proprietary data silos. The data may include text, images, audio, or other types of data. With the proper tools, the data extracted from the data sources may be used to provide analysis for various topics. For example, the extracted data may indicate patterns for domestic unrest or a customer&#39;s habits/patterns. More specifically, as an example, the extracted data may yield a consumers financial profile, retail history, social influences, and expressed interests. 
     Typical search engines are not designed for analyzing multiple data points in real time. Furthermore, the query of a search engine is limited to the exact search term. Moreover, a typical search engine is limited to querying indexed websites. That is, search engines are typically limited to searching structured data sources. By some accounts, nearly seventy percent of web pages are not indexed by search engines. The non-indexed web pages may be, for example, proprietary data silos, websites behind firewalls, and/or comment sections on a web page. Accordingly, the information obtained via search engines is limited and is not desirable for real-time data analysis that is specified for automated data extraction. 
     Thus, as the demand for real-time information continues to increase, there exists a need for further improvements in data mining and web searching technologies. Preferably, these improvements should be applicable to all real-time data extraction technologies and the digital systems that employ these technologies. 
     SUMMARY 
     In one aspect of the present disclosure, a method for analyzing data is disclosed. The method includes receiving, from a first user, a query for non-textual data. The method also includes expanding search terms of the query. The method further includes executing the query on multiple data sources. The method still further includes selecting one or more data sources from the multiple data sources, the one or more data sources are selected when results of the query are greater than or equal to an accuracy thresholds. The method also includes monitoring, based on a set schedule, the one or more data sources to extract non-textual data from the one or more data sources when an update to stored data matches the query and/or newly added non-textual data matches the query. The method further includes establishing a communication channel with a second user based on the data extracted from one or more monitored data sources. 
     Another aspect of the present disclosure is directed to an apparatus including means for receiving, from a first user, a query for non-textual data. The apparatus also includes means for expanding search terms of the query. The apparatus further includes means for executing the query on multiple data sources. The apparatus still further includes means for selecting one or more data sources from the multiple data sources, the one or more data sources are selected when results of the query are greater than or equal to an accuracy thresholds. The apparatus also includes means for monitoring, based on a set schedule, the one or more data sources to extract non-textual data from the one or more data sources when an update to stored data matches the query and/or newly added non-textual data matches the query. The apparatus further includes means for establishing a communication channel with a second user based on the data extracted from one or more monitored data sources. 
     In another aspect of the present disclosure, a non-transitory computer-readable medium with non-transitory program code recorded thereon is disclosed. The program code is for analyzing data. The program code is executed by a processor and includes program code to receive, from a first user, a query for non-textual data. The program code also includes program code to expand search terms of the query. The program code further includes program code to execute the query on multiple data sources. The program code still further includes program code to select one or more data sources from the multiple data sources, the one or more data sources are selected when results of the query are greater than or equal to an accuracy thresholds. The program code also includes program code to monitor, based on a set schedule, the one or more data sources to extract non-textual data from the one or more data sources when an update to stored data matches the query and/or newly added non-textual data matches the query. The program code further includes program code to establish a communication channel with a second user based on the data extracted from one or more monitored data sources. 
     Another aspect of the present disclosure is directed to an apparatus for analyzing data. The apparatus having a memory and one or more processors coupled to the memory. The processor(s) is configured to receive, from a first user, a query for non-textual data. The processor(s) is also configured to expand search terms of the query. The processor(s) is further configured to execute the query on multiple data sources. The processor(s) still further configured to select one or more data sources from the multiple data sources, the one or more data sources are selected when results of the query are greater than or equal to an accuracy thresholds. The processor(s) is also configured to monitor, based on a set schedule, the one or more data sources to extract non-textual data from the one or more data sources when an update to stored data matches the query and/or newly added non-textual data matches the query. The processor(s) is further configured to establish a communication channel with a second user based on the data extracted from one or more monitored data sources. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The features, nature, and advantages of the present disclosure will become more apparent from the detailed description set forth below when taken in conjunction with the drawings in which like reference characters identify correspondingly throughout. 
         FIG. 1  is a diagram illustrating a call flow for a data mining system according to an aspect of the present disclosure. 
         FIG. 2  is a block diagram for a data mining system according to an aspect of the present disclosure. 
         FIG. 3  illustrates various data visualization schemes according to aspects of the present disclosure. 
         FIGS. 4, 5A, 5B, 6, 7, and 8  are block diagrams for a data mining system according to aspects of the present disclosure. 
         FIG. 9  illustrates a hierarchy of security rights according to an aspect of the present disclosure. 
         FIGS. 10A and 10B  illustrate typical sequential data extraction schemes. 
         FIG. 10C  illustrates a parallel data extraction scheme according to an aspect of the present disclosure. 
         FIGS. 11 and 12  illustrate examples of data extraction and analysis according to an aspect of the present disclosure. 
         FIG. 13  illustrate a flow diagram of a method for analyzing data according to aspects of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     The detailed description set forth below, in connection with the appended drawings, is intended as a description of various configurations and is not intended to represent the only configurations in which the concepts described herein may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of the various concepts. It will be apparent to those skilled in the art, however, that these concepts may be practiced without these specific details. In some instances, well-known structures and components are shown in block diagram form in order to avoid obscuring such concepts. 
     Based on the teachings, one skilled in the art should appreciate that the scope of the present disclosure is intended to cover any aspect of the present disclosure, whether implemented independently of or combined with any other aspect of the present disclosure. For example, an apparatus may be implemented, or a method may be practiced using any number of the aspects set forth. In addition, the scope of the present disclosure is intended to cover such an apparatus or method practiced using other structure, functionality, or structure and functionality in addition to, or other than the various aspects of the present disclosure set forth. It should be understood that any aspect of the present disclosure may be embodied by one or more elements of a claim. 
     The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects. 
     Although particular aspects are described herein, many variations and permutations of these aspects fall within the scope of the present disclosure. Although some benefits and advantages of the preferred aspects are mentioned, the scope of the present disclosure is not intended to be limited to particular benefits, uses or objectives. Rather, aspects of the present disclosure are intended to be broadly applicable to different technologies, system configurations, networks, and protocols, some of which are illustrated by way of example in the figures and in the following description of the preferred aspects. The detailed description and drawings are merely illustrative of the present disclosure rather than limiting, the scope of the present disclosure being defined by the appended claims and equivalents thereof. 
     As discussed, various contributors generate large amounts of data on the Internet. The data may be monitored from data sources, such as, but not limited to, social media, web feeds, customer feedback, internal company databases, and proprietary data silos. The data may include text, images, audio, or other types of data. With the proper tools, the data extracted from the data sources may be used to provide analysis for various topics. For example, the extracted data may indicate patterns for domestic unrest or a customer&#39;s habits/patterns. More specifically, as an example, the extracted data may yield a consumers financial profile, retail history, social influences, and expressed interests. 
     Conventional search engines do not analyze multiple data points in real time. Furthermore, some conventional search engines are limited to analyzing text-based data. Additionally, the query of a conventional search engine is limited to the exact search term. Finally, a conventional search engine is limited to querying indexed websites. Accordingly, the information obtained via conventional search engines is limited and is not desirable for real-time data analysis of non-textual data. 
     Aspects of the present disclosure are directed to a data mining system for identifying textual and/or non-textual data in substantially real time. The identified data may be analyzed to determine sentiment or other aspects associated with the data. For simplicity, aspects of the present disclosure are presented with the non-textual data being an image. Still, the non-textual data may include video, audio, or other types of data. 
       FIG. 1  is a diagram illustrating a call flow for a data mining system  100  according to an aspect of the present disclosure. At block  102  a query is generated. The query may be a natural language query generated by a user. Alternatively, the query generation may be automated from a query generator. The query generator may be based on artificial intelligence or a similar system. The query may be a textual query, such as “find people that go to the beach.” Additionally, or alternatively, the query may be non-textual. For example, the query may include an image of a beach. In this example, the query is used to find images of a beach and/or people with images at the beach. 
     At block  104  the query is expanded. In one configuration, the search terms of the query are expanded based on search lexicons. For example, the lexicons may be applied to expand the search using misspellings and derivations of the topic of interest/sub-topics. The lexicon may be generated based on user input and/or may be generated based on automatic word association, such as synonym/antonym trees and lexicons of positive and/or negative modifiers. The expansion of the query at block  104  may be performed by a user and/or may be automated. 
     As another example, if the query is a non-textual query, the query is expanded to similar data. For example, if one picture of the beach is used as the query, the query is expanded to use various beach images. The beach images may be previously stored or retrieved as needed. 
     At block  106 , the query is executed. The query may be executed on open-source data and proprietary data. Specifically, in one configuration, the query may be limited to specific data sources (e.g., proprietary data sources and open-source data sources). In another configuration, the query searches all available data sources. In yet another configuration, the initial pass of the query is limited to open-source data sources. 
     Open-source data refers to publicly available data, such as data available via Wikipedia or the CIA factbook, for example. Sources that are indexed by search engines are also considered open-source data. Open-source data may also refer to data that is not proprietary but still not indexed by a search engine. For example, some sources, such as a blog, may include a comments section. The comments may be updated in real time and are typically not indexed by a search engine. Thus, because the comments are not indexed by a search engine, the contents of the comments are not available when conducting a search. Still, the information in the comments is publically available to anyone that visits a particular source. Therefore, the comments section, or any other non-indexed source, may be considered open-source data. 
