Systems and methods for lossless compression of data and high speed manipulation thereof

The present disclosure includes a system, method, and article of manufacture for lossless compression of data and high speed manipulation of the data. The method may comprise associating a customer with a plurality of levels, and counting, in near real time, a number of transactions at each level in the plurality of levels based on a transaction history of the customer at each of a plurality of merchants. The method may further comprise counting the number of transactions during a time period. Similarly, the method may comprise determining an opportunity comprising an offer based upon the counting, determining an opportunity based upon a count indicating a transaction by the customer with a merchant, and/or determining an opportunity with a first merchant based upon a count indicating a transaction by the customer with a second merchant.

BACKGROUND

The present disclosure generally relates to compression of data, and more particularly, to lossless compression of data and high speed manipulation thereof.

2. Related Art

There are many instances in which it is advantageous to inquire into the activity and/or attributes associated with a particular individual (e.g., a computer user, a transaction account holder, a consumer, and the like). For example, many organizations sometimes go to great expense to remain apprised of the network activities of their employees. That is, a large number of organizations often expend a large amount of money and time determining which websites/domains their employees surf (an activity), which network permissions their employees may be assigned (an attribute), and the like. Similarly, many organizations (e.g., sales organizations, transaction account issuers or payment processors, and the like) may utilize information associated with, for example, the browsing activities and/or transaction histories of their customers to better tailor content (e.g., offers, opportunities, rewards, and the like) to those customers.

In the past, the size and complexity of such data has limited the speed and efficiency with which this data may be analyzed or processed. For example, where an organization wishes to track a website that an employee visits and/or a database of each permission assigned to each employee, the processing and memory requirements may escalate rapidly, particularly where there are a large number of employees surfing to a large number of websites, or a large number of employees associated with a particular permission or group of permissions (only some of which may be necessary for the performance of the employee's assignments). Similarly, where a transaction account issuer or payment processor wishes to analyze, for example, transaction history data associated with its customers, often the complexity and quantity of this data may again give rise to rapidly escalating processing and memory requirements.

Accordingly, systems and methods capable of rapidly and inexpensively compressing and manipulating large amounts of data (e.g., data comprising an employee's activity or attributes on or within a network, transaction history, and the like) are desirable and would be of great advantage to a large number of organizations and businesses. In particular, solutions that reduce memory and processing requirements may be very advantageous.

SUMMARY

The present disclosure includes a system, method, and article of manufacture for lossless compression of data and high speed manipulation of data. The method may comprise associating a customer with a plurality of levels and/or merchants, where a number of transactions associated with one or more spend levels (e.g., high, medium, low) at each merchant may be counted (in near real time) for the customer. In various embodiments, the method may further comprise counting the number of transactions during a time period. Similarly, the method may comprise determining an opportunity comprising an offer based upon the counting, determining an opportunity based upon a count indicating a transaction by the customer with a merchant, determining an opportunity with a first merchant based upon a count indicating a transaction by the customer with a second merchant, determining a loyalty opportunity with a merchant based upon a count indicating a transaction by the customer with the merchant during a time period, and/or determining an opportunity with a merchant based upon a count and an indication by the customer that the customer is visiting the merchant.

DETAILED DESCRIPTION

The phrases consumer, customer, user, account holder, cardmember or the like shall include any person, entity, business, government organization, business, software, hardware, machine associated with a transaction account, buys merchant offerings offered by one or more merchants using the account and/or who is legally designated for performing transactions on the account, regardless of Whether a physical card is associated with the account. For example, the cardmember may include a transaction account owner, an transaction account user, an account affiliate, a child account user, a subsidiary account user, a beneficiary of an account, a custodian of an account, and/or any other person or entity affiliated or associated with a transaction account. In addition, as used herein, a user may comprise, in various embodiments, any person who interacts and/or interfaces with a computer system (e.g., an organizational and/or an employer computer system).

As used herein, the phrases “real time,” “near real time,” “pseudo real time,” “quasi real time,” and the like may mean any period of time during which a table or data structure, as described herein, is analyzed or processed. For example, in various embodiments, any of these terms may mean a period of time immediately following and/or shortly following a transaction, an event, an occurrence, and the like. In various embodiments, the period of time may include a period of picoseconds, nanoseconds, microseconds, milliseconds, seconds, minutes, hours, days, and the like.

Phrases and terms similar to “transaction account” may include any account that may be used to facilitate a financial transaction.

Phrases and terms similar to “financial institution” or “transaction account issuer” may include any entity that offers transaction account services. Although often referred to as a “financial institution,” the financial institution may represent any type of bank, lender or other type of account issuing institution, such as credit card companies, card sponsoring companies, or third party issuers under contract with financial institutions. It is further noted that other participants may be involved in some phases of the transaction, such as an intermediary settlement institution.

Phrases and terms similar to “business” or “merchant” may be used interchangeably with each other and shall mean any person, entity, distributor system, software and/or hardware that is a provider, broker and/or any other entity in the distribution chain of goods or services. For example, a merchant may be a grocery store, a retail store, a travel agency, a service provider, an on-line merchant or the like.

A system, method and/or computer program product for lossless compression of data is disclosed. The data may be manipulated in a compressed state very efficiently and at high speed. Referring broadly toFIGS. 1 and 15, exemplary systems100and1500for lossless compression and high-speed efficient manipulation of data are disclosed. Further, in various embodiments, system100may represent or illustrate a system through Which an employer or organization and an employee or individual interact, while in various embodiments, system1500may represent or illustrate a system for processing and/or analyzing a transaction history (e.g., a history of payment transactions and/or an activity history of a user and/or system).

With particular reference toFIG. 1, system100may comprise a web client102, a network104, and/or a database106. In an exemplary embodiment, system100may comprise a mainframe system and/or a single distributed system.

