Abstract:
A computer-implemented method comprising collecting data from a plurality of information sources, identifying a geographic location associated with the data and forming a corresponding event according to the geographic location, correlating the data and the event with one or more topics based at least partly on the identified geographic location and storing the correlated data and event and inferring the associated geographic location if the data does not comprise explicit location information, including matching the data against a database of geo-referenced data.

Description:
BACKGROUND 
       [0001]    1. Field 
         [0002]    Embodiments of the present invention generally relate to data correlation systems and, more particularly, to a method and apparatus for correlating and viewing disparate data. 
         [0003]    2. Description of the Related Art 
         [0004]    John Naisbitt&#39;s famous words often seem truer in today&#39;s world than ever before: “We are drowning in information, but starved for knowledge.” Increasingly, there are many different, widely available sources of data such as social networks, news sites and newsfeeds, blogs, webcams, and a wide variety of other private and public sources for diverse types of data including photos, videos, and textual content. This creates a growing need for better, more coherent ways to correlate, and to derive semantic information from, the multiple multi-modal sources of information, and to view and navigate all of this data in an organized and meaningful way. Conventional search engines and information retrieval systems, however, are often weak at synthesizing data from multiple sources and channels over multiple modalities that needs to be correlated and “aligned” along multiple dimensions such as geo-space, time, with other entities, events and their semantics. 
         [0005]    Current research on cross-modal association tends to rely on an underlying assumption that the different modalities have strongly correlated temporal alignment, which is not always the case. The “Semantic Web” (see http://www.w3.org/2001/sw/) is an example of a technological approach to enable derivation of meaning and associations from web-based content that has been manually semantically “tagged”. However, much of the data that is available and continues to be published on the Internet is not semantically tagged at present. Geo-location, for example, can potentially be an important cue in cross-modality association. However, much of the image and video content available on today&#39;s Internet may not include location metadata, much less precise geo-location and orientation coordinates, and so it cannot readily be correlated and reasoned about with regard to its geographical location, for example. Broadly speaking, cross-modality association is difficult in part because it entails interpreting signals at a semantic level in order to make correlations, and there remain significant technological challenges in solving the problem of correlating cross-modal data to produce meaningful inferences. 
         [0006]    Therefore, there is a need in the art for a method and apparatus for aligning, correlating and viewing disparate data along multiple dimensions (geo-space, time, entities, events and their semantics) in order to produce meaningful inferences, based on cross-modal data streams. 
       SUMMARY 
       [0007]    Embodiments of the present invention relate to an apparatus for collecting data from a plurality of information streams comprising a collection module, a geo-localization module, coupled to the collection module, for geo-localizing the plurality of information streams to identify a geographic location for multiple events extracted from the data, and a correlation module, coupled to the geo-localization module and the collection module, for correlating the plurality of information streams based on the geo-localization and the event and storing correlation data in a database. 
         [0008]    Embodiments of the present invention relate to a computer-implemented method comprising collecting data from a plurality of information sources, identifying a geographic location associated with the data and forming a corresponding event according to the geographic location, correlating the data and the event with one or more topics based at least partly on the identified geographic location and storing the correlated data and event and inferring the associated geographic location if the data does not comprise explicit location information, including matching the data against a database of geo-referenced data. 
         [0009]    Embodiments of the present invention relate to an apparatus for comprising a collection module for collecting data from a plurality of information sources, a geo-localization module, coupled to the collection module, for identifying a geographic location associated with the data and forming a corresponding event according to the geographic location, a correlation module, coupled to the geo-localization module and the collection module, correlating the data and the event with one or more topics based at least partly on the identified geographic location and storing correlation data in a database and an inferencing module for inferring the associated geographic location if the data does not comprise explicit location information, including matching the data against a database of geo-referenced data. 
         [0010]    Further embodiments of the present invention relate to a computer-implemented method comprising collecting data from a plurality of information sources, identifying a geographic location associated with the data for the event and displaying a computer-generated map wherein the data can be accessed via user interaction with the map based on the geographic locations identified respectively for the data. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0011]    So that the manner in which the above recited features of embodiments of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to typical embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments. 