     Proprietary data is data that may not be searchable via a search engine because the proprietary data is not publically available. This may include classified databases, data behind a firewall, or data that is available via a paid subscription, such as Lexis. 
     At block  108  the results of the query are reviewed. If the results are satisfactory, specific data sources may be selected to be monitored. If the results are not satisfactory, the query may be expanded and/or refined at block  104 . The refinement of the query may be refined based on data sources selected at block  108 . At block  110  the selected data sources are monitored. The monitoring may be performed by data extractors that monitor changes to a data source in real-time. When information at the data sources is updated or located to match the query terms, a data extractor extracts the data to be analyzed at block  112 . In one configuration, a quality control analysis may also be performed on the extracted data (not shown). After analyzing the data, the extracted data may be visualized at block  114 . 
     Additionally, after the data has been analyzed, at block  116 , a communication channel may be established with specific individuals and/or other users based on the analyzed data. For example, if the data analysis reveals that a customer may be planning a trip to another country, the data mining system  100  may engage the customer to discuss cell phone data roaming plans and/or other services that may be desired by a customer that is traveling to another country. Furthermore, at block  118 , customers and/or users may provide feedback. That is, the customers may provide feedback to the system via a pre-defined communication channel, such as a message board, phone, SMS, or email. The feedback is analyzed at block  112  to determine customer sentiment. Furthermore, in addition to or separate from the open forum, the feedback may also be generated based on the communication channel established at block  116 . 
     In another configuration, the communication channel of block  116  may be established after the extracted data has been visualized at block  114 . It should be noted, the communication channel of block  116  and the visualization of block  114  may be established in parallel or independently. Furthermore, in one configuration, the feedback received at block  118  may be visualized in block  114 . 
       FIG. 2  illustrates a data mining system  200  according to an aspect of the present disclosure. Those of skill would further appreciate that the various illustrative modules of  FIG. 2  may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure. 
     The data mining system  200  may include a query module  202  for generating a query. As discussed above, in one configuration, a query is generated for searching specific data sources. In one configuration, the query is a natural language query. For example, the natural language query may be “I want to know the sentiment of wireless phone customers.” The query represents a topic of interest (TOI), for example, the topic of interest may be a person, such as Michael Jackson, an event, such as the Arab Spring, a service, such as wireless phone, and/or any other informational element that may be queried. As another example, the query may be a non-textual query. For example, the query may be an audio clip used to find similar audio clips. As another example, the query may be an image used to find similar images. 
     The data sources may include websites, private databases, public databases, and/or any data source that may be electronically accessed. Furthermore, the query may further specify the parties that hold the opinion, such as the author of the content. Thus, in one configuration, the query may specify the sources of the data, such as URLs, websites of specific countries, and/or internal/external data silos. Furthermore, the query may further specify the author of the specified data sources, such as mainstream media, bloggers, user comments, and/or social media. 
     Furthermore, in one configuration, the query may be specified for a specific time frame. The time frame may be a specific time frame of the past, a time frame of the future, or a specific interval. For example, the query may only apply to events that occurred in the summer of 2012. As another example, the query may be generated to run in three months from the date of origin for the query. 
     In one configuration, after generating the query, open-source data is mined using the topic of interest and sub-topics. Sub-topics refer to topics related to the topic of interest. For example, for the query “I want to know the sentiment of wireless phone customers,” the topic of interest may be a specific wireless provider and/or “wireless phone customers.” Furthermore, in the present example, the sub-topics may be “iPhone customers” and “wireless customers.” The open-source data refers to data that is generally available to the public. For example, data that is not deemed classified by a sovereign entity may be one source of open-source data. Open-source data may also include, but is not limited to, data available via a website, a database, and/or a really simple syndication (RSS) feed. 
     After mining the open-source data using the topic of interest and sub-topics, the query parameters (e.g., search parameters) may be expanded and/or further processed. That is, during the initial query generation, the user defines the scope of the query. Specifically, the user determines, to the best of their ability, the topics of interest and their sub-topics. After the query is executed, the user may find unintended search results. Some of the unintended search results may be false positives, while others may be relevant to the context of the query. Consequently, the user may add additional items to the topics of interest and/or sub-topics based on the unintended search results. 
     According to one aspect of the present disclosure, the query expansion and/or further processing may be performed in the query module  202 . Alternatively, the query expansion and/or further processing may be performed in a module that is separate from the query module  202  (not shown). 
     In one configuration, a lexicon is applied to expand on the search parameters. The lexicon may be applied prior to the initial search and/or during the query expansion phase. The lexicon may also include misspellings and derivations of the topic of interest/sub-topics. The lexicon may be generated based on user input and/or may be generated based on automatic word association, such as synonym/antonym trees and lexicons of positive and/or negative modifiers. 
     As an example, for the query “Arab spring,” the lexicon may be applied to expand the search to include relevant search terms such as, for example, “revolution,” and/or “unrest.” In this example, the lexicon may also apply misspellings, such as “srping,” to the search terms. 
     The expanded query is run on the data sources and the results are analyzed via a results module  204 . The expanded query may be performed across meta-data for the web. Furthermore, in addition to, or separate from the meta-data search, in one configuration, the query is also performed on the selected data sources. If the results of the query are deemed satisfactory, data extractors are generated to monitor one or more of the specified data sources. That is, the results of the query are reviewed by a user, after reviewing the results, one or more of the data sources are selected based on the accuracy of the search results. The user may also select or exclude data sources that are false positives. False positives refer to data sources that are not relevant to the topic of interest but are still returned because the data source matches the query. 
     For example, the topic of interest for a query may be “Arab Spring,” and the results may include topics that are not related to the topic of interest (e.g., false positives). In this example, a search for a search of “Arab spring” may have generated false positive search results such as “Arab spring water” or sources related to the spring season in the Middle East. Accordingly, sources related to these false positives may be excluded from further iterations of the search. Moreover, sources specifically related to the Arab Spring uprisings may be selected by the user based on their accuracy. 
     As another example, the topic of interest for a query may be “people that visit the beach.” The query may be text-based and/or non-text based. For example, an image of a person at the beach may be used for the query. In one example, the query may be generated by a user intending to sell beach related products, such as beach chairs, towels, sun-screen, etc., to beachgoers. In this example, a search for images of a person at the beach may generate false positive search results such as sources related to people engaging in a beach clean-up or a painter that paints beach images. Although the sources are related to “people that visit the beach” and/or images of the beach, the sources may not be the intended customers for the user. The sources related to these false positives may be excluded from further iterations of the search. The sources related to people that visit the beach for recreation may be selected by the user based on their accuracy. 
     Once the specific data sources have been selected, data extractors are deployed to monitor the specific data sources. In one configuration, when the results of the query do not yield results that are satisfactory to the user, the query is further refined until the desired results are obtained. 
     The data mining system  200  may include a data extractor module  206  for deploying data extractors. The data extractors may be referred to as extractors. Extractors refer to applications that monitor and extract data from user-specified locations or sources of interest (e.g., data source). That is, upon deployment, the extractors extract the data from a given source for analysis. Moreover, until terminated, the extractors are maintained at the data source to monitor for any changes to the data. The data changes are analyzed for a match to the keywords of the query and the data is transmitted to a data analysis module when there is a match. 
     In some cases, the extractors may be bots or crawlers. The number of extractors deployed for a data source may be dynamic. In one configuration, the number of data extractors deployed for a data source increases when the activity on the data source increases. For example, if extractors are deployed on a social media source to follow a current event, such as the elections, the number of extractors may increase during times when traffic for the election increases. In this example, the social media traffic may increase during flashpoints of a campaign, such as primaries, debates, and/or election night. 
     In one configuration, after a data source has been identified, the content of the data source is extracted based on a schedule. For example, a user may identify a blog as a data source. Moreover, the identified blog may have multiple authors. Still, the user may specify to only extract posts by a first author, rather than all of the authors. The user may also set a schedule for the extractor to determine if there is new content from the first author. The schedule may be once a week, hour, month, minute, or other time. The extractor may extract the content to determine if there is new content. After determining that there is new content, the content will be transmitted to the data mining system. In this configuration, prior to deploying the extractor, the first author is identified as a topic of interest for the query. The extractor determines the identified topics of interest in external data sources such as social media, websites, proprietary data silos, and transmits the data to the data mining system for analysis. 
     Data is extracted when the extractor detects a relevant post. A relevant post refers to data on the data source that matches the query and/or keywords of the query. The extracted data may be cataloged and stored on a database in a data analysis module  208 . The data may also be connected to terrestrial feature markers. The data that is stored in the database may be transmitted to a data analytics module for sentiment analysis, indexing, and/or pattern detection. Archived data is kept for each data source that is monitored. 
     In one configuration, sentiment analysis is performed on the extracted data. The sentiment analysis may language agnostic so that analytics may be performed in any native language, such as, for example, Arabic, Chinese, or Persian. The data analysis may also perform pattern detection, document classification, and sentiment analysis on both structured and unstructured datasets. Additionally, an analysis lexicon may be applied to the sentiment analysis. 
