Attribute tracking, profiling, and recognition

Techniques for tracking, profiling and recognizing attributes associated with transactions associated with computer system services. A bitmap representation of the unique identifiers created from name-value pairs of attributes is created and one or more operations are performed on the representation to process the bitmap representation. The one or more operations include operations to, for example, compress the bitmap representation, sub-sample the bitmap representation, locate patterns of attributes within the bitmap representation and other such processing operations. The processed bitmaps may then be further queried to analyze the event data.

BACKGROUND

Modern computer systems are frequently implemented as a distributed and/or virtualized system comprising a plurality of computer system services operating on a plurality of computer systems using a plurality of computer system resources. Such computer systems may allow connections from a large number of users and/or clients to allow access to the computer system services in order to perform a variety of tasks. Users and/or clients may connect to the computer system services using many different types of devices over a variety of different connection types. When a provider of computer system services wants to track and analyze attributes and/or metadata associated with the connection such as attributes and/or metadata related to the user, the device, the network and/or other such attributes and/or metadata, the provider can be faced with the problem of storing and processing very large data sets in a data store such as a data warehouse. In some computer system environments, where thousands of users may be performing tasks simultaneously, large data sets may be produced and stored in a data warehouse every second. A data warehouse, which may be statically configured in order to facilitate processing the large data sets, may require reconfiguring the data gathering service each time a new device, a new service, a new user type and/or other such new entities with new attributes and/or metadata may join the system. Additionally, a data warehouse may require reconfiguring the data gathering service each time the provider may wish to gather different data, or focus data gathering on a certain attribute subset or perform other types of analysis. Storage and manipulation of the large data sets as well as reconfiguring the data gathering service may be resource intensive, requiring expensive dedicated resources.

DETAILED DESCRIPTION

Techniques described and suggested herein include techniques for high-performance gathering, tracking and analyzing computer system service attribute data associated with computer system service users and/or clients within a distributed and/or virtualized computing system environment as well as executable code operating thereon. In particular, techniques are disclosed for utilizing computing system capabilities to gather computer system service transactions and attribute data and/or metadata, accumulate transaction and attribute data and/or metadata name-value pairs, issue and process transaction name-value pair events, create a reduced-sized representation of the accumulated transaction name-value events and analyze the reduced-sized representation of the transaction name-value events in conjunction with the attribute data and/or metadata. Such computing systems may include, but not be limited to, computing systems that consist of distributed sets of a plurality of components, subsystems and resources and may further include, but not be limited to, virtualized and/or non-virtualized components, subsystems and resources. Computing systems with distributed sets of virtualized and/or non-virtualized components, subsystems and resources may be running a variety of services, modules, applications, processes and/or other such computing system entities and may allow connections to those computing system entities by a variety of clients, users, processes, services, modules applications and/or other such connecting computer system entities.

A computer system may be implemented as a distributed and/or virtualized system with a plurality of services. Such computer systems may allow a plurality of connections from a large number of users and/or clients, using a variety of devices and over many different connections such as, for example, the Internet. A provider of computer system services may need to track and analyze attributes and/or metadata associated with the transactions associated with computer system services. For example, a provider of a shopping service targeted to mobile devices may wish to track attributes associated with the usage of the shopping service such as attributes and/or metadata related to the user, the device, the network and/or other such attributes and/or metadata. The provider may wish to know demographic information about the users, what devices that the users may be using, how the users are connecting, which transactions are performed most, what features are used, the complexity of the transaction and/or other such attribute data and/or metadata. In many cases, a provider may use this data, attributes and/or metadata to make decisions about the efficiency of the service, target new services, understand load levels and/or otherwise improve the service and/or the connection to the service.

Such attribute tracking in typical systems may involve tracking the data across multiple dimensions by creating a data warehouse to receive and store transaction attribute data and/or metadata, which is then collected and organized for reporting. As used herein, unless otherwise stated or clear from context, the terms “transaction data” and “transaction attribute data” may be used to refer collectively to data related to the transaction as well as metadata related to the transaction. Data may be sorted and stored based on geographical location of the user and/or client, user preferences, user demographics such as gender, age, and/or other demographics, device type, device version, language, preferred marketplace, activities while connected to the service or a combination of these and/or other such data factors. For some types of data, a data warehousing approach may lead to large data sets, a long delay between gathering the data and producing useable reports, a requirement for expensive resources to store and analyze the large data sets, an inefficient response to changes in data gathering requirements due to the static nature of the data warehouse configuration and/or other such data inefficiencies.

For example, the shopping service targeted to mobile device users mentioned herein above may have a large number of simultaneous users numbering, at peak times, in the tens of thousands. Each user may be engaging in dozens of transactions such as connecting to the service, searching for products, downloading product data, downloading product metadata, uploading product reviews, initiating purchases, completing purchases, disconnecting or other such transactions. Each transaction may have attributes, data and/or metadata associated with the user, the client, the device, the network and/or other such connection items. In such a shopping service, multiple megabytes of attribute data may be produced every second leading to multiple gigabytes of attribute data every hour which must be stored, sorted, analyzed and/or reported.

