Patent Publication Number: US-10318510-B2

Title: Systems and methods of generating and using a bitmap index

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is a continuation of and claims priority to U.S. patent application Ser. No. 15/231,477 filed Aug. 8, 2016 and entitled “SYSTEMS AND METHODS OF GENERATING AND USING A BITMAP INDEX,” which is a continuation of and claims priority to U.S. patent application Ser. No. 14/757,800 filed Dec. 23, 2015 and entitled “SYSTEMS AND METHODS OF GENERATING AND USING A BITMAP INDEX,” which is a continuation of and claims priority to U.S. patent application Ser. No. 14/165,200 filed Jan. 27, 2014 and entitled “SYSTEMS AND METHODS OF GENERATING AND USING A BITMAP INDEX,” the content of each of which is expressly incorporated by reference herein in its entirety. 
    
    
     BACKGROUND 
     With the advent of the Internet, and the increasing popularity of Internet-based media content, significant advertising funds are being directed towards online advertising. Because online advertising is delivered to computing devices, various aspects of online advertising can be quantified. For example, statistics can be collected regarding how many people have viewed an online advertisement, clicked on an online advertisement, etc. The increasing prevalence of online advertising has resulted in the creation and storage of large amounts of measurement data. Analyzing such a large data set may be difficult. Large data sets may also be collected and stored in industries other than online advertising, such as the healthcare industry, the financial industry, etc. 
     SUMMARY 
     Systems and methods of using a bitmap index are disclosed. The bitmap index may index “big data,” such as data related to an advertising audience of a media property (e.g., a website). For example, an audience measurement system may track, on the basis of received event signals, various demographic properties, brand affinities, and behaviors of a media property&#39;s audience. The “raw” data collected by the audience measurement system may be stored in a distributed storage network and the bitmap index may include bit strings corresponding to the raw data (or a portion thereof). For example, the bitmap index may include a bit string for the demographic property “Male” and a bit string for the behavior “Reads articles.” The same position in each bit string may correspond to the same audience member. For example, if a particular user is assigned a user identifier (ID) of N, then the value of the N th  bit of the “Male” bit string indicates whether the particular user is male and the value of the N th  bit of the “Reads articles” bit string indicates whether the particular user has read an article on the media property. 
     The bitmap index may enable real-time or near-real-time computation of various metrics. For example, the question “what are the 50 most popular brands advertised on my website” may be answered by counting the number of ones in the brand affinity bit strings and then returning the 50 brands with the highest counts. More complex questions may also be answered. To illustrate, the question “what are the 50 most popular brands for men in my audience that have either made a purchase on the website or have an affinity for a particular coffee shop” may be answered by performing logical AND and OR operations on the bit strings to identify the audience subset (also referred to herein as a “segment”) that satisfies the query (male AND (purchased OR coffee shop)), and then performing a count operation on the brand affinity bit strings using the segment as a filter. 
     In selected implementations, the described bitmap index may be stored in a distributed fashion across multiple network nodes. Prior to executing a query, the system may formulate a query execution plan that parallelizes execution of the query and reduces or minimizes the amount of data that is transferred between storage nodes. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram to illustrate a particular embodiment of a system that is operable to generate and use a bitmap index; 
         FIG. 2  is a diagram to illustrate a particular embodiment of the bitmap index of  FIG. 1 ; 
         FIG. 3  is a diagram to illustrate a particular embodiment of a method of adding a user to a bitmap index; 
         FIG. 4  is a diagram to illustrate a particular embodiment of a method of updating a bitmap index; 
         FIG. 5  is a diagram to illustrate another particular embodiment of a method of updating a bitmap index; 
         FIG. 6  is a diagram to illustrate a particular embodiment of a method of using a bitmap index during execution of a query; 
         FIG. 7  is a diagram to illustrate another particular embodiment of a method of using a bitmap index during execution of a query; 
         FIG. 8  is a diagram to illustrate a particular embodiment of a method of generating a query execution plan corresponding to the query of  FIG. 7 ; 
         FIG. 9  is a diagram to illustrate a particular embodiment of a method of compressing and storing a bit string of a bitmap index; 
         FIG. 10  is a diagram to illustrate a particular embodiment of a graphical user interface (GUI) that includes a brand segment; 
         FIG. 11  is a diagram to illustrate a particular embodiment of a GUI that is operable to create a brand segment using logical operations; 
         FIG. 12  is a flowchart to illustrate another particular embodiment of a method of updating a bitmap index; 
         FIG. 13  is a flowchart to illustrate a particular embodiment of a method of using a bitmap index during execution of a query; 
         FIG. 14  is a diagram to illustrate a particular embodiment of an audience measurement system that is operable to generate and use a bitmap index; and 
         FIGS. 15A, 15B, 15C, and 15D  are diagrams to illustrate another particular embodiment of an audience measurement system that is operable to generate and use a bitmap index. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  illustrates a particular embodiment of a system  100  that is operable to generate and use a bitmap index. A measurement system  120  may be configured to receive event signals  110  associated with the audience of one or more media properties (e.g., websites). The event signals  110  may be received from sources that include, but are not limited to, computing devices, mobile devices, web servers, measurement logs, third-party data sources, and social networks. 
     The measurement system  120  may be implemented using one or more computing devices (e.g., servers). For example, such computing devices may include one or more processors or processing logic, memories, and network interfaces. The memories may include instructions executable by the processors to perform various functions described herein. The network interfaces may include wired and/or wireless interfaces operable to enable communication to local area networks (LANs) and/or wide area networks (WANs), such as the Internet. In the illustrated example, the measurement system  120  is communicably coupled to a network  130 . 
     The event signals  110  may include information associated with audience members of a media property. For example, when an audience member creates an account or otherwise registers with a media property using social networking identification, the measurement system  120  may retrieve event signals corresponding to data stored in social networking profiles of the audience member. As another example, the event signals  110  may identify specific interactions by the audience members with respect to the media property (e.g., what action was taken at a media property, when the action was taken, for how long the action was taken, etc.). The interactions may include interactions with advertisements presented by the media property and/or interactions with content presented by the media property. In a particular embodiment, each of the event signals  110  identifies a property (e.g., “Property 1”) and an audience member (alternately referred to herein as a “user”). For example, if a user having a user ID=N made a purchase on the website for Property 1, a corresponding event signal received by the measurement system  120  may be “(userID=N, property=‘Property 1’, behavior=Purchase)”. In alternate embodiments, a different format may be used to represent an event signal. 
     The measurement system  120  may include a data processing module  122  and a query execution module  124 , each of which may be implemented using instructions executable by one or more processors at the measurement system  120 . The data processing module  122  may receive the event signals  110  and store “raw” data corresponding to the event signals  110  (e.g., a copy of the event signals  110 ) in cloud-based storage  140 . The data processing module  122  may also store indexing data for the cloud-based storage  140  in a bitmap index  126 . In a particular embodiment, unlike the cloud-based storage  140 , the bitmap index  126  may be local (or more quickly accessible) to the measurement system  120 . To illustrate, data for the bitmap index  126  may be stored across one or more data storage devices (e.g., nodes) that are part of the measurement system  120  or accessible to the measurement system  120  via a LAN, or other private high-speed network, as opposed to a WAN. Thus, read and write operations with respect to the bitmap index  126  may be faster than corresponding read and write operations with respect to the cloud-based storage  140 . 
     The measurement system  120  may maintain a bitmap index  126  for each media property being measured. Data in the bitmap index  126  may be stored in the form of bit strings. The bitmap index  126  may store bit strings corresponding to at least a subset of the data stored in the cloud-based storage  140 . In a particular embodiment, the bitmap index  126  for a particular media property includes, for each audience member of the media property, data regarding one or more demographic attributes of the audience member, one or more brand affinities of the audience member, and/or one or more behaviors (e.g., interactions with the media property) of the audience member. 