           [0012]      FIG. 1  is a functional diagram of an exemplary embodiment of an apparatus for correlating and viewing disparate data in accordance with at least one of the embodiments of the present invention; 
           [0013]      FIG. 2  is a block diagram of the situational analysis module (SANM) in accordance with an exemplary embodiment of the present invention; 
           [0014]      FIG. 3  is an illustration of the output of a visualization module in accordance with at least one embodiment of the present invention 
           [0015]      FIG. 4  is a block diagram depicting an exemplary embodiment of a computer system in accordance with one or more aspects of the invention; 
           [0016]      FIG. 5  is a block diagram of a situational awareness module in accordance with one or more aspects of the invention; 
           [0017]      FIG. 6  is a functional block diagram showing an example of generating an inference through inferencing module; 
           [0018]      FIG. 7  is a flow diagram for a method for correlating disparate data in accordance with at least one embodiment of the present invention; 
           [0019]      FIG. 8  is a flow diagram for a method for extracting events in accordance with at least one embodiment of the present invention; and 
           [0020]      FIG. 9  is a flow diagram for a method for displaying related events to a user in accordance with at least one embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0021]    Embodiments of the present invention relate to a method and apparatus for automatically correlating data from independent, distributed data sources (online or offline), and ad-hoc sensors as well as planned sensors. Some embodiments include methods for geo-locating the data, in order to facilitate correlating data regarding currently occurring events with respect to particular locations. Various statistical analyses and image processing techniques are utilized in order to extract location specific information and to otherwise correlate the data; in some embodiments, such techniques are also employed to predict future movements and changes within a situation represented by the data. The analyzed and correlated data is presented in a navigable and interactive manner to a user of the system. In some embodiments, a common operating picture (COP) for a current situation of interest is produced, providing situational awareness to a plurality of commanders, analysts, or other cooperating users. 
         [0022]      FIG. 1  is a functional diagram of an exemplary embodiment of an apparatus  100  for correlating and viewing disparate data in accordance with one or more embodiments of the present invention. In an exemplary embodiment, the apparatus  100  comprises an information stream collection module (collection module)  102 , a geo-localization module  104 , an inferencing module  106 , a correlation module  108 , a situational awareness module (SAWM)  110 , a situational analysis module (SANM)  112 , a query module  114 , a visualization module  118  and a database  116 , all coupled to a central communication bus  103  for inter-module communication. The apparatus  100  is coupled to the internet via network  101 . The network  101  enables the apparatus to be remotely coupled to various data streams produced by a plurality of independent, distributed data sources, ad-hoc sensors (or “sensors in the wild”) such as DS 1 , DS 2  . . . DSN. In an exemplary embodiment, the apparatus  100  may also receive data from planned sensors S 1  . . . Z, where Z, the number of planned sensors and N, the number of ad-hoc sensors are adjustable based on the needs of particular applications. In an exemplary embodiment, the data streams DS 1 , DS 2  . . . DSN comprise online news feeds, Twitter® feeds, Facebook® feeds, data from various social networks, audio/video feeds, and the like. Accordingly, the apparatus  100  is configurable to monitor many diverse feeds, and users of apparatus  100  can add data feeds on a real-time basis. 
         [0023]    The collection module  102  is configured to periodically extract data from the various data streams DS 1  . . . DSN through the network  101 . The collection module  102  works with any type of data stream, can extract entities and events in the stream, and space-time semantic relationships between the events and entities. According to one embodiment, the collection module  102  works in continuous mode and operates on streaming data. The collection module  102  extracts data and stores the data in the database  116  according to configuration parameters of the apparatus  100  such as period of extraction, data stream list, and the like. The geo-localization module  104  analyzes the data in the database  116 , and preferably uses a variety of techniques to automatically identify the geographic location of particular scenes and situations that are depicted in or referred to in the data and to form an event based on the geo-localization. For example, for textual content, the system may use keyword and/or semantic analysis of the content to determine relevant location(s). For audio content, voice recognition techniques may first be employed to convert the audio signal to text. For video and other image content, if location metadata is included, then geo-localization module  104  may determine image location from the meta-data in a straightforward manner; otherwise, in some embodiments, module  104  automatically determines locations of images by employing techniques such as analysis and matching of landmarks and other image features against an existing geo-referenced image database (e.g. a satellite image database, Google Earth, etc.) as described in commonly assigned and co-pending U.S. provisional patent applications Attorney Docket #6346 and #6385, hereby incorporated in their entirety by this reference. If extremely precise registration of image content extracted by collection module  102  against reference images is desired, then registration techniques such as described in U.S. Pat. No. 6,597,818 filed Mar. 9, 2001, which is hereby incorporated by reference in its entirety herein, can also be employed. 