     Specifically, the data analysis module  208  may further analyze the data via linguistic patterns to determine the sentiment. That is, via the analysis lexicon, the data is processed using both a clausal and individual word level in English and/or the native language of the data. In one configuration, the data may be processed via successive passes. Numerical values may be assigned to denote sentiment. The sentiment may refer to whether the data is for or against a specific topic. 
     In one configuration the numerical values for the sentiment provide a specific range of numerical value to denote the strength of the sentiment. Positive sentiment may be given a value from 1 to X, 1 being the lowest value for a positive sentiment while X denoting the highest sentiment value. Additionally, negative sentiment may be given a value from −1 to −Y, −1 being the lowest value for a negative sentiment while −Y denoting the highest sentiment value. 
     The data mining system  200  may include a data visualization module  210  for visualizing data. The visualized data may be based on the data that is analyzed in the data analysis module  208 . That is, in one configuration, the data is visualized to display sentiment patterns across datasets. The visualization may be temporal and/or spatial. Furthermore, the visualized data may be exported so that a user may directly manipulate the results. 
     Additionally, data mining system  200  may include a customer communication module  212  for establishing a communication channel with customers and/or other users based on the analyzed data. As discussed above, in one example, if the data analysis reveals that a customer may be planning a trip to another country, the data mining system  200  may engage the customer to discuss cell phone data roaming plans and/or other services that may be desired by a customer that is traveling to another country. 
     Furthermore, the data mining system  200  may include a customer feedback module  214  for customers and/or users to provide feedback. In one configuration, the feedback is based on an open forum, such as a message board, for the customers to provide feedback to the system. The feedback is analyzed at the data analysis module  208  to determine customer sentiment. Furthermore, in addition to or separate from the open forum, the feedback may also be generated based on the communication channel established via the customer communication module  212 . 
     In another configuration, the communication channel of the customer communication module  212  may be established after the extracted data has been visualized via the data visualization module  210 . Furthermore, in one configuration, the feedback received via the customer feedback module  214  may be visualized at the data visualization module  210 . 
       FIG. 3  illustrates examples of data visualization according to aspects of the present disclosure. In one configuration, the data visualization is a sentiment heat map  308 . That is, as shown in the sentiment heat map  308 , the data may be visualized to show flashpoints for the extracted data. For example, if the topic of interest is “earthquakes,” the heat map may show the areas in the world that have the most activity on the topic of interest. The heat map is not limited to a world map and may be visualized for any region. 
     In another configuration, the data visualization is a bar graph  302  for the sentiment over time. As previously discussed, the analyzed sentiment may be assigned a numerical value. Accordingly, the numerical value for the sentiment may be plotted over time and visualized as shown in the bar graph  302 . For example, the topic of interest may be “alternative energy,” and the visualization of the bar graph  302  may show a specific group&#39;s sentiment towards alternative forms of energy over time. In another configuration, the data visualization is pie chart  304  and/or a plot graph  306 . 
     The data visualization is not limited to the schemes shown in  FIG. 3 , the data may be visualized via any desired scheme, such as, but not limited to Calendars, Box Plots, Bubble Charts and variations thereof, Bullet Charts, Chord Diagrams, Cartograms, Dendograms, Network Diagrams, Streamgraphs, Bar Graphs and variations thereof, Tree diagrams, maps, and variations thereof, Line graphs, Maps, Hierarchical Edge Bundling, Scatterplots and Scatterplot Matrices, Sankey diagram, Cluster maps and diagrams, Co-occurrence Matrices, Sunburst charts, Beziers, Word clouds, Heat maps, Timelines, Donuts and Pie charts, and/or Area charts. Moreover, the aforementioned visualizations may be based on dynamic factors, such as, but not limited to force directed movements, time elapse, pattern acceleration, and/or volume. More specifically, the data visualization may be specified to show relationships, variations, anomalies, trends, patterns, pinpoints, ranges, and/or actuals in the target data. It should be noted that the visualization options are configurable by the user and may vary based on the deployment domain and goal of the data mining system. 
       FIG. 4  is a conceptual data flow diagram illustrating the data flow between different modules/means/components in an exemplary query module  400  according to an aspect of the present disclosure. In one configuration, the data flow between different modules/means/components of the query module  202  is similar to the data flow between different modules/means/components of the query module  400 . 
     As shown in  FIG. 4 , query module  400  includes a query setup module  402  for creating a query. The query setup module  402  may receive query elements  406  via an input. The query elements  406  may specify the topic of interest and subtopics of interest. The query elements  406  may be an image, text (e.g., natural language query), or another type of input, such as audio. In one configuration, the query elements  406  are input by a user and/or automatically generated via a query module (not shown). 
     The query module  400  also includes a lexicon module  404  for applying lexicon elements to the query elements  406  received at the query setup module  402 . In one configuration, the lexicon elements are applied to the query elements  406  at the query setup module  402 . Alternatively, the query setup module  402  may transmit the query elements  406  to the lexicon module  404  so that the lexicon elements may be applied to the query elements  406 . 
     As discussed above, the lexicon elements may expand the query elements  406 . For example, the query may be a text query “find people at a beach.” As another example, the query may be an image of a person at a beach during the daytime. For the above example, the lexicon module  404  may expand the query elements  406  to include additional related queries, such as “find images of people at white sand beaches, at the black sand beaches, during sunrise, during sunset, etc.” That is, lexicon module  404  expands the query to other expressions of the same classification. The expanded query may be additional text queries, image queries, or other types of data queries. 
     As discussed, in one configuration, for a query, the lexicon is applied to expand the search to include relevant search terms. The lexicon may also apply misspellings and derivations of the topic of interest/sub-topics. The lexicon may be generated based on user input and/or may be generated based on automatic word association, such as synonym/antonym trees and lexicons of positive and/or negative modifiers. 
     The query module  400  also includes a language translator  408  for translating the query elements  406  received at the query setup module  402 . The language translator  408  may translate the query elements  406  and/or the results from English to another language, and vice versa. After applying the lexicon via the lexicon module  404  and translating the query elements  406  (if necessary), the query setup module  402  transmits the query to the natural language processor  410 . In one configuration, the natural language processor  410  converts the query elements  406  to system-understood operations, such as Boolean operations. 
     When an image query is specified, the image engine  420  may receive one or more inputs from the lexicon engine  404 , query setup module  402 , and/or language translator  408 . Additionally, or alternatively, the image engine  420  directly receives the query elements  406 . The image engine  420  may be a neural network, such as a deep convolutional neural network. Based on training, the image engine  420  converts the query to a machine translation for a set of features of the query. The features may include parameters, shapes, edges, etc., related to the query. 
     In one configuration, for an image query, the user may specify whether the search is for a match to an item in the image, a match for items/elements similar to those found in the image. For example, if the image is an image of the Statue of David, the user may indicate that the search should return other images of the Statue of David. As another example, if the image is an image of the Statue of David, the user may indicate that the search should return other images of statues, including, but not limited to, the Statue of David. 
     According to aspects of the present disclosure, upon generating the set of features, the image engine  420  transmits the set of features to a search module  412 . For simplicity, the set of features transmitted to the search module may be referred to as query elements  406 . In one configuration, the search module  412  distributes the query elements  406  to search data sources. In one configuration, the data sources are defined by a user and are received at the search module  412  via a signal  416 . Alternatively, the data sources may also be specified in the query elements  406  received at the search module  412 . Results of the search are transmitted to the results module  414 . The results module  414  may sort the results and display the results to the user. In one configuration, the user may select the desired results to be stored in memory. The user may select the desired results based on the accuracy of the results and/or the information provided in the results. In one configuration, the results module  414  may be separate from the query module  400 . According to an aspect of the present disclosure, the selected results are transmitted to a refinement module  418 . 
       FIG. 5A  is a conceptual data flow diagram illustrating the data flow between different modules/means/components in an exemplary refinement module  500  according to an aspect of the present disclosure. In one configuration, the refinement module  500  may be included in the query module  400 . Still, in the present example, the refinement module  500  is distinct from the query module  400 . Nonetheless, in the present configuration, the refinement module  500  uses some of the elements of the query module  400 . 
     As shown in  FIG. 5A , the selected results from the results module  502  may be transmitted to the lexicon updating module  504 . As previously discussed, at the results module  502 , the user selects relevant results for processing (e.g., calibration). That is, the user may judge the results for accuracy based on the intent of the query. Based on the results selected from the query, the data mining system may refine the lexicons and the query elements. 
     More specifically, a search for a topic of interest may yield a plethora of results. Some results may be relevant to the search while others may be false positives. Accordingly, by understanding the data sources that are relevant to the user, the system may further refine to search terms so that a more focused search may be performed in future iterations. 
     Thus, the lexicon updating module  504  adjusts the lexicons based on the results selected by the user. The lexicon updating module  504  may transmit the results to the search module  506  to perform another search with the updated lexicons. Additionally, the lexicon engine  508  may also be updated with the updated lexicons of the lexicon updating module  504 . The lexicon engine  508  may receive the updated lexicons from the search module  506  and/or the lexicon updating module  504 . The refinement module  500  may run in a loop until the search results reach the desired results threshold. In one configuration, once the desired results have been received via the search module  506 , the results module  502  transmits the results to a data analysis module  510 . 