A web client102includes any device (e.g., personal computer, point of sale or POS device) which communicates via any network, for example such as those discussed herein. Such browser applications comprise Internet browsing software installed within a computing unit or a system to conduct online transactions and/or communications. These computing units or systems may take the form of a computer or set of computers, although other types of computing units or systems may be used, including laptops, notebooks, tablets, hand held computers, mobile phones, smart phones, personal digital assistants, set-top boxes, workstations, computer-servers, main frame computers, mini-computers. PC servers, pervasive computers, network sets of computers, personal computers, such as WADS, iMACs, and MacBooks, kiosks, terminals, point of sale (POS) devices and/or terminals, televisions, or any other device capable of receiving data over a network. A web-client102may run Microsoft Internet Explorer, Mozilla Firefox, Google Chrome, Apple Safari, or any other of the myriad software packages available for browsing the internet.

Practitioners will appreciate that a web client102may or may not be in direct contact with an application server. For example, a web client102may access the services of an application server through another server and/or hardware component, which may have a direct or indirect connection to an Internet server. For example, a web client102may communicate with an application server via a load balancer. In an exemplary embodiment, access is through a network or the Internet through a commercially-available web-browser software package.

As those skilled in the art will appreciate, a web client102includes an operating system (e.g., Windows NT, 95/98/2000/CE/Mobile, OS2, UNIX, Linux, Solaris, MacOS, PalmOS, etc.) as well as various conventional support software and drivers typically associated with computers. A web client102may include any suitable personal computer, network computer, workstation, personal digital assistant, cellular phone, smart phone, minicomputer, mainframe or the like. A web client102can be anywhere there is any type of wireless network connectivity (e.g., in a home or business environment with access to a network). In an exemplary embodiment, access is through a network or the Internet through a commercially available web-browser software package. A web client102may implement security protocols such as Secure Sockets Layer (SSL) and Transport Layer Security (TLS). A web client102may implement several application layer protocols including http, https, ftp, and sftp.

“Cloud” or “Cloud computing” includes a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing may include location-independent computing, whereby shared servers provide resources, software, and data to computers and other devices on demand. For more information regarding cloud computing, see the NIST's (National institute of Standards and Technology) definition of cloud computing at http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf (last visited June 2012), which is hereby incorporated by reference in its entirety.

Database106may comprise any type of hardware and/or software (e.g., a computer server) configured or configurable to store data and/or host a database. For example, database106may comprise a server appliance running a suitable server operating system (e.g., IIS) and having database software (e.g., SQL Server 2008, an Oracle database, and the like), stored thereon. Database106may, in various embodiments, compress and/or analyze data, as described herein. Similarly, in various embodiments, database106may be coupled to a system for analyzing and/or compressing data, as described herein.

With particular reference toFIG. 15, system1500may comprise a client1502, a network1504, a global authorization network (“GAN”)1506, and/or an analytics system1508.

In various embodiments, web client1502may comprise a client similar or identical to web client102, as described above. For example, in various embodiments, client1502may comprise a personal computer, a mobile computer, a mobile phone, a POS device, and the like.

Similarly, in various embodiments, network1504may comprise a network similar or identical to network104, as described above. Further, in various embodiments, network1504may comprise an inbound acquiring network associated with a financial institution or other transaction account issuer or manager.

In various embodiments, GAN1506may comprise a system (e.g., a front end or initial authorization system) capable of or configured to perform all or part of an authorization process in relation to a payment transaction associated with a transaction account.

In various embodiments, analytics system1508may comprise a system (e.g., a back end or secondary authorization system) capable of or configured to perform all or part of an authorization process in relation to a payment transaction associated with a transaction account. For example, analytics system1508may comprise a card authorization system (“CAS”).

Referring broadly now toFIGS. 2-14and16-17, the process flows, logical representations, and/or screen shots depicted are merely embodiments and are not intended to limit the scope of the disclosure. For example, the steps recited in any of the method or process descriptions may be executed in any order and may, in various embodiments, apply to the systems100and/or1500depicted, respectively, inFIGS. 1 and 15. It will be appreciated that the following description makes appropriate references not only to the steps depicted inFIGS. 2-14and16-17but also to the various system components and/or logical representations as described above with reference toFIGS. 1 and 15.

With reference now toFIG. 2, an exemplary process200for lossless compression and efficient manipulation of data is disclosed. As a primer, the following definitions apply.

Broadly, a “collection” may comprise a group or cluster of “entities,” and an “entity” may comprise any element, part, or aspect of a collection. To clarify, although the foregoing definitions are not so limited, an entity may comprise any transaction, activity, occurrence, event, system, user, customer, consumer, merchant, attributes, and the like that may be associated with a collection, and a collection may comprise any group or cluster of transactions, activities, occurrences, events, systems, users, merchants, customers, consumers, attributes, and the like. Moreover, a collection may in one instance operate as an entity, and an entity, likewise, as a collection.

Take, for example, a collection comprising a user (a “user collection”). A variety of entities may be associated with the user collection; but, to take a single entity for purposes of illustration, a variety of transaction entities (e.g., visits to a website/domain or file access attempts) may be associated with the user collection. Thus, the user may comprise a collection of transaction entities. More particularly, in this case, the user collection may comprise one or more visits by the user to a website/domain or accesses by the user of a file.

However, and as mentioned above, a collection may in one instance operate as an entity, and the entity a collection. For example, a transaction entity (e.g., a website or a file) may operate as a collection, while a user may operate as an entity that accesses the website/file collection. Thus, in this example, the website or file may comprise a collection of user entities which have accessed the website or file collection.

Thus, the terms “entity” and “collection” may be, at the most basic level, defined by their relationship to one another. In other words, because a user may comprise a collection in one scenario, but an entity in another scenario, it is helpful to realize that the terms are best understood as a one-to-one, one-to-many, or many-to-many relationship between two interrelated data sets or elements.

