Aggregation of data from disparate sources into an efficiently accessible format

Abstract

Methods and apparatus are presented for aggregating data from disparate sources into an efficiently accessible format. For example, an aggregation tool may receive attribute-based data from one source and metrics-based data from another source. Given this data, the aggregation tool may store attribute data from the attribute-based data into a data object, where the data object includes multiple time slots corresponding to defined time ranges. The aggregation tool may then determine from the metrics-based data, respective metrics data for each of the multiple time slots of the data object, where each time slot is associated with the attribute data. The aggregation tool may store the respective metrics data into each of the multiple time slots of the data object. In this way, the data object may serve to efficiently provide an answer to a query requiring data from multiple data sources.

What is claimed is: 
     
         1 . A computer implemented method, comprising:
 receiving attribute-based data from a transactional data source, wherein the attribute-based data is associated with interactions with one or more search engines;   receiving web analytics data from an analytics data source, wherein the web analytics data includes metrics data based on interactions with one or more web pages;   storing attribute data from the attribute-based data in a data object having a plurality of time slots, wherein each time slot corresponds to a respective time period and is associated with the attribute data;   identifying respective metrics data in the web analytics data corresponding to respective ones of the plurality of time slots; and   storing the respective metrics data in respective ones of the plurality of time slots.   
     
     
         2 . The method of  claim 1 , wherein the transactional data source is a database storing attribute-based data generated as a result of user interactions with a search marketing tool. 
     
     
         3 . The method of  claim 1 , wherein the analytics data source is an analytics database storing metrics measured over defined time periods, and wherein the analytics database is organized according to time. 
     
     
         4 . The method of  claim 1 , further comprising:
 receiving a query, wherein data satisfying the query includes attribute-based data received from the transactional data source, web analytics data received from the analytics data source, and metrics data stored in one or more time slots of the data object; and   generating a response to the query, the response including the metrics data stored in the one or more time slots of the data object.   
     
     
         5 . The method of  claim 1 , further comprising:
 responsive to receiving a query indicating a time range, generating a report based on:
 attribute data in the data object corresponding to the time range; and 
 metrics data from time slots of the plurality of time slots corresponding to the time range. 
   
     
     
         6 . The method of  claim 1 , further comprising:
 receiving updated attribute-based data from the transactional data source without receiving updated web analytics data from the analytics data source; and   updating the attribute data stored in the data object with attribute data from the updated attribute-based data without updating the respective metrics data stored in respective ones of the plurality of time slots.   
     
     
         7 . The method of  claim 1 , further comprising:
 responsive to receiving a selection of a date range, creating a new time slot within the data object;   responsive to querying the analytics data source for additional web analytics data corresponding to the new time slot, receiving the additional web analytics data from the analytics data source; and   storing the received additional web analytics data in the new time slot.   
     
     
         8 . A system, comprising:
 a processor; and   a memory having instructions stored thereon that, if executed by the processor, cause the processor to:
 receive attribute-based data from a transactional data source, wherein the attribute-based data is associated with interactions with one or more search engines; 
 receive web analytics data from an analytics data source, wherein the web analytics data includes metrics data based on interactions with one or more web pages; 
 store attribute data from the attribute-based data in a data object having a plurality of time slots, wherein each time slot corresponds to a respective time period and is associated with the attribute data; 
 identify respective metrics data in the web analytics data corresponding to respective ones of the plurality of time slots; and 
 store the respective metrics data in respective ones of the plurality of time slots. 
   
     
     
         9 . The system of  claim 8 , wherein the memory further comprises instructions that, if executed by the processor, cause the processor to:
 receive additional attribute data from the transactional data source;   store the additional attribute data in another data object including the plurality of time slots;   determine, based on the web analytics data, other respective metrics data for each of the plurality of time slots of the another data object, wherein each time slot of the plurality of time slots is associated with the additional attribute data; and   store the respective other metrics data into each of the plurality of time slots of the another data object.   
     
     
         10 . The system of  claim 8 , further comprising a display device, wherein the memory further comprises instructions that, if executed by the processor, cause the processor to:
 receive a query indicating a time range;   in response to the query, generate a report based on:
 attribute data in the data object corresponding to the time range; and 
 metrics data from time slots of the plurality of time slots corresponding to the time range; and 
   display, on the display device, the report.   
     
     
         11 . The system of  claim 8 , wherein the transactional data source is a database storing attribute-based data generated as a result of user interactions with a search marketing tool. 
     
     
         12 . The system of  claim 8 , wherein the analytics data source is an analytics database storing metrics measured over defined time periods, and wherein the analytics database is organized according to time. 
     
     
         13 . The system of  claim 8 , wherein the memory further comprises instructions that, if executed by the processor, cause the processor to:
 receive updated attribute-based data from the transactional data source without receiving updated web analytics data from the analytics data source; and   update the attribute data stored in the data object with attribute data from the updated attribute-based data without updating the respective metrics data stored in respective ones of the plurality of time slots.   
     
     
         14 . A non-transitory computer-readable storage medium having instructions stored thereon, which when executed by a computing device, cause the computing device to perform operations comprising:
 receiving attribute-based data from a transactional data source, wherein the attribute-based data is associated with interactions with one or more search engines;   receiving web analytics data from an analytics data source, wherein the web analytics data includes metrics data based on interactions with one or more web pages;   storing attribute data from the attribute-based data in a data object having a plurality of time slots, wherein each time slot corresponds to a respective time period and is associated with the attribute data;   identifying respective metrics data in the web analytics data corresponding to respective ones of the plurality of time slots; and   storing the respective metrics data in respective ones of the plurality of time slots.   
     
     
         15 . The non-transitory computer-readable storage medium of  claim 14 , wherein the transactional data source is a database storing attribute-based data generated as a result of user interactions with a search marketing tool. 
     
     
         16 . The non-transitory computer-readable storage medium of  claim 14 , wherein the analytics data source is an analytics database storing metrics measured over defined time periods, and wherein the analytics database is organized according to time. 
     
     
         17 . The non-transitory computer-readable storage medium of  claim 14 , the operations further comprising:
 receiving a query, wherein data satisfying the query includes attribute-based data received from the transactional data source, web analytics data received from the analytics data source, and metrics data stored in one or more time slots of the data object; and   generating a response to the query, the response including the metrics data stored in the one or more time slots of the data object.   
     
     
         18 . The non-transitory computer-readable storage medium of  claim 14 , the operations further comprising:
 responsive to receiving a query indicating a time range, generating a report based on:
 attribute data in the data object corresponding to the time range; and 
 metrics data from time slots of the plurality of time slots corresponding to the time range. 
   
     
     
         19 . The non-transitory computer-readable storage medium of  claim 14 , the operations further comprising:
 receiving updated attribute-based data from the transactional data source without receiving updated web analytics data from the analytics data source; and   updating the attribute data stored in the data object with attribute data from the updated attribute-based data without updating the respective metrics data stored in respective ones of the plurality of time slots.   
     
     
         20 . The non-transitory computer-readable storage medium of  claim 14 , the operations further comprising:
 responsive to receiving a selection of a date range, creating a new time slot within the data object;   responsive to querying the analytics data source for additional web analytics data corresponding to the new time slot, receiving the additional web analytics data from the analytics data source; and   storing the received additional web analytics data in the new time slot.