COMPUTING A HIERARCHICAL PATTERN QUERY FROM ANOTHER HIERARCHICAL PATTERN QUERY

• US 20130103638A1
• Filed: 10/25/2011
• Published: 04/25/2013
• Est. Priority Date: 10/25/2011
• Status: Abandoned Application

• Current Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Gupta, Chetan Kumar, Mehta, Abhay, Liu, Mo, Rundensteiner, Elke, Wang, Song
• Application Number:
  US13/280,342
• Publication Number:
  US 20130103638A1
• Time in Patent Office: Days
• US Class Current: 707/603
• CPC Class Codes: G06F 16/24542 Plan optimisation
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP

Abstract

A method analyzes event patterns in multi-dimensional data and based on this analysis of the event patterns computes a hierarchical event pattern query from another hierarchical event pattern query. The method executes the hierarchical event pattern query on the multi-dimensional data.

Claims

1. ) A method executed by a computer, comprising:

analyzing, by the computer, event patterns in multi-dimensional data;

computing, by the computer and based on analysis of the event patterns, a hierarchical event pattern query from another hierarchical event pattern query; and

executing, by the computer, the hierarchical event pattern query on the multi-dimensional data.

2. ) The method of claim 1 further comprising, utilizing an E-Cube to integrate complex event processing (CEP) and online analytical processing (OLAP) techniques to provide the analysis of the event patterns.

3. ) The method of claim 1 further comprising, determining a processing cost to execute the hierarchical event pattern query and the another hierarchical event pattern query.

4. ) The method of claim 1 further comprising, reusing results from an upper level query to compute a lower level query by considering only pattern changes.

5. ) The method of claim 1 further comprising, reusing results from an upper level query to compute a lower level query by considering only concept changes.

6. ) A non-transitory computer readable storage medium comprising instructions that when executed causes a computer system to:

analyze multi-dimensional streaming data to determine multiple hierarchical pattern queries that exist a different abstraction levels;

compute, with an E-Cube, one hierarchical pattern query from another hierarchical pattern query of the multiple hierarchical pattern queries; and

execute the hierarchical event pattern query on the multi-dimensional streaming data.

7. ) The non-transitory computer readable storage medium of claim 6 including instructions to further cause the computer system to:

leverage, with the E-Cube, online analytical processing (OLAP) techniques to enable navigation of the multi-dimensional streaming data at different abstraction levels while simultaneously supporting real-time multi-dimensional sequence data analysis.

8. ) The non-transitory computer readable storage medium of claim 6 including instructions to further cause the computer system to:

calculate a cost to compute a child q_i from a parent q_j given q_i and q_j in an E-Cube hierarchy with simultaneous concept and pattern changes, where q_i and q_j are pattern queries.

9. ) The non-transitory computer readable storage medium of claim 6 including instructions to further cause the computer system to:

identify, by the E-Cube, concept and pattern relationships between the multiple hierarchical pattern queries in order to reduce redundant computations among the multiple hierarchical pattern queries.

10. ) The non-transitory computer readable storage medium of claim 6 including instructions to further cause the computer system to:

roll up one of the multiple hierarchical pattern queries into another of the multiple hierarchical pattern queries.

11. ) A computer system, comprising:

a memory storing instructions; and

a processor executing the instructions to analyze multi-dimensional data to determine multiple hierarchical pattern queries, use an E-Cube to compute one hierarchical pattern query from another hierarchical pattern query of the multiple hierarchical pattern queries, and execute the hierarchical event pattern query on the multi-dimensional data.

12. ) The computer system of claim 11 wherein the processor further executes the instructions to:

given queries q_i and q_j in a pattern hierarchy and results of q_j, compute the q_i by reusing the results of q_j and unioning the results of q_j with delta results not captured by the q_j.

13. ) The computer system of claim 11 wherein the processor further executes the instructions to:

given queries q_i and q_j in a concept hierarchy and results of q_j, compute the q_i by reusing the results of q_j and unioning the results of q_j with delta results not captured by the q_j.

14. ) The computer system of claim 11, wherein the processor further executes the instructions to:

compute a lower level query, return results from the lower level query to an upper level query in order to compute the upper level query by reusing the results from the lower level query.

15. ) The computer system of claim 11 wherein the processor further executes the instructions to evaluate each of the multiple hierarchical pattern queries by one of computing each query independently by stack-based join and computing each query from one of its descendants.

16. ) The computer system of claim 11 wherein the processor further executes the instructions to:

given q_i and q_j in an E-Cube hierarchy with simultaneous concept and pattern changes, calculate an intermediate query with either only concept or pattern changes from q_j, where q_i and q_j are pattern queries.