Accordingly, and for purposes of illustration, several examples of collection-entity relationships are provided. In various embodiments, an entity may comprise one or more transactions, users, user activities, internet sites, internet proxy activities, systems, alerts from systems or processes, anti-virus activities, data leakage prevention events, system activities, and the like. Likewise, a collection may comprise, in various embodiments, any of the foregoing, including one or more users, user groups, transaction clusters, activities associated with one or more users, files, file permissions, system alerts, websites/domains, and the like.

Similarly, in various embodiments, a collection may comprise an individual or customer (e.g., a customer of a transaction account issuer), while an entity may comprise one or more merchants from whom the customer collection has made purchases. However, as described above, the collection-entity relationship between merchants and customers may be defined such that a merchant comprises a collection, while customers comprise entities.

With continuing reference toFIG. 2, an entity may be associated with a unique power of the number two (step202). For example, six entities, labeled A, B, C, D, E, and F, may be associated with unique powers of two using the number two as the base and (n−1) as the exponent, or 2(n−1), where n=1 to 6, in this example, but where n generally begins with the number one and increases until each entity is associated with a unique value. The value associated with each entity may be further converted to binary (step204). Thus, where there are six entities, A through F, each entity may receive base 10 and binary values as follows:

Although a variety of mechanisms may be employed, in an embodiment, the relationships between each entity and its base 10 and/or binary value may be saved in a table or array, which may comprise a portion of a database (other mechanisms may include flat files, objects in an object oriented program, and the like). For example, database106may store these relationships as part of an entity cross-reference table, which is depicted inFIG. 3.

As stated, a collection may comprise a cluster, collection, and/or group of one or more entities. Thus, where there are, for example, five collections, C1, C2, C3, C4, and C5, each collection may include entities as follows:

Like each entity, each collection C1-C5 may be associated with a base 10 and/or binary value. This may be accomplished by summing the base 10 values of each unique entity in a collection and converting those values to binary. In an embodiment, the binary values associated with each unique entity in a collection may be logically ORed to produce the same result (step206). In the example above, the following results are obtained:

As described above with reference to each entity value, each collection value may be stored in a database (e.g., database106) as part of a collections table, which is depicted inFIG. 4.

At this point, in one embodiment, 512 unique entities (to pick a number solely for purposes of illustration) may be mapped uniquely using 512 bits, or 64 bytes. Assuming a period of 30 days at a rate of 500 entities per day per collection (e.g., a user collection performing up to 500 distinct transactions per day) and 20,000 collections (e.g., 20,000 users), only about 38 megabytes of storage are required to store all of the data generated for the entire month. Thus, the data is compressed, in that a fraction of the storage previously required to track a collection's entities is required by the systems and methods described herein. Moreover, the data is stored losslessly, because all of the information associated with a collection's entities is stored intact as set of binary values.

The unique numbering system described above gives rise to a variety of unique and useful results (step208). For instance, if a collection has a same entity sum as another collection, those collections are identical. That is, observe that collections C2 and C5, which both comprise entities A, B, and C, are associated with a base 10 sum of 7, or a binary sum of 000111. Thus, a simple numerical comparison shows duplicate collections. Further, because computing devices are designed to process data in a binary format (as opposed to a character or character string format, or even a base 10 format), the comparison described above may be performed at very high speed. That is, no computationally intensive string matching is required with the disclosed system.

Observe further that any collection that is associated with a value of 2(n−1)necessarily includes only a single entity (e.g., a collection associated with a value of 1, 2, 4, 8 . . . only includes the entity associated with that value). On the other hand, any collection that is not associated with a value of 2(n−1)is not limited to a single entity, and may include entities in common with other collections. For example, with reference to collections C1 and C3, system100may use a logical AND operation to quickly determine that these collections share entities in common and, indeed, which entities are shared. The calculation is provided below:
001111(C1) AND 010011(C3)=000011(Result)

Having reached this Result, system100may use the entity cross-reference table (depicted inFIG. 3) to determine that the Result contains entities A and B (as stated, A−00001 and B=000010). Thus, system100may quickly determine which entities a plurality of collections share in common. In addition, system100may associate an entity count with a plurality of collections, whereby system100may determine collections that are candidates for entity reduction/consolidation and/or likely include entities included in other collections. That is, collections with a high entity count may be more likely to share common entities.

From a practical standpoint, although many uses are possible, assume that a first and second collection comprise two different network user groups (e.g., group C1 and group C2). Assume further that these groups comprise a variety of network users (e.g., users A, B, and C). More particularly, assume:

Group C1: user A, user B, user C

Group C2: user A, user B

System100may determine, based upon the processes described above, that Groups C1 and C2 both include users A and B. A system or network administrator may use this information to collapse, remove, and/or delete group C2, particularly where the administrator is able to place user C in a different group (not shown). Thus, the disclosed system and method may, in an example, permit a system or network administrator to remove redundant or unnecessary user groups.

With further regard to the manner in which entities may be assigned a value, the speed at which system100operates may be further improved by calculating, prior to assigning each entity in a collection a value, the relative frequencies of each entity across a plurality of collections. Those entities that occur most commonly may be assigned lower values (e.g., 1 or 000001), while those entities that occur less commonly may be assigned higher values (e.g., 32 or 100000). In this way, long binary representations can be partitioned into subsets that enable faster logical operations. For example, using the 512 entity example above, frequent entities are placed in the low order bytes (i.e., to the right, as shown in the example). Searching (or updating) the collections for a given common entity can be completed by performing the logical AND (respectively OR) on only the right most bytes and not the full 64 byte blocks. In an embodiment, the binary encoding can be completed so that similar or related entities are “close” together in the binary representation. This too enables partitioning the binary representation for efficient searching and/or updates of similar or related entities. With reference toFIG. 4, further efficiencies may be achieved by including a field for the number of entities in the collection or several fields for the number of entities in each of the partitioned subsets.