Techniques described and suggested herein include techniques to store, sort, analyze and produce reports on attribute data using a high performance system which may reduce the amount of attribute data stored by multiple orders of magnitude and which may produce a corresponding increase in the efficiency of attribute data sorting, analysis and/or report generation. A transaction service may be configured to receive and process transactions such as the shopping transactions described herein above. Each transaction may contain one or more pieces of attribute data and/or metadata relating to the transaction. For example, a transaction to connect to a service to initiate a shopping transaction may include attributes such as a user ID, a device ID, a connection type, the transaction type, a time stamp associated with the transaction and/or other such transaction attributes. The transaction may also include metadata associated with one or more of these attributes. For example, the user ID may include metadata about the user such as name, location, nationality, gender, age, birthdate, market preferences and/or other such metadata. The metadata associated with an attribute may be included with the transaction data and/or may be stored in one or more secondary data locations accessible by the service. In some embodiments, a user may enter such metadata upon initially registering an account with the service and then the service may gather such metadata as needed to facilitate data analysis needs. In some other embodiments, the metadata will be derived from, for example, the user's device, survey data, interpolated data, situational data and/or other such data gathering techniques. As may be contemplated, the types of attribute data, the types of metadata and the methods of receiving attribute data and/or metadata illustrated here are examples and other types of attribute data, types of metadata and methods of receiving attribute data and/or metadata may be considered as within the scope of the present disclosure.

Upon receiving a transaction, the transaction service may next begin gathering name-value pairs for attribute data and/or metadata associated with the transaction. Each attribute data and/or metadata item may have a name or identifier and a value for that item. For example, an attribute for a customer with a first name of “Joe” would have a name-value pair of “First Name=Joe”. As each name-value pair in a transaction is received, the transaction service may check against a name-value pair repository (such as, for example, a database) to see if that name-value pair has been previously received. New name-value pairs may be entered into the repository and assigned a unique index while previously received name-value pairs may be assigned the previously assigned index. So in the above example, the name value pair of “First Name=Joe” would be assigned the next available unique index if this were the first time a user with a first name of “Joe” engaged in a transaction with the service. Each subsequent user with a first name of “Joe” would have the same unique index assigned to that transaction for that name-value pair. In such a system, new data items may be automatically discovered and entered into the system so that, for example, if a new mobile device manufacturer entered the marketplace, the identifiers for that manufacturer may be automatically discovered and added to the transaction service upon the first connection from the first user of such a new device.

After each name-value pair is associated with a unique identifier from the name-value pair repository, the name-value pair identifiers may be collected into an event associated with the transaction. The event may include an event identifier, an event time stamp, an event type and one or more name-value pair identifiers corresponding to the attribute data and/or metadata associated with the transaction. The events may then be aggregated into a matrix wherein each column of the matrix may represent a single event and each row of the matrix may represent a single name-value pair identifier. Such a matrix may relate events to name-value pair identifiers, and thus to name-value pairs by indicating, for each event (column), the name-value pairs associated with that event and for each name-value pair (row), the events that that name-value pair is associated with. In some embodiments, the events may be aggregated by configuring the transaction service to create an event bus, the event bus configured to issue the events which may then be received and processed by one or more event processing services running within the computer system. In some embodiments, the events may be aggregated directly by the transaction service.

The aggregated events may then be further processed be creating a bitmap of the data represented by the aggregation matrix. A bitmap may be a data representation mapping one or more values to one or bit-values as described herein. In some embodiments, the event data may be directly aggregated into the bitmap without intermediate storage in the matrix. The bitmap may be constantly grown as new events are received, or may be collected and aggregated for a set time period, or may be collected and aggregated for a certain number of samples or may be collected and aggregated according to some other organization of the data. For example, a bitmap that records the events for the previous thirty minute period may have one column for each event that occurred during that thirty minute period and may have one row for each unique name-value pair that occurred during that thirty minute period. A bit value of one for a particular row and column may indicate that the corresponding name-value pair (the row) was associated with the event (the column). The bitmap, combined with the name-value pair repository may be a lossless representation of the transaction data as, for each event, the name value pairs associated with that event may be determined and thus, the transaction data may also be determined.

One or more services running on the computer system may then further process the aggregated data bitmap by, for example, compressing the data. Image processing techniques such as run-length encoding or various mathematical transformations may be used to compress the bitmap, reducing the bitmap data size. In some embodiments, the image compression may be required to be lossless to preserve the source data. In some embodiments, some lossy compression techniques may be allowed. Frame-to-frame and/or other movie compression techniques may also be used to compress the data, using frame-to-frame coherence and/or other properties of a collection of aggregated data bitmaps. Sampling of the data such as time-based sampling and/or statistically-based sampling may be used to further reduce the size of the aggregated data bitmaps. Such sampling may be used when, for example, the aggregated data bitmaps are sparse. Other data processing techniques to reduce the data size may be used such as, for example, normalizing the data of time based at least in part on data patterns, gathering data types or event types into data regions, compressing or aggregating data across similar events and/or similar attributes, pruning unneeded data and/or other such processing techniques. As may be contemplated, the data processing techniques described herein are illustrative examples and other such data processing techniques may be considered as within the scope of the present disclosure.