       FIG. 5B  is a conceptual data flow diagram illustrating the data flow between different modules/means/components in another example of a refinement module  550  according to an aspect of the present disclosure. In one configuration, the refinement module  550  may be included in the query module  400 . Still, in the present example, the refinement module  550  is distinct from the query module  400 . Nonetheless, in the present configuration, the refinement module  550  uses some of the elements of the query module  400 . 
     As shown in  FIG. 5B , the selected results from the results module  552  may be transmitted to the image updating module  554 . As previously discussed, at the results module  552 , the user selects relevant results for processing (e.g., calibration). That is, the user may judge the results for accuracy based on the intent of the query. Based on the results selected from the query, the data mining system may refine the images, the query elements, and/or the lexicon used to expand the query terms. The query elements may refer to the features used to identify images. 
     More specifically, a search for a topic of interest may yield a plethora of results. Some results may be relevant to the search while others may be false positives. Accordingly, by understanding the data sources that are relevant to the user, the system may further refine to search terms so that a more focused search may be performed in future iterations. 
     Thus, the image updating module  554  adjusts the images based on the results selected by the user. The image updating module  554  may transmit the results to the search module  556  to perform another search with the updated images and/or features. Additionally, the image engine  558  may also be updated with the updated images of the image updating module  554 . The image module  558  may receive the updated images from the search module  556  and/or the image updating module  554 . The refinement module  550  may run in a loop until the search results reach the desired results threshold. In one configuration, once the desired results have been received via the search module  556 , the results module  552  transmits the results to a data analysis module  560 . 
       FIG. 6  is a conceptual data flow diagram illustrating the data flow between different modules/means/components in an exemplary data analysis module  600  according to an aspect of the present disclosure. As shown in  FIG. 6 , the data analysis module  600  includes a pre-processing module  602  that pre-processes the received data. The data may include results found by the extractors. That is, the data from the extractors may include data generated during the first pass of a search in addition to data from subsequent passes. More specifically, the data from subsequent searches may be refined and/or expanded based on the modification of the query. Moreover, the results obtained from the extractors may be time stamped, or given a unique identifier, so that the data of each pass is distinguishable from data of other passes. 
     In some cases, the data may be in various formats. For example, data extracted from a website may be different from data extracted from a database. Therefore, the preprocessing  602  may normalize the data to a desired format. After normalizing the data, the pre-processing module transmits the processed data to the data analysis module  604 . The data may include text, images, audio, or other types of data. 
     The data analysis module  604  may use various functions to analyze the normalized data. In one configuration, the data analysis module  604  includes a user-generated function module  606 , a machine learning module  608 , a lexicon based matching module  610 , an image data module  630 , and a predictive module  612 . In one configuration, the user-generated function module  606  executes a function specified by the user. The machine learning module  608  may detect patterns in the data and the patterns may be applied to future data. The lexicon based matching module  610  finds keywords and/or indexes in the body of the data. The predictive module  612  determines patterns in previous data and applies a probability for an occurrence of an event based on the present data. The image data module  630  may extract features from the image. The features may include shapes, patterns, and/or other data, such as time stamp, location, etc. The image data module  630  may also classify and label elements in an image. In one configuration, the data analysis module  604  includes all of the aforementioned modules or any combination thereof. Additionally, in another configuration, the user may configure the algorithm modules  606 - 612  that are used by the data analysis module  604 . 
     After the data is analyzed, the data is transmitted to a pattern detecting module  614  to detect patterns in the data. In one configuration, the pattern detecting module  614  detects a spike in specific words, phrases, and/or image elements. For example, the pattern detecting module  614  may detect a spike in the phrase “I love my iPhone 5.” As another example, the pattern detecting module  614  may detect a spike in a number of images with red cars. As yet another example, the pattern detecting module  614  may detect a spike in a number of images at a specific location (e.g., Las Vegas) or a type of location (e.g., beach). 
     The pattern detecting module  614  may also determine any causality for the spike. That is, the pattern detecting module  614  may determine what caused the spike to occur. For example, the pattern detecting module  614  may determine that the spike for the phrase “I love my iPhone 5” is based on a recent software update to the iPhone. As another example, an increase in the number of images at a location, such as Las Vegas, may be due to a recent event, such as a concert or sports event. The pattern detecting module  614  transmits the analyzed data and/or the detected patterns to the post-processing module  616 . The post-processing module  616  may process the data to a format that is desired for an external system. 
     The data and/or detected patterns may be transmitted to a quality control module  618 . In one configuration, the data may be transmitted directly from the data analysis module  604  to the quality control module  618 . Likewise, the analyzed data from the data analysis module  604  may bypass either the pattern detecting module  614  or the post-processing module  616 . The quality control module  618  may include a scoring module  620  for assessing the accuracy of the results over time and as compared to a human analyst. Additionally, the quality control module  618  may include a logging module  622  for logging changes to the analysis of the data over time and/or per iteration. The logging may be similar to a software version control function. Furthermore, the quality control module  618  may include a labeling module  624  for labeling the data and the datasets for later reference. The labeling may include time stamping, pattern stamping, topic labeling, or any other desired type of label (e.g., identification). 
     The results from the quality control are transmitted to the user profile and used for future analysis by the data analysis module  604 . The results of the quality control and/or the analyzed data may be stored in a storage module  626 . The storage module may also store the patterns detected via the pattern detecting module  614 . The results of the quality control and/or the analyzed data may also be displayed via a display module  628 . The display module  628  may also display the patterns detected via the pattern detecting module  614 . Furthermore, the display module may display a notification when the quality is below a quality threshold and/or when specific analyzed data is detected. Additionally, the analyzed data from the data analysis module  604  may be used to generate notifications and/or open communication channels with users based on various criteria. 
       FIG. 7  is a conceptual data flow diagram illustrating the data flow between different modules/means/components in an exemplary customer communication module  700 . The customer communication module may include modules to both communicate with a customer and receive feedback from a customer. In one configuration, the communication module may be separate from the feedback module. 
     As shown in  FIG. 7 , the customer communication module  700  may include a communication generation module  702 . The communication generation module  702  may receive data from the analyzed results. Furthermore, the communication generation module  702  may receive information regarding a specific user from a user profile module  704 . The user information may include information such as the user&#39;s name, address, occupation, spoken languages, physical attributes, preferred communication method, and/or any other desired details. Additionally, the communication generation module  702  may be coupled to a translating module  706  to translate the received data based on the user profile. Furthermore, the communication generation module  702  may be coupled to a natural language processor  714  to process any natural language data. That is, the natural language processor  714  may analyze the received natural language and determine semantic information from the received natural language. 
     Based on the data from the analyzed results, the user profile information, and the processing by the translator  706  and/or the natural language processor  714 , the communication generation module  702  determines a strategy for communicating with a user. In one configuration, the strategy may be based on historical data. Historical data may include, but is not limited to, company records, sales records, and/or logged customer feedback. Once the strategy has been developed, the communication generation module  702  transmits the strategy to a communication execution module  708  for communicating with the user via one or more determined communication channels. The communication channels may include email, short message service (SMS), click-to-voice, interactive voice response (IVR), or any other desired form of communication. In one configuration, the user specifies their desired form of communication. 
     The customer communication module  700  may include a feedback module  710  for receiving a communication from a customer. The communication may be in response to the communication initiated via the communication execution module  708 . Alternatively, the communication may be received via a portal, such as a webpage, twitter, message board, or any other communication channel. After receiving the communication from the user, the feedback module  710  transmits the data of the communication to the data analysis module  712  to analyze the data. The data is analyzed similar to the results of a query (e.g., data analysis module  510 ). That is, the data analysis module  712  may be the same module as the data analysis module  510  of  FIG. 5 . In another configuration, the data analysis module  712  may be a different module as the data analysis module  510  of  FIG. 5 ; still, both modules would have similar functionality. 
     In one configuration, the data analysis module  712  may analyze the data for pattern detection and/or natural language processing. The results of the analyzed data may be reported to the user of the data mining system. Furthermore, the results may be transmitted to the communication generation module  702  for further refinement of the communication generation. 
       FIG. 8  is a conceptual data flow diagram illustrating the data flow between different modules/means/components in an exemplary customer communication module  800 . The customer communication module  800  may include the feedback module  710  of  FIG. 7 . In one configuration, the customer communication module  800  of  FIG. 8  is integrated with the customer communication module  700  of  FIG. 7 . 
     As shown in  FIG. 8 , the customer communication module  800  may include a feedback module  802  for receiving an input from a user via a communication channel. The communication channel may include, but is not limited to, SMS, email, web-based communication, or a user-defined method. The feedback module  802  receives the communication, which may be received as a string of characters, an image, or another type of data. The feedback module  802  processes the input to a desired format. The input is then transmitted to a parsing module  804  to be parsed based on lexicon sets  820 , image data  822 , and/or relationship maps  824  (e.g., ontology sets), to classify the topic of the input. For example, if the image is of a smiling customer, the image data  822  may be used to classify the image as a smiling customer (e.g., happy customer). 