The foregoing processes, tables, and/or computations may be enhanced in a variety of ways. For instance, a collection table may be enhanced to show periodic (e.g., hourly, daily, weekly, monthly, 90 day, annual, etc.) entity sums, in which case a collection may be evaluated more granularly. This is depicted atFIG. 5. With respect toFIG. 5, note that Day_M is merely intended to represent the final day in a period of days. So, if the period is one month, M may equal 28, 29, 30, or 31, depending upon the month and year. Moreover, where a period is very granular (e.g., hourly) a trending analysis may be performed by system100to show a real time (or almost or pseudo-real time/quasi-real time) behavior of one or more collections and/or entities.

In an embodiment, a collection table may include entries for entities “used” by a collection and entries for entities “assigned” to a collection. This enhancement may be helpful, in one example, to system administrators in determining whether a user collection is using all, fewer, or greater than the permissions entities that it (or he) is assigned. Table 6 shows this enhancement, and “used” and “assigned” may be merely illustrative and are not exclusive of other similar columns, such as “detected,” for an alert or event, and “base events,” where a collection comprises a group, such as a business unit within an organization.

Occasionally, it may be desirable to add granularity to the data stored for an entity. That is, it may be useful to know/determine more than whether an entity occurred or exists. For example, where an entity comprises a file, the method described above may enable determination of whether the file was accessed. Likewise, where an entity comprises a merchant (as described herein), the systems and methods described herein may enable determination of whether a customer made a purchase from the merchant. However, if it is important to know, for example, a type of file access (e.g., none, read, write, alter, control, update, etc.), a type or category of transaction that occurred with a particular merchant, and the like, additional tables and/or a different table structure may be helpful and/or useful. Hereinafter, such data may be tracked using “level” data, or simply “levels,” depending upon the context in which the terms appear. As described herein, and for purposes of illustration, level data may include a type of file access (e.g., read, write, etc.), a type of transaction (e.g., a transaction of a first value, a transaction of a second value, a transaction with a merchant in a particular industry group, and the like). In certain embodiments, any number of levels may be associated with an entity, depending upon the nature of the entity (e.g., user entity, file entity, etc.) and the granularity and information desired.

With reference now toFIG. 7, a process700for associating level data with an entity is described. The process is similar to the process described above, with reference toFIG. 2. Specifically, each desired level must be associated with a unique value (step702). The unique value may comprise a base 10 number, a binary number, a character or character string, and/or any combination thereof (Note that in cases with large numbers of levels one may want to avoid using character or character strings for performance reasons.) For example, where an entity comprises a file or file access attempt (and the collection is a user), the file or file access attempt may be associated with a level depending upon the result of the access attempt. In this example, potential levels include none, read, update (e.g., writer or alter), control, and/or execute. The levels can be used to distinguish between granted and consumed access, e.g., read attempted and read granted, read attempted and read denied, write attempted and write granted, write granted and write denied, and the like. Potential unique values for each level include, N (none), R (read), W (write), U (update), A (alter), 0 (non), 1 (read), 2 (write), 3 (update), 4 (alter). In an embodiment, encoding can be completed to capture attempted and granted access, such as RR or 11 or 1 (read attempted and read granted), RN or 10 or 0 (read attempted and none granted), WW or 22, or 2 (write attempted and write granted), WN or 20 or 3 (write attempted and none granted), etc. In certain embodiments, any value, string, and/or combination thereof may be assigned to a level or the combination of attempted versus granted, etc. The only requirement is that each level be assigned a unique identifier. As described above with reference to the entity cross-reference table (seeFIG. 3), so too, the level data associated with each entity may be stored in a level cross-reference table. Exemplary level cross-reference tables are depicted atFIGS. 8A and 8B.

With further reference toFIG. 7, in order to capture level data associated with an entity (e.g., in order to capture the results of a file access attempt), each entity in a collection may be associated with an appropriate level, based upon the data contained in the level cross-reference table (step704). This may be achieved by creating a table or array of level data, where the ntharray element for the level is associated with the nthbit position for the entity in the binary representation for the entities in the associated collection. Likewise, in various embodiments, level data may be represented as an integer (e.g., a big integer) or string, where, for example, the nthbit position corresponds to the nthentity. The following example, which uses entities A, C, and D, as above with reference toFIG. 2, is illustrative.

Level Data for Entities A, C, and D:

Level Data Array for Collection 1:

Thus, the level data for each entity A, C, and D in Collection 1 is stored in a level data array, where the array position corresponds to the bit position of the entity in Collection 1 with which it is associated. In certain embodiments, vertical bars may be used herein to separate level data elements in a level data array to depict the array nature of a level data array. However, in practice, a level data array may not include vertical bars. One or more level data arrays may be stored in a level data collections table, an example of which is depicted atFIG. 9. Each level data array comprising a level data collections table may, in conjunction with a level data cross-reference table, permit a variety of more advanced analyses (step706). For example, where a collection, C1, comprises a file, and the entities assigned to C1 comprise users A, B, and C attempting to access the file, system100may use the process described with reference toFIG. 2to determine that users A, B, and C accessed (or attempted to access) the file collection, and the process described with reference toFIG. 7to determine specific details about the access attempts by users A, B, and C (e.g. read attempted, read granted, etc.) Thus, for example, system100may identify potential excessive granted access that is not being used during a given time period, possible accidental or malicious attempted access, and the like.

A level data collections table may be enhanced in a variety of ways. For instance, a level data collection table may be enhanced to show periodic (e.g., hourly, daily, weekly, monthly, 90 day, annual, etc.) level data for the associated collection, which enables evaluation with more granularity. An enhanced monthly level data collections table is depicted atFIG. 10. With reference toFIG. 10, note that Day_M is merely intended to represent the final day in a period of days. So, if the period is one month, M may equal 28, 29, 30, or 31, depending upon the month and/or year. Moreover, where a period is very granular (e.g., hourly) a trending analysis may be performed by system100to show a real time (or almost or pseudo-real time or quasi-real time) behavior of one or more collections and/or entities. Further still, a Boolean flag may indicate whether an entity has been active (e.g., existed and/or occurred) during an interval (e.g., the last 30, 60, 90, etc. days).

