Data analysis system

• US 9,405,914 B2
• Filed: 05/10/2011
• Issued: 08/02/2016
• Est. Priority Date: 05/10/2011
• Status: Active Grant

• Current Assignee: Ground Transportation Systems Canada Incorporated
• Original Assignee: Thales Canada Incorporated (Thales SA)
• Inventors: Amorim, Aaron
• Primary Examiner(s): SOMERS, MARC S
• Application Number:
  US13/104,196
• Publication Number:
  US 20120290576A1
• Time in Patent Office: 1,911 Days
• Field of Search: 707/600, 707/687, 707/690, 707/694, 707/702, 714/100, 714/1, 714/25, 714/37, 714/47.1, 714/48
• US Class Current: 1/1
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/258 Data format conversion from or to a database
• CPC Class Codes: G06F 21/577 Assessing vulnerabilities and evaluating computer system security

Abstract

A data analysis system for analyzing data from multiple devices has a database service module including a data storage subsystem storing data from collected from different devices. The data is stored in a meta-structure using primitives to classify the data. An analysis engine analyzes the data to determine whether the data defined by the meta-structure meets certain criteria in accordance with a stored set of rules. The system is useful, for example, in the detection of faults in railway infrastructure.

Claims

1. A data analysis apparatus for analyzing data from multiple devices forming part of an infrastructure, comprising:

a database service module including a data storage subsystem storing data collected from disparate devices in a generalized data structure that allows the data collected from all devices to be treated in a common manner, wherein meaning from the data is stripped such that the data is reduced to just a number, and wherein the data from the disparate devices is stored as homogenized data regardless of the form or source of the original data in a database defined by a meta-structure that uses primitives to classify the homogenized data, said primitives comprising device type, device instance, and data type, wherein device type determines the type of device that is the source of the data, device instance identifies the particular device within a device type that is the source of the data, and data type indicates the nature of the stored data;

an analysis engine configured to analyze the data to determine whether the data defined by the meta-structure meets certain criteria in accordance with a stored set of rules; and

a workstation configured to permit the user to view and analyze the homogenized data in a common manner without regard to the source thereof, wherein the meta-structure separates and categorizes the data such that all data is handled identically and such that the data analysis apparatus is prevented from knowing what data was collected.

2. A data analysis apparatus as claimed in claim 1, wherein the device type comprises the status of the device constituting the source of the data.

3. A data analysis apparatus as claimed in claim 1, wherein the rules are stored in a database forming part of the database service module.

4. A data analysis apparatus as claimed in claim 1, wherein the database service module further comprises a data collector interface and a data management processing module for receiving data from multiple devices via the data collector and storing the data in the data storage subsystem in accordance with said meta-structure.

5. A data analysis apparatus as claimed in claim 4, further comprising a data collection service module comprising a data collection manager for managing collection of data from the multiple devices through a device interface and communicating with the database service module through a database service interface.

6. A data analysis apparatus as claimed in claim 5, wherein the data collection service module includes a data staging module for temporarily staging data prior to forwarding to the database service module.

7. A data analysis apparatus as claimed in claim 1, wherein the data storage subsystem is Structured Query Language (SQL) database.

8. A data analysis apparatus as claimed in claim 1, wherein the analysis engine includes an analysis engine manager for determining which rules are run, in what order, against which types of device or device instance, and the frequency at which the rules are processed.

9. A data analysis apparatus as claimed in claim 8, wherein the analysis engine manager further comprises an analysis queue module for processing rules assigned to it by the analysis engine manager.

10. A data analysis apparatus as claimed in claim 9, wherein the analysis queue module is configured to issue an alert notification to the processed data meeting certain criteria.

11. A data analysis apparatus as claimed in claim 10, wherein the analysis queue module is configured to trigger the processing of other rules in response to the processed data meeting certain criteria.

12. A data analysis apparatus as claimed in claim 10, wherein the analysis queue module is configured to trigger on-demand data collection in response to the data processed under a certain rule meeting certain criteria.