     After the string is parsed to determine the topic of the input, the parsed string is transmitted to a routing module  806  to be routed based on the identified topic. That is, the routing module  806  may look up the topic in a topic index so that the proper response is determined. After determining the proper index, the routing module  806  may transmit the topic index to the response module  808 . The response module  808  determines the response from responses stored in a database  810 . For example, if the string includes the words “appointment” and “schedule,” the routing module  806  may determine that an index is “scheduling an appointment” and transmits the index to the response module  808 . In this example, the response module  808  may then communicate with the user to schedule an appointment. 
     The responses in the database  810  may be updated in real time based on a machine learning module  812 . For example, an index of “scheduling an appointment” may be determined based on a string including the words “appointment” and “schedule.” Over time, the system may determine that communicating a response to schedule an appointment is not the appropriate response. That is, based on customer feedback, or other types of feedback, the system may determine that the proper response is to provide the user with the schedule of their appointments, or another type of response. 
     After determining the proper response, the response module  808  transmits the response to an outbound communication module  814  to communicate the response to the user via a desired form of communication, such as, but not limited to, email, SMS, or phone. 
     According to an aspect of the present disclosure, the data mining system may provide an ability for managers or high-level user to subdivide the analysis into discrete, secure modules. A variety of permissions can be assigned to the analysts to control both the input and processed data.  FIG. 9  illustrates a flow diagram for the levels of security. 
     For example, as shown in  FIG. 9 , a user designated as an administrator may have access to all of the data obtained via the system. The administrator may be referred to as a level 1 user. The administrator may provide specific permissions to level 2 users, such as a first manager (manager 1) and a second manager (manager 2). The managers may have the same access as the administrator or may have less access than the administrator. Furthermore, the levels of access for each manager may be the same as or different from each other. The managers may then control the level of access by each data analyst (Analysts A-D). The levels of access for each analysis may be the same as or different from each other. 
     As previously discussed, extractors may be deployed on target data sources. The data extracted by the extractors may be transmitted to a cloud-based infrastructure. The cloud-based infrastructure allows for on-demand scaling of resources and deployment of multiple extractors that may be processed in parallel. 
     The dynamic parallel computing structure may be used to process data without impeding speed. Specifically, the extractors and data analysis modules are dynamically configurable between sequential and parallel processing to improve efficiency. 
       FIG. 10A  illustrates a typical sequential data extraction system. As shown in  FIG. 10A  data sources (e.g., data source  1 -data source n) are sequentially analyzed. More specifically, each record (e.g., record  1 -record n) of the data source is extracted one record at a time. The speed of the extraction is limited by the speed of the processor(s). After extracting the record from a data source, the record is written to a data base. 
     Furthermore, as shown in  FIG. 10B , a sentiment analysis module may also be specified to process each extracted page. The sentiment analysis module may also be a data analysis module. As shown in  FIG. 10B , each extracted data source (e.g., data source  1 -data source n) is sequentially analyzed by the sentiment analysis module. The sentiment analysis module may receive an input from the lexicon module. The records processed by the sentiment analysis module may be output from each sentiment analysis module for further processing. 
     Although sequential processing is a very straightforward approach to data-mining and analysis, it is limited by processing speed. For example, various sub-processes may be slower than others, leading to bottlenecks in the system. Moreover, page extraction speed (X) may be faster than the sentiment analysis processing speed (Y), thus leading to bottlenecks. 
     In one configuration, as shown in  FIG. 10C , scalable processing capabilities are specified by deploying multiple instances/sub-sentries to perform sentiment analysis processing in parallel. As shown in  FIG. 10C , multiple sentiment analysis modules may be deployed to analyze the records of the extracted data in parallel. In  FIG. 10C , the sentiment analysis module may receive an input from the lexicon module. The records processed by the sentiment analysis module may be output from each sentiment analysis module for further processing. 
     In one example, based on the present configuration, if the extraction speed is four times the sentiment analysis processing speed, 4 sub-sentries may be deployed to perform the sentiment analysis in parallel. Additional sub-sentries may be deployed for buffering needs. In another configuration, if the sentiment analysis processing speed is limited by a large lexicon, sub-sentries may be deployed to process a portion of the lexicon, in parallel. The data mining system of the aspects of the present disclosure monitors the speed of each sub-process, which is input into a controller that dynamically allocates parallel processing instances on-demand. 
     The data mining system described in the aspects of the present disclosure may obtain large amounts of data with varying levels of sensitivity. That is, some information may include customer sensitive data or data that should only be viewed by individuals who have varying degrees of clearance. Thus, it may be desirable to provide a data security system that may be modified based on the procedures of a client of the data mining system. 
     As previously discussed, the data mining system described in the various aspects of the present disclosure may be used to mine and analyze various forms of data. Provided below are various examples of how the data mining system may be used by governments, individuals, corporations, and/or any desired user. Of course, the use of the data mining system of the present application is not limited to the discussed examples. 
     In one configuration, the data mining system may be used by a government and/or intelligence agency to monitor global hot spots. For example, governments and/or intelligence agencies may move through vast amounts and types of data, and in multiple languages to find patterns indicating the likelihood of domestic unrest, or the next terrorist attack. However, integrating and manually analyzing multiple data sources in real time is technically challenging, time intensive, and marred by human bias, oversight, and even fraud. 
     The data mining system described in aspects of the present disclosure finds, monitors, and summarizes the information needed to make the most informed decision in real time. Rather than relying on the manual sifting and analysis of intelligence, data mining system of the present application may use a combination of rules engines and artificial intelligence schemes to detect threat patterns within large datasets before problems reach a tipping point. Through iterations of searches and search results, the data mining system learns to filter data noise and refine queries to prioritize the most relevant results. 
     The data mining system described in aspects of the present disclosure may determine political sentiment by sampling mass media conversations in any language. Social media outlets, such as Twitter, may be monitored via the data extractors to identify user-defined events, shifts in traffic volume, and/or sentiment. The data mining system may report frequencies on topics, entities, and persons of interest, which may signal the development of a physical event posing a risk to public security. 
     After identifying a user-defined event, the data mining system notifies the appropriate personnel in real time, communicating the event type, location, and optimal response strategy via a communication channel, such as email or SMS. 
     In addition to monitoring mass online conversations and their physical impact, in one configuration, the data mining system may also monitor a specific individual. That is, the data mining system may analyze, verify, and cross check a person of interest&#39;s information, such as travel, financial, vital, and law enforcement records to identify patterns indicative of fraud, criminal activity, or other threatening behavior. The data mining system may then build a person of interest profile based on verifiable intelligence determined from all available institutional and open-source data feeds, including social media. 
     As an example, an intelligence agency may monitor sentiment of specific topics and/or groups during. In this example, the intelligence agency would have developed a query for monitoring sentiment of a topic/group. For example, the query may be “show me people upset with the government.” In some cases, the query may be further refined to a specific group and/or a specific location. 
     After the query has been selected, the query may be expanded based on lexical and rule-set based techniques to search structured and unstructured datasets for the keywords of the query. Specifically, the expanded query may generate a topic of interest in addition to sub-topics. The expanded query may then be run on open-source data and closed-source data. After the first pass of the search, the user may expand the topic of interest based on unintended search results. 
     That is, after the first pass of the search, the user may add additional topics and/or sub-topics in addition to narrowing the search to specific sources. After the search has been narrowed to a desired results threshold, the data extractors are deployed to the selected data sources for monitoring of the data sources. 
     As an example, the user may have searched for “Arab Spring” as a topic of interest with “unrest” as sub-topics. In this example, a search for the topic of interest and other sub-topics may have generated unintended search results such as results related to social revolutions. Therefore, the user may determine to add “revolution” as sub-topics to a search. Additionally, other false positives may include topics that are not related to the topic of interest. For example, a search for a search of “Arab spring” and other sub-topics may have generated unintended search results such as “Arab spring water” or sources related to the spring season in the Middle East. Accordingly, sources related to these false positives may be excluded from further iterations of the search. 
       FIG. 11  illustrates an example of data from a data source that is identified by a sentry. The sentry may identify a phrase from a message and/or image posted on a data source, such as a message board and/or a social media site. Specifically, as shown in  FIG. 11 , the sentry may detect a post  1102  in the data source, such as a message board. The post may include an image  1104  and text  1106 . In the example data of  FIG. 11 , the image  1104  is an image of protestors with a sign  1110 . In this example, the query may include a text query for “protest” and/or a non-text query, such as an image of a protestor. The query may be expanded to include images of various types of protestors. 
     In one configuration, an artificial neural network (ANN), such as a convolutional neural network may be trained to identify images related to a query. For example, the ANN may be trained to identify images of protestors. In this configuration, images extracted from a source are processed by the ANN to determine the content of each image. That is, the ANN extracts features from the images to classify each image. The classified images are then processed to determine if the classification matches the query. 
     Additionally, or alternatively, the data mining system may use the ANN to find images that match a specific element in an image. That is, rather than identifying images that relate to a specific topic, the ANN may identify images that are substantially similar to the query image and/or the expanded query images. For example, if the query image is the Statue of David, the ANN may only identify images of the Statue of David, rather than all images of statues. 