A level data collections table may be further enhanced to provide a minimum or maximum array level for each associated entity for a given collection during a given period. For example, a level data collections table may include a maximum level associated with one each entity in or more collections during a given month. An exemplary summary level data collections table including this data is depicted atFIG. 11. Again, a Boolean flag may indicate whether an entity has been active (e.g., existed and/or occurred) during an interval (e.g., the last 30, 60, 90, etc. days).

In addition to providing details about an entity, level data may be leveraged to assess the risk associated with a particular entity. For example, where an entity comprises a file, and the collection associated with the entity comprises a user, level data may be leveraged to determine the risk associated with the user's file access attempts. That is, where a user attempts to access a file and access is denied (because the user does not have permission to access the file), a higher risk may be associated with the user or the user's activities. This risk may be associated with a risk level, which may be defined in any suitable manner. For example, a risk may be assigned a risk level of 1 to 10, 0 to 9, low, medium, high, etc., depending upon a variety of factors (e.g., likelihood of harm, impact of harm, etc.) This data may be stored in a risk level cross-reference table. An exemplary risk level cross-reference table is depicted atFIG. 12. Further, a risk level may be stored and associated with an entity and/or a collection in the manner described above with reference to level data. That is, each entity in a collection may be associated with a risk level by storing a risk level in a risk level collections table in the array position corresponding to the bit position of the entity in the collection with which it is associated. An exemplary risk level collections table is depicted atFIG. 13.

Further still, one or more fields, columns, arrays, and/or tables may be implemented to capture the number of transactions associated with each risk level (e.g., on an entity, collection, and/or system wide level), and this data may, for instance and in the example provided above, form the basis for a report highlighting excess user access or attempted access violations, either of which may be accidental or malicious. For example, and with reference toFIG. 14, which depicts an exemplary monthly risk level transaction count table, the ntharray element may hold a count for the number of times that the nthassociated entity in the collection (e.g., activity, transaction type and/or resource) has been attempted to be performed, occurred, and/or accessed. During multiple events for accessing or attempting to access a resource, transaction, system, etc., the level array may simply capture the maximum access attempted. This may ensure that the worst case (i.e., the maximum level) is identified. For example, if a user is granted ALTER accesses to a particular file and then accesses it 99 times with READ access and once with WRITE or ALTER access then the level entry in the associated array may show ALTER ATTEMPTED and ALTER GRANTED, e.g., 22, while the associated count in the risk level transaction count table (FIG. 14) would be 100. Additionally, the array elements may be broken down into separate bit maps, where there may be an indicator for the various levels and the associated counts. For example, a 64 bit array element may be used to allocate bits for different levels, with either assumed (based on position) or explicit level identifiers. Counts that exceed the maximum value may simply be left at their maximum value and not rolled to restart counting from zero. Alternatively, counts exceeding the maximum may be restarted (e.g., from zero), and a separate counter incremented each time the maximum is achieved.

With reference now toFIG. 16, a table or array1602is depicted which may, in various embodiments, be useful for capturing or collecting information associated with a level, an entity, a collection, and/or a time period in a single table or array. In other words, table1602may permit storage of both level data and event counts in a single data structure. Moreover, although table1602is described below with reference to collection and analysis of transaction history data (e.g., spending or purchasing history data), table1602may apply equally to any instance in which it is desirable to organize data according to entity, collection, level, and/or time period. For example, as described above, table1602may include level data associated with a number of file access attempts and/or types of file access attempts. In addition, although table1602is described as being useful for the storage and analysis of information associated with each of an entity, a collection, a level, and/or a time period, those of skill will appreciate that the systems and methods described above with reference toFIGS. 1-14are also capable of capturing the same data. Similarly, in various embodiments, a one dimensional array may be formed that represents one or more entities. Such an array may comprise one or more multi-bit elements (e.g., bytes), where each element is tied to an entity and where the bits comprising each multi-bit element are associated with level data for the entity.

Thus, although those of skill in the art will recognize as described briefly above) that other structures capable of performing the same or a similar function may be constructed based upon this disclosure, in various embodiments, table1602may be organized such that a group of rows may comprise a single collection (e.g., customers, such as Cust1, Cust2, and Cust3, as shown, users, and the like), while each column may comprise an entity (e.g., merchants, such as M1 . . . Mx, as shown, categories of merchants or industry groups of merchants, and the like). Further, as described below, in certain embodiments, a count or level count may be associated each entity. A count or level count may be stored as a two dimensional array (e.g., as bytes and/or small integers). Thus, a first dimension of a two dimensional array for storing level counts may correspond to each of a plurality of entities (e.g., M1 . . . Mx), while a second dimension of the two dimensional array may correspond to one or more levels (e.g., L, M, H, as described below). Thus, each element in a two dimensional array may store a level count for an entity with respect to a particular level (e.g., for the nthentity at the mthlevel).

Further, in various embodiments, a group of columns or entities may be grouped into a particular time period (e.g., minutes, hours, days, weeks, months, etc.), and a group of rows comprising a collection may comprise level data. As discussed briefly above, level data may comprise, for example, a transaction amount or value, such as low, medium, or high (or L, M, H, as shown), a number of transactions or purchases performed by a customer collection with a merchant entity, a merchant type (e.g., discount merchant, food retail merchant, jewelry merchant, etc.), a type of file access attempt (e.g., Read, Write, etc.), and the like. In various embodiments, although many other arrangements are possible, for purposes of illustration, a low transaction (L) may comprise a transaction having a value under a first amount (e.g., $100), while a medium transaction (M) may comprise a transaction having a value between the first amount and a second amount (e.g., $100 and $500), and a high transaction (H) may comprise a transaction having a value greater than the second amount (e.g., $500).

