Patent Publication Number: US-2018039636-A1

Title: Prioritized retrieval and/or processing of data via query selection

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This present application is a continuation of U.S. patent application Ser. No. 15/409,499, entitled “PRIORITIZED RETRIEVAL AND/OR PROCESSING OF DATA VIA QUERY SELECTION,” filed on Jan. 18, 2017, which is a continuation-in-part of U.S. patent application Ser. No. 15/010,986, entitled “PRIORITIZED RETRIEVAL AND/OR PROCESSING OF DATA,” filed on Jan. 29, 2016, which is a continuation of U.S. patent application Ser. No. 14/634,680, entitled “PRIORITIZATION OF RETRIEVAL AND/OR PROCESSING OF DATA,” filed on Feb. 27, 2015, the content of each of which is hereby incorporated herein by reference in its entirety. 
    
    
     FIELD OF THE INVENTION 
     The invention relates generally to prioritizing retrieval and/or processing of data over retrieval and/or processing of other data. 
     BACKGROUND OF THE INVENTION 
     With the recent influx of smartphones, tablets, and/or other mobile devices having connectivity to the Internet, businesses, consumers, or other users are frequently receiving, analyzing, sharing, and creating new information. Obtaining such information may involve querying for data across a plurality of data sources. Because retrieval and/or processing of such data may be time consuming, and users are often not provided with their requested information until retrieval and processing of such data have finished, there may be considerable delay before a user is provided with any substantive information related to their request. These and other drawbacks exist. 
     SUMMARY OF THE INVENTION 
     The invention addressing these and other drawbacks relates to methods, apparatuses, and/or systems for prioritizing retrieval and/or processing of data over retrieval and/or processing of other data. In certain implementations, retrieval and/or processing of data related to one subset of attributes may be prioritized over retrieval and/or processing of data related to other subsets of the attributes to reduce latency related to providing a presentation of values associated with the attributes and/or to provide other benefits. As an example, the prioritization may be based on a prediction that one subset of the attributes (and its associated values) will be presented in a portion of a user interface that is initially “in-view” (e.g., seen by the user) and/or that the other subsets of the attributes (and their associated values) will be presented in a portion of a user interface that is initially “out-of-view” (e.g., not seen by the user). Based on the prioritization, for example, (1) data related to the prioritized attribute subset may be retrieved and/or processed before the retrieval and/or processing of data related to the other attribute subsets, (2) more computing resources may be allocated for retrieving and/or processing data related to the prioritized attribute subset than for retrieving and/or processing data related to the other attribute subsets, or (3) other prioritization techniques may be performed. 
     In some implementations, retrieval and/or processing of data related to a subset of attributes may be prioritized based on a prediction of values associated with the attributes. As an example, based on a request for values associated with a set of attributes, an initial query (or an initial set of “micro-queries”) may be performed to obtain an initial subset of data related to calculating values associated with the attributes. The associated values may then be predicted based on the initial data subset, and the predicted values may be utilized to prioritize retrieval and/or processing of data related to one subset of the attributes over retrieval and/or processing of data related to other subsets of the attributes. In one use case, for example, priority may be assigned to a particular subset of attributes when the predicted values associated with the subset of the attributes are greater than the predicted values associated with other subsets of the attributes (e.g., if the attributes and their associated values are to be presented in descending order). In another use case, priority may be assigned to a particular subset of attributes when the predicted values associated with the subset of the attributes are less than the predicted values associated with other subset of the attributes (e.g., if the attributes and their associated values are to be presented in ascending order). 
     In some implementations, an “attribute” may comprise an object, an entity, a property, a characteristic, etc., of the entity-attribute-value model (which may also be referred to as the “object-attribute-value model”) or other data model. As an example, with respect to the entity-attribute-value model, a value associated with two attributes may comprise a value associated with a characteristic of an entity where the characteristic is a first attribute and the entity is a second attribute. 
     These and other features of the present invention, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. As used in the specification and in the claims, the singular form of “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. In addition, as used in the specification and the claims, the term “or” means “and/or” unless the context clearly dictates otherwise. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The invention is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawing and in which like reference numerals refer to similar elements. 
         FIG. 1  illustrates a diagram of a system for prioritizing retrieval and/or processing of data, in accordance with one or more implementations. 
         FIG. 2  illustrates a data visualization user interface, in accordance with one or more implementations. 
         FIG. 3  illustrates a data visualization user interface, in accordance with one or more implementations. 
         FIG. 4  illustrates a data visualization user interface, in accordance with one or more implementations. 
         FIG. 5  illustrates an information exchange flowchart for prioritized retrieval and/or processing of data, in accordance with one or more implementations. 
         FIG. 6  illustrates an information exchange flowchart for prioritized retrieval and/or processing of data, in accordance with one or more implementations. 
         FIG. 7  illustrates a flowchart of processing operations for prioritizing retrieval and/or processing of data, in accordance with one or more implementations. 
         FIG. 8  illustrates another flowchart of processing operations for prioritizing retrieval and/or processing of data, in accordance with one or more implementations. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the implementations of the invention. It will be appreciated, however, by one skilled in the art that the implementations of the invention may be practiced without these specific details or with an equivalent arrangement. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the implementations of the invention. It should be noted that features (e.g., components, operations, or other features) described herein may be implemented separately or in combination with one another. 
       FIG. 1  illustrates a diagram of system  100  for prioritizing retrieval and/or processing of data related to a subset of attributes based on a prediction of associated values, in accordance with one or more implementations. System  100  may comprise one or more servers  102 . Server  102  (or servers  102 ) may be configured to communicate with one or more user devices  104  according to a client/server architecture (e.g., over communication network  106  or via other communication medium). Users may access system  100  via user devices  104 . 
     Server  102  may be programmed to execute one or more computer program components to facilitate prioritization of retrieval and/or processing of data. The computer program components may include a data request component  108 , a query component  110 , a value component  112 , a prioritization component  114 , a resource component  116 , a presentation component  118 , and/or other components. 
     The data request component  108  may be programmed to receive a request for values associated with respective attributes. A value may be associated with one or more attributes (e.g., an object, an entity, a property, a characteristic, etc.). As an example, with respect to  FIG. 2 , values represented by data elements  206   a - 206   y  may each be associated with at least one attribute represented in sections  202   a - 202   e , at least one attribute represented in sections  204   a - 204   e , and at least one attribute represented in sections  208   a - 208   e.    
     Presentation component  118  may be programmed to effectuate presentation of a data visualization user interface (e.g., at user devices  104 ). The data visualization user interface may be programmed to receive entry and/or selection by users of a first attribute group (associated with first attributes), a second attribute group (associated with second attributes), and/or other information. The user entry and/or selection may correspond to a request for values. The data visualization user interface may be programmed to present (predicted, updated, and/or calculated) values associated with a set of attributes (or a representation thereof) based on the request. 
     In some implementations, a data visualization user interface provided by presentation component  118  may be in the form of a pivot table (or other display style). The data visualization user interface may include rows, columns, data elements, other portions, and/or other interface elements. The rows and columns may represent respective ones of at least a first set of attribute (or first attributes) and a second set of attributes (or second attributes). Data elements may individually represent a value associated with a respective one of the first attributes and a respective one of the second attributes. For example, a data visualization user interface may include a construction of an N by M matrix where N represents a series of first attributes in rows and M represents a series of second attributes as columns. The intersection of N and M may represent the data elements representing the values associated with the first and second attributes. 
     For example,  FIG. 2  illustrates a diagram of a data visualization user interface  200  that facilitates the generation of requests for values and displays results from prioritized retrieval and/or processing of data. It is noted that the depiction of the user interface  200  and accompanying descriptions are intended for illustrative purposes only. For example, in some implementations, the procedures and/or processes described in connection with the user interface  200  may instead be performed without the presentation of values to a user. Instead, for instance, predicted values may simply be stored in a database for later use, placed into a queue for further processing, etc. 
     With that being said, in  FIG. 2 , portion  210  may represent a first attribute group (e.g., State, Gender, Zip Code, City, Income, Product Category, or other attribute group). Portion  202  may include sections  202   a - 202   e  that may represent at least a subset of attributes (e.g., Ala., Ak., Ariz., and/or other state; Male, Female, or other characteristic; 92007, 92014, 92024, and/or other zip code; 0- $25k, $25k - $50k, and/or other income or range; Electronics, Books, Jewelry, Accessories, Arts, Audio, Men&#39;s, Woman&#39;s, and/or other product category; and/or other attributes) included in the first attribute group. Portion  210  may include a first control element  211 . The first control element  211  may facilitate setting of a sorting preference for the attributes included in the first attribute group (e.g., thereby affecting the order in which the attributes may be presented). Sorting may be based on a description (e.g. name, numerical range, and/or other description) of respective ones of the attributes of the first attribute group, the values associated with the attributes of the first attribute group, or other criteria. For example, the first control element  211  may be used to sort the attributes of the first attributes group in ascending order based on their respective values, descending order based on their respective values, alphabetically based on the names of the first attributes, etc. As an example, the sorting may modify the subset of attributes that are displayed in sections  202   a - 202   e.    
     In  FIG. 2 , portion  212  may represent a second attribute group (e.g., State, Gender, Zip Code, City, Income, Product Category, and/or other attribute group). Portion  204  may include sections  204   a - 204   e  that may represent a subset of attributes (e.g., Ala., Ak., Ariz., and/or other state..; Male or Female; 92007, 92014, 92024, and/or other zip code; $0- $25k, $25k - $50k, and/or other income or range; Electronics, Books, Jewelry, Accessories, Arts, Audio, Men&#39;s, Woman&#39;s, and/or other product category; and/or other attributes) included in the second attribute group. Portion  212  may include a second control element  213 . The second control element  213  may facilitate setting a sorting preference for the attributes included in the second attribute group. The sorting may, for example, affect the attributes of the second attribute group that may be included in the subset of attributes displayed in sections  204   a - 204   e.    
     Portion  206  may include data elements  206   a - 206   y  that may represent at least some values associated with the attributes of the first and/or second attribute groups. The values conveyed by the data elements  206   a - 206   y  may be associated with respective ones of the attributes of the first attribute group in sections  202   a - 202   e  and respective ones of the attributes of the second attribute group in sections  202   a - 202   e . For example, data element  206   a  may be associated with a first attribute in section  202   a  and a second attribute in section  204   a.    
     Control elements  224 ,  226 , and/or  228  may facilitate user modifications to the data visualization user interface  200 . As an example, control element  224  may correspond to configuring and/or setting the attribute groups represented by the Columns and Rows of the pivot table (e.g.,  FIG. 3 ). Control element  226  may be selected to apply filters to explore other available data elements  206   a - 206   y  that may not currently be in the viewable area of portion  206  or may initiate requests for other values. Control element  228  may be selected to change the display style for depicting the attribute groups, attributes, values, and/or other information. Display styles may comprise a pivot table, a bar chart, a bubble graph, a pie chart, a scatter plot, a word cloud, a zoomable map, and/or other display style. 