Compressed aggregate data bitmaps may then be used to analyze usage patterns related to the users of the service, using a query service. In some embodiments, bitmap comparisons between regions of the aggregated data bitmaps, even if compressed, may be used to locate data usage patterns which may then be used to make business decisions. For example, if each time a certain best-selling product is updated, the aggregated data bitmaps show that there is a large upswing in transactions from a certain marketplace, the service provider may determine that doing a system update that prevents access from that marketplace immediately after updating that best-selling product may lead to a degraded customer experience. Similarly, if customers from one geographical region take twice as many steps to complete a sales transaction as compared to customers from another geographical region, the data showing this fact may be an indicator that the instructions in the language used in that region are faulty, misleading and/or confusing.

FIG. 1illustrates an example environment100where one or more computer systems running within a computing resource service provider environment, as well as the associated code running thereon, may be used to store, sort, analyze and produce reports on attribute data. A user102may connect106to a computer system instance112through a computer system client device104and may initiate a connection with and/or interaction with one or more applications running on the computer system instance112. The computer system instance112may be operating within a computing resource service provider110. In some embodiments, the computing resource service provider110may provide a distributed, virtualized and/or datacenter environment within which one or more applications, processes, services, virtual machines and/or other such computer system entities may be executed. In some embodiments, the user102may be a person, or may be a process running on one or more remote computer systems, or may be some other computer system entity, user or process. The command or commands to connect to the computer system instance may originate from an outside computer system and/or server, or may originate from an entity, user or process on a remote network location, or may originate from an entity, user or process within the computing resource service provider, or may originate from a user of the computer system client device104, or may originate as a result of an automatic process or may originate as a result of a combination of these and/or other such origin entities. In some embodiments, the command or commands to initiate the connection106to the computing resource service provider110may be sent to the computer system instance112, without the intervention of the user102. The command or commands to initiate the connection106to the computer system instance112may originate from the same origin as the command or commands to connect to the computing resource service provider110or may originate from another computer system and/or server, or may originate from a different entity, user or process on the same or a different remote network location, or may originate from a different entity, user or process within the computing resource service provider, or may originate from a different user of a computer system client device104, or may originate as a result of a combination of these and/or other such same and/or different entities.

The user102may request connection to the computing resource service provider110via one or more connections106and, in some embodiments, via one or more networks108and/or entities associated therewith, such as servers connected to the network, either directly or indirectly. The computer system client device104that may request access to the computer system instance112may include any device that is capable of connecting with a computer system via a network, including at least servers, laptops, mobile devices such as smartphones or tablets, other smart devices such as smart watches, smart televisions, set-top boxes, video game consoles and other such network enabled smart devices, distributed computing systems and components thereof, abstracted components such as guest computer systems or virtual machines and/or other types of computing devices and/or components. The network may include, for example, a local network, an internal network, a public network such as the Internet, a wide-area network, a wireless network, a mobile network, a satellite network, a distributed computing system with a plurality of network nodes or other such networks. The network may also operate in accordance with various protocols, such as those listed below, Bluetooth, WiFi, cellular network protocols, satellite network protocols and/or others.

The computing resource service provider110may provide access to one or more host machines as well as provide access to computer services such as virtual machine (VM) instances, automatic scaling groups, file-based database storage systems, block storage services, redundant data storage services, data archive services, data warehousing services, user access management services, content management services and/or other such computer system services as may be running thereon. The computing resource service provider110may also provide access to computer system resources such as user resources, policy resources, network resources and/or storage resources. In some distributed and/or virtualized computer system environments, the resources associated with the computer services may be physical devices, virtual devices, combinations of physical and/or virtual devices or other such device embodiments. In some embodiments, the host machines may be physical machines located within the computer system environment. In some embodiments, the host machines may be guest virtual machines operating on physical machines located within the computer system environment.

The user102may connect to a service120which may be one of a plurality of services running on one or more computer system112instances within a computing resource service provider110. The service120may be a back-end service, or data provider service, or a web service or a combination of these and/or other such services. The service120may report data associated with one or more transactions122received from the user102of the computer system client device104via the network108over the connection106. The data associated with the one or more transactions122may include data and/or transaction metadata relating to the user102, to the device,104, to the network108, to the service120or to a combination of these and/or other such entities associated with the one or more transactions122. For example, the data associated with the one or more transactions122(also referred to herein as “transaction data”) may include a user identifier (user ID) for the user102which may be a previously established user ID associated with the user102, or may be a dynamically generated user ID associated with the user102or may be some other such user ID. In another example, the transaction data may include a device identifier (device ID) for the computer system client device104which may be a previously established device ID based at least in part on the device manufacturer, the device model, the device operating system, the device instance or a combination of these and/or other such device data. As may be contemplated, the transaction data items described herein are illustrative examples and other such transaction data items may be considered as within the scope of the present disclosure.