     Once the post  1102  has been identified, the image  1104  may be analyzed by a data analysis module or a sentiment module. In one configuration, the sentiment is analyzed by the data analysis module  208  and/or a sentiment weight module  1108 . The sentiment may be determined based on a classification of one or more elements (e.g., objects) in the image. For example, if the image includes smiling people, the sentiment analysis assigns a positive sentiment. As another example, images of frowning or crying people may indicate a negative sentiment. The sentiment is not limited to the sentiment of an individual. Other factors, such as location and/or surrounding objects may be used to determine sentiment. For example, a cemetery may indicate an “unhappy” or “sad” sentiment. As another example, an amusement park may indicate a “fun” or “happy” sentiment. 
     In one configuration, the sentiment analysis assigns weights (e.g., scores) to specific features that are extracted from a data source. For example, positives features are assigned a value from 1 to X and negative words are assigned a value from −1 to −Y. Positive features refer to features that indicate a positive sentiment, such as “good,” “happy,” etc. Negative features refer to features that indicate a negative sentiment, such as “hate,” “angry,” etc. The more positive or negative the word, the higher the value. For example, the positive features “good” may be assigned a value of 1 while the positive features “fantastic” may be assigned a value of 3. The value assignment may be set by the user or pre-defined based on a specific vernacular. 
     In one configuration, in addition to, or alternate from, determining the sentiment of an image, the keywords in the text  1106  may be analyzed by the sentiment weight module  1108 . The sentiment weight module  1108  may assign a sentiment weight to the words and/or phrases extracted from the text  1106 . In this example, the words “opposition” and “strength” are both given a weight of −1. Thus, based on the analysis of the sentiment weight module  1108  a user and/or the data mining system may determine that the detected post  1102  carries a negative sentiment. The total sentiment weight for the extracted data may be the sum of all sentiment weight. In this example, the total sentiment weight is −2 (e.g., sum of “opposition” sentiment weight (−1) and “strength” sentiment weight (−1). 
     In one configuration, after determining a total sentiment weight for extracted data, the data analysis module, user, and/or data mining system may determine whether to proceed with a second pass for sentiment analysis. Specifically, in one configuration, for the second pass, the data analysis module performs a more formal computational linguistic analysis to dive deeper into the textual opinions. Additionally, or alternatively, the second pass may determine more specific details of an image, such as a person&#39;s facial expression. That is, the first pass may determine general sentiment based on a location and/or surrounding objects, while the second pass may analyze a person&#39;s sentiment. Still, the first pass may also determine a person&#39;s sentiment. 
     When analyzing text, the second pass may use a natural language processing toolset on the native script to process and determine sentiment including lemmatization (root-finding), semantic feature analysis, statistical processing to discover patterns, etc. In one aspect of the disclosure, linguistic patterns are specified to populate the topic and modifier tables and create custom lexicons. After the first pass, the system can then reprocess the opinionated text string (OTS) with the improved tables and increase accuracy. The opinionated text string refers to the set of words where the author holds the opinion. 
     In the exemplary data shown in  FIG. 11 , during the second pass, the sign  1110  in the image  1104  may be analyzed to determine the sentiment of the text in the sign  1110 . In this example, the keywords of the sign, such as, “change,” “constitutions,” and “tyrants,” may be given sentiment weights. In one configuration, after determining the total sentiment weight from the second pass, the data analysis module determines to extract the user info for the post for further analysis. In this configuration, the data analysis module determines that the user information should be extracted when the total sentiment weight is above a threshold. The thresholds may be set for positive and/or negative sentiment weights. 
     The user information may be extracted via an information extraction module  1112 . The information extraction module  1112  may be part of the data analysis module, sentiment weight module  1108 , and/or a distinct module. The information that is extracted may include the username, actual name, age, location, and/or any other information related to the user that posted the data. In one configuration the extracted information may be used for a new query to follow the user that posted the data. 
     It should be noted that although  FIG. 11  shows the data in English, the aspects of the present disclosure may be applied to data of any language. 
     In one configuration, after the sentiment of the data is analyzed, quality control is specified for the results of the sentiment. That is, the data and the sentiment weights may be transmitted to a quality control module. A user may review the data transmitted to the quality control module to determine whether the proper sentiment weights were specified for the extracted data. Specifically, the user may determine whether the sentiment analysis missed specific words, gave weights to false positives, and/or applied the wrong weights to specific words. Based on the user analysis, the sentiment analysis may be provided with a quality control value, such as a percentage. 
     Furthermore, based on the on the user analysis, the lexicon modules and/or sentiment analysis modules may be updated to correct missed words, false positives, incorrect analysis, and/or any other detected errors. After updating the lexicon modules and/or sentiment analysis modules, the sentiment analysis may be subsequently performed. The results of the subsequent sentiment analysis may be further analyzed by a user. The iterations of the sentiment analysis may be performed until the quality control value is equal to or greater than a threshold. 
     As shown in  FIG. 11 , in one configuration, the data mining system may be specified to determine political sentiment. Still, in another configuration, the data mining system of the present application may be used for customer service purposes and/or revenue enhancement services. In one configuration, a revenue enhancement service (RES) platform is an end-to-end big solution that generates actionable intelligence and executes individually targeted outreach strategies. The RES platform may use predictive analytics, natural language processing, image processing, audio processing, and/or automated engagement capabilities, to increase revenue by improving sales, identifying emerging trends, and or streamlining customer interaction. 
     Consumers may generate large amounts of data that indicate how they spend, what they desire, and where and how they engage. In one configuration, the data mining system may continuously monitor the customer information from data sources, such as social media, web feeds, customer feedback, internal company databases, and proprietary data silos. In this configuration, the data mining system extracts attributes for each customer, such as financial profiles and retail history, to social influences and expressed interests. The RES continues to monitor and analyze the data for product sentiment, consumer patterns, and purchasing behaviors to build customer profiles and generate market intelligence in real-time. Using this intelligence, the customer of the data mining system may determine what the most likely products and services that each customer is looking for, as well as ways to engage with that particular customer. 
     In one configuration, based on the data retrieved via the data mining system, a company may improve product placement or determine design trends. Moreover, the customer may communicate with the company through their choice of interaction medium (e.g., SMS, email, voice, avatar chat, or a combination thereof) so that the customer may ask questions, resolve issues, and express satisfaction/dissatisfaction. Furthermore, the RES platform may determine customer profiles as well as customer sentiment so customer communications may be accurately routed for the appropriate response. That is, companies may improve outreach to engage with customers knowing who they are, how they feel and what they need. 
       FIG. 12  illustrates an exemplary flow chart  1200  for determining user sentiment based on an aspect of the present disclosure. As shown in  FIG. 12 , at block  1202  an individual may post data on a data source, such as a public forum or social media. In this example, the data is an indication of a phone user&#39;s sentiment for their phone service, specifically, “my phone service is terrible.” At block  1204  an extractor that is monitoring the data source determines that the data post of block  1202  matches the query, therefore, the data extractor extracts the data and transmits the data to the data analysis module. At block  1206  a data analysis module analyzes the data to determine the sentiment of the post. At block  1208  the data analysis module outputs the sentiment word, such as “terrible,” and the sentiment weight, such as “−1”. Based on the analysis of the data analysis module, a customer communication module may determine an appropriate response. The response may be determined based on the determined sentiment. 
     In yet another configuration, the data mining system may be used to determine fraud, waste, and/or abuse of a system. In one configuration, the data mining system uses pattern detection based on historical data and correlating factors in datasets to determine potential fraud, waste, and/or abuse of a system. For example, the data mining system may be used in a health care or insurance system to analyze for fraud and/or abuse. 
     In one configuration, the data mining system is specified to analyze reported claims. The contents of the claim, such as user information, claim type, injury type, etc. are queried at data sources. The data sources may include a health provider database, subscriber database, and/or affiliate database. The query is specified to determine whether the submitted claim has specific fraud characteristics. If it is determined that a claim has fraud characteristics, the data mining system may take appropriate actions, such as notifying an investigation, denying the claim, and/or flagging the claim for further monitoring. In one configuration, when a claim does not have fraud characteristics, the claim may still be flagged so that the individual that made the claim is monitored for future activity. 
     In one configuration, the data mining system is specified to query one or more data sources for various types of data, such as financial data, operational data, and/or human resources data. For example, one of the data sources may be an operations center. The query may be specified to extract data for further analysis. Thus, the query may be specified for updates and/or additions to all data in a data source or specific data in the data source. In one configuration, the data that matches the query is extracted and aggregated to a pre-defined format. That is, the data in the data source(s) may be in various proprietary formats. Therefore, it is desirable to aggregate the extracted data to a pre-defined format. Furthermore, after aggregating the extracted data, the data may be analyzed. The analysis may be performed via a data analysis module, such the data analysis module  604 . 
     The analysis may use machine learning, a user-generated system, lexicon based matching, natural language processing, and/or predictive processing to analyze the data. The analysis may detect patterns, find key works and/or indexes, determine a probability for an occurrence of an event, and/or perform other types of analysis. In one configuration, the analyzed data may be visualized. The visualization may be a sentiment heat map  308 , bar graph  302 , the pie chart  304 , plot graph  306 , and/or any other type of visualization. 
     Finally, in one configuration, based on the analyzed data, the data mining system may take appropriate actions, such as generating an actionable alert. As an, an actionable alert may be generated when a maximum allowed expense for a cost center exceeds a threshold. For example, a threshold of a maximum expense of $1 million dollars per month is set, and the actual expense data is $1.2 million dollars. In this example, the cost center Manager will be notified that the threshold has been passed. The Manager may then take appropriate actions based on the alert. 