     Still referring to  FIG. 2 , portion  214  may include a pause/play button  216  for pausing or playing the data visualization, an indicator  218  specifying that the data visualization is live data, and a sliding-based control element  220  having a movable control element  222  for rewinding to visualize historical data, fast-forwarding back to visualizing live data, fast-forwarding to visualize predictions of future data, etc. As shown by indicator  218 , the data visualization that is depicted in portion  206  may represent live data. A user may, for example, slide movable control element  222  to the left on sliding-based control element  220  to switch the data visualization of the live data to a data visualization of historical data. 
     In one use case, with respect to  FIG. 2 , data elements  206   a - 206   y  may represent values associated with attributes displayed in sections  202   a - 202   e  (and/or with value types displayed in section  208   a - 208   e ). The associated values may, for example, include the most up-to-date values for a certain time period thus far (e.g., a current day, week, month, year, etc.). Upon moving of movable control element  222  toward the left along sliding-based control element  220  onto a point associated with a particular time, a request for data elements associated with the displayed attribute (with respect to the particular time and/or time period) may be initiated. As an example, responsive to the request, a historical data store may be determined and used to obtain data necessary for determining values that may be represented by the data elements  206   a - 206   y.    
     Returning to  FIG. 1 , based on the selection of a first and/or a second attribute group, the query component  110  may be programmed to perform queries for data relating to attributes of the first attribute group, attributes of the second attribute group, and/or other attributes. The queries may be performed across a plurality of data sources. The queries may be performed over one or more query intervals (e.g., based on data sources, time periods, geographic areas, randomized, etc.). As an example, responsive to a user request for a data visualization of overall sales volume of various product categories during a particular year, at least first and second sets of queries may be performed (where each set comprises one or more queries). The first and second sets of queries may be performed in series or in parallel. When data from one set of queries is obtained, the obtained data may be processed to predict the values associated with the overall sales volume of the various product categories during the requested year without having to wait for data from the other set(s) of queries to be obtained. When data from at least one remaining set of queries is obtained, this data may then be processed to update the predicted values (e.g., if not all of the queries has returned data) and/or calculate the final values associated with the overall sales volume of the various product categories during the requested year (e.g., if data from all of the queries has been obtained). 
     In one use case, the first set of queries may comprise one or more queries to a first subset of the relevant data sources to be searched for the requested data, and the second set of queries may comprise one or more queries to a second subset of the relevant data sources for the requested data. In another use case, the first set of queries may comprise one or more queries for such data related to one or more time periods during the requested year, and the second set of queries may comprise one or more queries for such data related to one or more other time periods during the requested year. The time periods for each set of queries may, for example, comprise randomly-selected time periods during the requested year (e.g., randomly-selected amount of time per period, randomly-selected time periods with predefined amount of time per period), a predefined set of time periods during the requested year, or other time periods which together comprise a subset of the requested year. 
     In another yet use case, the first set of queries may comprise one or more queries for such data related to one or more geographic areas (e.g., a subset of zip codes per state, a subset of cities per state, a subset of regions per country, etc.), and the second set of queries may comprise one or more queries for such data related to one or more other geographical areas. The geographical areas for each set of queries may, for example, comprise randomly-selected geographical areas, a predefined set of geographical areas, or other geographical areas which together comprise a subset of an overall requested geographical area. 
     In some implementations, the query component  110  may randomly determine a first subset of queries to be performed (e.g., randomly selecting which queries to be executed), and the value component  112  may predict one or more values based on at least some data obtained from the first subset of queries. As an example, the obtainment of the data and/or the prediction of the values may be performed prior to performing one or more other queries derived from the same user request as the first subset of queries, prior to obtaining data from the performance of the other queries, etc. In one use case, the first subset of queries may comprise one or more randomly-selected queries to search a first subset of the relevant data sources for requested data. In another use case, the first subset of queries may comprise one or more randomly-selected queries for data related to one or more time periods (e.g., by day, by month, by year, etc.). In another use case, the first subset of queries may comprise one or more randomly-selected queries for data related to one or more geographic areas (e.g., by zip code, by city, by state, by country, etc.). In some implementations, the random selection may be a pseudorandom selection (e.g., by executing a RdRand instruction and applying a seed value, by applying probabilities or other weights to each query and sampling the queries based on such probabilities or other weights to select a subset of the queries, etc.). 
     In some implementations, responsive to a user request, query component  110  may randomly select a first subset of queries related to the user request to be performed, a second subset of queries related to the user request to be performed, or other subsets of queries to be performed. As an example, when data from the first subset of queries is obtained, the obtained data may be processed to predict one or more values associated with attributes of the requested data. When data from the second subset of queries is obtained, this data may then be processed to update the predicted values (e.g., if not all of the queries has returned data) and/or calculate the final values associated with the attributes of the requested data. In some implementations, the first and second subset of queries may be randomly selected in parallel. In some implementations, the first subset of queries may be randomly selected and processed prior to initiating the random selection of other subsets of queries (e.g., prior to the random selection of the second subset of queries or other subsets of queries). 
     In one use case, responsive to a user request for a data visualization of the overall sales volume of various product categories for a particular income range, a set of queries related to the user request may be generated. A first subset of queries may be randomly selected from the set of generated queries and executed to obtain data. When data from the first subset of queries is obtained, the obtained data may be processed to predict the values associated with the overall sales volume of various product categories for a particular income range without having to wait the other subsets of queries to be executed, without having to wait for data from the other subsets of queries to be obtained, etc. When at least one remaining subset of queries is executed and data from that subset of queries is obtained, this data may then be processed to update the predicted values and/or calculate the final values associated with the overall sales volume of various product categories for a particular income range. 
     In some implementations, the query component  110  may randomly select queries (e.g., from a set of queries related to a user request) to be executed, where the random selection of the queries is performed without regard to whether a particular query was previously selected. For example, a first subset of queries that are randomly selected (as potential queries to be executed) and a second subset of queries that are randomly selected (as potential queries to be executed) after the random selection of the first subset of queries may comprise overlapping queries selected at random. In some implementations, the query component  110  may add randomly-selected queries to one or more execution queues, and perform filtering of queries randomly selected for execution so that one or more queries are removed from an execution queue before the queries are executed. As an example, if a query (added to an execution queue) was previously randomly selected and executed to obtain data related to the user request, the previously selected-and-executed query may be removed from the execution queue prior to the query being executed (e.g., responsive to detecting that the query was previously executed to obtain data for the user request). 
     In some implementations, the query component  110  may randomly select queries that have not been previously selected for execution (to obtain data related to a given user request). As an example, based on one or more rules prohibiting random selection of queries previously selected for execution to obtain data related to the same user request, a first subset of queries that are randomly selected (as potential queries to be executed) and a second subset of queries that are randomly selected (as potential queries to be executed) after the random selection of the first subset of queries would not comprise any of the same queries as the first subset of randomly-selected queries. 
     In one implementation, the value component  112  may be programmed to predict values associated with a set of attributes based on a subset of data obtained via an initial query that corresponds to an initial query interval (e.g., a subset of relevant data sources, a subset of a requested time period, a subset of a requested geographic area, etc.). In some implementations, the quantity of available and/or possible attributes included in a first attribute group may be unknown and/or may be large (e.g., hundreds, thousands, tens of thousands, and/or other amount). Due to these or other factors, performing queries for data related to all possible and/or available attributes of the first attribute group may cause unnecessarily delay in getting an adequate representation of the data to a user. The prioritization component  114  may be programmed to determine a first subset of attributes (e.g., that should be prioritized) of the first attribute group based on the prediction of the values, priority criteria, and/or other information. The first subset of attributes may include respective ones of the first attributes associated with predicted values most relevant to the priority criteria. The priority criteria may relate to a threshold quantity of attributes (e.g.,  10 ,  100 ,  200 , and/or other considerations) for the first subset, sorting preference of the attributes (e.g., sorted from highest value to lowest value), or other criteria. As an example, the first subset of the attributes (that is to be given priority over other subsets of the attributes) may include up to the threshold quantity of attributes with predicted values that are higher than predicted values associated with other attribute subsets. 
     By way of illustration referring back to  FIG. 2 , once a first attribute group (or other attribute group), priority criteria, and/or other information has been determined (e.g., set by a user), an initial query may be performed to obtain a subset of data related to calculating values associated with attributes of the first attribute group. A prediction of the values may be calculated based on the subset of data obtained via the initial query. The predicted values may be reflected in the display of data elements  206   a - 206   y . Based on the predicted values, priority criteria, and/or other information, the attributes of the first attribute group to be included in sections  202   a - 202   e  may be selected. 
     In the current depiction, the data visualization user interface  200  may be limited to displaying only five attributes of the first attribute group at a given time. As such, the priority criteria may specify that the number of attributes to be included in the subset of attributes displayed in sections  202   a - 202   e  to be equal to five. However, this amount is provided for illustrative purposes only and is not to be considered limiting. In other implementations, this amount may vary depending on the size of the data visualization user interface  200 , user input, the available quantity of the attributes of the first attribute group, and/or other criteria. In this manner, the user (or other entity having interest in the values) may be provided an initial presentation of the selected subset of the attributes and their predicted values. 
     In one scenario, with respect to  FIG. 2 , a first attribute group “Product Category” may include attributes such as Books, Electronics, Jewelry, Men, Women, Toys, Pet Products, and/or other attributes. A second attribute group “User Income” may include attributes such as $0 to $25,000, $25,000 to $50,000, $50,000 or more, and/or other attributes. A third attribute group “Sale Numbers” may include sales volume and/or other attributes. As an example, Table 1 below may represent predicted values associated with sale volumes (e.g., by unit), respective ones of the “Product Category” attributes (e.g., rows), and respective ones of the “User Income” attributes (e.g., columns) that may be streamed in real-time. 
     