In some embodiments, the transaction data associated with the one or more transactions122may be based at least in part on one or more user attributes114. The user attributes114may be stored in a location accessible by the service120or may be stored in a location accessible by one or more other services, modules, applications, processes and/or other such computer system entities operating on, under the control of or accessible by the computing resource service provider110. Access to the user attributes may be based at least in part on one or more data items contained in the transaction data associated with the one or more transactions122. For example, as mentioned herein above, the transaction data associated with the one or more transactions may include a user ID. In such an example, the service may access additional user information such as a user name, user demographics such as gender or age, user preferences, user purchase data or other such user attributes based at least in part on the user ID. Such user attributes114may be received by the service120from another service or services connected to and/or otherwise accessible by the service120. The transaction data associated with the one or more transactions122may also be based at least in part on one or more device attributes116and/or on one more network attributes118. The device attributes116and/or the network attributes118may also be stored in a location accessible by the service120and/or by one or more other services as described herein.

An interaction between a user and a service may include a plurality of transactions. For example, a user interacting with the shopping service described herein above to make a purchase may initiate a series of transactions to make the purchase. Such transactions may include, but not be limited to, connecting to the service, logging in, sending user preferences, displaying a home page based on preferences, searching for a product, receiving the search results, selecting a product to purchase, transferring the selected product to a shopping cart, entering credit card information, entering shipping information, completing the purchase, logging out and/or other such transactions. In such an example, some of the transaction data may remain static while some of the transaction data may be mutable. For example, each transaction may include a transaction identifier (transaction ID) associated with the type of transaction (e.g. logging in, sending user preferences and so on) which may change with each transaction. Similarly, the user may be moving from place to place and so a location of the user or a network identifier (network ID) may change as the user moves within a network.

The transaction data associated with the one or more transactions122may be received from the service120by a transaction service configured to receive and process the transaction data. The transaction service124may receive the transaction data and may first identify one or more transaction data name-value pairs contained in the transaction data. For example, a user attribute item associated with the transaction data may indicate that the user102is female. The name value pair for such an attribute item may be “Gender=Female” for the user102. Each transaction data item (including the data items from the user attributes114, the device attributes, the network attributes118, the transaction data, the transaction metadata or other such data items) may result in the identification of a name-value pair.

The transaction service124may then query126a name-value repository128to determine whether one or more of the identified name-value pairs may have been previously identified by the transaction service124. The transaction service124may determine whether one or more of the identified name-value pairs may have been previously identified by the transaction service124by searching for an entry in the name-value repository128corresponding to the name-value pair. For those name-value pairs that have not been previously identified by the transaction service124, the name-value repository128may create a new entry in the name-value repository128corresponding to the name-value pair including a unique identifier associated with the new entry in the name-value repository128. In some embodiments, the unique identifier associated with the new entry in the name-value repository128may be associated with the new entry by assigning a monotonically increasing value for each new entry. In such an embodiment, the first name-value pair identified may be assigned an identifier of one; the second name-value pair identified may be assigned an identifier of two and so on. In some embodiments, the name-value repository may assign unique identifiers based on some other schema such as by using database keys, globally unique identifiers (GUIDs) or some other such identifying schema. Those name-value pairs that have been previously identified by the transaction service124may have the previously assigned unique identifier returned as a result of the query126.

The transaction service124may then create an event130comprising at least a subset of the unique identifiers associated with the name-value pairs contained in the transaction data. The event130, which may include an event identifier and may include event metadata such as the type of the event or a timestamp corresponding to the transaction may include each of the unique identifiers associated with the name-value pairs contained in the transaction data or may include a reduced subset of the unique identifiers associated with the name-value pairs contained in the transaction data. In some embodiments, the transaction service124may be configured to remove some of the unique identifiers associated with the name-value pairs contained in the transaction data and stored in the event130in order to reduce the data size, or to focus a query based on specific data such as data related to certain services, users, user demographics, locations, devices, networks and/or other such data, or to focus a query based on certain business needs or based on a combination of these and/or other such reasons. In some embodiments, an event130may be created by, for example, placing the event in an event queue which may issue the events to be received by an event service132. In some embodiments, an event130may be created and sent directly to an event service132. In some embodiments, the event service132and the transaction service124may be the same service.

A plurality of events may be collected by an event service132to build an event matrix134. Each column of the event matrix134may comprise a single event130. The rows of the event matrix134may correspond to the unique identifiers contained in the name-value repository, with one row for each unique identifier. For each event130received by the event service132, if the event contains a unique identifier, an value of one may be entered in the matrix entry corresponding to that row indicated by the unique identifier, for the column corresponding to the event. For example, if an event contains unique identifiers one, three, seven and fifteen, a value of one may be entered in the first, third, seventh and fifteenth rows of the column corresponding to the event and values of zero may be entered for the other rows. As new events with new unique identifiers are received by the event service, new rows corresponding to those unique identifiers may be added to the event matrix134.