         [0024]    The geo-localization module  104  is coupled to the collection module  104  as well as the correlation module  108  through the communication bus  103 . According to an exemplary embodiment, the correlation module  108  is also coupled to the collection module  102  and the database  116 . The database  116  stores geographical matching data as well as the correlated data from the various data streams S 1  . . . SZ and DS 1  . . . DSN. The database  116  is indexed in a way that accessing data is fast and efficient. In exemplary embodiments, the database  116  is indexed categorically, i.e., by keywords, geographic location as determined by the geolocalization module  104  and event and object indexing by the situational analysis module  112 , amongst others. Image descriptors and visual features, descriptions and visual features of videos, categorizing tags and the like are stored in the database  116  to facilitate semantic alignment of the multiple media streams, as described in more detail below, and to facilitate user querying through the query module  114 . 
         [0025]    The correlation module  108  correlates data from the various information streams in the database  116  with each other. The correlation module  108  generates groups of correlations and stores these in, for example, relational database tables in database  116  corresponding to the formed events. For example, if a textual news report regarding the formation of a protest is extracted from DS 1 , video data of the protest is extracted from DS 2 , and audio clips from analysts discussing the protest and possible spill-over into other locations and the like are extracted from DS 3 , the correlation module  108  correlates all of these various streams with each other as relating to a particular protest, by semantically analyzing text (including text derived by recognition of audio) and/or imagery to recognize, for example, the overarching theme of a protest and perhaps keywords, names, faces, or other characteristics associated with the particular protest event, along with geo-location data determined by the geo-localization module  104 . In other instances, sets of definitions and models describing current events of interest, are manually input to correlation module  108  to determine correlations. The correlation module  108  is coupled to the situational analysis module  112  for analyzing the correlated data by extracting entity classes such as groups of people, vehicles and the like, and reasoning about the extracted entities within a scene (e.g., recognizing what appears to be a group of people chasing someone or surrounding a building, etc.). Additionally, the correlation module  108  couples with the SAWM  110  to determine, based on the extracted entities, possible strategies for addressing the protest from a particular perspective, for example, a law enforcement perspective, or a journalist&#39;s perspective. 
         [0026]    The extracted entity, event and relationship information (including correlation and situational analysis) is stored in database  116  and clients 1-N can subsequently submit a query through the query module  114  relating to, for example, a particular entity associated with the protest or about the protest itself. In some embodiments, query module  114  accepts simple natural language search terms such as “protest on Smith St.” and the like, and generates a corresponding database query. The visualization module  118  renders a view for users of client 1 . . . N of the correlated data responsive to a user&#39;s query. Based on the users selection of output preferences for client 1 . . . N, the visualization module  118  will form the data and only show those entities/events that are responsive to the user&#39;s query, or that the correlation module  108  has determined are correlated to events and entities responsive to the user&#39;s query, based on a statistical analysis. To use the prior example, if a user is interested in the “Protest on Smith St.,” the correlation module also correlates similar protests in other locations, and shows media related to those events through the visualization module  118  to clients 1 . . . N. The correlation module  108 , according to this embodiment, performs correlations in real-time based on user&#39;s searches. In other embodiments, the correlation module  108  performs correlations passively, i.e., correlates data collected through the collection module  102  and performs the correlations in the background and these correlations are the basis of search results provided to users. In addition, in some embodiments, the SAWM  110  creates a common operating picture (COP)  120 , where the COP shows strategic routes and paths, movement of entities within an area, danger zones and the like. The COP assists collaborating analysts in evaluating breaking events by providing them with a consistent, up-to-date picture of a crisis through the visualization module  118 . 
         [0027]    In exemplary embodiments, the apparatus  100  is a server which accepts query requests through the query module  114  and sends responses of events and analysis based on the received query to various external sources such as search engines, direct clients, downstream information services and the like. In other embodiments, the apparatus  100  hosts an interactive online portal where users can view the data in various ways as shown in  FIG. 3  below. The inferencing module  106  is coupled to the database  116  as well as the query module  114  and makes predictions based on recognizing patterns of movement, moods of the crowd, and the like. 