     To illustrate, a media property may have a known audience of one hundred thousand registered members. The bitmap index  126  for the media property may include bit strings representing demographics, brand affinities, and behaviors of each of the hundred thousand audience members. Each of the bit strings may be one hundred thousand bits in length. Further, the same location in each bit string may correspond to the same audience member. For example, if the N th  location in a “Male” bit string has a value of “1” and the N th  location in a “Watches video” bit string has a value of “1,” this indicates that the N th  audience member (who has a userID=N) is a male that has watched at least one video on the property. 
     It should be noted that the bitmap index  126  for a media property may store bit strings corresponding to less than all of the data stored in the cloud-based storage  140 . For example, although the cloud-based storage  140  may include “raw” data corresponding to brand affinities of millions of brands, the bitmap index  126  may store bit strings for a smaller subset of the most popular brands (e.g., the top fifty thousand brands). An example of the bitmap index  126  is further described with reference to  FIG. 2 . 
     The query execution module  124  may be configured to use the bitmap index  126  to execute queries regarding measurement data for a media property. For example, the query execution module  124  may receive a query  150  corresponding to the question “What are the top 50 brand affinities for my audience?” In a particular embodiment, the query  150  may be generated using a query generation interface, as further described with reference to  FIG. 11 . To resolve the query  150 , the query execution module  124  may perform count operations on brand affinity bit strings stored in the bitmap index  126 . In a particular embodiment, because the bit strings may be stored across multiple nodes, the query execution module  124  may formulate a query execution plan that parallelizes execution of the query  150  across multiple nodes and minimizes the amount of data that is transferred between nodes during execution of the query  150 . By executing the query  150  on bit strings stored in the bitmap index  126  in parallel without retrieving data from the cloud-based storage  140  and by reducing or minimizing data transfers, the query execution module  124  may achieve real-time or near-real-time performance. For example, the query execution module  124  may have a maximum query execution latency less than or equal to one hundred milliseconds. Further examples regarding query execution are described with reference to  FIGS. 6-8 . 
     During operation, the measurement system  120  may receive the event signals  110  from various event sources. Each event signal may include a unique identifier, such as a user ID. If the user is a “new” audience member, the user may be assigned a new user ID and a user profile may be created for the user. Data for the user profile may be stored in the cloud-storage  140  and/or the bitmap index  126 . In a particular embodiment, data for the user profile may be retrieved from third party data sources, including but not limited to social networks. For example, the data may include demographic information associated with the user (e.g., a name, an age, a geographic location, a marital/family status, a homeowner status, etc.), social information associated with the user (e.g., social networking activity of the user, social networking friends/likes/interests of the user, etc.), and other types of data. The cloud-based storage  140  and the bitmap index  126  may be updated as additional event signals  110  are received (e.g., when additional users register with the media property, interact with the media property, etc.). 
     When the measurement system  120  receives the query  150 , the query execution module  124  may execute the query  150  based on a query execution plan that parallelizes execution and reduces/minimizes the amount of bit string data that is transferred between nodes during execution of the query  150 . The system  100  of  FIG. 1  may thus enable audience measurement and analysis based on data (e.g., event signals) received from various sources. For example, event signals may be generated in response to user interactions with websites, web pages, audio items, video items, games, and/or text associated with various media properties. Further, the system  100  of  FIG. 1  may enable real-time or near-real time execution of queries on the collected data. For example, the query execution module  124  may execute complex “top N” queries using the bitmap index  126  in real-time or near-real-time (e.g., within one hundred milliseconds). 
     It should be noted that although various embodiments are described herein in the context of advertising, this is for illustration only and not to be considered limiting. In alternate embodiments, the described bitmap index may be used in other contexts. For example, a tracked “audience” may correspond to patients and the event signals  110  may identify a patient and may correspond to a healthcare event (e.g., a visit to a doctor&#39;s office, a prescription being filled, etc.). Bit strings of the bitmap index  126  may correspond to medical attributes, such as medical history, allergy information, medication taken, etc. The same location in each bit string may correspond to the same patient. As another example, a tracked “audience” may correspond to inventory or customers of a store. Bit strings of the bitmap index  126  may correspond to attributes of inventory, such as color, price, demand, etc., and/or attributes of customers. The same location in each bit string may correspond to the same inventory item or the same customer. As yet another example, in the financial industry, the same location in each bit string may correspond to the same investor, the same financial advisor, the same financial product, etc. 
     Referring to  FIG. 2 , a particular embodiment of a bitmap index  250  is shown. In an illustrative embodiment, the bitmap index  250  is the bitmap index  126  of  FIG. 1 . The bitmap index  250  may correspond to a particular media property tracked by the measurement system  120  of  FIG. 1 . The bitmap index  250  stores a plurality of bit strings, where each bit string represents a demographic attribute  206 , a brand affinity  208 , or a behavior  210  associated with the audience of the media property. For purposes of illustration, the bitmap index  250  is shown as a grid, where each row  204  of the bitmap index  250  corresponds to a bit string. In  FIG. 2 , a bit string count is shown for the brand affinity bit strings. For example, the bit string for “Coffee Shop A” has a total of 7, indicating that 7 audience members have a brand affinity for “Coffee Shop A.” 
     As described with reference to  FIG. 1 , the same location in each bit string may correspond to the same audience member (e.g., user ID). Thus, each column  202  of the bitmap index  250  corresponds to a particular audience member. For example, a zero (0) at the intersection of the row  204  and the column  202  indicates that the user having a user ID equal to “Q” does not have an income in the range of $100,000-$110,000. It should be noted that the specific demographic attributes  206 , brand affinities  208 , and behaviors  208  are for example only. Bit strings may be stored with various other demographic attributes, brands, and behaviors/interactions. 
     In a particular embodiment, each bit string in the bitmap index  250  is subdivided into “slices” (e.g., sub-strings). In the illustrated example, each slice includes S bits, where S is a positive integer. Thus, a first slice  212  of each bit string includes data related to audience members having IDs 0 to S−1. A last (e.g., X th ) slice  214  includes data related to audience members having IDs (X−1)*S to Q. When bit strings are subdivided into slices, different slices of the same bit string may be stored in different locations (e.g., storage nodes). A hashing algorithm, such as consistent hashing, may be used (e.g., during read operations, write operations, query execution, etc.) to identify locations of the slices of a bit string. The value of S, which represents the maximum length of each slice, may be set based on a desired query execution latency. In a particular embodiment, S is equal to 65,535 (e.g., each slice includes 2 16  bits). 
     In a particular embodiment, the bitmap index  250  stores brand affinity bit strings for fewer brands than the total number of brands tracked by a measurement system (e.g., the measurement system  120  of  FIG. 1 ). For example, the measurement system  120  may track millions of brands, but the bitmap index  250  may store bit strings for the N (e.g., fifty thousand) most popular brands. As additional event signals are received, the bitmap index  250  may be updated to replace a bit string for one brand with a bit string for another brand, as further described with reference to  FIG. 5 . 
     Various operations may be performed using the bitmap index  250 . For example,  FIG. 3  illustrates an embodiment of adding a new user  302  to the bitmap index  250  and is generally designated  300 . In a particular embodiment, adding the new user  302  to the bitmap index  250  may involve a capture processor  304 , an event processor  306 , a data store  308 , and a database (e.g., an illustrative structured query language (SQL) database  310 ). In an illustrative embodiment, the capture processor  304  and the event processor  306  correspond to the data processing module  122  of  FIG. 1 . The data store  308  and/or the SQL database  310  may correspond to the cloud-based storage  140  of  FIG. 1 . 
