System and method for analyzing and utilizing data, by executing complex analytical models in real time
First Claim
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.
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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.
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Citations
20 Claims
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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:
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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. - View Dependent Claims (2, 3, 4, 5)
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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:
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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. - View Dependent Claims (7, 8, 9, 10, 11, 12, 13, 14)
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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:
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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.
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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:
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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.
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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:
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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. - View Dependent Claims (18, 19)
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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:
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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.
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Specification