         [0028]      FIG. 2  is a block diagram of the situational analysis module (SANM)  112  and its various couplings in apparatus  100  in accordance with an exemplary embodiment of the present invention. The SANM  112  analyzes the correlated data by extracting entity classes such as groups of people, vehicles and the like, and reasoning about the extracted entities within a scene. The SANM  112  comprises an event extractor, and a semantic correlation module  210 . The Event Extractor can work with multiple modalities such as text streams, documents, images and videos amongst others to extract textual and image data of interest and their attributes from the events stored in the database  116  by the correlation module  108 . Events and their attributes can be extracted from text streams and documents using statistical text analysis and distributional alignment and co-clustering as described in the paper entitled “New Experiments in Distributional Representations of Synonymy,” Ninth Conference on Computational Natural Language Learning (CoNLL), Ann Arbor, Mich., (2005) by Dayne Freitag, Matthias Blume, John Byrnes, Edmond Chow, Sadik Kapadia, Richard Rohwer, and Zhiqiang Wang as well as “Simmered Greedy Optimization for Co-clustering,” ITNG, Seventh International Conference on Information Technology, Las Vegas, pp. 410-419, (2010) by Sadik Kapadia and Richar Rowher, both herein incorporated by reference in their entirety. The SANM  112  is coupled with the database  116  as well as the query module  114  and the visualization module  118  through communication bus  103  (not shown). SANM  112  performs analysis on the data correlated by the correlation module  108 . The SANM  112  couples with the database  116  to retrieve and analyze the data. The event extractor  202  parses the multi-media data and extracts frames  204  of video or images, or descriptions of those frames and textual data  206  highlighting newscasts, TWEETS®, blog and FACEBOOK® posts and the like. 
         [0029]    The feature extraction module  208  analyzes frames  204  by algorithmically classifying entities such as moving persons and vehicles into groups for tracking, for example by performing a “history of oriented occurrences” algorithm as described in commonly owned pending U.S. patent application Ser. No. 12/489,667, which is incorporated by reference in its entirety herein. In addition to the HO2 like techniques, event extraction in images and videos can be carried out by computing features related to movement, appearance, shape and semantic entities such as people, vehicles, etc. Furthermore, unknown events of interest can be discovered by computing distributions over features and looking for anomalies with respect to known events or statistical trends. 
         [0030]    The semantic extraction module  210  parses the textual data  206  to extract entities based on correlated data from the correlation module  108 . The output of the semantic extraction module  210  and the feature extraction module  208  combine to generate a complete analysis  209  related to the clients&#39; 1 . . . N particular query through the query module  114 . The visualization module  118  organizes and groups the various data points into a seamless view for simple navigation and easy access to relevant data by the clients  116   1 . . . n . Additionally, spatial and temporal gating is used to limit the size of semantic alignment problems associated with the semantic extraction module  210  For example, if a first sensor (not shown in  FIG. 2 ) observed an event, say people running out of a building, and a second sensor (not shown in  FIG. 2 ) observed people running into a building, based on the geo-location of the two sensors and buildings, and the time of the two events, it can be determined whether the two events are correlated. Semantic alignment addresses the issue of where multiple streams from the data streams are related to the same event and need to be aligned semantically. 
         [0031]    In other exemplary embodiments, face-name association is performed using statistical matching to present the user with associated text, video and other multimedia about particular popular personalities, as described in the [Nadeu 2007], [Wacholder 1997] and [Berg 2004] papers, herein incorporated by reference in their entirety.
   [Nadeu 2007]: http://cogprints.org/5859/1/Thesis-David-Nadeau.pdf   [Wacholder 1997]: Nina Wacholder, Yael Ravin, Misook Choi:  Disambiguation of Proper Names in Text.    ANLP  1997: 202-208.   [Berg 2004]:  Names and Faces in the News,  Tamara L. Berg, Alexander C. Berg, Jaety Edwards, Michael Maire, Ryan White, Yee Whye Teh, Erik Learned-Miller, David A. Forsyth  Computer Vision and Pattern Recognition  ( CVPR ), 2004.   
 
         [0035]      FIG. 3  is an illustration of the output of the visualization module  118  in accordance with at least one embodiment of the present invention. Client window  302  represents a client&#39;s view when using a web-browser to access services offered by the apparatus  100 . In other embodiments, client  302  may be a thick client or a thin client accessing web services published by the apparatus  100 . Several different types of clients can simultaneously access the services of apparatus  100 . A user accesses the services of apparatus  100  through client window  302 , by submitting a search term or the like to the query module  114 . The query module  114  couples to the database  116  through the communication bus  103  to generate a real-time or near real-time list of events related to the input query as they occur and as data for such events is publicly posted in online data sources. In an exemplary embodiment, the view is divided into several sections of news, where group  304   1  shows real-time local geographic news,  304   2  shows real-time entertainment news and group  304   n  showing real-time sports news. These groups  304   1 . . . n  are populated according to the user&#39;s query. Additionally, the client window  302  can show related relevant items and predictions inferred by inferencing module  106  of the apparatus  100 . The groups  304   1 . . . n  are being continually updated by the SANM  112  which is constantly monitoring database changes for new information streams based on the user&#39;s interests. The SANM  112  may also be driven by system level criteria in some embodiments such as preconfigured settings for particular topics, events, locations or the like, according to the preference of a user or organization. 