     During operation, the capture processor  304  may receive an event signal corresponding to a new user registration event  303  for the user  302 . The event signal indicates that the user  302  is to be assigned a new user ID and is a new user for the media property “Property 1.” The event signal also indicates (e.g., on the basis of retrieved social networking data and/or third-party data) that the user  302  is a male, has an income of $105,000, and is single. In alternate embodiments, such demographic information may be automatically retrieved by a measurement system after the new user registration event, as further described with reference to  FIG. 15 . 
     The capture processor  304  (which may implement a capture application programming interface (API)) may send the event signal to the event processor  306 . Because the user  302  is a new audience member, the event processor  306  may generate and assign a new user ID to the user  302 . For example, the event processor  306  may atomically increment a largest previously assigned user ID (e.g., Q) to generate a new user ID  314  (e.g., Q+1). In a particular embodiment, the event processor  306  requests the new user ID  314  from an atomic incrementer service (e.g., a web service). The event processor  306  may then store data corresponding to the event signal in the data store  308 , the SQL database  310 , and/or the bitmap index  250 . For example, a new column  312  may be created in the bitmap index by storing a new (Q+1) th  bit in each of the bit strings in the bitmap index. When allocating and storing data in the (Q+1) th  column involves creating a new slice, the event processor  306  may automatically generate a new slice for each bit string of the bitmap index  250 . The value of the (Q+1) th  bit in the “Male,” “Income [100-110 k],” and “Single” bit strings may be set to “1” based on the event signal. The value of the (Q+1) th  bit in the remaining bit strings may be zero (e.g., a default value). 
       FIG. 4  illustrates a particular embodiment of updating the bitmap index  250  and is generally designated  400 . During operation, the capture processor  304  may receive an event signal  403  corresponding to updated information for the user  302 . In the illustrated example, the event signal  403  is generated based on the user  302  making a purchase on the media property. The event processor  306  may receive the event signal  403  and determines which row(s) and column(s) of the bitmap index  250  are affected by the event signal  403 . In the illustrated example, the event processor  306  determines that the event signal  403  will cause the value of the (Q+1) th  bit  412  of a “Purchase” bit string  414  to be set to “1.” The event processor  306  may also update the data store  308  and/or the SQL database  310  based on the received event signal  403 . 
     Although  FIG. 4  illustrates updating the bitmap index  250  in response to receiving an event signal for a user behavior, the bitmap index  250  may similarly be updated in response to receiving an event signal for a demographic attribute or a brand affinity. In the case of brand affinities, additional operations may also be performed. In particular, because the bitmap index  250  stores brand affinity bit strings for the top N (e.g., fifty thousand) brands, receiving a brand affinity event signal may cause a particular brand to become elevated into or fall out of the top N brands.  FIG. 5  illustrates a particular embodiment of updating the bitmap index  250  responsive to a brand affinity event signal, and is generally designated  500 . 
     For ease of illustration, the bitmap index  250  is depicted as storing three brand affinity bit strings (i.e., N=3). Initially, the three brands may be “Coffee Shop A,” “Store B,” and “Television Network C.” The brand affinity counts for the three brands are 7, 5, and 10 audience members, respectively. Brand affinity data for additional brands (e.g., brands outside the top N brands) may be stored in the data store  308 . 
     A received event signal  503  may indicate that the user  302  has an affinity for “Clothing brand D.” Upon receiving the event signal  503 , the event processor  306  may determine that a brand affinity bit string for “Clothing Brand D” is not stored in the bitmap index  250 . Thus, the event processor  306  may store data for the event signal  503  in the data store  308 . The event processor  306  (or a background process or thread) may determine that because of the event signal  503 , “Store B” (which has a count of 5) has fallen outside of the top N brands and that “Clothing Brand D” (which now has a count of 6) has become elevated into the top N brands. In response to the determination, a bit string  510  for “Store B” may be replaced in the bit string index  250  with a bit string  512  for “Clothing Brand D.” 
       FIGS. 3-5  thus illustrate various operations that may be performed during generation and maintenance of the bitmap index  250 . The bitmap index  250  may also be used during execution of queries. For example,  FIG. 6  illustrates a particular embodiment of executing (e.g., resolving) a query  602  using the bitmap index  250  and is generally designated  600 . The query  602  may be received and executed by the query execution module  124  of  FIG. 1 . The result of executing the query  602  is another bit string  614 , referred to herein as a “filter string.” In the illustrated example, the query  602  corresponds to an intersection operation between the “Male” and “Income [100-110 k]” demographic properties (i.e., corresponds to the question “Which of my audience members is male and has a household income between $100,000 and $110,000?”). Thus, the filter string  614  may correspond to a custom segment of an audience of a particular property that is associated with the bitmap index  250 . The custom audience segment may correspond to an aggregation of audience segments generated using one or more set operations, such as logical AND operations and logical OR operations. 
     Resolving the query  602  may including ANDing each bit string location (i.e., each user) of a “Male” bit string  610  with a corresponding location of an “Income [$100-110 k] bit string  612 , as shown. When both corresponding locations contain a “1,” the corresponding location of the filter string  614  is set to 1. At the conclusion of the AND operations, the filter string  614  corresponds to a custom audience segment of men who earn $100,000-$110,000. 
     In a particular embodiment, the filter string  614  is stored and available for use during execution of subsequent queries. The filter string  614  may also be used to query the data store  308  (e.g., cloud-based storage) or the SQL database  310  (e.g., a user profile database) regarding the custom audience segment. It should be noted that while the illustrated query  602  calls for a single set operation to generate the filter string  614 , the described techniques may be used with more complex queries that involve any number of union operations, intersection operations, and/or count operations. For example,  FIG. 7  illustrates a particular embodiment of resolving a (more complex) second query  702  to generate a second filter string  716 , and is generally designated  700 . In  FIG. 7 , the query  702  is a top Z brand affinities query (where Z is a positive integer). 
     The query  702  requests identification of audience members that are male and that like “Coffee Shop A” or have made a purchase on the media property. The filter string  716  may be generated by ORing a “Coffee Shop A” bit string  712  with a “Purchase” bit string  714  to generate an intermediate result string (not shown). The filter string  716  may be generated by ANDing the “Male” bit string  610  with the intermediate result string. The audience members having a “1” in the filter string  716  represent the audience members who are male and either have a brand affinity for “Coffee Shop A” or have made a purchase. In a particular embodiment, the filter string  716  may be stored and used during execution of subsequent queries. For example, to answer the question “What are the top 50 brands for men in my audience that either have an affinity for Coffee Shop A or have made a purchase?” the filter string  716  may be generated. The filter string  716  may be ANDed with each of the brand affinity bit strings to generate result strings. Count operations may be performed on the result strings and the 50 brand affinities with the highest counts may be returned in response to the question. 
     It will be appreciated that during query execution, the AND/OR operations performed on bit strings are performed one bit at a time, and the result of an operation on any single bit location does not impact the result of the operation on any other bit location. Thus, query execution may be parallelized. For example, when slices of the bit strings are stored at different network nodes, performing an operation with respect to bit strings may be parallelized into performing the operation with respect to individual slices at individual nodes. To determine where and in what order such parallel operations should be performed, a query execution module may generate a query execution plan. 
     For example,  FIG. 8  illustrates a particular embodiment of generating of a query execution plan  804  to resolve the query  702  of  FIG. 7 , and is generally designated  800 . Upon receiving the query  702 , a query execution module  806  may generate a query execution plan  804 . In a particular embodiment, because data transfers between nodes may represent a bottleneck, the query execution plan  804  may be generated such that data transfers are reduced/minimized. In a particular embodiment, the query execution module  806  is part of one of the nodes  808 - 814 . Alternately, the query execution module  806  may be part of a separate node (e.g., a load-balancing node). 
