System and method for analyzing and utilizing data, by executing complex analytical models in real time

• US 20030084053A1
• Filed: 11/01/2001
• Published: 05/01/2003
• Est. Priority Date: 11/01/2001
• Status: Active Grant

• Current Assignee: Actimize Limited (Nice Ltd)
• Original Assignee: Actimize Limited (Nice Ltd)
• Inventors: Peer, Boaz, Govrin, David, Sosna, David, Greenberg, Guy
• Granted Patent: US 6,965,886 B2
• Time in Patent Office: Days
• US Class Current: 707/100
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: Y10S 707/99932 Access augmentation or optimizing

Abstract

A system and method for collecting, filtering, analyzing, distributing and effectively utilizing highly relevant events (such as key business events) in real time, from huge quantities of data. The present invention analyzes both historic and real-time data stemming from operational activity, by interfacing with internal data repositories (such as Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM)), external data sources (such as suppliers and clients), and real time operational systems in order to create an Active Intelligence Platform. This Active Intelligence Platform is positioned as a layer between the organization'"'"'s data sources and its applications, monitoring inputs and relaying only the important data items to the relevant individuals and/or systems. This allows individuals and systems to respond immediately and effectively to key events.

Claims

1. A platform for identifying key events from a plurality of data sources, according to user-defined rules, and delivering actionable messages of these events to a relevant target using real time analytics, comprising:

i. a data pump layer, for capturing and loading data into the platform, from a plurality of external data sources;

ii. a feeder component for filtering said data, and routing relevant said data;

iii. a MDPDS component, for aggregating and storing said data routed by said feeder component;

iv. a solver component for filtering said data from said MDPDS, according to user defined rules, in order to determine events;

v. a matcher component for matching said key events with target addresses, and transferring said events into user readable messages; and

vi. a distributor component for distributing said messages to said target addresses in real time.

2. The platform of claim 1, wherein said data pump layer includes:

a. at least one loader driver for loading static data from external data sources; and

b. at least one listener adaptor for capturing external messages from a plurality of messaging systems.

3. The platform of claim 1, wherein said MDPDS component is a multi dimensional data structure that holds a minimal set of data necessary to solve pre-defined integrated analytical rules, said data includes:

a) dimensions, for holding descriptive information about analysis dimensions; and

b) parameters, for holding a plurality of accumulative data types for analyzing said data.

4. The platform of claim 1, wherein said matcher component includes a matching engine, said matching engine:

I. categorizes said data into small hierarchical communities, for matching data to targets in heterogeneous communities; and

II. categorizes said data into dimension based communities, for matching data to targets in homogenous communities.

5. The platform of claim 1, wherein said distributor component furthermore:

I) transforms events into human readable messages; and

II) distributes said events to at least one target address, using a plurality of communications mediums.

6. A method of matching patterns in data to pre-defined events, and delivering actionable messages of these events to at least one relevant target in real time, comprising the steps of:

i. defining key events to be detected within a data environment, according to user determined rules;

ii. detecting relevant records from at least one external data source, and discarding irrelevant records;

iii. analyzing said relevant records;

iv. recognizing impact of said records, according to said definitions of key events, in order to match said records to at least one target address; and

v. targeting significant said matches, by distributing said events as messages to said at least one relevant recipient, using a plurality of communications mediums.

7. The method of claim 6, wherein said detecting further comprises:

i—

loading static data from batch data sources, by a loader driver; and

ii—

capturing dynamic data from messaging systems/online sources, by a listener adaptor.

8. The method of claim 6, wherein said analyzing further comprises:

i) monitoring values of said records;

ii) routing each said record according to relevant parameters; and

iii) where necessary, changing value of said record.

9. The method of claim 6, wherein said recognizing further comprises:

I. evaluating said data in terms of monitored values;

II. evaluating said data in terms of additional attributes of the dimensions involved;

III. evaluating said data in terms of additional information that exists in an information system;

IV. triggering a distribution function; and

V. sending a message with said data to at least one relevant target address.

10. The method of claim 6, wherein said targeting further comprises:

I) comparing the context of each said triggered event with context attributes of each member of a community attached to each said event, by a matching engine; and

II) turning said event into an action message, by a distribution engine.

11. The method of claim 6, where said parameters are used for a plurality of rules.

12. The method of claim 6, wherein said targeting uses context sensitive routing.

13. The method of claim 10, further comprising:

I—

generating at least one short message by using at least one pre-defined template;

II—

sending said at least one message to relevant recipients using a communications medium selected from the group of mediums consisting of E-mail, SMS, Fax, instant messaging, Database Transactions, etc.

III—

generating reports containing tabular enriched information using templates;

IV—

creating triggers for message distribution functions; and

V—

distributing said message to at least one recipient.

14. The method of claim 13, further comprising:

VI—

generating at least one transaction in external systems, according to pre-defined definitions.

15. A method for managing data, such that data from a plurality of sources is processed into key events and distributed in real time, comprising the steps of:

i. defining business needs against product scope and data availability in enterprise IT systems;

ii. providing a sizing assessment by estimating complexity and volume of a specific project in terms of hardware, communication and software specifications;

iii. installation of an Active Intelligence platform and applications;

iv. integration of additional necessary system integration elements;

v. creating business models and dimensions, and mapping said business models and dimensions to said/an external IT environment, in order to define business events; and

vi. defining and designing messages and reports, producing alerts and distribution said alerts to end users.

16. A method for integrating real time transactions with corporate databases and transaction systems, such that a minimal demand is placed on data sources, and data is limited to single usage, said minimizing the usage of system resources is achieved by the steps of:

i. selecting a minimal data set from data sources, according to pre-configured rules;

ii. getting each data element only once, even if said element is required to solve a plurality of rules;

iii. selecting data incrementally, such that historical data is not required to be transferred every time said data is needed to participate in a calculation; and

iv. performing virtual joins between different data sources within the databases.

17. A method of filtering raw data from multiple sources and defining key events from the raw data, in real time, comprising the steps of:

i. capturing data from at least one data source, and loading said data into the system;

ii. filtering said data according to filtering rules;

iii. aggregating filtered data, and storing aggregated data in a multi-dimensional Parametric Data Structure according to data parameters and dimensions; and

iv. filtering said aggregated data in real time, upon request.

18. The method of claim 17, wherein said aggregation of data is achieved by defining key events, and processing changes in data for said events against current data status for said events, such that raw data is excluded from the multi-dimensional Parametric Data Structure, and only processed data is stored in said multi-dimensional Parametric Data Structure.

19. The method of claim 17, wherein said filtering said aggregated data in real time is achieved by applying analytical rules to requests from said multi-dimensional Parametric Data Structure.

20. A method for creating a Multi-Dimensional Parametric Data Structure (MDPDS) for effectively storing data, such that the MDPDS holds the minimal set of data necessary to solve pre-defined integrated analytical rules in an efficient way, thus avoiding the need to use a historical database to process transactions, comprising the steps of:

i. dividing the data into descriptive and quantitative elements, said descriptive elements being dimension based, and said quantitative elements being parameter based, as determined by users;

ii. configuring parameters to be accumulative in nature, such that said parameters can hold only updated data, such that said parameters exclude raw data;

iii. storing said dimensions and said parameters in said MDPDS; and

iv. updating the data at any given time.