     According to an aspect of the present disclosure, the data mining system is specified to improve a customer&#39;s travel experience. For example, a query may be generated to query one or more data sources for information related to traveling. The information may be travel information prior to traveling, during traveling, and/or after traveling. In one configuration, the query is related to itinerary changes, local deals, expenses accrued in relation to traveling, corporate travel policy, and/or other travel related information. The corporate travel policy may be information for budget caps, preferred hotels, preferred airlines, and/or other information related to corporate travel. 
     The data source(s) may include a traveler&#39;s calendar, a database of deals, a traveler&#39;s rewards account, enterprise data, credit card information, the traveler&#39;s location, and/or any other data source that includes travel information. The rewards account may be an account such as a frequent flyer account. In this configuration, the data mining system generates an actionable alert when new data and/or a change in data match the query. 
     As an example, once a traveler has booked a trip (e.g., hotel and/or flight) to a destination, a query is generated to monitor the one or more data sources. In this example, if there is a change to an itinerary, an actionable alert may be generated for the traveler. The change in itinerary may be detected based on a change to the traveler&#39;s calendar, a change in a rewards account, a change in a credit card transaction, and/or a change any other data source that includes travel information. 
     As another example, prior to embarking on the planned travel, the query may determine local deals based on the traveler&#39;s destination. The local deals may be determined from one or more data sources that include local deals. Furthermore, an actionable alert may be generated for the traveler if a local deal is found. As another example, the local deals may be determined while the traveler is at a specific destination. In this example, the local deals may be determined using a data source that includes the traveler&#39;s location as well as a data source that includes local deals. 
     In yet another example, after travel has been booked, a traveler may be notified that their booked hotel, airline, rental car, and/or other travel related item is no longer qualified by a corporate travel policy. The notification may also indicate that travel expenses accrued prior and/or during travel have exceeded the corporate travel limit. Furthermore, in one example, the query may search expenses from a credit card data source and/or other financial data source to determine travel related expenses. The data mining system may then generate and submit an expense report based on the matched query. 
     In one configuration, the data mining system is configured to query one or more data sources for information regarding bullying. For example, the query may search for specific words, phrases, images, and/or videos related to bullying in a bullying report database and/or social media. The bullying reports may be submitted by victims and/or witnesses. The reports may include a description, audio, and/or video of an incident. 
     In this configuration, an actionable alert is generated when a query identifies a new and/or updated bullying incident. The actionable alert may be transmitted to authorities, such as school officials, parents, and/or police. 
     In one configuration, the data mining system is configured to query one or more data sources for a user-defined topic of interest. The data source(s) may include surveys, feedback forms, user comments, and/or any type of data. For example, the data source may be a survey data source and/or social media. The query may be specified to search for updates and/or additions to all data in a data source or specific data in the data source. After extracting the data, the data may be analyzed. The analysis may be performed via a data analysis module, such the data analysis module  604 . 
     In one configuration, the analysis is specified to determine public sentiment. Of course, aspects of the present disclosure are not limited to determining public sentiment and the analysis may be performed to determine any metric. The analysis may use machine learning, a user-generated system, lexicon based matching, natural language processing, and/or predictive processing to analyze the data. The analysis may detect patterns, find key works and/or indexes, determine a probability for an occurrence of an event, and/or perform other types of analysis. The results of the analysis may be communicated to a user for further processing. 
     In one configuration, the data mining system is configured to query one or more data sources for financial transactions. The data source(s) may include a bank account and/or a credit card account. The query may be specified to identify updates and/or additions to the financial information. The financial information may be in different formats. For example, one format may be transactional data, such as a posted transaction. Another format may be non-transactional data or user-provided data, such as an image of a receipt or check. A match to the query may result in an actionable alert to a customer. Additionally, a match to the query may result in an expense report being generated for the financial transaction. 
     As an example, the user may set a query to determine when financial transactions at a coffee shop exceed a pre-defined budget. The query may then monitor one or more of the user&#39;s financial data sources. When a transaction at a coffee shop is posted, the query receives a match and increments a variable with the amount of transaction. Accordingly, if the variable is greater than the pre-defined budget, an alert may be sent to the user. 
     As an example, the user may set a query to determine when a new image is added to a financial data source. The query may then monitor one or more of the user&#39;s financial data sources for a new image. When an image, such as an image of a check or an image of a receipt is posted, the query receives a match. In this example, data from the image is extracted and the amount may be incremented to a ledger and/or an expense report may be generated based on the extracted information. 
     In one configuration, the data mining system is configured to monitor one or more data sources for a user&#39;s financial transactions. Spending patterns may be determined from the financial transactions via a data analysis module, such the data analysis module  604 . Additionally, the data mining system is configured to monitor one or more data sources for deals, such as coupons, deals, rewards, and/or other incentives for existing and/or potential customers. 
     In one example, the data mining system may determine a deal based on an identified spending pattern. For example, based on a query for spending patterns, the data mining system identifies that a user has increased spending at a specific coffee merchant. Based on the identified coffee spending pattern, the data mining system may query the deal data source(s) to identify deals related to the coffee spending pattern. The query may identify existing deals and/or the query may continuously monitor the data source to determine when a deal is identified. Furthermore, once a deal is identified, an actionable alert may be generated for the user. 
     In another example, the query may identify a new deal from a merchant. Furthermore, after determining the new deal, the query may then monitor the spending patterns of users to identify potential targets for the deal. Once a target (e.g., user) for the deal is identified, an actionable alert may be generated for the user. 
     In yet another example, the query may identify a user&#39;s spending patterns. Furthermore, based on the spending patterns, a user may earn points to be used for deals, trips, and/or merchandise. The points and/or spending patterns may be updated when new financial transactions are identified by the query. 
     In one configuration, the data mining system is configured to query one or more data sources for user information as well as other financial information. The financial information may include public information and/or user received information, such as mortgage rates, interest rates, housing prices, location data, a user&#39;s financial data (e.g., credit score, account balances, income, etc.). The user&#39;s financial data may be information received via application forms, such as a loan or credit application, and/or tax forms. The user information may also include the user&#39;s location (e.g., current location, business location, and/or residence). In response to identifying data via the query, an actionable alert may be generated to a user. The alert may suggest a vendor/service and/or a path to a goal. Vendors/services may include loan vendors, credit vendors, and/or other vendors of financial related services. 
     For example, the alert may suggest a low-interest credit card based on the user&#39;s credit score and account balances. As another example, the alert may suggest a service to repair a credit score based on the user&#39;s credit score. In yet another example, a path to a goal may include suggestions for how to save money in view of current spending patterns to reach a user-defined goal or a system suggested goal. 
     In one configuration, the data mining system is configured to query one or more data sources for user information as well as other health-related information. The health-related information may include locations for health care providers, a user&#39;s insurance information, and/or a user&#39;s health records. The user information may include the user&#39;s location (e.g., current location, business location, and/or residence). In response to identifying data via the query, an actionable alert may be generated to a user. 
     The alert may suggest a nearby health care provider that is approved by insurance. The alert may also suggest updated insurance information. Furthermore, the alert may include an application for enrollment in health plans. In one configuration, the information in the application is pre-populated with user information obtained from one or more of the data sources. 
     In one configuration, the data mining system is configured to query one or more data sources for customer service issues and/or a user-defined topic of interest. The data source may be one or more data sources that include knowledge centers, FAQs, user comments, or any type of data. The query may be specified to search for updates and/or additions to all data in a data source or specific data in the data source. After extracting the data, the data may be analyzed for sentiment, trends, or other analysis. The analysis may be performed via a data analysis module, such the data analysis module  604 . 
     In one configuration, the data mining system is configured to query one or more data sources for data that is to be processed for summarization (e.g., abstraction). For example, the data may include documents and/or images. The data may be unstructured (e.g., not in machine-readable format). In this configuration, when the data has been identified, the data is converted to a machine-readable format and summarized. For example, optical character recognition may be applied to convert the data into structured data. Furthermore, the summarized data may be available for further analysis. The summarized data may be referred to as abstracted data. 
     As an example, the one or more data sources may include lease information. In this example, when an update to an existing lease or a new lease is provided, the query extracts the lease and the information is converted to a machine-readable format. Furthermore, after the conversion, the lease may be summarized. Additionally, the summary may then be available for further queries or analysis. 
     In another example, the one or more data sources may include legal documents, such as court decisions. In this example, when a new court decision has been entered, the query extracts the court decision and the information is converted to a machine-readable format. Furthermore, after the conversion, the court decision may be summarized. Additionally, the summary may then be available for further queries or analysis. 
     In one configuration, the data mining system is configured to query one or more data sources that include a user&#39;s location, device information, as well as a user&#39;s contact preferences. The information may include a user/device GPS location, wireless device information, a radio access technology (RAT) the device is connected to (e.g., connected to WiFi or LTE), a cell tower the device is connected to, a SIM card used by the device, and/or other communication related information. In this configuration, the information obtained from the data sources may be used for virtual contact card as well as contact rules based on a user&#39;s preference. That is, when an update or new information is provided for a first user&#39;s location, device information, and/or the first user&#39;s contact preferences, an alert may be generated to one or more second users. The alert may inform the second user regarding the first user&#39;s contact preferences. The alert may be transmitted to the second user&#39;s device, such that the second user is not identified that the first user&#39;s contact information has been updated. The alert may also be sent to one or more of the first user&#39;s devices to update outgoing communication rules. 