13. A data analysis apparatus as claimed in claim 12, wherein the queue analysis module is configured to trigger processing of the on-demand data at a higher frequency than the data processed under said certain rule.

14. A data analysis apparatus as claimed in claim 1, wherein the workstation is configured to permit the user to run specific rules against particular data stored by the database service module.

15. A computer-implemented method of analyzing data from multiple devices forming part of an infrastructure, comprising:

collecting the data from disparate devices on an on-going basis;

storing the collected data in a data storage subsystem of a database service module in a generalized data structure that allows the data collected from all devices to be treated in a common manner, wherein meaning from the data is stripped such that the data is reduced to just a number, and wherein the data from the disparate devices is stored as homogenized data in a cohesive manner regardless of the form or source of the original data in a database defined by a meta-structure that uses primitives to classify the homogenized data, said primitives comprising device type, device instance, and data type, wherein device type determines the type of device that is the source of the data, device instance identifies the particular device within, a device type that is the source of the data, and data type indicates the nature of the stored data;

analyzing the data to determine whether the data defined by the meta-structure meets certain criteria in accordance with a stored set of rules; and

accepting user instructions through a workstation permitting the user to analyze the homogenized data, wherein the meta-structure separates and categorizes the data such that all data is handled identically and such that the data analysis apparatus is prevented from knowing what data was collected.

16. A method as claimed in claim 15, wherein the device type comprises the status of the device constituting the source of the data.

17. A method as claimed in claim 15, further comprising storing the rules in a database forming part of the database service module.

18. A method as claimed in claim 15, wherein the analysis engine determines which rules are run, in what order, against which types of device or device instance, and the frequency at which the rules are processed.

19. A method as claimed in claim 18, wherein the analysis engine module issues an alert notification to the processed data meeting certain criteria.

20. A method as claimed in claim 19, wherein the analysis engine triggers the processing of other rules in response to the processed data meeting certain criteria.

21. A method as claimed in claim 19, wherein the analysis engine triggers on-demand data collection in response to the data processed under a certain rule meeting certain criteria.

22. A method as claimed in claim 21, wherein the analysis engine triggers processing of the on-demand data at a higher frequency than the data processed under said certain rule.

23. A method as claimed in claim 15, wherein the collected data is temporarily staging data prior to forwarding to the database service module.

24. A method as claimed in claim 15, further comprising supplying the data stored in the database service module for viewing by a user.

25. A method as claimed in claim 15, further comprising configuring rules to be used by the analysis engine through a user interface.

26. A method as claimed in claim 25, further comprising commanding the running of specific rules against particular data stored by the database service module through a user interface.

27. A non-transitory computer readable medium storing instructions for analyzing data from multiple devices forming part of an infrastructure, said instructions when implemented on a computer providing:

a data collector for collecting data from different devices on an on-going basis;

a database service module for storing the data in a generalized data structure that allows the data collected from all devices to be treated in a common manner, wherein meaning from the data is stripped such that the data is reduced to just a number, the data from the disparate devices being stored as homogenized data in a cohesive manner regardless of the form or source of the original data in a database defined by a meta-structure using primitives to classify the data, said primitives comprising device type, device instance, and data type, wherein device type determines the type of device that is the source of the data, device instance identifies the particular device within a device type that is the source of the data, and data type indicates the nature of the stored data;

an analysis engine configured to analyze the data to determine whether the data defined by the meta-structure meets certain criteria in accordance with a stored set of rules; and

a workstation interface for interfacing with a workstation to provide a user interface to permit a user to analyze the homogenized data, wherein the meta-structure separates and categorizes the data such that all data is handled identically and such that the data analysis apparatus is prevented from knowing what data was collected.

28. A non-transitory computer readable medium as claimed in claim 27, wherein the workstation is configured to permit the user to create and modify the stored set of rules.

29. A non-transitory computer readable medium as claimed in claim 28, wherein the workstation is configured to permit the user to run particular rules stored in the system.