Smart integration engine and metadata-oriented architecture for automatic EII and business integration

• US 7,580,946 B2
• Filed: 08/09/2007
• Issued: 08/25/2009
• Est. Priority Date: 08/11/2006
• Status: Expired due to Fees

• Current Assignee: Majed Amer, Munir Badir, Rabia Mansour, Talea Sarsour
• Original Assignee: Bizweel Limited
• Inventors: Mansour, Rabia, Sarsour, Talea, Amer, Majed, Badir, Munir, Badir, Naila
• Primary Examiner(s): Fleurantin; Jean B.
• Assistant Examiner(s): MYINT, DENNIS Y
• Application Number:
  US11/836,164
• Publication Number:
  US 20080082569A1
• Time in Patent Office: 747 Days
• Field of Search: 707/10, 707/102
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

The present invention discloses systems and methods for automating the EII, using a smart integration engine based on metadata. On-line execution (i.e. data access, retrieval, or update) is automated by integrating heterogeneous data sources via a centralized smart engine based on metadata of all data sources managed in a metadata repository. The data-source assets are mapped to business metadata (terminology) giving programmers the ability to use business terms, and overcome technical terms. IT departments can use the business-level terms for easy and fast programming of all services “at the business level”. The integration is performed by the engine (via pre-configuration) automatically, dynamically, and on-line, regardless of topology or technology changes, without user or administrator intervention. MDOA is a high-level concept in which the metadata maps the technical low-level terms to business high-level terms. MDOA is used for seamless integration of a fully-distributed organization with many data sources and technologies.

Claims

1. A method for automatically generating data-service-execution flows, based on metadata objects, for executing data services from heterogeneous data sources, the method comprising the steps of:

(a) providing a smart integration engine, having at least one smart integration server with a solution resolver residing therein, configured to receive dynamic service schema (DSS) requests, said DSS requests each having a DSS metadata input and a DSS metadata output, for executing the data services from the heterogeneous data sources, wherein said solution resolver has access to data assets stored in a metadata repository; and

(b) generating solution flows of said DSS requests based on metadata criteria and on said data assets, each said solution flow utilizes a plurality of nodes that are inter-related such that a node output of a preceding node serves as a node input of a subsequent node for producing said DSS metadata output, a portion of said plurality of nodes to be executed subsequent to said step of generating said solution flows according to an optimized node sequence determined by said solution resolver solely during said step of generating said solution flows.

2. The method of claim 1, the method further comprising the step of:

(c) optimizing said solution flows based on client-defined criteria, on said metadata criteria, and on said data assets, said step of optimizing based on at least one optimization parameter selected from the group consisting of;

a data quality, a node quantity, a solution-execution response time, and a data-source quantity.

3. The method of claim 1, the method further comprising the step of:

(c) aggregating common data-source requests into a request bundle to be sent for execution from a specific data source.

4. The method of claim 1, the method further comprising the step of:

(c) sending internal requests, based on said solution flows, to smart integration agents for executing data from the data sources.

5. The method of claim 4, the method further comprising the steps of:

(d) discovering new data sources using said agents; and

(e) modifying said solution flows based on said new data sources.

6. The method of claim 4, wherein said agents include sensors for monitoring status information of the data sources.

7. The method of claim 6, the method further comprising the step of:

(d) modifying said solution flows based on said status information.

8. The method of claim 6, wherein said status information includes at least one item selected from the group consisting of:

a data-source availability, a data-source validity, and an execution performance.

9. The method of claim 4, wherein said agents include smart integration workers for operationally connecting to the data sources.

10. The method of claim 9, wherein said workers include adapters for facilitating an operational connection to the data sources.

11. The method of claim 1, the method further comprising the step of:

(c) recovering from incomplete-request faults during execution, upon said incomplete-request faults occurring, by;

(i) generating sub-flows of alternate data sources for incomplete-request elements of said incomplete-request faults; and

(ii) executing an optimized sub-flow.

12. The method of claim 1, the method further comprising the step of:

(c) storing said DSS requests and user-defined requests and previously executed requests in a schema store for faster execution via alias calls and/or web services.

13. The method of claim 1, the method further comprising the steps of:

(c) before generating said solution flows, verifying said DSS requests are allowed to be executed by a client application; and

(d) upon failing to obtain verification, ignoring said DSS requests in said step of generating said solution flows.

14. The method of claim 1, the method further comprising the step of:

(c) monitoring said DSS requests to obtain execution information.

15. The method of claim 14, the method further comprising the step of:

(d) using said execution information in order to determine a subsequent optimized node sequence for a subsequent DSS request.

16. The method of claim 14, wherein said execution information includes at least one item selected from the group consisting of:

an execution time, an execution duration, and an execution status.

17. The method of claim 14, the method further comprising the step of:

(d) auditing said execution information for use in obtaining a history rollback and/or to search said execution information.

18. The method of claim 1, the method further comprising the step of:

(c) triggering an operation upon execution of a designated request.

19. The method of claim 1, the method further comprising the step of:

(c) automatically updating the data sources upon executing update DSS requests.

20. The method of claim 1, the method further comprising the step of:

(c) modeling execution of the data services using said solution flows, said modeling being performed using mock metadata and mock components associated with mock data sources in order to evaluate said solution flows.

21. The method of claim 1, the method further comprising the step of:

(c) aggregating returned data values from all the data sources into a single DSS response according to a DSS metadata output format.

22. The method of claim 1, wherein said metadata criteria include at least one criterion selected from the group consisting of:

a priority rule, a data-source availability, a data quality, a number of request steps, a data-source validity, and an execution performance.

23. The method of claim 1, wherein said metadata repository includes technical metadata and business metadata, wherein said technical metadata provide data-source information for executing said DSS requests, and wherein said business metadata represent said technical metadata in business terms.

24. The method of claim 23, wherein said technical metadata and business metadata include metadata selected from the group consisting of:

metadata objects, metadata components, metadata fields, compound metadata objects, organizational information, site information, data-source information, metadata object-metadata field (MDO-MDF) association information, metadata object-metadata object (MDO-MDO) association information, metadata field-metadata field (MDF-MDF) association information, association transformation information, security, permission privilege information, policy information, validation information, and data-schema alias information.

25. The method of claim 1, wherein said optimized node sequence is obtained using a backward-chaining algorithm.

26. The method of claim 1, wherein said solution flows exclude previously-executed nodes from previous DSS requests.

27. A computer-readable storage medium having computer-readable code embodied on the computer-readable storage medium, the computer-readable code comprising:

(a) smart-integration-engine (SIE) program code for enabling a smart integration engine to execute a solution flow, said solution flow based on metadata objects;

(b) smart-integration-server (SIS) program code for enabling a smart integration server to receive at least one dynamic service schema (DSS) request, said DSS requests each having a DSS metadata input and a DSS metadata output, for executing data services from heterogeneous data sources;

(c) metadata-repository (MDR) program code for enabling a metadata repository to have at least one data asset that serves as an input for said solution flow; and

(d) solution-resolver (SR) program code for enabling a solution resolver to generate said solution flow of said at least one DSS request based on metadata criteria and said at least one data asset, said solution flow utilizes a plurality of nodes that are inter-related such that a node output of a preceding node serves as a node input of a subsequent node for producing said DSS metadata output, a portion of said plurality of nodes to be executed subsequent to generate of said solution flow according to an optimized node sequence determined by said solution resolver solely during said generation of said solution flow.