     For example, the query execution module  806  may determine that resolution of the query  702  of  FIG. 7  involves performing operations on the “Male” bit string, the “Coffee Shop A” bit string, and the “Purchase” bit string. In the illustrated example, each of the bit strings has three slices. A first slice  816  of the “Male” bit string, designated Male 1  is stored on Node A  808 . A Male 2  slice  818  and a Male 3  slice  820  are stored on Node B  810 . Coffee Shop A 1 , Coffee Shop A 2 , Coffee Shop A 3 , and Purchase 3  slices  822 ,  824 ,  826 , and  828  are stored on Node C  812 . Purchase 1  and Purchase 2  slices  830  and  832  are stored on Node D  814 . 
     The query execution plan  804  identifies operations and at what nodes are to perform the operations. For example, the query execution plan  804  indicates that in a first step, Node C  812  is to perform a union (OR) operation between Coffee Shop A 3  slice  826  and the Purchase 3  slice  828  to generate an intermediate result slice Union 3 . In parallel, Node A  808  is to transfer a copy of the Male 1  slice  816  to Node C  812  and Node B  810  is to transfer copies of the Male 2  slice  818  and the Male 3  slice  820  to Node C  812 . Node D is to transfer copies of the Purchase 1  slice  830  and the Purchase 2  slice  832  to Node C  812 . 
     In a second step, Node C  812  performs two operations in parallel: ORing the Purchase 1  slice  830  and the Coffee Shop A 1  slice  822  to generate an intermediate result slice Union 1 , and ORing the Purchase 2  slice  832  and the Coffee Shop A 2  slice  824  to generate an intermediate result slice Union 2 . 
     In a third step, Node C  812  performs three operations in parallel to generate three intermediate bit strings. The first intermediate bit string Result 1  is generated by ANDing the Union 1  slice with the Male 1  slice. The second intermediate bit string Result 2  is generated by ANDing the Union 2  slice with the Male 2  slice. The third intermediate bit string Result 3  is generated by ANDing the Union 3  slide with the Male 3  slice. In a fourth step, Node C concatenates the Result 1 , Result 2 , and Result 3  bit strings to generate the filter string  716  of  FIG. 7 . 
       FIG. 8  thus illustrates generation of a query execution plan for a query. In a particular embodiment, the query execution plan is generated prior to performing any set operations. The query execution plan may be generated so as to increase the number of parallel operations and reduce the number of bit string (or slice) transfers between nodes, which may result in improved query execution latency. 
     In the foregoing description, bit strings are described as being subdivided into slices. For example, each slice may include 64 kibibits (1 kibibit=2 10  bits=1,024 bits). In a particular embodiment, slices may be further divided into “chunks.” For example, chunks may be up to 2 kibibits in length (e.g., each slice is subdivided into 32 chunks). To reduce the amount of space occupied by bit strings of a bitmap index, chunks may be stored in a compressed fashion. For example,  FIG. 9  illustrates a particular embodiment of compressing and storing data of a bitmap index and is generally designated  900 . In particular,  FIG. 9  depicts Node A  808  of  FIG. 8 , which stores the Male 1  slice  816 . The Male 1  slice  816  may be compressed in accordance with various compression schemes. In the illustrated compression scheme, chunks that have only zeroes are not stored. Chunks that include a one are stored. A flag corresponding to each chunk is stored. If a flag has a value of zero, the corresponding chunk is stored in its entirety. For example, a first flag  902  and a third flag  908  have a value of zero, indicating that corresponding first chunk  904  and third chunk  910  are stored at Node A  808 . If a flag has a value of one, the corresponding chunk is “compressed” by not being stored. For example, a second flag  906  has a value of one, indicating that a corresponding second chunk includes only zeroes and is not stored. During queries, the second chunk may be dynamically generated by introducing zeroes (e.g., 2,048 zeroes) between the first chunk  904  and the third chunk  910 . 
     While  FIG. 9  illustrates one example of a compression scheme, in alternate embodiments, different compression schemes may be used. Further, data for the compression scheme may be stored in different places. For example, the chunks  904  and  910  may be stored at Node A  808 , and the flags  902 ,  906 , and  908  may be stored in a different location (e.g., as part of a hash table that is used to identify where slices/chunks of a bit string are stored). 
     Referring to  FIG. 10 , a diagram of a graphical user interface (GUI)  1000  corresponding to a brand segment is shown. The GUI  1000  includes options  1001  to view particular types of segments. For example, the options  1001  include a behavioral option  1002 , a brand option  1004 , a category option  1006 , a demographic option  1008 , an education option  1010 , a geographic option  1012 , a technology option  1016 , a favorites option  1018 , and a custom collections option  1020 . It should be noted that while the aforementioned ten options are shown in  FIG. 10 , the GUI  1000  may include more, fewer, and/or different options. Selecting a particular segment type option  1001  may cause the GUI  1000  to display segments of the particular segment type. Each segment may correspond to a bit string of a bitmap index. 
     In the illustrated example, the brand option  1004  is selected, and the GUI  1000  accordingly displays brand segments. Each brand segment corresponds to a brand affinity bit string of the bitmap index  250 . Thus, the GUI  1000  displays icons corresponding to a “Clothing Brand D” brand segment  1022 , a “TV Network C” brand segment  1024 , and a “Coffee shop A” brand segment  1026 . Each of the icons may be selectable (e.g., by clicking on the icon, hovering a pointer over the icon, etc.). 
     Upon receiving a selection of a particular icon, the GUI  1000  may display an overlay that includes additional information about the selected segment. In the illustrated example, the GUI  1000  displays that ten audience members have a brand affinity for “TV Network C.” Thirty percent of those audience members are female, seventy percent are male, the average age of the audience is 27, and the average household income is $85,000. 
     The GUI  1000  also displays a create aggregate segment option  1030 . The create aggregate segment option  1030  may be selected to create a custom aggregate segment using the selected “TV Network C” segment  1024 . To illustrate, referring to  FIG. 11 , an overlay  1102  may be displayed on the GUI  1000 . The overlay  1102  includes an icon for the “TV Network C” segment. The overlay  1102  also includes a drop-down menu  1104  to select a set operation, such as an AND operation or an OR operation. In the illustrated example, the AND operation is selected. The overlay  1102  further includes an option  1106  to search for a segment to AND with the “TV Network C” segment. The overlay  1102  includes an option  1108  to assign a name to the created custom segment, so that the segment may be retrieved in the future (e.g., via the custom collections option  1020  of  FIG. 10 ). 
     In a particular embodiment, the overlay  1102  includes a profile option  1110 . When selected, the profile option  1110  may generate a query based on the segments and set operations selected in the overlay  1102 . The query may be resolved to generate an aggregate segment, and profile data for the aggregate segment may be displayed. Thus, the GUI  1000  of  FIGS. 10-11  may enable a user to view segments, create custom segments and queries, execute queries, and view results of query execution. 
     Referring to  FIG. 12 , a particular embodiment of a method  1200  of updating a bitmap index is shown. In an illustrative embodiment, the method  1200  may be performed by the measurement system  120  of  FIG. 1  and may be illustrated with reference to  FIG. 3 . 
     The method  1200  includes receiving an event signal, at  1202 . For example, in  FIG. 3 , the event processor  306  may receive an event signal corresponding to the new user registration event  303  for the user  302 . As another example, in  FIG. 4 , the event processor  306  may receive the event signal  403 , indicating that the user  302  made a purchase. 
     The method  1200  also includes determining an identifier associated with the event signal, at  1204 . For example, in  FIG. 3 , the event processor  306  may determine that the user  302  is a new user and is to be assigned a new user ID. In response, the event processor  306  may assign the identifier (Q+1)  314  to the user  302 . As another example, in  FIG. 4 , the event processor  306  may determine that the event signal is associated with the user ID (Q+1)  314  for the user  302 . 
     The method  1200  further includes storing the event signal (or data corresponding thereto) in a data store, at  1206 . For example, the event signal may be stored in offsite storage, such as the cloud-based storage  140  of  FIG. 1  or the data store  308  of  FIG. 3 . 