       
         
           
               
               
             
               
                   
                 TABLE 1 
               
             
            
               
                   
                   
               
               
                   
                 User Income 
               
            
           
           
               
               
               
               
            
               
                   
                 $0 to $25,000 
                 $25,000-$50,000 
                 $50,000 or more 
               
               
                 Product Category 
                 Volume 
                 Volume 
                 Volume 
               
               
                   
               
            
           
           
               
               
               
               
            
               
                 Books 
                 107,460,342 
                 107,476,521 
                 71,653,491 
               
               
                 Electronics 
                 80,622,191 
                 80,610,360 
                 53,752,435 
               
               
                 Jewelry 
                 67,181,209 
                 67,180,592 
                 44,799,309 
               
               
                 Men 
                 26,858,564 
                 26,871,587 
                 17,906,847 
               
               
                 Women 
                 26,874,505 
                 26,877,982 
                 17,917,377 
               
               
                   
               
            
           
         
       
     
     The values in Table 1 may be predicted sales volumes of the respective “Product Category” and “User Income” attributes for a certain time period (e.g., current day, week, month, year, etc.). The predicted values may represent a first prediction based on a first subset of data received from a first query. For example, the above values may represent a prediction of sales volumes for a 1 hour period of a given day and/or other time period. From the first prediction, it may be determined that Books, Electronics, Jewelry, Men, and Women product categories occupy the top five highest sales volumes with regard to all product categories. For example, in Table 1, the highest predicted sales volume on a product category basis for users in the income range of $0 to $25,000 is Books at 107,460,342 units. Such values may be reflected by data elements  206   a - 206   y  in the pivot table of the data visualization user interface  200 . The values may be updated continuously to represent the most up-to-date prediction of sales volume values for each of the attributes as more queries for data are performed. 
     Returning to  FIG. 1 , the prioritization component  114  may be programmed to prioritize, based on a prediction of values associated with a set of attributes, retrieval and/or processing of data related to one subset of the attributes over retrieval and/or processing of data related to one or more other subsets of the attributes. Prioritizing retrieval and/or processing may be based on a comparison of the predicted values with one another (e.g., based on highest to lowest sorting, lowest to highest sorting, and/or other considerations). In  FIG. 2 , the retrieval and/or processing of data related to the attributes in sections  202   a - 202   e  may be prioritized over attributes not included in the sections  202   a - 202   e  (e.g., the “Product Category” attributes not within the “top five” list). In the scenario of Table 1, the retrieval and/or processing of data related to Books, Electronics, Jewelry, Men, and Women may be prioritized over retrieval and/or processing of data related to Toys, Pet Products, and/or other “Product Category” attributes. 
     Returning to  FIG. 1 , in some implementations, the resource component  116  may be programmed to allocate, based on the prioritization of retrieval and/or processing of data related to a subset of attributes, a greater amount of resources (e.g., processing power, random access memory space, non-volatile storage space, network resources, or other resources) to retrieval and/or processing of data related to the prioritized subset of the attributes than an amount of resources allocated to retrieval and/or processing of data related to the one or more other subsets of the first attributes. As a result of such allocation, for example, the amount of resources allocated to the prioritized subset of attributes (for retrieval and/or processing of its related data) may remain greater than the amount of resources allocated to the other subsets of attributes until the retrieval and/or processing of data related to the prioritized subset of attributes is completed. 
     In some implementations, the query component  110  may be programmed to perform, based on the prioritization of a subset of attributes, additional queries for data related to the prioritized subset of attributes. The additional queries may be performed before performing further queries for data related to other subsets of attributes. The query component  110  may be programmed to receive, based on the additional queries, additional data related to calculating the values of the prioritized subset of attributes. The value component  112  may be programmed to update, based on the additional data, the prediction of the values associated with the prioritized subset of attributes. As an example, with respect to  FIG. 2 , the update of the prediction of the values may be conveyed to a user by updating the data elements  206   a -206 y  that represent the values. In the scenario shown in Table 1, the additional queries may be for data related to Books, Electronics, Jewelry, Men, and Women&#39;s apparel. An example of an updated prediction of the values for Books, Electronics, Jewelry, Men, and Women is shown in Table 2 below (as compared with Table 1 above). 
     
       
         
           
               
               
             
               
                   
                 TABLE 2 
               
             
            
               
                   
                   
               
               
                   
                 User Income 
               
            
           
           
               
               
               
               
            
               
                   
                 $0 to $25,000 
                 $25,000-$50,000 
                 $50,000 or more 
               
               
                 Product Category 
                 Volume 
                 Volume 
                 Volume 
               
               
                   
               
            
           
           
               
               
               
               
            
               
                 Books 
                 106,554,102 
                 107,481,412 
                 72,112,474 
               
               
                 Electronics 
                 80,630,007 
                 79,985,547 
                 53,741,345 
               
               
                 Jewelry 
                 68,045,998 
                 67,223,879 
                 45,000,199 
               
               
                 Men 
                 26,910,094 
                 27,747,203 
                 18,023,814 
               
               
                 Women 
                 25,887,789 
                 26,702,986 
                 17,830,223 
               
               
                   
               
            
           
         
       
     
     In some implementations, the query component  110  may be programmed to perform one or more other queries for data related to subsets of attributes other than a prioritized subset. The query component  110  may be programmed to receive, based on the other queries, one or more other subsets of data related to calculating the values associated with the other attributes. The value component  112  may be programmed to predict (e.g., a subsequent prediction) the values associated with the other attributes based on the other subsets of data. 
     In some implementation, the subset of attributes for which priority is given may be modified to add other attributes and/or to remove attributes from the prioritized subset. As an example, prioritization component  114  may be programmed to compare the updated predicted values associated with a current prioritized subset of the attributes with the predicted values associated with other subsets of the attributes. The prioritization component  114  may be programmed to update, based on the comparison, respective ones of the attributes that are included in the prioritized subset of the attributes. That is, by comparing the updated predicted values to one another, it may be determined whether one or more attributes should added to and/or removed from the prioritized subset of the attributes. As an example, the prioritized subset of the attributes may be updated to add one or more attributes to the prioritized subset if the updated predicted values of those attributes satisfy the priority criteria, and thus, should be included in the prioritized subset. As another example, the prioritized subset of the attributes may be updated to remove one or more attributes from the prioritized subset if the updated predicted values of those attributes no longer satisfy the priority criteria. 
     As an illustrative example, following the scenarios of Tables 1 and 2, Table 3 shown below depicts predicted values for other “Product Category” attributes (e.g., Toys and Pet Products) based on one or more other queries. 
     
       
         
           
               
               
             
               
                   
                 TABLE 3 
               
             
            
               
                   
                   
               
               
                   
                 User Income 
               
            
           
           
               
               
               
               
            
               
                   
                 $0 to $25,000 
                 $25,000-$50,000 
                 $50,000 or more 
               
               
                 Product Category 
                 Volume 
                 Volume 
                 Volume 
               
               
                   
               
            
           
           
               
               
               
               
            
               
                 Toys 
                 85,142,348 
                 83,174,635 
                 55,636,759 
               
               
                 Pet Products 
                 12,111,619 
                 14,221,465 
                 13,159,001 
               
               
                   
               
            
           
         
       
     
     A comparison of values in Table 3 with those in Table 2 may show that attribute “Toys” (Table 3) may be associated with predicted values that may hierarchically fall between the updated predicted values of “Electronics” and “Books” (e.g., Table 2). As such, the subset of attributes in Table 2 may be updated to include the attribute “Toys,” to be positioned between the attributes “Books” and “Electronics,” as shown in Table 4 below. As depicted in Table 4 below, the attribute “Women” may be removed from the prioritized subset of attributes since it no longer fits within the “top five” criteria. 
     