The data in the event matrix may then be used as the basis for producing one or more bitmaps144. A query service138may update142the bitmaps144based at least in part on the event matrix134. The query service138may also update the event matrix134and/or the bitmaps144using additional attribute data obtained from a backfill service146. The backfill service146may query148the user attributes114, the device attributes116and/or the network attributes118via a network150which may, in some embodiments, be the same as the network108. The backfill service146may also query other attributes152from one or more computing systems, services, applications, processes, modules and/or other such computing system entities that may be used to further characterize the transaction and/or event data. For example, an event may be characterized by an operating system type of a particular service if a business unit desires such information. The operating system type of the particular service may be queried by the backfill service and that information included in the event along with the other attributes152. In some embodiments, the query service138may use the attributes obtained by the backfill service146and may obtain154new name-value pair identifiers from the name-value repository128, allowing entry of the new attributes in the event matrix134and/or in the bitmaps144.

The one or more bitmaps144may be produced from the event matrix134by, for each non-zero value in the event matrix134, a corresponding pixel in the bitmap may be turned on. In some embodiments, the data from the transaction service124and the data from the backfill service146may be written directly to the bitmaps144by the query service138, without writing to an event matrix134. The data and/or the event matrix134may be used to produce bitmaps144on a periodic basis (for example, every thirty minutes), or after a certain number of events have been received, or as a result of having received a specific event, or after a certain total aggregation of event data has been received, or as a result of receiving a signal from a service such as the transaction service124and/or the query service138, or for business reasons or as a result of these and/or other such conditions. The query service138may process the events using one or more data processing nodes140so that data may both be received and processed at the same time. The data processing nodes140may timeslice the data (separate by time), or may shard the data (separate by category as described herein below) or process the data in some other such manner. The query service138may also use one or more data processing nodes140to further process the bitmaps144by, for example, compressing, normalizing, subsampling, statistically sampling, image reconstruction, image comparison and/or other such techniques. As may be contemplated, the bitmap processing techniques described herein are merely illustrative examples and other such bitmap processing techniques may be considered as within the scope of the present disclosure.

To further aid in processing of the event data, the query service138, the event service132and/or the transaction service124may specify one or more categories associated with the events. For example, a transaction relating to a user purchase may be in a different event category than a transaction relating to a user creating an account. Similarly, a new computer system client device104associated with a user102and/or adding a corresponding new set of device attributes116may be a different event category that a device migrating from one network location to another network location. The categories may be stored in the name-value repository128or may be stored in a separate location accessible by the transaction service124, the event service132and/or the query service138. Categories may be used to improve the efficiency of storing, processing, scaling and/or reporting the events. Other types of event categories may also be specified and each event may be associated with one or more of the categories, allowing multiple categorizations of events. For example, one or more errors may be received by the transaction service124, and those errors may be processed and/or categorized as error events. The event categories may be transactional, informational, functional or a combination of these and/or other such category types. Events may be further categorized by event aggregations, event combinations, event category operations, event namespaces, event meta-categories (categories of categories) and/or other such event categorization types. As may be contemplated, the event categories and event categorization methods described herein are merely illustrative examples and other such event categories and event categorization methods may be considered as within the scope of the present disclosure.

The query service138may further process the bitmaps144by performing a plurality of queries associated with the bitmaps. The queries, which may be based on binary operations associated with the compressed, normalized and/or otherwise processed bitmaps, may be done in real-time, allowing multiple simultaneous queries relating to a plurality of events, event categories, event types and/or other such event specifications. For example, the query service138may issue a plurality of queries to determine peak usage of a certain device type from a certain marketplace by comparing binary data representing the device type and marketplace identifiers against the bitmaps. The query may yield a set of one or more events which may also be represented in a bitmap or in a collection of bitmaps. The set of one or more events may then be further queried for secondary and/or related event information.

FIG. 2illustrates an example environment200where data and/or attributes relating to one or more transactions may be compiled into transaction data for further processing as described herein at least in connection withFIG. 1and in accordance with at least one embodiment. A transaction208may be initiated by a user or some other such computer system client entity, the client entity which may be connected to a service using a device as described herein. A transaction such as the transaction208may include data items such as, for example, a user ID, a device ID, a network, a service, an operation, a time or a combination of these and/or other such data items. The data may be collected210and combined with additional data items including, but not limited to, user attributes202, device attributes204, network attributes206or other such attributes, to produce transaction data212. A transaction such as the transaction214may also include data items such as, for example, a user ID, a device ID, a network, a service, an operation, a time or a combination of these and/or other such data items which may also be collected216and combined with additional data items to produce transaction data218. In some embodiments, the type and/or number of data items contained in a transaction such as transaction214may differ from the type and/or number of data items contained in a transaction such as transaction208. The type and/or number of data items contained in a transaction may be based on system conditions, business needs, customer and/or provider policies and/or other such considerations.