     In one example, a query may be generated to monitor what type of RAT a first user&#39;s device is connected to. Furthermore, a data source may receive an update indicating that the first user&#39;s device is only connected to WiFi. Based on the update, an alert is sent to one or more second users. The alert may indicate that until further notice, the first user should only be contacted via voice over IP (VoIP) or other Internet-based communications. Additionally or alternatively, in this example, when the query determines that the first user is only connected to WiFi, an alert may be generated for the first user&#39;s device so that the first user&#39;s device may only perform outgoing communications via VoIP or other Internet-based communications. 
     In another example, a query may be generated to monitor a user&#39;s calendar. In this example, when a new calendar entry or updated calendar entry is determined from the query, the user&#39;s contact preference may be alerted to the user or other users. For example, a first user may update their calendar to include a business meeting during a specific data. Based on the updated calendar, an alert is transmitted to one or more second users to update the first user&#39;s contact preference. For example, the first user may only desire to receive SMS messages during the meeting. Thus, if a second user attempts to contact the first user during the scheduled meeting, the second user may only be able to contact the first user using SMS based on the updated contact preference. 
     In one configuration, the data mining system is configured to query data sources for health-related information, secure messaging, and/or scheduling information. For example, the information may include a physician&#39;s schedule, such that an alert is generated when a new or updated schedule is provided to the data source. As another, in a healthcare environment, the queried data queried may include contact information, health records, and/or a physician&#39;s schedule. An alert may be generated, when a new schedule or an update to a physician&#39;s schedule is received in the database. Alerts may also be generated upon the entry of a new communication in the database. 
     As previously discussed, according to aspects of the present disclosure the search terms of the query may be expanded. In the above-mentioned examples, the search terms may be expanded if desired. Additionally, according to aspects of the present disclosure, in some cases, one or more data sources from the multiple data sources are selected when results of the query are greater than or equal to an accuracy thresholds. In the above mentioned examples, the selection of one or more data sources based on the accuracy threshold may be performed as desired. Still, the above mentioned examples are not limited to expanding the query and/or selecting one or more data based on the accuracy threshold. 
       FIG. 13  illustrates a flow diagram for a method  1300  of analyzing data according to aspects of the present disclosure. As shown in  FIG. 13 , at block  1302 , a data extraction system receives a query for non-textual data. The query may be generated via a first user. The non-textual data may be an image, audio, or other type of data. The non-textual data may be the entire data or an object in the data. For example, the query may be for an entire image or an object in the image. 
     At block  1304 , the data extraction system executes the query on multiple data sources. The data sources may be public and/or private data sources. For example, one of the data sources may be a deep web data source. At block  1306 , the data extraction system selects one or more data sources from the multiple data sources. The one or more data sources may be selected based on results of the query being greater than or equal to an accuracy thresholds. 
     At block  1306 , the data extraction system monitors, based on a set schedule, the one or more data sources to extract non-textual data when an update to stored data matches the query and/or newly added non-textual data matches the query. The monitoring may include deploying extractors to each of specific data source. The number of extractors may be dynamic. For example, the number of extractors may be based on a traffic volume of each specific data source Finally, at block  1308  the data extraction system establishes a communication channel with a second user based at least in part on the data extracted from the at least one monitored data source. 
     As used herein, the term “determining” encompasses a wide variety of actions. For example, “determining” may include calculating, computing, processing, deriving, investigating, looking up (e.g., looking up in a table, a database or another data structure), ascertaining and the like. Additionally, “determining” may include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory) and the like. Furthermore, “determining” may include resolving, selecting, choosing, establishing, and the like. 
     As used herein, a phrase referring to “at least one of” a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover: a, b, c, a-b, a-c, b-c, and a-b-c. 
     The various illustrative logical blocks, modules and circuits described in connection with the present disclosure may be implemented or performed with a processor configured to perform the functions discussed in the present disclosure. The processor may be a neural network processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array signal (FPGA) or other programmable logic device (PLD), discrete gate or transistor logic, discrete hardware components or any combination thereof designed to perform the functions described herein. The processor may be a microprocessor, controller, microcontroller, or state machine specially configured as described herein. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or such other special configuration, as described herein. 
     The steps of a method or algorithm described in connection with the present disclosure may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in storage or machine-readable medium, including random access memory (RAM), read only memory (ROM), flash memory, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, a hard disk, a removable disk, a CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. A software module may comprise a single instruction, or many instructions, and may be distributed over several different code segments, among different programs, and across multiple storage media. A storage medium may be coupled to a processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. 
     The methods disclosed herein comprise one or more steps or actions for achieving the described method. The method steps and/or actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of steps or actions is specified, the order and/or use of specific steps and/or actions may be modified without departing from the scope of the claims. 
     The functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in hardware, an example hardware configuration may comprise a processing system in a device. The processing system may be implemented with a bus architecture. The bus may include any number of interconnecting buses and bridges depending on the specific application of the processing system and the overall design constraints. The bus may link together various circuits including a processor, machine-readable media, and a bus interface. The bus interface may be used to connect a network adapter, among other things, to the processing system via the bus. The network adapter may be used to implement signal processing functions. For certain aspects, a user interface (e.g., keypad, display, mouse, joystick, etc.) may also be connected to the bus. The bus may also link various other circuits such as timing sources, peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further. 
     The processor may be responsible for managing the bus and processing, including the execution of software stored on the machine-readable media. Software shall be construed to mean instructions, data, or any combination thereof, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. 
     In a hardware implementation, the machine-readable media may be part of the processing system separate from the processor. However, as those skilled in the art will readily appreciate, the machine-readable media, or any portion thereof, may be external to the processing system. By way of example, the machine-readable media may include a transmission line, a carrier wave modulated by data, and/or a computer product separate from the device, all which may be accessed by the processor through the bus interface. Alternatively, or in addition, the machine-readable media, or any portion thereof, may be integrated into the processor, such as the case may be with cache and/or specialized register files. Although the various components discussed may be described as having a specific location, such as a local component, they may also be configured in various ways, such as certain components being configured as part of a distributed computing system. 
     The processing system may be configured with one or more microprocessors providing the processor functionality and external memory providing at least a portion of the machine-readable media, all linked together with other supporting circuitry through an external bus architecture. Alternatively, the processing system may comprise one or more neuromorphic processors for implementing the neuron models and models of neural systems described herein. As another alternative, the processing system may be implemented with an application specific integrated circuit (ASIC) with the processor, the bus interface, the user interface, supporting circuitry, and at least a portion of the machine-readable media integrated into a single chip, or with one or more field programmable gate arrays (FPGAs), programmable logic devices (PLDs), controllers, state machines, gated logic, discrete hardware components, or any other suitable circuitry, or any combination of circuits that can perform the various functions described throughout this present disclosure. Those skilled in the art will recognize how best to implement the described functionality for the processing system depending on the particular application and the overall design constraints imposed on the overall system. 
     The machine-readable media may comprise a number of software modules. The software modules may include a transmission module and a receiving module. Each software module may reside in a single storage device or be distributed across multiple storage devices. By way of example, a software module may be loaded into RAM from a hard drive when a triggering event occurs. During execution of the software module, the processor may load some of the instructions into cache to increase access speed. One or more cache lines may then be loaded into a special purpose register file for execution by the processor. When referring to the functionality of a software module below, it will be understood that such functionality is implemented by the processor when executing instructions from that software module. Furthermore, it should be appreciated that aspects of the present disclosure result in improvements to the functioning of the processor, computer, machine, or other system implementing such aspects. 
     If implemented in software, the functions may be stored or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media include both computer storage media and communication media including any storage medium that facilitates transfer of a computer program from one place to another. Additionally, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared (IR), radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray® disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Thus, in some aspects computer-readable media may comprise non-transitory computer-readable media (e.g., tangible media). In addition, for other aspects computer-readable media may comprise transitory computer-readable media (e.g., a signal). Combinations of the above should also be included within the scope of computer-readable media. 
     Thus, certain aspects may comprise a computer program product for performing the operations presented herein. For example, such a computer program product may comprise a computer-readable medium having instructions stored (and/or encoded) thereon, the instructions being executable by one or more processors to perform the operations described herein. For certain aspects, the computer program product may include packaging material. 
     Further, it should be appreciated that modules and/or other appropriate means for performing the methods and techniques described herein can be downloaded and/or otherwise obtained by a user terminal and/or base station as applicable. For example, such a device can be coupled to a server to facilitate the transfer of means for performing the methods described herein. Alternatively, various methods described herein can be provided via storage means, such that a user terminal and/or base station can obtain the various methods upon coupling or providing the storage means to the device. Moreover, any other suitable technique for providing the methods and techniques described herein to a device can be utilized. 
     It is to be understood that the claims are not limited to the precise configuration and components illustrated above. Various modifications, changes, and variations may be made in the arrangement, operation, and details of the methods and apparatus described above without departing from the scope of the claims.