       
         
           
               
               
             
               
                   
                 TABLE 4 
               
             
            
               
                   
                   
               
               
                   
                 User Income 
               
            
           
           
               
               
               
               
            
               
                   
                 $0 to $25,000 
                 $25,000-$50,000 
                 $50,000 or more 
               
               
                 Product Category 
                 Volume 
                 Volume 
                 Volume 
               
               
                   
               
            
           
           
               
               
               
               
            
               
                 Books 
                 106,554,102 
                 107,481,412 
                 72,112,474 
               
               
                 Toys 
                 85,142,348 
                 83,174,635 
                 55,636,759 
               
               
                 Electronics 
                 80,630,007 
                 79,985,547 
                 53,741,345 
               
               
                 Jewelry 
                 68,045,998 
                 67,223,879 
                 45,000,199 
               
               
                 Men&#39;s apparel 
                 26,910,094 
                 27,747,203 
                 18,023,814 
               
               
                   
               
            
           
         
       
     
     The above description is for illustrative purposes only and is not to be considered limiting with respect to the prediction of values, updating of a prioritized subset of attributes, and/or priority criteria. For example, in other implementations, priority criteria may specify other sorting preferences where attributes may be sorted by their respective values from lowest to highest, alphabetically, and/or other criteria. 
     Returning to  FIG. 1 , the value component  112  may be programmed to calculate, based on the prioritization, the final values of the values associated with the (current) prioritized subset of the attributes before calculating the final values associated with other subsets of the attributes. In some implementations, one or more other attributes not included in the prioritized subset of the attributes may be made available to the user via the data visualization user interface. For example, a first page may display the first subset of attributes, and a user may scroll between different pages in order to access and/or otherwise view the other attributes not included in the prioritized subset. 
     In some implementations, the query component  110  may assign weights to queries related to a user request. The query component  110  may randomly select one or more queries from the weighted queries based on the assigned weights and execute the randomly-selected queries. In some implementations, a respective weight may be assigned to a query based on how influential the query or data obtained from the query is predicted to be on one or more requested results (e.g., certain final values to be provided in response to a user request, representations of such values, or other requested results). As an example, if certain requested results are to be prioritized over other requested results (e.g., certain results being placed before other results based on highest to lowest sorting, lowest to highest sorting, and/or other considerations), the query component  110  may assign weight to queries based on (i) the predicted influence of the respective data derived from such queries on the requested results, (ii) the priorities of the requested results, (iii) or other considerations. In one use case, with respect to  FIG. 2 , data related to the attributes in sections  202   a - 202   e  may be prioritized over attributes not included in the sections  202   a - 202   e  (e.g., the “Product Category” attributes not within the “top five” list). With respect to Table 1 above, data related to Books, Electronics, Jewelry, Men, and Women may be prioritized over data related to Toys, Pet Products, and/or other “Product Category” attributes. As an example, a set of queries related to a request for values may comprise queries to obtain data for calculating sales volumes associated with various product categories. Queries for obtaining data for calculating the sales volumes for Books, Electronics, Jewelry, Men, and Women may be assigned greater weights (e.g., because they are within the “top five” list), as compared to weights assigned to queries for obtaining data for calculating sales volumes for Toys, Pet Products, and/or other “Product Category” attributes. In another use case, if the priorities of the related data changes (e.g., based on updated predictions of requested results), the weights assigned to the queries may be updated based on the changed priorities. If Toys and Pet Products are subsequently predicted to be in the “top five” list, queries for obtaining data for calculating the sales volumes for Toys and Pet Products may be reassigned greater weights, as compared to weights assigned to queries for obtaining data for calculating sales volumes for “Product Category” attributes not within the “top five” list (e.g., even if one or more of such attributes were initially in the “top five” list). 
     In some implementations, a respective weight may be assigned to a query based on proximity of the query to a given query (e.g., a previously selected-and-executed query via which data for calculating a high-prioritized requested result is obtained, another particular query, etc.). The proximity of the query to a given query comprises proximity of the respective query to the given query with respect to a presentation order (e.g., a predefined presentation sorting order or other presentation order), a data source location attribute of the query (e.g., virtual or physical data source location for which the query is constrained or other data source location attribute), a time attribute of the query (e.g., a specified date or time period for which the query is constrained or other time attribute), a geographic location attribute of the query (e.g., a country, state, city, or other geographic region for which the query is constrained or other geographic location attribute), or other considerations. Proximity of two items may, for example, include (1) similarity of the two items (e.g., the same or similar data source location attribute values, time attribute values, geographic location attribute values, etc., of two queries), (2) closeness in distance of the two items (e.g., closeness in the order of execution of two queries, closeness in distance of results derived from two queries as presented on a user interface, closeness in distance of the queries in a dataset, etc.), or other proximities of the two items. As an example, in some scenarios, data obtained via adjacent or more proximate queries (e.g., proximate in data source location, in time, geographic location, etc.) tends to be more similar or related to one another, as compared to the data obtained via less proximate queries. 
     In one use case, with respect to Table 1 above, the sales volumes for Books, Electronics, Jewelry, Men, and Women may be prioritized over the sales volumes for Toys, Pet Products, and/or other “Product Category” attributes based on the sales volumes predicted using data obtained via execution of an initial randomly-selected subset of queries. The foregoing priority may, for instance, be based on a highest-to-lowest sales volume sorting order (e.g., indicating how results are to be sorted on the user interface) and the predicted sales volumes for Books, Electronics, Jewelry, Men, and Women being greater than the predicted sales volumes for the other “Product Category” attributes. As an example, if sales volume data obtained via selected-and-executed queries for Books, Electronics, Jewelry, Men, and Women was used to predict that the sales volumes for the foregoing product categories were the highest (compared to sales volumes for other product categories), other queries for sales volume data for the foregoing product categories or for product categories adjacent or more proximate to the foregoing product categories (e.g., as currently ordered for presentation of the sales volumes to a user) may be assigned greater weights, as compared to weights assigned to queries for sales volume data for product categories that are less proximate to the foregoing product categories on the current presentation order. In this way, although the next set of queries to be executed may still be randomly selected, the greater weights assigned to queries for sales volume data for the more proximate product categories provide a higher probability that such queries for obtaining more prioritized results are randomly selected and executed (e.g., at least prior to other queries for obtaining less prioritized results being selected and executed). 
     As another example, with respect to the foregoing use case for Table 1 above, if sales volume data obtained via selected-and-executed queries for Books, Electronics, Jewelry, Men, and Women was used to predict that the sales volumes for the foregoing product categories were the highest, queries to search the same data source location or more proximate data source locations may be assigned greater weights, as compared to weights assigned to queries to search less proximate data source locations. For example, in some scenarios, data stored in databases of the same company (or other more proximate databases) tends to be more similar or related with one another, as compared to data stored in databases of different companies (or other less proximate databases). As such, the greater weights assigned to such proximate queries may provide a higher probability that queries for obtaining more prioritized results are randomly selected and executed (e.g., at least prior to other queries for obtaining less prioritized results being selected and executed). 