     The method  1200  includes modifying a value of at least one bit stored in the bitmap index based on the identifier, at  1208 . The bitmap index stores a plurality of bit strings. A particular location in each of the plurality of bit strings corresponds to the identifier. For example, in  FIG. 3 , the event processor  306  may set the (Q+1) th  values of the “Male,” “Income [100-110 k]” and “Single” bit strings to one. As another example, in  FIG. 4 , the event processor  306  may set the (Q+1) th  value of the “Purchase” bit string to one. The method  1200  may thus enable updating and maintaining a bitmap index as additional measurement data is received. Keeping the bitmap index updated may enable quick resolution of queries. 
     Referring to  FIG. 13 , a particular embodiment of a method  1300  of using a bitmap index during execution of a query is shown. In an illustrative embodiment, the method  1300  may be performed by the measurement system  120  of  FIG. 1  and may be illustrated with reference to  FIG. 8 . 
     The method  1300  includes receiving a query directed to a bitmap index, at  1302 . The bitmap index includes a plurality of bit strings, where a particular location in each of the plurality of bit strings corresponds to a particular user identifier. The plurality of bit strings is stored in a distributed fashion across a plurality of nodes. For example, referring to  FIGS. 7-8 , the query execution module  806  may receive the query  702  directed to the bitmap index  250 . For ease of illustration, the bitmap index  250  is illustrated as a grid, where each row corresponds to a bit string and each column corresponds to a particular user ID. In a particular embodiment, the bit strings may be subdivided into slices and chunks, as described with reference to  FIG. 9 . 
     The method  1300  also includes determining a subset of nodes, where each node in the subset stores at least a portion of a bit string related to the query, at  1304 . For example, in  FIG. 8 , the query execution module  806  may determine that nodes A-D  808 - 814  store slices  816 - 832  of bit strings related to the query  702 . 
     The method  1300  further includes generating a query execution plan for the received query, at  1306 . The query execution plan identifies one or more set operations, one or more nodes of the subset of nodes to perform each of the one or more set operations, and a particular node to consolidate results of the one or more set operations to generate a result bit string that indicates a result of the query. For example, in  FIG. 8 , the query execution module  806  may generate the query execution plan  804 . The query execution plan  804  identifies a plurality of set operations and which node each set operation is to be performed on. The query execution plan  804  also indicates, at step  4 , that Node C  812  generates the filter string  716  that represents the resolution of the query  702 . 
       FIGS. 1-13  thus illustrate systems and methods of generating, updating, and using a bitmap index. The bitmap index may enable a measurement system, such as the measurement system  120 , to quickly provide analysis for “raw” data stored in an offsite (e.g., cloud-based) storage location. The bitmap index may represent an on-the-fly index of binary representations of different audience traits that can be mined to determine what set of audience members is most likely to be receptive to particular content or a particular advertisement. Audience traits may be combined into long bit strings, where each bit string represents a single trait for an entire audience. By keeping the bitmap index “hot” in memory, ad-hoc queries may be performed efficiently and with reduced latency. Moreover, it will be appreciated that maintaining the bitmap index in memory may be less memory-intensive than keeping all “raw” data in memory. The described techniques may also be used with other types of systems. For example, in alternate embodiments, the same location in each bit string of the bitmap index may correspond to an identifier other than a user ID, such as an inventory number, an employee number, a hospital patient identifier, etc.  FIGS. 14 and 15  illustrate additional scenarios in which a bitmap index may be generated and used. 
     In particular,  FIG. 14  illustrates an alternate embodiment of a measurement system  1440 , and is generally designated  1400 . The measurement system  1440  may be communicatively coupled to one or more user devices (e.g., illustrative user devices  1412 ,  1414 , and  1416 ), to one or more content delivery networks (CDNs) (e.g., illustrative CDN  1422 ), and to properties (e.g., websites)  1432  and  1434 . In  FIG. 14 , the properties  1432  and  1434  are illustrated by corresponding servers (e.g., web servers). The measurement system  1440  may be implemented using one or more computing devices (e.g., servers). For example, such computing devices may include one or more processors or processing logic, memories, and network interfaces. The memories may include instructions executable by the processors to perform various functions described herein. The network interfaces may include wired and/or wireless interfaces operable to enable communication to local area networks and/or wide area networks (e.g., the Internet). 
     The user devices  1412 - 1416  may be associated with various users. For example, the desktop computing device  1412  and the tablet computing device  1414  may be associated with a first user  1402 , and the mobile telephone device (e.g., smartphone)  1416  may be associated with a second user  1404 . It should be noted that the user devices  1412 - 1416  are shown for example only and are not to be considered limiting. In alternate embodiments, fewer, additional, and/or different types of user devices may be present in the system  1400 . For example, a radio-frequency identification (RFID)-enabled device may be carried by a user and may transmit a signal in response to detecting that the user is visiting a particular physical location. In a particular embodiment, the user devices  1412 - 1416  may execute applications that are operable to access the properties  1432  and  1434 . For example, the user devices  1412 - 1416  may include applications developed using a mobile software development kit (SDK) that includes support for audience measurement functions. To illustrate, when the SDK-based applications interact with the properties  1432  and  1434 , the applications may generate first event signals  1410  that are transmitted by the user devices  1412 - 1416  to the measurement system  1440 . 
     The first event signals  1410  may include information identifying specific interactions by the users  1402 - 1404  via the user devices  1412 - 1416  (e.g., what action was taken at a media property, when the action was taken, for how long the action was taken, etc.). The user interactions may include interactions with advertisements presented by the media property and/or interactions with content presented by the media property. The event signals  1410  may also include an identifier, such as a browser identifier (browser ID) generated by the SDK. In a particular embodiment, browser identifiers are unique across software installations and devices. For example, a first installation of a SDK-based application at the desktop computing device  1412  and a second installation of the same SDK-based application at the tablet computing device  1414  may use different browser IDs, even though both installations are associated with the same user  1402 . 
     In another particular embodiment, Browser IDs may remain consistent until applications or web browsers are “reset” (e.g., caches/cookies are cleared). In some embodiments, the user devices  1412 - 1416  may execute applications other than browser applications, such as downloadable mobile applications, that generate the event signals  1410  based on user interactions with advertisements and/or content presented by the applications. 
     The user devices  1412 - 1416  may access content provided by the properties  1432  and  1434  directly or via the CDN  1422 . The CDN  1422  may provide distributed, load-balanced access to audio, video, graphics, and web pages associated with the media properties  1432  and  1434 . For example, the CDN  1422  may include geographically distributed web servers and media servers that serve Internet content in a load-balanced fashion. The CDN  1422  may send second event signals  1420  to the measurement system  1440 . The second event signals  1420  may include information identifying interactions with media properties and browser IDs provided to the CDN  1422  by the user devices  1412 - 1416  and/or the properties  1432  and  1434 . For example, the second event signals  1420  may include CDN logs or data from CDN logs. 
     The media properties  1432  and  1434  may be controlled by the same entity (e.g., may be part of a federated property) or by different entities. The properties  1432  and  1434  may send third event signals  1430  to the measurement system  1440 . The third event signals  1430  may include information identifying interactions with the media properties and browser IDs provided by the user devices  1412 - 1416  during communication with the properties  1432  and  1434  (e.g., communication via hypertext transfer protocol (HTTP), transport control protocol/internet protocol (TCP/IP), or other network protocols). 
     In a particular embodiment, the third event signals  1430  may include server logs or data from server logs. Alternately, or in addition, the third event signals  1430  may be generated by SDK-based (e.g., web SDK-based) applications executing at the properties  1432  and  1434 , such as scripts embedded into web pages hosted by the properties  1432  and  1434 . 
     The first event signals  1410  from the user devices  1412 - 1416  and the second event signals  1420  generated by the CDN  1422  may be considered “first-party” event signals. The third event signals  1430  from the properties  1432  and  1434  may be considered “third-party” event signals. First party event signals may be considered more trustworthy and reliable than third party event signals, because of the possibility that third party event signals could be modified by a media property owner prior to transmission to the measurement system  1440 . 
