MODELING MULTI-DIMENSIONAL SEQUENCE DATA OVER STREAMS

• US 20100280857A1
• Filed: 04/30/2009
• Published: 11/04/2010
• Est. Priority Date: 04/30/2009
• Status: Active Grant

• Current Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Original Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Inventors: Liu, Mo, Ari, Ismail, Mehta, Abhay, Gupta, Chetan Kumar, Wang, Song
• Granted Patent: US 9,767,427 B2
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

One embodiment is a method that builds a model of multi-dimensional sequence data in real-time with cuboids that aggregate the multi-dimensional sequence data over both patterns and dimensions. The model provides search results for a query.

Claims

1. ) A method, comprising:

building, with a computer, a model of multi-dimensional sequence data in real-time with cuboids that aggregate the multi-dimensional sequence data over both patterns and dimensions; and

providing, with a computer, search results for a query in real-time.

2. ) The method of claim 1, wherein the multi-dimensional sequence data is a stream of radio-frequency identification (RFID) data.

3. ) The method of claim 1 further comprising, integrating both complex event processing (CEP) which includes detection of a sequence or a set of events that are related spatially or temporarily and online analytical processing (OLAP) techniques to provide pattern analysis functionalities.

4. ) The method of claim 3, wherein the pattern analysis functionalities include negation and querying over multi-dimensional spatio-temporal stream data.

5. ) The method of claim 1 further comprising, using online analytical processing (OLAP) techniques to analyze event patterns in real-time for the multi-dimensional sequence data.

6. ) The method of claim 1 further comprising, displaying search results on a display of a computer.

7. ) The method of claim 1, wherein the query requests tracking of radio-frequency identification (RFID) data for supply chain management of products using E-Cube.

8. ) The method of claim 1, wherein the model is an E-Cube that is a collection of cuboids such that a base cuboid corresponds to a most general pattern in a pattern hierarchy, and other cuboids are obtained by rolling up an event pattern or categories of an attribute.

9. ) A tangible computer readable storage medium having instructions for causing a computer to execute a method, comprising:

modeling multi-dimensional sequence data in real-time by building an E-Cube that integrates both complex event processing (CEP) and online analytical processing (OLAP) techniques to perform pattern analysis over event streams.

10. ) The tangible computer readable storage medium of claim 9, wherein the model is an E-Cube that is a collection of cuboids such that a base cuboid corresponds to a most general pattern in a pattern hierarchy, and other cuboids are obtained by rolling up an event pattern or categories of an attribute.

11. ) The tangible computer readable storage medium of claim 9, wherein the multi-dimensional sequence data is stream data from a sensor or radio-frequency identification (RFID).

12. ) The tangible computer readable storage medium of claim 9 further comprising, detecting patterns in the multi-dimensional sequence data.

13. ) The tangible computer readable storage medium of claim 9 further comprising, querying the E-Cube to track movement and status of products in a supply chain.

14. ) A computer system, comprising:

a database that stores an E-Cube model of multi-dimensional sequence data in real-time with cuboids that aggregate the multi-dimensional sequence data over both patterns and dimensions; and

a computer that processes a query and provides results for the query in real time.

15. ) The computer system of claim 14, wherein patterns in the multi-dimensional sequence data are rolled up and aggregated to report results.

16. ) The computer system of claim 14, wherein the E-Cube model that is a collection of cuboids such that a base cuboid corresponds to a most general pattern in a pattern hierarchy, and other cuboids are obtained by rolling up an event pattern or categories of an attribute.

17. ) The computer system of claim 14, wherein the computer processes the query over multi-dimensional stream data in real time.

18. ) The computer system of claim 14, wherein the E-Cube model integrates both complex event processing (CEP) and online analytical processing (OLAP) techniques to provide pattern analysis functionalities for queries to the database.

19. ) The computer system of claim 14, the E-Cube model is a collection of cuboids with each cuboid having one pattern and multiple attribute dimensions and patterns and attributes are aggregated.

20. ) The computer system of claim 14, the E-Cube model is a collection of cuboids and all event instances in one event cuboid have a same pattern and a same aggregated attribute level.