         [0036]    Client window  306  is also accessible remotely through a web-browser window or on a thick client display via execution of a desktop application, and the like. Client window  306  displays an Earth model  307 , manipulable by a client cursor to display differing sections of the globe. A user may select a presentation filter using drop-down box  310 . In exemplary embodiments, filters can be entities such as individuals or cars, objects, specific events and the like. Further, the model  307  is shown according to a modality selected by the user. The user may also select a different modality such as a conceptual modality showing groupings of concepts, a breaking-news modality or a climate modality each of which may be represented as a different graphical user interface. A user may view all related media for a particular filter and the global view  307  displays various event labels such as  308   1  to  308   4 . The user may select a particular event label to read a story and watch associated videos and an analysis of a particular event, enter a new query term to repopulate the global view with new labels, or rotate the globe view as new relevant stories are correlated by apparatus  100  and updated to the view by the visualization module  118 . 
         [0037]    In an exemplary embodiment, the apparatus  100  makes predictions based on the existing data in database  116  and, while the correlation module  108  is correlating data, statistical interpretation is performed by the SAWM  110  (as described below) and the situational assistant  111  assists those in need of “on the ground” knowledge, exit strategies, paths, and the like. 
         [0038]    In some embodiments, client windows  302  and  306  present an organized view of events selectively chosen for their particular relevancy to not only the user&#39;s query, but an individual long-term profile of the user indicating e.g. locations, categories, and people of interest to that user. Such a profile can be created by soliciting explicit user input (e.g. by questionnaire), and/or can be inferred by system  100  by employing machine learning algorithms with respect to the interactive viewing selections made over time by various users. A meaningful organization of images and videos is presented to the user, where the images and videos are a subset of relevant data items that are also viewable on user, as opposed to all relevant data available in the database  116 . The user may also place the views into static mode that does not update in real-time, or customize the updating of the information streams to only update with a particular frequency. Alternatively, the user may view all event data to synthesize responsive data in a visually coherent way, for example, by integrating time-sequenced, geo-located images and text with a map of the location of interest for important events and the like for future querying, or just as a world-wide event viewer. 
         [0039]      FIG. 4  is a block diagram depicting an exemplary embodiment of a computer system  400  in accordance with one or more aspects of the invention. The computer system  400  represents one embodiment of a system used to implement the apparatus  100 . The computer system  400  includes a processor  402 , a memory  404  and various support circuits  406 . The processor  402  may include one or more microprocessors known in the art, and/or dedicated function processors such as field programmable gate arrays programmed to perform dedicated processing functions. The support circuits  406  for the processor  402  include microcontrollers, application specific integrated circuits (ASIC), cache, power supplies, clock circuits, data registers, I/O interface  407 , and the like. The I/O interface  407  may be directly coupled to the memory  404  or coupled through the supporting circuits  406 . The I/O interface  407  may also be configured for communication with input devices and/or output devices  408 , such as, network devices, various storage devices, mouse, keyboard, displays, sensors and the like. 
         [0040]    The memory  404  stores non-transient processor-executable instructions and/or data that may be executed by and/or used by the processor  402 . These processor-executable instructions may comprise firmware, software, and the like, or some combination thereof. Modules having processor-executable instructions that are stored in the memory  404  comprise a collection module  410 , a geo-localization module  412 , an inferencing module  412 , a correlation module  416 , a situational awareness module  420 , a situational analysis module  418 , a query module  436  and a visualization module  438 . The situation analysis module  418  further comprises an event extractor  422 , a feature extraction module  424  and a semantic extraction module  426 . The situational awareness module  420  comprises a planning module  428 , a response module  430  and a situation assistant  432  for generating a plan of action  433 . In an exemplary embodiment, the memory  404  may include one or more of the following: random access memory, read only memory, magneto-resistive read/write memory, optical read/write memory, cache memory, magnetic read/write memory, and the like, as well as signal-bearing media as described below. 