Thus, table1602may be organized by entity, collection, level, and/or time period, and each coefficient or element in table1602may correspond to a count or tally associated with each entity in a collection at a particular level. For instance, where level data comprises a transaction amount or value, such as L, M, or H, as depicted, each element in table1602may comprise a number of transactions of one of these types by a particular customer collection with a particular merchant entity during a given time period. In various embodiments, and as shown, system1500may limit an element to a value of one or zero (e.g., based upon the assumption that a customer typically only makes one purchase from a given merchant during a single time period). However, in various embodiments, each element may simply act as a counter which may be incremented each time the customer makes a purchase from the merchant of the particular level type during the particular time period. An array dimension or element may be sized (or a size may be allocated for a particular element) based upon an expected or maximum number of bits required. Thus, in various embodiments, a structure1602may losslessly store data, as described herein. In addition, a count may be converted to and stored as (e.g., as described above) a binary value, which may speed data analysis and processing.

Referring toFIG. 17, a table1702, much akin to table1602, is shown. Table1702may, like table1602, aggregate a count associated with an entity in a collection by level. Likewise, table1702may aggregate such a count for one or more time periods, e.g., one or more months, as shown. Thus, in various embodiments, table1702may aggregate one or more monthly totals, such as monthly totals for a number of transactions a customer has engaged in with a particular merchant at a particular level. In various embodiments, it may be advantageous (e.g., storage requirements may again be reduced) to remove or delete from any table summarizing a time period (e.g., table1702, which summarizes or aggregates a total number of counts for a particular month) any level totals for a collection which are zero across all of a set of entities. For example, with regard to table1702, it may be advantageous to remove the medium (or M) level data for Cust2, since the customer is not shown to have a transaction with any merchant entity during the month. In various embodiments, level data may be removed, as described above, with the assistance of a cross reference table formed between a summary table, such as table1702, and a deletion table, which may track which rows (having zero values) have been removed from a table, such as table1702. Although such an approach may, in certain embodiments, reduce a size associated with a summary table, e.g., table1702, it may nevertheless require the creation of cross reference and deletion tables, which may require additional time and resources. Thus, in various embodiments, it may be advantageous to forgo the removal of level data from a summary table.

In various embodiments, the systems and methods discussed herein may be used for a large variety of purposes. For example, in various embodiments, each of the systems and methods may assist in the determination of one or more post purchasing opportunities, such as one or more offers (e.g., loyalty offers), one or more discounts on future purchases, one or more rewards points, and the like. Further, as described variously above, transaction history data may be manipulated and analyzed in real time and/or pseudo or near real time, such that a customer is provided a post purchasing opportunity within a short time after completing a transaction with a merchant (e.g., seemingly almost instantly, such as, within several seconds or minutes of a transaction). A customer may be provided a post purchasing opportunity, in various embodiments, in association with a same merchant from which a purchase was recently made, in association with a merchant partnered with a merchant from whom a sale was recently made (e.g., such that the merchant and partner merchant may offer cross-sales opportunities to boost sales between themselves), and/or in association with a merchant that is frequently attended or visited by the customer (e.g., to reward customer loyalty to the frequently attended merchant).

Further, in various embodiments, the systems and methods discussed herein may assist in the determination of one or more pre-purchasing opportunities. For example, a customer may indicate, prior to or during a visit to a merchant (e.g., an online visit and/or a visit to a brick and mortar store), that the customer intends to visit or is visiting the merchant, whereupon the systems and methods described herein may be used to provide an opportunity (e.g., a discount, an offer, etc.) to the customer.

Any of the opportunities described above may be provided, in various embodiments, based upon the transaction history of the customer, which may be maintained, as described herein, in a data structure such as, for example, any of the tables described above (including tables1602and or table1702). Tables may be analyzed by a system (e.g., system1502) to determine that a customer shopped at a particular merchant on a certain number of days during a month and/or that the customer shopped at merchants within a particular industry group, and a (post purchasing and/or pre-purchasing opportunity) may be determined and/or offered to the customer in accordance with the determination. For instance, where it is determined that a customer shopped with a particular merchant (e.g., a low priced merchant), as discussed above, the customer may be provided with an opportunity (e.g., a coupon or offer) associated with another or the same merchant (e.g., the low priced merchant). Further, the customer may be provided with such an opportunity in real time and/or near real time, because the systems and methods discussed herein permit the storage and manipulation of large quantities of data within a relatively short amount of time and based upon one or more logical or bitwise operations.

Any communication, transmission and/or channel discussed herein may include any system or method for delivering content (e.g. data, information, metadata, etc.), and/or the content itself. The content may be presented in any form or medium, and in various embodiments, the content may be delivered electronically and/or capable of being presented electronically. For example, a channel may comprise a website, a uniform resource locator (“URL”), a document (e.g., a Microsoft Word document, a Microsoft Excel document, an Adobe .pdf document, etc.), an “ebook,” an “emagazine,” an application or microapplication (as described below), an SMS or other type of text message, an email, facebook, twitter, MMS, data communication over a financial acquirer network, and/or other type of communication technology. In various embodiments, a channel may be hosted or provided by a data partner.

In various embodiments, the server may include application servers (e.g. WEB SPHERE, WEB LOGIC, JBOSS). In various embodiments, the server may include web servers (e.g. APACHE, IIS, GWS, SUN JAVA SYSTEM WEB SERVER).

As used herein, “issue a debit”, “debit” or “debiting” refers to either causing the debiting of a stored value or prepaid card-type financial account, or causing the charging of a credit or charge card-type financial account, as applicable.