Data such as the user attributes202, the device attributes204, the network attributes206and/or other such data may be combined with the data in the transaction208and/or may be combined with the data in the transaction214based at least in part on the contents and/or values of data items contained in the transaction208and/or the transaction214. Data such as the user attributes202, the device attributes204, the network attributes206and/or other such data may be combined with the data in the transaction208and/or may be combined with the data in the transaction214based at least in part on data items contained in other locations within the computer system such as, for example, an external data store, a system policy repository, a user policy repository, a business policy repository or other such locations. As may be contemplated, the types of data contained in the transaction data, the methods for gather such data, the locations of such data and/or the policies regarding gathering such data described herein are illustrative examples and other such data, methods, locations and/or policies may be considered as within the scope of the present disclosure.

FIG. 3illustrates an example environment300where name-value pairs may be identified based at least in part on transaction data as described herein at least in connection withFIG. 1and in accordance with at least one embodiment. Transaction data such as transaction data302may contain data from a transaction304and/or event-time attribute data308from attributes306as described herein at least in connection withFIG. 2and in accordance with at least one embodiment. The event-time attribute data308may be attribute data that is available at the time of the transaction. One of more name-value pairs310may be identified from the transaction data302and each name-value pair of the one or more name-value pairs310may be assigned a unique identifier based at least in part on determining whether the name-value pair was identified from a previous transaction by a service such as a transaction service124(as described herein at least in connection withFIG. 1) and stored in a name-value repository such as name-value repository128(also as described at least in connection withFIG. 1). One or more additional name-value pairs may be later determined and/or stored in the name-value repository such as name-value repository128by a backfill service322which may gather additional attributes320from the attributes306and which may also gather additional attributes326from the other attributes324as described herein at least in connection withFIG. 1and in accordance with at least one embodiment.

In the example illustrated inFIG. 3, each name-value pair of the name-value pairs310was not identified from a previous transaction and so each name-value pair of the name-value pairs310may be assigned a unique identifier (in this example, the numbers one to ten). Subsequent transaction data such as transaction data312may contain data from a transaction314and/or event-time attribute data316from attributes306as described herein at least in connection withFIG. 2and in accordance with at least one embodiment. One or more name-value pairs318may be identified from the transaction data312of transaction314. The name-value pairs318may include one or more name-value pairs that were identified from a previous transaction by a stored in a name-value repository. The name-value pairs318may also include one or more name-value pairs that were not identified from a previous transaction and were not stored in a name-value repository. The name-value pairs318may also include one or more attributes330provided by the backfill service322as described herein above. In the example illustrated inFIG. 3, four of the name-value pairs of the name-value pairs318were not identified from a previous transaction and may be assigned a unique identifier (in this example, the numbers eleven to fourteen).

FIG. 4illustrates an example process400for identifying name-value pairs from transaction data as described herein at least in connection withFIG. 1and in accordance with at least one embodiment. A transaction service such as the transaction service124described herein at least in connection withFIG. 1may perform at least a portion of the process illustrated inFIG. 4. A backfill service such as the backfill service146described herein at least in connection withFIG. 1may perform at least a portion of the process illustrated inFIG. 4. Other entities operating within a computing system may also perform at least a portion of the process illustrated inFIG. 4including, but not limited to, services, applications, modules, processes, operating system elements, virtual machine elements, network hardware, controlling domains (e.g. a hypervisor) or combinations of these and/or other such entities operating within the computing system.

A service such as a transaction service may wait for a transaction402, and when a transaction is received404may begin identifying name-value pairs from the transaction data in the transaction by selecting the first data item name and value406. The data item name and value may be selected in a determined order, or based upon one or more selection criteria such as, for example, first selecting all data relating to the user, or selected randomly or selected according to some other such selection order. The service such as a transaction service may then determine if the name and the value are known408due to, for example, having been identified from a previous data item name and value in this or in a previous transaction. Previously identified names and values may be stored and/or searched for within a repository of name-value pairs based on an indexing of the name-value pair, or based on indexing first the name and then the value, or based on indexing first the value and then the name, or based on a hash of the name-value pair, or based on a hash of first the name and then the value, or based on a hash of first the value and then the name, or based on a GUID, or based on a lookup table, or stored with a generated index in a structureless system or based on some other such storage and/or search schema. Name and value combinations that are not known may be identified for storage and/or searching by first getting a new index410for the name-value pair and second by storing the name-value pair with the index in a name-value repository412. The process of selecting names and values from the transaction data may continue while there are more pairs414to identify.

In some embodiments, name-value pairs may be further enhanced by attribute data provided by a backfill service as described herein at least in connection withFIG. 1and in accordance with at least one embodiment. The first backfill data item may be selected416, checked against a name-value pair repository418and, if not known, a new index may be provided420and a new entry placed in the repository422. This process may continue while there are more data pairs424to identify. The collected name-value pair identifiers may then be used to create an event426as described herein at least in connection withFIG. 1. In some embodiments, the addition of new backfill data to the event may continue after the event is created.