         [0041]      FIG. 5  is a block diagram of the situational awareness module  110  with its various couplings in apparatus  100  in accordance with one or more aspects of the invention. The SAWM  110  comprises a planning module  502 , a response module  504 , and a situation assistant  506 . The SAWM  110  is coupled with the database  116  to retrieve correlated objects, events and locations. The planning module  502  uses six degrees of freedom (6 DOF) estimates to plan exit strategies and exit routes, as described in commonly assigned pending U.S. patent application Ser. No. 13/217,014, which is hereby incorporated by reference in its entirety. The response module  504  uses the 6 DOF estimates to plan coordinated responses to situations which may be possibly dangerous. The situation assistant  506  evaluates the planning and responses and develops a plan of action  510  for personnel involved in the situation or event being analyzed to follow and execute. 
         [0042]      FIG. 6  is a functional block diagram showing an example of generating an inference through inferencing module  106 . Sensors in the wild  602  along with planned sensors, such as aerial cameras and the like, capture videos and images of objects and things such as people and locations, collectively referred to as entities  604 . In this example, image, audio, and/or other sensory data captured by sensors  602  is augmented with related information about each entity, such as the names of buildings, the distances between them, actors and their paths, and location information. According to one embodiment, the augmentation is accomplished by transmitting a request to a geographical information system (GIS) web service database with road network and urban infrastructure data. The entities  604  and the related data for each are collected by the collection module  102  and stored in the database  116 . As needed, geolocalization module  104  locates the entity in a particular location and the correlation module  108  correlates the various different types of data streams involving the entities. The inferencing module  106  draws one or more inferences  606  based on scene data gathered by sensors  602 . For example, analysis of video frames captured by one or more of sensors  602  could result in recognition of events including: (a) at 7:46 AM, a car rapidly gained speed and departed the area of 30 th  St. Station, (b) at 7:47 AM, an explosion occurred on Track 6 of 30 th  St Station, (c) at 7:48 AM, crowds of people ran towards Exits 2 and 3, and (d) a stampede was observed at 7:50 AM″ Higher-level, explanatory inferences drawn by module  106  may include that the explosion was the reason for the running crowd and the resulting stampede, and may include a hypothesis that the speeding car was driven by perpetrators of the explosion. Inferences will be derived using representation of entities, events and their relationships including locations, time, etc, with graphical models. Graphical models enable representation of complex events and interactions using local dependencies. Inferencing with graphical models is used to derive statistical distributions for the represented entities, events and their relationships. 
         [0043]      FIG. 7  is a flow diagram for a method  700  for correlating disparate data in accordance with at least one embodiment of the present invention. The method  700  is an implementation of the operation of apparatus  100  in  FIG. 1 , stored in memory  404  of the computer  400  and executed by the processor  402 . The method begins at step  702  and proceeds to step  704  where data is collected from online data sources or sensors, including “Sensors in the wild” (ad-hoc sensors) shown in  FIG. 1  DS 1 . . . N  as well as planned sensors S 1 . . . Z . Step  704  is performed by the collection module  102 , where in one embodiment the collection module includes one or more web-crawlers searching online data sources such as news feeds, Twitter® feeds, Facebook® posts and the like. Such crawling may be performed, in some embodiments, at periodic intervals according to a base metric determined by a configuration of the apparatus  100 . The online data sources may be publicly available, and in some embodiments one or more of the data sources may comprise personal or private sources available only to an individual user or to a select group of users. 
         [0044]    At step  706 , the geo-localization module  104  identifies the location of (“localizes”) the data collected from the sensors and encodes these locations in searchable tags associated with the corresponding items of localized data. If location information is explicitly provided (e.g. as metadata) for a given item of data, then localizing such data is straightforward. When such metadata is not provided, then module  104  seeks to infer location using one or more applicable automated techniques. For example, textual content is parsed for any locations mentioned; the IP address of a live feed can be examined to establish a presumed or estimated geographic location of the data collected from that source; video and other image content can be analyzed using feature matching techniques with respect to a known geo-referenced image database. For example, given an area-of-interest such as a neighborhood or city, a feature database is created using both satellite (SAT) and oblique bird&#39;s eye-view (BEV) imagery covering the area. The SAT and BEV data are fully geo-referenced and, according to an exemplary embodiment, are received through satellite communications. SAT provides orthographic top-down views of the scene while BEV provides oblique viewpoints. By combining the two, building outlines as well as building facades can be extracted. SAT images provide the outlines while one or more of the BEV images are used to locate the corresponding buildings and extract facades. Features are computed that capture the statistical self-similarity (or dissimilarity) of local patches on a building facade with respect to other patches on the facade. Since these features essentially capture the local appearance statistics, they are robust to viewpoint and global appearance changes and can be computed in a similar manner for street view images collected from the sensors shown in  FIG. 1  as well and then robustly matched with the features stored in the feature database. The location of the street view image can thus be determined from the known location of the matching, geo-referenced satellite data. For further information, refer to U.S. Provisional Patent Application Attorney Docket Number SRI #6346-2P. 