     In some implementations, the value component  112  may assign weights to subsets of data obtained via one or more queries related to a user request and predict one or more requested results based on the subsets of data and the assigned weights. In some implementations, a respective weight may be assigned to a given subset of data based on how influential the given subset of data is predicted to be on one or more requested results (e.g., certain final values to be provided in response to a user request, representations of such values, or other requested results). As an example, if the given subset of data is predicted to accurately reflect a final value to be provided (in response to the user request), the given subset of data may be assigned a high weight for predicting the final value (e.g., prior to the final value being calculated using all subsets of data for calculating such final value, prior to all the subsets of data being obtained, etc.). As another example, the given subset of data may be assigned a high weight for predicting another final value based on proximity of the given subset of data to at least another subset of data (e.g., where the other subset of data is for calculating the other final value but has not yet been obtained, where the given subset of data&#39;s influence on the other final value is predicted based on the proximity, etc.). The proximity of the given subset of data may comprise proximity of the given subset of data to one or more other subsets of data with respect to a data source location attribute of the given subset of data, a time attribute of the given subset of data, a geographic location attribute of the given subset of data, or other considerations. As an example, in some scenarios, adjacent data or more proximate data in datasets tends to have more similar or related values (compared to less proximate data in the datasets). In one use case, a subset of data derived from a query for calculating a given requested value may be assigned a high weight for predicting another requested value (e.g., prior to obtaining another subset of data for calculating the other requested value, prior to executing a query to obtain such other subset of data, etc.) if it is known or predicted that the two subsets of data are adjacent or proximate one another in a given dataset. For example, the closer the two subsets of data (e.g., the subset of data and the other subset of data) are to one another in the dataset, the greater the weight that may be assigned to the subset of data for predicting the other requested value. 
     In another use case, a set of queries may comprise queries for data related to various geographic location attributes (e.g., a subset of zip codes per state, a subset of cities per state, a subset of regions per country, etc.). In some scenarios, the set of queries may comprise a randomly-selected subset of queries of relevant data sources to be searched for data for calculating requested results. The geographical location attributes for each set of queries may, for example, comprise pre-determined selected geographical areas or other geographical areas which together comprise at least a portion of an overall requested geographical area. If, for instance, only some data for calculating a requested value (or values) (associated with the overall requested geographic area) has been derived from one or more queries, the derived data may be used predict other subsets of data, and the predicted other subsets of data may be used to predict the requested value (or other values). In some scenarios, weights may be assigned to one or more subsets of the derived data for predicting the other subsets of data respectively based on how close the geographic locations corresponding to the subsets of derived data and the geographic locations corresponding to the other subsets of data are to one another. As an example, the data derived from one query associated with a particular geographic area may be assigned a high weight for predicting data related to adjacent or proximate geographic areas (e.g., because, in some scenarios, data for similar or related geographic attributes tends to be closely located within a dataset). As another example, the data derived from another query associated with a geographic area may be assigned a low weight for predicting data related to geographic areas that are not proximate the geographic area associated with the other query. 
     In some implementations, the value component  112  may assign respective weights to data derived from queries based on one or more projected error rates (e.g., a weight may be assigned to a subset of the derived data based on a projected error rate for the subset of derived data). For example, the value component  112  may select a sampling of data derived from each query of a set of queries. In one implementation, the value component  112  may calculate a projected error rate between the sampled subsets of derived data based on the difference in values of the sampled subsets of derived data and the proximity of the sampled subsets of derived data to each other. The projected error rate may then be utilized to assign weights to the data derived from each query. In one use case, a sampling of data derived from a query may comprise an approximate 10% difference in value between the sampled subsets of derived data. Based on the proximity of the sampled subsets of derived data, a projected error rate is calculated (e.g., approximately 10% if the sampled subsets of derived data are adjacent or less than 10% if the sampled subsets data are located further away from one another). The projected error rate may then be utilized to determine weights to be used to predict the requested values based on the subset of data obtained via the first and/or second set of queries. In some implementations, the value components may assign respective weights to data derived from queries based on data volatility predicted for the queries. 
     The above descriptions illustrate the an implementation involving a single attribute group defining rows and/or a single attribute group defining columns of a pivot table. However, in other implementations, users may request values associated with more than one attribute group for the rows and/or more than one attribute group for the columns. 
     As an illustrative example in  FIG. 3 , control element  224  may allow a user to add or remove attribute groups that define the rows and/or columns of the pivot table and the subsequent retrieval of data. Responsive to selection of control element  224 , the user may be provided an additional user interface portions (e.g., pop up window or the like) which may facilitate configuring the Rows and Columns. In the Rows window, element  230   a  may represent the currently selected first attribute group for portion  210  (e.g., one of State, Gender, Zip Code, City, Income, Group, and/or other attribute group). The data visualization user interface  200  may be programmed such that a user may change the current selection of the first attribute group (e.g., deselect element  230   a ), add additional attribute groups to define the Rows of the pivot table through selection of the “+” element (e.g., facilitating selection of elements  230   b ,  230   c ,  230   d , and/or other elements), and/or to perform more or less operations. By way of illustration, element  230   b  is shown selected such that the pivot table in data visualization user interface  200  (in  FIG. 4 ) may have Rows corresponding to the first attribute group defined by element  230   a  and a fourth attribute group defined by element  230   b.    
     Similarly, in the Columns window, elements  232   a  and  232   b  may represent the currently selected second attribute group for portion  212  (e.g., one of State, Gender, Zip Code, City, Income, Group, and/or other attribute group) and the currently selected third attribute group represented in portion  208 . The data visualization user interface  200  may be programmed such that a user may change the current selection of the attribute groups (e.g., deselect elements  232   a  and/or  232   b ), add additional attribute groups to define the Columns of the pivot table, and/or to perform more or less operations. For example, a similar pop-up window as that showing elements  230   b - d  may be provided when a user selects the “+” element for the Columns portion to show the other available attribute groups that may be selected to define the Columns. 
       FIG. 4  illustrates an implementation of data visualization user interface  200  wherein the rows of the table are defined by the first attribute group represented by portion  210  and a fourth attribute group represented by portion  236 . The data visualization user interface  200  is additionally shown programmed such that columns of the table may be defined by the second attribute group represented by portion  212 , the third attribute group represented by portion  208 , and a fifth attribute group represented by portion  238 . Portion  203  may include sections  203   a - 203   f  that may represent a subset of attributes included in the fourth attribute group. Portion  205  may include sections  205   a - 205   e  that may represent a subset of attributes included in the fifth attribute group. 
     It is noted that the current depiction of data visualization user interface  200  is provided merely for illustrative purposes and is not to be considered limiting with respect to the amount of attribute groups selected to define the rows and/or columns. For example, in other implementations, the rows may be defined by more than one attribute groups while the columns may be defined by one attribute group, etc. However, it is noted that the manner in which queries for data are performed, subsets of data are retrieved, and/or values associated with at least one of attributes may be similar to implementation shown and described with respect to  FIG. 2 . For example, given the selections of attributes groups shown in  FIG. 4  in accordance with an implementation, a request for values associated with respective ones of the attributes of the various attribute groups may be received. A subset of data related to the attributes may be received based on an initial query (or queries) for such data. Values associated with respective ones of the attributes (e.g., a value associated with at least an attribute of the first attribute group and with an attribute of the fourth attribute group, and/or other values) may be predicted based on the initial subset of data. Based on the prediction of the values, retrieval and/or processing of data related to at least one subset of the attributes may be prioritized over retrieval and/or processing of data related to one or more other subset of the attributes. The manner in which the respective subsets of the attributes may be determined, the values may be predicted and/or updated, and/or other operations related to prioritized retrieval and/or processing of data as described herein may follow the same or similar processes described above. As an example, in one implementation with respect to  FIG. 4 , the data that may be retrieved may be related to all depicted attribute groups. Table 5 below depicts an example of data retrieval result in accordance with the implementation of the data visualization user interface  200  shown in  FIG. 4 . 
     