     In a particular embodiment, the properties  1432  and  1434  may send data to the measurement system  1440  and receive data from the measurement system  1440  regarding advertisements and/or content presented by the properties  1432  and  1434 . Such communication is illustrated in  FIG. 14  as advertisement/content communication  1460 . For example, an advertisement (or software associated with the advertisement that is executing on a client device, such as web server, a computer, a mobile phone, a tablet device, etc.) may collect and transmit data on a per-advertisement, per-user basis. The data may include or identify a profile of a user, a duration that the user viewed the advertisement, action(s) performed by the user with respect to the advertisement, etc. As another example, a content item or software associated therewith may collect and transmit data regarding user interactions with the content item. 
     In a particular embodiment, the measurement system  1440  includes a data filtering module  1442 , a data processing module  1444 , a data reporting module  1446 , and a query execution module  1447 . In a particular embodiment, each of the modules  1442 - 1447  is implemented using instructions executable by one or more processors at the measurement system  1440 . 
     The data filtering module  1442  may receive the event signals  1410 ,  1420 , and  1430 . The data filtering module  1442  may check the event signals  1410 ,  1420 , and  1430  for errors and may perform data cleanup operations when errors are found. The data filtering module  1442  may also receive and perform cleanup operations on advertisement measurement data and content measurement data received from the properties  1432  and  1434  and from applications executing on the user devices  1412 - 1416 . In a particular embodiment, the data filtering module  1442  may implement various application programming interfaces (APIs) for event signal collection and inspection. The data filtering module  1442  may store authenticated/verified event signals in a database, event cache, archive, and/or cloud storage  1452 . In a particular embodiment, the measurement system  1440  includes or has access to a brand database that tracks brands. For example, “raw” data corresponding to the brand database and other collected data may be stored in the cloud storage  1452 . Signals received from the properties  1432  and  1434  and from applications executing the user devices  1412 - 1416  may identify a brand that matches one of the brands in the brand database. The measurement system  1440  may thus track advertisements/content for various brands across multiple properties. 
     The data processing module  1444  may operate as described with reference to the data processing module  122  of  FIG. 1 . Alternately, or in addition, the data processing module  1444  may associate received event signals (and interactions represented thereby) with user profiles of users. For example, when an event signal having a particular browser ID is a social networking registration event (e.g., when a user logs into a website using a Facebook® account, a Twitter® account, a LinkedIn® account, or some other social networking account), the data processing module  1444  may retrieve a corresponding social networking profile or other user profile data from third party data sources  1450 . Facebook is a registered trademark of Facebook, Inc. of Menlo Park, Calif. Twitter is a registered trademark of Twitter, Inc. of San Francisco, Calif. LinkedIn is a registered trademark of LinkedIn Corp. of Mountain View, Calif. 
     It will be appreciated that interactions that were previously associated only with the particular browser ID (i.e., “impersonal” alphanumeric data) may be associated with an actual person (e.g., John Smith) after retrieval of the social networking profile or user profile. Associating interactions with individuals may enable qualitative analysis of the audiences of media properties. For example, if John Smith is a fan of a particular sports team, the measurement system  1440  may indicate that at least one member of the audience of the first property  1432  or the second property  1434  is a fan of the particular sports team. When a large percentage of a media property&#39;s audience shares a particular characteristic or interest, the media property may use such information in selecting and/or generating advertising or content. User profiles (e.g., a profile of the user John Smith) and audience profiles (e.g., profiles for the media properties associated with the properties  1432  and  1434 ) may be stored in the cloud storage  1452  and/or in another database, as further described with reference to  FIG. 15 . An audience profile for a particular media property may be generated by aggregating the user profiles of the individual users (e.g., including John Smith) that interacted with the particular media property. 
     Audience profiles may be generated using as few as one or two user profiles, although any number of user profiles may be aggregated. In a particular embodiment, audience profiles may be updated periodically (e.g., nightly, weekly, monthly, etc.), in response to receiving updated data for one or more users in the audience, in response to receiving a request for audience profile data, or any combination thereof. Audience profiles may similarly be generated for audiences of a particular mobile application based on signals generated by installations of the mobile application on various user devices. 
     The data reporting module  1446  may generate various interfaces, such as the GUI  1000  of  FIGS. 10-11 . The data reporting module  1446  may also support an application programming interface (API) that enables external devices to view and analyze data collected and stored by the measurement system  1440 . In a particular embodiment, the data reporting module  1446  is configured to segment the data. 
     As used herein, a “segment” is based on a group of people (e.g., an audience or a subset thereof). As further described herein, a digital genome may be determined for each segment. Examples of segments include, but are not limited to, brand affinity segments (also called brand segments), demographic segments, geographic segments, social activity segments, employer segments, educational institution segments, professional group segments, industry category of employer segments, brand affinity category segments, professional skills segments, job title segments, and behavioral segments. In a particular embodiment, behavioral segments are defined by a client (e.g., property owner or publisher) or by the measurement system  1440 , and represent actions taken on a client&#39;s property. 
     Additional examples of segments include segments based on an advertisement, an advertisement campaign, an advertisement placement, an advertisement context, a content item, a content context, content placement, a platform (e.g., desktop/laptop computer vs. mobile phone vs. tablet computer), etc. Segments may be used to understand or evaluate characteristics of an audience, craft a content strategy, generate advertising leads, create advertising pitches, and respond to inbound advertising requests. Segments may also be used to acquire additional audience members, receive information from advertisements/content items, and send information to advertisements/content items. In a particular embodiment, the measurement system  140  may be operable to define “new” segments based on performing logical operations (e.g., logical OR operations and logical AND operations), as described with reference to  FIGS. 7, 8, and 11 . 
     The measurement system  1440  may also include a bitmap index  1448  (e.g., the bitmap index  126  of  FIG. 1  and/or the bitmap index  250  of  FIG. 2 ). The bitmap index  1448  may store bit strings corresponding to at least a subset of the “raw” data stored in the cloud storage  1452 . In one example, a different bitmap index  1448  is maintained for each property  1432 ,  1434 . The bitmap index  1448  for a particular property may include, for each audience member of the property, data regarding a demographic attribute of the audience member, a brand affinity of the audience member, and/or behaviors (e.g., interactions with the media property) of the audience member. The same location in each bit string of the bitmap index  1448  may correspond to the same user, as described with reference to  FIGS. 1-2 . 
     The data processing module  1444  may also be configured to, upon receiving an event signal, parse the event signal to identify what user and media property the event signal corresponds to. The data processing module  1444  may store data corresponding to the event signal in one or more databases (e.g., the cloud storage  1452 , a user profile database, etc.). The data processing module  1444  may also store indexing data corresponding to the event signal in the bitmap index  1448  for the identified media property. If the user is a new audience member for the media property, the data processing module  1444  may assign a new ID to the user. Event signals may be processed as described above with reference to  FIGS. 2-8 . 
     The query execution module  1447  may operate as described with reference to the query execution module  124  of  FIG. 1  and/or the query execution module  806  of  FIG. 8 . For example, the query execution module  1447  may receive a query and generate a query execution plan that parallelizes execution and reduces/minimizes data transfers between storage nodes during query execution. 
     During operation, the users  1402 - 1404  may interact with the media properties  1432  and  1434  and with applications executing on the user devices  1412 - 1416 . In response to the interactions, the measurement system  1440  may receive the event signals  1410 ,  1420 ,  1430 , and/or  1460 . Each event signal may include a unique identifier, such as a browser ID and/or an audience member ID. If the user is a “new” audience member, the data processing module  1444  may create a user profile. Data for the user profile may be stored in the cloud storage  1452  and/or the bitmap index  1448 . In a particular embodiment, data for the user profile may be retrieved from the third party data sources  1450 . 