         [0045]    Once each feed is geo-localized, the results are stored in the database  116 . The method then moves to step  708  where the textual, graphical, audio, video and other data items are correlated with each other based on the geographic location identified at step  706  and based on other information (tags descriptors, subject matter, topics, etc.). The correlations are also stored in database  116 . The method  700  uses the situational analysis module  112  to analyze the correlated data and extract entities to base a situational analysis on in step  710 . The entities may comprise, for example, traffic patterns, road blocks, famous personalities who have tweeted they are in the area, breaking news nearby, places of interest and the like. For example, if a user searches for “Times Square,” the SANM  112  may generate information about shopping stores, restaurants and the like. A user may also filter the search query for more precise information about Times Square. At step  712 , the situational awareness module  110  determines possible strategies for addressing the situational based on the situational analysis performed by the SANM  112 , such as exit strategies, suggested tactical maneuvers, efficient routes for shopping and the like. 
         [0046]    At step  714 , the inferencing module  106  infers predictions based on the situational analysis and possible strategies and infers the location of data where location information was not initially available. The inferencing module  106  may also use the correlated data and situational analysis to statistically determine predictive attributes of a situation developing at a sporting event or shopper&#39;s moods during a holiday and predicting where the best sales are or where people are focusing their attention. The method ends at step  716 . 
         [0047]      FIG. 8  is a flow diagram for a method  800  for extracting events in accordance with at least one embodiment of the present invention. The method  800  is an implementation of the operation of situational analysis module  112  in  FIG. 1  stored in memory  404  of the computer  400  and executed by the processor  402 . The method  800  begins at step  802  and proceeds to step  804  where the information streams from S 1  . . . Z and DS 1  . . . DSN are analyzed to extract events performed by the SANM  112 . The SANM retrieves data from database  116  and uses the event extractor  202  to extract video frames  204  and textual data  206 . At step  806  of the method, the feature extraction module  208  performs a histogram of oriented occurrences of the video frames to establish entity classes and to track movement in those classes and actions taken by members of the classes, as described in pending U.S. patent application Ser. No. 12/489,667. At step  808 , the semantic correlation module  210  performs semantic correlation for correlating the frames and text data to establish that the frames and text are linked, as described in commonly assigned, U.S. Patent Applications Attorney Docket #6503, #6515, and #6554, herein incorporated by reference in their entirety. At step  810 , the entities are inferred based on correlation followed by entity extraction using HO2 or other suitable algorithmic techniques. The method ends at step  812 . 
         [0048]      FIG. 9  is a flow diagram for a method  900  for displaying related events to a user in accordance with at least one embodiment of the present invention. The method  900  is an implementation of the operation of visualization module  118  of  FIG. 1  stored in memory  404  of the computer  400  and executed by the processor  402 . The method begins at step  902  and proceeds to step  904  where the query module  114  of  FIG. 1  receives a query from a user such as a search term. The query is parsed and run against the database  116  at step  906  to search for events that are associated in database  116  with a searchable term, tag, descriptor or the like that matches the parsed query. The visualization module  118  groups retrieved events into categories at step  908  based on the correlated data from the correlation module  108 . At step  910 , an interactive view of the categories of events is presented to the user, where a user may manipulate the view and change the modality of the view, i.e., to a view of people of interest, or locations of interest. At step  912 , the view is updated periodically with breaking feeds relating to the entered query, until the user closes the viewer, ending the method at step  914 . 
         [0049]    The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the present disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as may be suited to the particular use contemplated. 
         [0050]    Various elements, devices, and modules are described above in association with their respective functions. These elements, devices, and modules are considered means for performing their respective functions as described herein. 
         [0051]    While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.