       
         
           
               
               
               
             
               
                 TABLE 5 
               
             
            
               
                   
               
               
                   
                   
                 User Gender 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                 Male 
                 Female 
                 Male 
                 Female 
                 Female 
               
            
           
           
               
               
               
            
               
                   
                   
                 User Income 
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Product 
                   
                   
                   
                 $25,000- 
                 $25,000- 
                 $50,000 or 
               
               
                 Sub- 
                 Product 
                 $0-$25,000 
                 $0-$25,000 
                 $50,000 
                 $50,000 
                 more 
               
               
                 Category 
                 Category 
                 Volume 
                 Volume 
                 Volume ↓ 
                 Volume 
                 Volume 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Watches 
                 Jewelry 
                 33,012,040 
                 34,662,170 
                 116,381,612 
                 34,836,617 
                 44,214,836 
               
               
                 Accessories 
                 Electronic 
                 20,279,672 
                 37,480,551 
                 103,016,203 
                 46,337,399 
                 55,790,438 
               
               
                 Bracelets 
                 Jewelry 
                 74,045,124 
                 32,662,423 
                 75,316,569 
                 97,711,350 
                 71,867,982 
               
               
                 Women&#39;s 
                 Books 
                 47,526,656 
                 44,672,173 
                 72,635,786 
                 71,242,063 
                 70,516,363 
               
               
                 Audio 
                 Electronics 
                 83,296,596 
                 25,383,349 
                 65,450,089 
                 55,185,292 
                 26,676,082 
               
               
                   
               
            
           
         
       
     