Any databases discussed herein may include relational, hierarchical, graphical, or object-oriented structure and/or any other database configurations. Common database products that may be used to implement the databases include DB2 by IBM (Armonk, N.Y.), various database products available from Oracle Corporation (e.g., MySQL) (Redwood Shores, Calif.), Microsoft Access or Microsoft SQL Server by Microsoft Corporation (Redmond, Wash.), or any other suitable database product. Moreover, the databases may be organized in any suitable manner, for example, as data tables or lookup tables. Each record may be a single file, a series of files, a linked series of data fields or any other data structure. Association of certain data may be accomplished through any desired data association technique such as those known or practiced in the art. For example, the association may be accomplished either manually or automatically. Automatic association techniques may include, for example, a database search, a database merge, GREP, AGREP, SQL, using a key field in the tables to speed searches, sequential searches through all the tables and files, sorting records in the file according to a known order to simplify lookup, and/or the like. The association step may be accomplished by a database merge function, for example, using a “key field” in pre-selected databases or data sectors. Various database tuning steps are contemplated to optimize database performance. For example, frequently used files such as indexes may be placed on separate file systems to reduce In/Out (“I/O”) bottlenecks.

The data set annotation may also be used for other types of status information as well as various other purposes. For example, the data set annotation may include security information establishing access levels. The access levels may, for example, be configured to permit or monitor only certain individuals, levels of employees, companies, or other entities accessing data sets, or to permit or monitor access to specific data sets based on the transaction, merchant, issuer, user or the like. Furthermore, the security information may restrict, permit, and/or monitor only certain actions such as accessing, modifying, and/or deleting data sets. In one example, the data set annotation may indicate or track that only the data set owner or the user are permitted to delete a data set, various identified users may be permitted to access the data set for reading, and others are altogether excluded from accessing the data set. However, other access restriction or monitoring may also be used which may allow various entities to access a data set with various permission levels as appropriate, and/or which monitoring may also be used to track various entities (e.g., users or systems) accessing a data set with various permission levels. Thus, in various embodiments, tracking information may enable a system administrator to inquire into one or more user activities, which inquiry may permit the system administrator to adjust one or more access controls, modify one or more user groups or transaction clusters, and the like.

Encryption may be performed by way of any of the techniques now available in the art or which may become available—e.g., AES, Twofish, RSA, El Gamal, Schorr signature, DSA, PGP, GPG (OnuPU) ECC, and symmetric and asymmetric cryptosystems.

The computing unit of the web client may be further equipped with an Internet browser connected to the Internet or an intranet using standard dial-up, cable, DSL or any other Internet protocol known in the art. Transactions originating at a web client may pass through a firewall in order to prevent unauthorized access from users of other networks. Further, additional firewalls may be deployed between the varying components of CMS to further enhance security.

Firewall may include any hardware and/or software suitably configured to protect CMS components and/or enterprise computing resources from users of other networks. Further, a firewall may be configured to limit or restrict access to various systems and components behind the firewall for web clients connecting through a web server. Firewall may reside in varying configurations including Stateful Inspection, Proxy based, access control lists, and Packet Filtering among others. Firewall may be integrated within an web server or any other CMS components or may further reside as a separate entity. A firewall may implement network address translation (“NAT”) and/or network address port translation (“NAPT”). A firewall may accommodate various tunneling protocols to facilitate secure communications, such as those used in virtual private networking. A firewall may implement a demilitarized zone (“DMZ”) to facilitate communications with a public network such as the Internet. A firewall may be integrated as software within an Internet server, any other application server components or may reside within another computing device or may take the form of a standalone hardware component.

The computers discussed herein may provide a suitable website or other Internet-based graphical user interface which is accessible by users. In one embodiment, the Microsoft Internet Information Server (IIS), Microsoft Transaction Server (MTS), and Microsoft SQL Server, are used in conjunction with the Microsoft operating system, Microsoft NT web server software, a Microsoft SQL Server database system, and a Microsoft Commerce Server. Additionally, components such as Access or Microsoft SQL Server, Oracle, Sybase, Informix MySQL, Interbase, etc., may be used to provide an Active Data Object (ADO) compliant database management system. In one embodiment, the Apache web server is used in conjunction with a Linux operating system, a MySQL database, and the Perl, PHP, and/or Python programming languages.

Each participant is equipped with a computing device in order to interact with the system and facilitate online commerce transactions. The customer has a computing unit in the form of a personal computer, although other types of computing units may be used including laptops, notebooks, hand held computers, set-top boxes, cellular or mobile telephones, touch-tone telephones and the like. The merchant has a computing unit implemented in the form of a computer-server, although other implementations are contemplated by the system. The bank has a computing center shown as a main frame computer. However, the bank computing center may be implemented in other forms, such as a mini-computer, a PC server, a network of computers located in the same of different geographic locations, or the like. Moreover, the system contemplates the use, sale or distribution of any goods, services or information over any network having similar functionality described herein

The merchant computer and the bank computer may be interconnected via a second network, referred to as a payment network. The payment network which may be part of certain transactions represents existing proprietary networks that presently accommodate transactions for credit cards, debit cards, and other types of financial/banking cards. The payment network is a closed network that is assumed to be secure from eavesdroppers. Exemplary transaction networks may include the AMERICAN EXPRESS, VISANET and the VERIPHONE networks.

The electronic commerce system may be implemented at the customer and issuing bank. In an exemplary implementation, the electronic commerce system is implemented as computer software modules loaded onto the customer computer and the banking computing center. The merchant computer does not require any additional software to participate in the online commerce transactions supported by the online commerce system.

Phrases and terms similar to “account”, “account number”, “account code” or “consumer account” as used herein, may include any device, code (e.g., one or more of an authorization/access code, personal identification number (“PIN”). Internet code, other identification code, and/or the like), number, letter, symbol, digital certificate, smart chip, digital signal, analog signal, biometric or other identifier/indicia suitably configured to allow the consumer to access, interact with or communicate with the system. The account number may optionally be located on or associated with a rewards account, charge account, credit account, debit account, prepaid account, telephone card, embossed card, smart card, magnetic stripe card, bar code card, transponder, radio frequency card or an associated account.