FIG. 5illustrates an example environment500where an event matrix may be created from events as described herein at least in connection withFIG. 1and in accordance with at least one embodiment. A collection of events502, each comprising a collection of unique identifiers may be used to create an event matrix with, for example, with the events stored in the columns of the matrix and with values in the columns indexed by the unique identifiers. A first single event504of from the collection of events502may be used to create an event matrix506with a single column representing the single event504. Entries in the rows of the event matrix506correspond to the unique identifiers (ID) in the single event504(one, two and four in this case, with no entry in row three). A second event508, with two new unique identifiers (five and six) may add two new rows to the event matrix510and one new column to the event matrix510representing the new event. A third event512, may add a new column to the event matrix514representing the new event.

FIG. 6illustrates an example environment600where a bitmap may be created from an event matrix as described herein at least in connection withFIG. 1and in accordance with at least one embodiment. The event matrix602may be used to create604the bitmap606by, for each value stored in the matrix, determining the row and column in the bitmap corresponding to row and column in the matrix. A bitmap may contain two states variously described as “on and off”, “one and zero” or “true and false”. In the example illustrated inFIG. 6, if a non-zero value is stored in the matrix the corresponding bit may be set to “on” in the bitmap (represented by a black square) and if a zero value is stored in the matrix the corresponding bit may be set to “off” in the bitmap (represented by a white square). In some embodiments, the bitmap may be set in an opposite manner so that non-zero values in the matrix result in an “off” value in the bitmap and zero values in the matrix result in an “on” value in the bitmap. In some embodiments, there may be a mapping between the order of the rows and/or columns in the matrix and the rows and/or columns in the bitmap. For example, the rows and columns may be swapped so that rows in the matrix correspond to columns in the bitmap and columns in the matrix correspond to rows in the bitmap. In another example, the rows and/or the columns may be inverted, so that, for example, the first row in the matrix corresponds to the last row in the bitmap and the last row in the matrix corresponds to the first row in the matrix. In some embodiments, the data from the events may be written directly to the bitmap, without writing to a matrix as described herein at least in connection withFIG. 5. It should be noted that while the bitmaps shown herein are illustrated with “on” bit values shown as black squares and “off” bit values shown as white squares, this is merely for illustrative purposes.

The bitmap606and/or the matrix602may be very sparse, containing a limited amount of data for a particular row, column and/or other such region. In one particular time period such as, for example, an off-peak time period, a large number of events may occur with no events relating to a particular attribute. For example, during an early morning time period for a particular marketplace, no transactions of a particular type may occur. Similarly, in a service with, for example, millions of attributes and thousands of transactions per hour, an event related to a particular user (which may be unique name-value pair identifier) may occur one time in billions of samples. Such sparse data may be efficiently compressed in a lossless manner as removing a large number of zero values from a bitmap representation may be highly efficient and lossless.

FIG. 7illustrates an example process700for creating a bitmap from an event matrix as described herein at least in connection withFIG. 1and in accordance with at least one embodiment. A transaction service such as the transaction service124described herein at least in connection withFIG. 1may perform at least a portion of the process illustrated inFIG. 7. An event service such as the event service132described herein at least in connection withFIG. 1may perform at least a portion of the process illustrated inFIG. 7. A query service such as the query service138described herein at least in connection withFIG. 1may perform at least a portion of the process illustrated inFIG. 7. Other entities operating within a computing system may also perform at least a portion of the process illustrated inFIG. 7including, but not limited to, services, applications, modules, processes, operating system elements, virtual machine elements, network hardware, controlling domains (e.g. a hypervisor) or combinations of these and/or other such entities operating within the computing system.

An event service such as event service132described herein may first wait for events702and, when an event is received704a new matrix column may be created706. The event service may then select the first identifier708from the event and may set the value in the matrix column for the row corresponding to the identifier710as described herein at least in connection withFIG. 6and in accordance with at least one embodiment. Values in the matrix column corresponding to the identifiers may continue being set until there are no more identifiers to set712. In some embodiments a new event may trigger a decision to update the bitmap714and, if the bitmap is to be updated, new entries in one or more bitmaps may be updated716as a result of one or more events. In some embodiments, each event may result in an update to one or more bitmaps. In some embodiments, a multitude of events may be collected into an event matrix before one or more bitmaps may be created based at least in part on the event matrix.

FIG. 8illustrates an example environment800where commands or operations may be dispatched to one or more downstream services as described herein at least in connection withFIG. 1and in accordance with at least one embodiment. A query service802may issue one or more commands804to one or more downstream services808. The issued commands806may include commands to store, retrieve and/or process the transaction data810from the downstream service808. For example, a downstream service may be gathering transactions and generating a new data log of transactions every hour. The query service802may require data from a certain hour, on a certain day from the downstream service and, upon determining that that data has not yet been sent for processing into a bitmap representation, may request that the downstream service808send the data for processing812to produce bitmaps814which may then be used by the query service802for analysis. Similarly, the query service may instruct816a backfill service818to provide820additional data related to the transaction data810as described herein at least in connection withFIG. 1and in accordance with at least one embodiment.