     In one scenario, a fourth attribute group “Product Sub-Category” may be added to the rows along with the first attribute group “Product Category.” The fourth attribute group may include attributes such as Accessories, Arts, Audio, Bracelets, Men&#39;s, Woman&#39;s, and/or other attributes. A fifth attribute group “User Gender” may be added to the columns along with the second attribute group “User Income.” The fifth attribute group may include attributes such as Male or Female. Their associated values may comprise predicted and/or calculated values representing sales volumes with respect to product category, product subcategory, user income, and user gender for a certain time period (e.g., day, week, month, year, etc.). The values shown in Table 5 may represent an initial prediction based on a subset of data received from an initial query. The “Product Category” attributes depicted in Table 5 may represent a prioritized subset of attributes, while the depicted “Product Sub-Category” attributes may represent a prioritized subset of third attributes. It is noted that Table 5 is sorted from highest to lowest sales volume value associated with at least the “Product Sub-Category” attribute and, in particular, with respect to Male&#39;s with an income range between $25,000 and $50,000. However in other implementations, the sorting may be changed based on user input. Based on a prediction of the values, it may be determined that the product sub-category “Watches” within the product category “Jewelry” occupies the highest sales volume at 116,381,612 units with respect to males having a salary range between $25,000 and $50,000. Such values may be reflected by data elements  206   a - 206   y  of the data visualization user interface  200  in  FIG. 4 . The values may be updated continuously to represent the most up-to-date overall sales volume values associated with the respective attributes as more queries for data are performed and/or as the most update-to-date subsets of attributes are determined. 
       FIGS. 5 and 6  illustrate information exchange flow charts for prioritized retrieval and/or processing of data, in accordance with one or more implementations. The information may be exchanged between a server  102  (e.g., such as server  102  in  FIG. 1 ) and one or more data sources (e.g., distributed data sources  500 ) over a network  106 , such as the Internet. 
     In  FIG. 5 , a request for values associated with respective first attributes may be received  502  at server  102 . First queries may be performed  504  at server  102 . The first queries may be received  506  at appropriate data source(s) included in the distributed data sources  500 . A first subset of data based on the first queries may be sent  508  from the data sources and received  510  at the server  102 . The server  102  may include computer program components to predict  512  at least some of the values of respective ones of the first attributes based on the received first subset of data. A first subset of first attributes may be determined  514  based on the predicted values. Second queries for data related to the first subset of first attributes may be performed  516  by the sever  102 . The second queries may be received  518  at appropriate ones of the distributed data sources  500 . A second subset of data may be sent  520  from the data sources and received  522  at the server  102 . Based on the second subset of data, the prediction of the values associated with the first subset of first attributes may be updated  524 . One or more other queries for data may be performed  524 . 
     In  FIG. 6 , one or more other queries for data related to one or more other first attributes may be performed  602  at server  102 . The one or more other queries may be received  604  at appropriate ones of the data sources  500 . The data sources may send  606  one or more other subsets of data based on the one or more other queries to server  102 . The one or more other subsets of data may be received  608  at server  102  and used to predict  610  values of the one or more other first attributes. The predicted values of the one or more other first attributes may be compared  612  to the updated predicted values from step  524  shown in  FIG. 5 . The first subset of first attributes may be updated  614  based on the comparison. If the updated first subset of first attributes includes the other first attributes, the predicted values for the other first attributes may be updated based on data included in the second subset of data received at step  522  in  FIG. 5 . 
     In accordance with one or more implementations presented herein, the server  102  may be programmed to perform numerous micro-queries on the distributed data sources  500 . As an example, server  102  (e.g., query component) may perform a plurality of micro-queries on distributed data sources  500  (e.g., comprising numerous data nodes at which data is stored) to update a current data visualization (e.g., updating a current data element set), generate a new data visualization (e.g., generating a new data element set), or facilitate other automated or user-requested operations. In response to the micro-queries, the query component may begin receiving streams of data from multiple ones of the data nodes. In response to the query component receiving results from a subset of the micro-queries, the value component may approximate or otherwise predict the final data values to generate data elements representing the final data values based on the subset of results. When the results from all of the micro-queries have been received, the actual final data values may be calculated, and the actual final data values may be utilized to generate data elements to replace the approximated data elements. 
     By way of example, micro-query delegation may be performed to enable faster real-time results of large streaming data by performing numerous micro-queries on a distributed data store. An approximation of the final data values may be performed based on results from a subset of the micro-queries that are completed by extrapolating from the resulting data that has been processed thus far to estimate the final data values before all of the micro-queries are completed. Data elements may be generated based on the approximated data values to present users with approximated visualized information without having to wait for all of the micro-queries to be completed. Upon completion of all the micro-queries or a determination that no further results is to be received in response to any further micro-queries, the final data values may be calculated and then utilized to generate data elements based on the final data values to replace the approximated data elements, thereby “sharpening” the data elements. In this way, among other benefits, user experience may be improved by providing users with visualized information representing final data values without having to wait for all of the results from the micro-queries to be received by predicting what the final data values will likely be based on results from a subset of the micro-queries that have been received. 
     In some scenarios, different types of data may be joined based on any number or criteria (e.g., time, geography, or other criteria), and visualization of the joined data may be presented using any number of approaches. 
     Returning to  FIG. 1 , user device  104  may comprise any type of mobile terminal, fixed terminal, and/or other device. For example, user device  104  may comprise a desktop computer, a notebook computer, a netbook computer, a tablet computer, a smartphone, a navigation device, an electronic book device, a gaming device, and/or any other user device. In some implementations, user device  104  may comprise the accessories and peripherals of these devices. User device  104  may also support any type of interface to the user (such as “wearable” circuitry, etc.). 
     Communication network  106  of system  100  may comprise one or more networks such as a data network, a wireless network, a telephony network, and/or other communication networks. A data network may comprise any local area network (LAN), metropolitan area network (MAN), wide area network (WAN), a public data network (e.g., the Internet), short range wireless network, and/or any other suitable packet-switched network. The wireless network may, for example, comprise a cellular network and may employ various technologies including enhanced data rates for global evolution (EDGE), general packet radio service (GPRS), global system for mobile communications (GSM), Internet protocol multimedia subsystem (IMS), universal mobile telecommunications system (UMTS), etc., as well as any other suitable wireless medium (e.g., worldwide interoperability for microwave access (WiMAX), Long Term Evolution (LTE) networks, code division multiple access (CDMA), wideband code division multiple access (WCDMA), wireless fidelity (WiFi), wireless LAN (WLAN), Bluetooth, Internet Protocol (IP) data casting, satellite, mobile ad-hoc network (MANET), etc.). 
     The external resource(s)  105  may include sources of information that are outside of system  100 , external entities participating with system  100  (e.g., distributed data sources, and/or other considerations), and/or other resources. In some implementations, some or all of the functionality attributed herein to external resources  105  may be provided by resources included in system  100 . 
     In some implementations, server  102  may include an electronic storage  118 , one or more processors  120 , and/or other components. Server  102  may include communication lines, or ports to enable the exchange of information with network  106  and/or other computing platforms. Illustration of server  102  in  FIG. 1  is not intended to be limiting. Server  102  may include a plurality of hardware, software, and/or firmware components operating together to provide the functionality attributed herein to server  102 . For example, server  102  may be implemented by a cloud of computing platforms operating together as server 102. 