The system may include or interface with any of the foregoing accounts, devices, and/or a transponder and reader (e.g. RED reader) in RF communication with the transponder (which may include a fob), or communications between an initiator and a target enabled by near field communications (NFC). Typical devices may include, for example, a key ring, tag, card, cell or mobile phone, wristwatch or any such form capable of being presented for interrogation. Moreover, the system, computing unit or device discussed herein may include a “pervasive computing device,” which may include a traditionally none computerized device that is embedded with a computing unit. Examples may include watches. Internet enabled kitchen appliances, restaurant tables embedded with RF readers, wallets or purses with imbedded transponders, etc. Furthermore, a device or financial transaction instrument may have electronic and communications functionality enabled, for example, by: a network of electronic circuitry that is printed or otherwise incorporated onto or within the transaction instrument (and typically referred to as a “smart card”); a fob having a transponder and an RED reader; and/or near field communication (NFC) technologies. For more information regarding NFC, refer to the following specifications all of which are incorporated by reference herein: ISO/IEC 18092/ECMA-340, ISO 14443, Near Field Communication Interface and Protocol-1 (NFCIP-1); ISO/IEC 21481/ECMA-352, Near Field Communication interface and Protocol-2 (NFCIP-2); and EMV 4.2 available at http://www.emvco.com/default.aspx.

The account number may be distributed and stored in any form of plastic, electronic, magnetic, radio frequency, wireless, audio and/or optical device capable of transmitting or downloading data from itself to a second device. A consumer account number may be, for example, a sixteen-digit account number, although each credit provider has its own numbering system, such as the fifteen-digit numbering system used by American Express. Each company's account numbers comply with that company's standardized format such that the company using a fifteen-digit format will generally use three-spaced sets of numbers, as represented by the number “0000 000000 00000”. The first five to seven digits are reserved for processing purposes and identify the issuing bank, account type, etc. In this example, the last (fifteenth) digit is used as a sum check for the fifteen digit number. The intermediary eight-to-eleven digits are used to uniquely identify the consumer. A merchant account number may be, for example, any number or alpha-numeric characters that identify a particular merchant for purposes of account acceptance, account reconciliation, reporting, or the like.

In various embodiments, an account number may identify a consumer. In addition, in various embodiments, a consumer may be identified by a variety of identifiers, including, for example, an email address, a telephone number, a cookie id, a radio frequency identifier (RFID), a biometric, and the like.

The terms “payment vehicle,” “financial transaction instrument,” “transaction instrument” and/or the plural form of these terms may be used interchangeably throughout to refer to a financial instrument.

Phrases and terms similar to “merchant,” “supplier” or “seller” may include any entity that receives payment or other consideration. For example, a supplier may request payment for goods sold to a buyer who holds an account with a transaction account issuer.

Phrases and terms similar to “internal data” may include any data a credit issuer possesses or acquires pertaining to a particular consumer. Internal data may be gathered before, during, or after a relationship between the credit issuer and the transaction account holder (e.g., the consumer or buyer). Such data may include consumer demographic data. Consumer demographic data includes any data pertaining to a consumer. Consumer demographic data may include, for example, consumer name, address, telephone number, email address, employer and social security number. Consumer transactional data is any data pertaining to the particular transactions in which a consumer engages during any given time period. Consumer transactional data may include, for example, transaction amount, transaction time, transaction vendor/merchant, and transaction vendor/merchant location. Transaction vendor/merchant location may contain a high degree of specificity to a vendor/merchant. For example, transaction vendor/merchant location may include a particular gasoline filing station in a particular postal code located at a particular cross section or address. Also, for example, transaction vendor/merchant location may include a particular web address, such as a Uniform Resource Locator (“URL”), an email address and/or an Internet Protocol (“IP”) address for a vendor/merchant. Transaction vendor/merchant, and transaction vendor/merchant, location may be associated with a particular consumer and further associated with sets of consumers. Consumer payment data includes any data pertaining to a consumer's history of paying debt obligations. Consumer payment data may include consumer payment dates, payment amounts, balance amount, and credit limit. Internal data may further comprise records of consumer service calls, complaints, requests for credit line increases, questions, and comments, A record of a consumer service call includes, for example, date of call, reason for call, and any transcript or summary of the actual call.

Phrases similar to a “payment processor” may include a company (e.g., a third party) appointed (e.g., by a merchant) to handle transactions. A payment processor may include an issuer, acquirer, authorizer and/or any other system or entity involved in the transaction process. Payment processors may be broken down into two types: front-end and back-end. Front-end payment processors have connections to various transaction accounts and supply authorization and settlement services to the merchant banks' merchants. Back-end payment processors accept settlements from front-end payment processors and, via The Federal Reserve Bank, move money from an issuing bank to the merchant hank. In an operation that will usually take a few seconds, the payment processor will both check the details received by forwarding the details to the respective account's issuing bank or card association for verification, and may carry out a series of anti-fraud measures against the transaction. Additional parameters, including the account's country of issue and its previous payment history, may be used to gauge the probability of the transaction being approved. In response to the payment processor receiving confirmation that the transaction account details have been verified, the information may be relayed back to the merchant, who will then complete the payment transaction. In response to the verification being denied, the payment processor relays the information to the merchant, who may then decline the transaction. Phrases similar to a “payment gateway” or “gateway” may include an application service provider service that authorizes payments for e-businesses, online retailers, and/or traditional brick and mortar merchants. The gateway may be the equivalent of a physical point of sale terminal located in most retail outlets. A payment gateway may protect transaction account details by encrypting sensitive information, such as transaction account numbers, to ensure that information passes securely between the customer and the merchant and also between merchant and payment processor.