     For example, the data processing module  1444  may retrieve and store data from one or more social network profiles of the user. The data may include demographic information associated with the user (e.g., a name, an age, a geographic location, a marital/family status, a homeowner status, etc.), social information associated with the user (e.g., social networking activity of the user, social networking friends/likes/interests of the user, etc.), and other types of data. The data processing module  1444  may also collect and store data associated with advertisements and content served by the properties  1432  and  1434  and by applications executing on the user devices  1412 - 1416 . In a particular embodiment, the measurement system  1440  is further configured to receive offline data from external data sources. For example, the measurement system  1440  may receive data regarding transactions (e.g., purchases) made by an audience and may use the transaction data to generate additional signals that contribute to the digital genome of an audience, brand, property, etc. Another example of offline data may be a “data dump” of data collected by an RFID-enabled device or an RFID detector. Offline data may be stored in one or more computer-readable files that are provided to the measurement system  1440 . In a particular embodiment, offline data can include previously collected data regarding users or audience members (e.g., names, addresses, etc.). 
     The data reporting module  1446  may report data collected by the measurement system  1440 . For example, the data reporting module  1446  may generate reports based on an audience profile of a media property (or application), where the audience profile is based on aggregating user profiles of users that interacted with the media property (or application). To illustrate, the data reporting module  1446  may generate an interface, such as the GUI  1000  of  FIGS. 10-11 , indicating demographic attributes of the audience as a whole (e.g., a percentage of audience members that are male or female, percentages of audience members in various age brackets, percentages of audience members in various income bracket, most common audience member cities/states of residence, etc.). The interface may also indicate social attributes of the audience as a whole (e.g., the most popular movies, sports teams, etc. amongst members of the audience). Audience profiles may also be segmented and/or aggregated with other audience profiles, as further described herein. Audience profiles may further be segmented based on advertisement, advertisement campaign, brand, content item, etc. Audience profiles may also be constructed by combining segments, as further described herein. 
     In a particular embodiment, the system  1400  may also receive event signals based on measurements (e.g., hardware measurements) made at a device. For example, an event signal from the tablet computing device  1414  or the mobile telephone device  1416  may include data associated with a hardware measurement at the tablet computing device  1414  or the mobile telephone device  1416 , such as an accelerometer or gyroscope measurement indicating an orientation, a tilt, a movement direction, and/or a movement velocity of the tablet computing device  1414  or the mobile telephone device  1416 . As another example, the system  1400  may receive a signal in response to an RFID device detecting that a user is visiting a particular physical location. The system  1400  of  FIG. 14  may also link interactions with user profiles of users. This may provide information of “how many” viewers and “how long” the viewers watched a particular video (e.g., as in direct response measurement systems), and also “who” watched the particular video (e.g., demographic, social, and behavioral attributes of the viewers). 
     The system  1400  of  FIG. 14  may thus enable audience measurement and analysis based on data (e.g., event signals) received from various sources. Further, the system  1400  of  FIG. 14  may enable real-time or near-real time execution of queries on collected data, such as execution of “top N” queries using the bitmap index  1448 . 
       FIG. 15  illustrates another particular embodiment of a system  1500  that is operable to generate and use a bitmap index. The system  1500  includes a data collection tier (e.g., subsystem)  1510 , an event processing tier  1550 , and a monitoring tier  1570 . Components of the data collection tier  1510  are illustrated in further detail in  FIG. 15B . Components of the event processing tier  1550  are illustrated in further detail in  FIG. 15C . Components of the monitoring tier are illustrated in further detail in  FIG. 15D . 
     The system  1500  includes (or has access to) an authentication provider  1532 , third party data sources  1534 , an audience web application  1546 , a first framework  1544 , a second framework  1542 , a database  1548 , an interrogator  1538 , a data store  1536 , and a bitmap index  1540 . In an illustrative embodiment, the third party data sources  1534  are the third party data sources  1450  of  FIG. 14 , the event processing tier  1550  and the interrogator  1538  correspond to the data processing module  1444  of  FIG. 14 , and the bitmap index  1540  is the bitmap index  1448  of  FIG. 14 . 
     The data collection tier  1510  includes a content management system (CMS)  1512 , cloud storage  1516 , content delivery networks  1518 , client browsers  1520 , and client servers  1522 . The data collection tier  1510  may further include an application programming interface (API)  1521 . The API  1521  includes a load balancer  1524 , capture servers  1526 , and cloud storage  1530 . 
     The event processing tier  1550  includes a job queues module  1551 , an anonymous buffer  1560 , and an event bundle buffer  1562 . The job queues module  1551  includes an authentication token handler  1552 , a backplane hander  1554 , an event dispatch  1556 , and an event bundle handler  1558 . In alternate embodiments, the job queues module  1551  may include more, fewer, and/or different handlers than illustrated in  FIG. 15 . 
     The monitoring tier  1570  includes an internal monitoring module  1572 , a ping monitor  1584 , and a notifications module  1582 . The internal monitoring module  1572  includes a penetration monitor  1574 , a performance analysis module  1576 , a system monitor  1578 , and an alert rules module  1580 . 
     During operation, the content management system  1512  may be used to generate a client specific script (e.g., webscript)  1514  for various clients (e.g., media properties). The client specific script  1514  may be stored in the cloud storage  1516  and replicated to the content delivery networks  1518 . As audience members register and interact with a media property, the content delivery networks  1518  may deliver the client specific script  1514 , along with property content, to the client browsers  1520 . Based on the client specific script  1514 , the client browsers  1520  may generate tags (e.g., a tag corresponding to a particular user activity, such as watching a video) or tokens (e.g., a social networking registration token). The tags or tokens may be sent to the load balancer  1524 . The client servers  1522  may also generate tags or tokens to send to the load balancer  1524  based on user registrations and user activity at media properties. The tags or tokens from the client servers  1522  may be authenticated by the authentication provider  1532 . 
     The load balancer  1524  may send the tags or tokens to the capture servers  1526  based on a load balancing algorithm. The capture servers  1526  may generate event data (e.g., event signals) based on the tags or tokens. The capture servers  1526  may store the event data in event logs  1528  in the cloud storage  1530  and send the event data to the job queues module  1551 . 
     The job queues module  1551  may distribute the event data to different event handler(s) based on the type of the event data. For example, event data including an authentication token may be sent to the authentication token handler  1552 . In addition, event data requiring additional information from social media sources may be sent to the backplane handler  1554 . The handlers  1552 - 1554  may perform asynchronous event collection operations based on the received event data. For example, when a new user registers with a media property using a social networking profile, a token may be provided by the data collection tier to the authentication token handler  1552  and/or the backplane handler  1554 . The handlers  1552 - 1554  may use the token to retrieve demographic and brand affinity data for the user from the user&#39;s social networking profile. 
     Event signals may also be sent to the event dispatch  1556 , which determines whether the event signals correspond to known or unknown users. When event data corresponds to an unknown user, the event dispatch  1556  buffers the event data in the anonymous buffer  1560 . After a period of time (e.g., three days), event data from the anonymous buffer  1560  may be sent to the job queues module  1551  to be processed again. 
     When event data corresponds to a “known” user (e.g., a user that has already been assigned a user ID), the event dispatch  1556  may send the event data to the event bundles buffer  1562 . The event bundle handler  1558  may retrieve event data stored in the event bundles buffer  1562  every bundling period (e.g., one hour). The event bundles processor  1558  may bundle event data received each bundling period into an event bundle that is sent to the interrogator  1538 . 