     Electronic storage  118  may comprise non-transitory storage media that electronically stores information. The electronic storage media of electronic storage  118  may include one or both of system storage that is provided integrally (i.e., substantially non-removable) with server  102  and/or removable storage that is removably connectable to server  102  via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). Electronic storage  118  may include optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. Electronic storage  118  may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and/or other virtual storage resources). Electronic storage  118  may store software algorithms, information determined by processor  120 , information received from server  102 , information received from user devices  104 , and/or other information that enables server  102  to function as described herein. In some implementations, electronic storage may comprise a non-transitory, tangible computer-readable storage medium with an executable program stored thereon, wherein the program instructs a microprocessor to perform some or all of the functionality of components  108 ,  110 ,  112 ,  114 ,  116 ,  118  and/or other components. 
     Processor  120  may be configured to provide information processing capabilities in server  102 . As such, processor  120  may include a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. Although processor  120  is shown in  FIG. 1  as a single entity, this is for illustrative purposes only. In some implementations, processor  120  may include a plurality of processing units. These processing units may be physically located within the same device, or processor  120  may represent processing functionality of a plurality of devices operating in coordination. Processor  120  may be configured to execute components  108 ,  110 ,  112 ,  114 ,  116 ,  118  and/or other components. Processor  120  may be configured to execute components  108 ,  110 ,  112 ,  114 ,  116 ,  118 , and/or other components by software; hardware; firmware; some combination of software, hardware, and/or firmware; and/or other mechanisms for configuring processing capabilities on processor  120 . 
     It should be appreciated that although components  108 ,  110 ,  112 ,  114 ,  116 , and  118  are illustrated in  FIG. 1  as being co-located within a single processing unit, in implementations in which processor  120  includes multiple processing units, components  108 ,  110 ,  112 ,  114 ,  116 , and/or  118  may be located remotely from the other components. The description of the functionality provided by the different components  108 ,  110 ,  112 ,  114 ,  116 , and/or  118  described below is for illustrative purposes, and is not intended to be limiting, as any of components  108 ,  110 ,  112 ,  114 ,  116 , and/or  118  may provide more or less functionality than is described. For example, components  108 ,  110 ,  112 ,  114 ,  116 , and/or  118  may be eliminated, and some or all of its functionality may be provided by other ones of components  108 ,  110 ,  112 ,  114 ,  116 , and/or  118 . As another example, processor  120  may be configured to execute one or more additional components that may perform some or all of the functionality attributed below to one of components  108 ,  110 ,  112 ,  114 ,  116 , and/or  118 . 
       FIG. 7  illustrates a flowchart of processing operations of prioritizing retrieval and/or processing of data, in accordance with one or more implementations. The operations of process  700  presented below are intended to be illustrative. In some implementations, process  700  may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of process  700  are illustrated in  FIG. 7  and described below is not intended to be limiting. 
     In certain implementations, one or more operations of process  700  may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices executing some or all of the operations of process  700  in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of process  700 . 
     In an operation  702 , a request for values associated with respective first attributes may be received. Operation  702  may be performed by a data request component that is the same as or similar to data request component  108  in accordance with one or more implementations. 
     In an operation  704 , first queries for data related to the first attributes may be performed. The first queries may be based on the request at operation  702 . Operation  704  may be performed by a query component that is the same as or similar to query component  110 , in accordance with one or more implementations. 
     In an operation  706 , a first subset of data related to calculating at least some of the associated values may be received. The first subset of data may be received based on the first queries. Operation  706  may be performed by a query component that is the same as or similar to query component  110 , in accordance with one or more implementations. 
     In an operation  708 , at least some of the associated values may be predicted based on the first subset of data. Operation  708  may be performed by a value component that is the same as or similar to value component  112 , in accordance with one or more implementations. 
     In an operation  710 , retrieval and/or processing of data related to a first subset of the first attributes may be prioritized over retrieval and/or processing of data related to one or more other subsets of the first attributes. The prioritization may be based on the prediction of the associated values. Operation  710  may be performed by a prioritization component that is the same as or similar to prioritization component  114 , in accordance with one or more implementations. 
       FIG. 8  illustrates a flowchart of processing operations of prioritizing retrieval and/or processing of data, in accordance with one or more implementations. The operations of process  800  presented below are intended to be illustrative. In some implementations, process  800  may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of process  800  are illustrated in  FIG. 8  and described below is not intended to be limiting. 
     In certain implementations, one or more operations of process  800  may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices executing some or all of the operations of process  800  in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of process  800 . 
     In an operation  802 , a request for values associated with respective first attributes may be received. Operation  802  may be performed by a data request component that is the same as or similar to data request component  108 , in accordance with one or more implementations. 
     In an operation  804 , a set of queries for data related to the first attributes is generated based on the request. The first set of queries may be based on the request at operation  802 . Operation  804  may be performed by a query component that is the same as or similar to query component  110 , in accordance with one or more implementations. 
     In an operation  806 , a first subset of queries for data related to the first attributes may be selected from the generated query set. As an example, the first subset of queries may be randomly selected from the generated query set. The random selection may, for instance, be a pseudorandom selection (e.g., by executing a RdRand instruction and applying a seed value, by applying probabilities or other weights to each query and sampling the queries based on such probabilities or other weights to select a subset of the queries, etc.). Operation  806  may be performed by a query component that is the same as or similar to query component  110 , in accordance with one or more implementations. 
     In an operation  808 , the first subset of queries for data related to the first attributes may be performed. Operation  808  may be performed by a query component that is the same as or similar to query component  110 , in accordance with one or more implementations. 
     In an operation  810 , a first subset of data related to calculating at least some of the associated values may be received. The first subset of data may be received based on the first subset of queries. Operation  808  may be performed by a query component that is the same as or similar to query component  110 , in accordance with one or more implementations. 
     In a step  812 , the first subset of data is assigned a respective weight based on how influential the first subset of data is predicted to be on one or more of the associated values. Operation  812  may be performed by a value component that is the same as or similar to value component  112 , in accordance with one or more implementations. 
     In an operation  814 , at least some of the associated values may be predicted based on the first subset of data and the respective weights. Operation  814  may be performed by a value component that is the same as or similar to value component  112 , in accordance with one or more implementations. 
     In an operation  816 , retrieval and/or processing of data related to a first subset of the first attributes may be prioritized over retrieval and/or processing of data related to one or more other subsets of the first attributes. The prioritization may be based on the prediction of the associated values. Operation  816  may be performed by a prioritization component that is the same as or similar to prioritization component  114 , in accordance with one or more implementations. 
     Although the present invention has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred implementations, it is to be understood that such detail is solely for that purpose and that the invention is not limited to the disclosed implementations, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present invention contemplates that, to the extent possible, one or more features of any implementation can be combined with one or more features of any other implementation.