Transaction data810may include data retrieved from system logs, data stored as a result of previous commands from the query service and/or other such transactional data. Transaction data810may be produced from browser plugins, application plugins, application programming interface (API) calls, sensors, monitors, logs and/or other such data gathering services, processes, applications, modules and/or the like. As mentioned herein above, the transaction data may be enhanced by attribute data gathered at the time of the various transactions and may also be later enhanced by attribute data gathered by, for example, the backfill service818. Additionally, the transaction data810and/or the resulting event data may be categorized based on one or more event categories (or event types) to aid in storing, processing and analyzing the data.

Other types of command processing services may be instantiated in connection with the environment800illustrated inFIG. 8to send other types of commands to downstream services. For example, an event dispatching service, where event gathering commands may be dispatched to downstream services may be instantiated. As an example, an event dispatch service may select one or more transactions to gather based on the business value of the transactions and/or other such factors. The transactions to gather may include new transactions, previously gathered transactions and/or a combination of new and previously gathered transactions. The transactions to gather may be chosen based on reasons including, but not limited to, system policies, business decisions, in preparation for system maintenance, in preparation for system outages or other such reasons. The dispatch service may then direct one or more downstream services such as the downstream service808to begin gathering transaction data related to the transactions to gather. In some embodiments, a command to begin gathering may instruct the downstream services to gather the specific transactions. In some embodiments, the command to begin gathering may instruct the downstream services808to gather at least a subset of all transactions.

In response to the command to begin gathering, one or more downstream services may begin gathering and sending transaction data to a transaction service for processing as described herein at least in connection withFIG. 1. After a number of transactions have been gathered and processed, the dispatch service may select one or more transactions being gathered and may direct the downstream services to stop gathering those transactions. The one or more transactions may include one or more of the transactions previously chosen to gather. In some embodiments, the command to stop gathering may instruct the downstream services to stop gathering specific transactions. In some embodiments, the command to stop gathering may instruct the downstream services to stop gathering all transactions. As a result of the command to stop gathering the transactions, one or more downstream services may continue gathering other transactions until instructed to stop gathering those transactions.

FIG. 9illustrates an example process900for processing bitmaps as described herein at least in connection withFIG. 1and in accordance with at least one embodiment. A transaction service such as the transaction service124described herein at least in connection withFIG. 1may perform at least a portion of the process illustrated inFIG. 9. An event service such as the event service132described herein at least in connection withFIG. 1may perform at least a portion of the process illustrated inFIG. 9. A query service such as the query service138described herein at least in connection withFIG. 1may perform at least a portion of the process illustrated inFIG. 9. Other entities operating within a computing system may also perform at least a portion of the process illustrated inFIG. 9including, but not limited to, services, applications, modules, processes, operating system elements, virtual machine elements, network hardware, controlling domains (e.g., a hypervisor) or combinations of these and/or other such entities operating within the computing system.

The bitmap processing service may first process the data in the bitmap by compressing sparse bitmap data902using techniques including, but not limited to, run length encoding, coordinate lists, dictionaries, hashes or other such sparse data compression methods. The bitmap processing service may next normalize the bitmap data904such as by redistributing more dense and/or otherwise more interesting columns to the beginning of the matrix. The bitmap processing service may next aggregate the bitmap data over time906and may use such aggregation to locate patterns within the data, further compress the data and/or other such processing. In some embodiments, the bitmap processing service may also examine the aggregation to determine whether there is any frame-to-frame coherence of the bitmap data908. The bitmap processing service may also sub-sample the bitmap data by time910and/or may statistically sample the bitmap data912to further reduce the data size. As may be contemplated, the order of the bitmap processing operations described herein and/or the type of bitmap processing operations described herein are merely illustrative examples and, as such, other orders of bitmap processing operations as well as other bitmap data compression and/or reduction techniques914may be considered as within the scope of the present disclosure.

FIG. 10illustrates an example environment1000where data in a bitmap may be searched to locate events as described herein at least in connection withFIG. 1and in accordance with at least one embodiment. A bitmap1002such as the bitmaps described herein may contain a plurality of columns corresponding to events with bits corresponding to event identifiers. The bitmap1002may be a compressed bitmap, or a sampled bitmap, or a statistically sampled bitmap, or a normalized bitmap or a combination of these and/or other such processed bitmaps. A search pattern1004may be used to locate events within the bitmap corresponding to one or more unique identifiers associated with one or more name-value pairs. As described herein, the operations on the bitmaps may be fast binary operations doing in-place comparisons of the data. In the example illustrated inFIG. 10, the search pattern1004is set to locate events containing the second, sixth and eighth unique identifiers. The pattern of the second, sixth and eighth unique identifiers may correspond to an event that, for example, was originated from a certain device type, in a certain market, for a certain type of user. In the example illustrated inFIG. 10, five of the events collected contain the second, sixth and eighth unique identifiers (indicated by check marks above the event column) and may be of interest. In some embodiments, the five events of interest may be collected into a separate bitmap for further processing such as, for example, to determine whether there are other events that correspond to these events. In the example illustrated inFIG. 10, the five events of interest also each contain the seventh unique identifier, possibly indicating a correspondence between the associated name-value pairs.