     The interrogator  1538  may parse the event bundle and update the data store  1536 , the SQL database  1548 , and/or the bitmap index  1540 . For example, the interrogator  1538  may perform bitmap index generation and update operations as described with reference to  FIGS. 1-13 . In a particular embodiment, the database  1548  corresponds to a profiles database that is accessible the first framework  1544  to the audience web application  1546 . For example, the first framework  1544  may be a database-driven framework that is operable to dynamically generate webpages based on data in the database  1548 . The audience web application may be operable to generate various graphical user interfaces (e.g., the GUI  1000  of  FIGS. 10-11 ) to analyze the data collected by the system  1500 . The bitmap index  1540  may be accessible to the audience web application  1546  via the second framework  1542 . In one example, the second framework  1542  supports representational state transfer (REST)-based data access and webpage navigation. Although not shown, in particular embodiments, the data store  1536  may also be accessible to the audience web application  1546 . 
     The monitoring tier  1570  may monitor the various components of the system  1500  during operation to detect errors, bottlenecks, network intrusions, and other issues. For example, the penetration monitor  1574  may collect data indicating unauthorized access to or from the capture servers  1526  and the first framework  1544 . The penetration monitor  1574  may provide the data to the alert rules module  1580 . Similarly, the system monitor  1578  may collect performance data from the capture servers  1526 , from the second framework  1542 , and from the data store  1536 . The system monitor  1578  may provide the performance data to the performance analysis module  1576 , which may analyze the data and send the analyzed data to the alert rules module  1580 . The alert rules module  1580  may compare received data to alert rules and, based on the comparison send an alert to the notifications module  1582 . For example, the alert rules module  1580  may determine that an intruder has accessed components of the system  1500  or that the system  1500  is not operating at a desired level of efficiency, and may send an alert to the notifications module  1582 . 
     The notifications module  1582  may also receive alerts from the ping monitor  1584 . The ping monitor  1584  may monitor the load balancer  1524  and the audience web application  1546  and collect data regarding uptime, downtime, and performance, and provide alerts to the notification module  1582 . 
     The notification module  1582  may send notifications (e.g., via short message service (SMS), e-mail, instant messaging, paging, etc.) to one or more technical support staff members  1564  to enable timely response in the event of errors, performance bottlenecks, network intrusion, etc. 
     In accordance with various embodiments of the present disclosure, the methods, functions, and modules described herein may be implemented by software programs executable by a computer system. Further, in an exemplary embodiment, implementations can include distributed processing, component/object distributed processing, and parallel processing. Alternatively, virtual computer system processing can be constructed to implement one or more of the methods or functionality as described herein. 
     Particular embodiments can be implemented using a computer system executing a set of instructions that cause the computer system to perform any one or more of the methods or computer-based functions disclosed herein. A computer system may include a laptop computer, a desktop computer, a mobile phone, a tablet computer, a set-top box, a media player, or any combination thereof. The computer system may be connected, e.g., using a network, to other computer systems or peripheral devices. For example, the computer system or components thereof can include or be included within any one or more devices, modules, and/or components illustrated in  FIGS. 1-15 . In a networked deployment, the computer system may operate in the capacity of a server or as a client user computer in a server-client user network environment, or as a peer computer system in a peer-to-peer (or distributed) network environment. The term “system” can include any collection of systems or sub-systems that individually or jointly execute a set, or multiple sets, of instructions to perform one or more computer functions. 
     In a particular embodiment, the instructions can be embodied in a computer-readable or a processor-readable device. The terms “computer-readable device” and “processor-readable device” include a single storage device or multiple storage devices, such as a centralized or distributed database, and/or associated caches and servers that store one or more sets of instructions. The terms “computer-readable device” and “processor-readable device” also include any device that is capable of storing a set of instructions for execution by a processor or that cause a computer system to perform any one or more of the methods or operations disclosed herein. For example, a computer-readable or processor-readable device or storage device may include random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, a hard disk, a removable disk, a disc-based memory (e.g., compact disc read-only memory (CD-ROM)), or any other form of storage device. A computer-readable or processor-readable device is not a signal. 
     In accordance with at last one described embodiment, a method includes receiving an event signal at a computing device including a processor. The method also includes determining an identifier associated with the event signal and storing the event signal in a data store. The method further includes modifying a value of at least one bit stored in a bitmap index based on the identifier. The bitmap index includes a plurality of bit strings and a particular location in each of the plurality of bit strings corresponds to the identifier. 
     In another particular embodiment, a method includes receiving a first signal at a computing device comprising a processor, where the first signal includes information corresponding to a first identifier associated with a first source. The method also includes modifying a value of a first bit of a bitmap index responsive to the first signal, where the bitmap index includes a plurality of bit strings, where a value stored in a particular location in each of the bit strings indicates whether a corresponding signal associated with a corresponding identifier has been received, and where the first bit is stored in a first location of the bitmap index that corresponds to the first identifier and to the first signal. The method further includes receiving a second signal that includes second information corresponding to a second identifier associated with a second source. The method includes modifying a value of a second bit of the bitmap index responsive to the second signal, where the second bit is stored in a second location of the bitmap index that corresponds to the second identifier and to the second signal. The method also includes receiving a query directed to the bitmap index and outputting a result of the query based on performing one or more set operations with respect to the first bit and the second bit. 
     In another particular embodiment, a system includes a processor and a memory storing instructions executable by the processor to perform operations including receiving a first signal, where the first signal includes information corresponding to a first identifier associated with a first source. The operations also include modifying a value of a first bit of a bitmap index responsive to the first signal, where the bitmap index includes a plurality of bit strings, where a value stored in a particular location in each of the bit strings indicates whether a corresponding signal associated with a corresponding identifier has been received, and where the first bit is stored in a first location of the bitmap index that corresponds to the first identifier and to the first signal. The operations further include receiving a second signal that includes second information corresponding to a second identifier associated with a second source. The operations include modifying a value of a second bit of the bitmap index responsive to the second signal, where the second bit is stored in a second location of the bitmap index that corresponds to the second identifier and to the second signal. 
     In another particular embodiment, a computer-readable storage device stores instructions that, when executed, cause a computer to perform operations including receiving a first signal at a computing device comprising a processor, where the first signal includes information corresponding to a first identifier associated with a first source. The operations also include modifying a value of a first bit of a bitmap index responsive to the first signal, where the bitmap index includes a plurality of bit strings, where a value stored in a particular location in each of the bit strings indicates whether a corresponding signal associated with a corresponding identifier has been received, and where the first bit is stored in a first location of the bitmap index that corresponds to the first identifier and to the first signal. The operations further include receiving a second signal that includes second information corresponding to a second identifier associated with a second source. The operations include modifying a value of a second bit of the bitmap index responsive to the second signal, where the second bit is stored in a second location of the bitmap index that corresponds to the second identifier and to the second signal. 
     The illustrations of the embodiments described herein are intended to provide a general understanding of the structure of the various embodiments. The illustrations are not intended to serve as a complete description of all of the elements and features of apparatus and systems that utilize the structures or methods described herein. Many other embodiments may be apparent to those of skill in the art upon reviewing the disclosure. Other embodiments may be utilized and derived from the disclosure, such that structural and logical substitutions and changes may be made without departing from the scope of the disclosure. Accordingly, the disclosure and the figures are to be regarded as illustrative rather than restrictive. 
     Although specific embodiments have been illustrated and described herein, it should be appreciated that any subsequent arrangement designed to achieve the same or similar purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all subsequent adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the description. 
     The Abstract of the Disclosure is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, various features may be grouped together or described in a single embodiment for the purpose of streamlining the disclosure. This disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter may be directed to less than all of the features of any of the disclosed embodiments. 
     The above-disclosed subject matter is to be considered illustrative, and not restrictive, and the appended claims are intended to cover all such modifications, enhancements, and other embodiments, which fall within the true scope of the present disclosure. Thus, to the maximum extent allowed by law, the scope of the present disclosure is to be determined by the broadest permissible interpretation of the following claims and their equivalents, and shall not be restricted or limited by the foregoing detailed description.