CLUSTERING ANALYTIC FUNCTIONS

• US 20090248722A1
• Filed: 03/27/2008
• Published: 10/01/2009
• Est. Priority Date: 03/27/2008
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Kamath, Monty, Pair, Stephen, Pikovsky, Alexander, McKeown, Robert Joseph, Pennell, David Joel SR.
• Application Number:
  US12/056,890
• Publication Number:
  US 20090248722A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: H04L 41/142 using statistical or mathematical methods

Abstract

A method, system, and computer usable program product for clustering analytic functions are provided in the illustrative embodiments. Information about a set of analytic function instances is received. Information about a set of time series is received. A subset of time series may be a set of input time series to an analytic function instance in the set of analytic function instances. An analytics clustering rule is applied to the information about the set of analytic function instances and the information about the set of time series. A subset of time series is clustered as a group in response to applying the analytics clustering rule. An analytics clustering rule may determine whether all time series in the set of input time series to an analytic function instance are members of a group, and group an output time series of the analytic function instance in the group if all time series in the set of input time series are members of the group.

Claims

1. A computer implemented method for clustering analytic functions, the computer implemented method comprising:

receiving information about a set of analytic function instances;

receiving information about a set of time series, the set of time series comprising data produced by a set of physical components in an environment, a first subset of the set of time series being a set of input time series received over a data network in an analytic function instance in the set of analytic function instances;

applying an analytics clustering rule to the information about the set of analytic function instances and the information about the set of time series; and

clustering a second subset of time series in a group responsive to applying the analytics clustering rule.

2. The computer implemented method of claim 1, wherein receiving the information about the set of analytic function instances further comprises:

receiving an information about an input binding of the analytic function instance;

receiving information about a temporal semantics of the analytic function instance; and

receiving information about an output time series of the analytic function instance, wherein the output time series comprises data produced by the analytic function instance.

3. The computer implemented method of claim 1, wherein receiving the information about the set of time series further comprises:

receiving information about a source of a time series in the set of time series, the information about the source including information about a location of the source, wherein the source corresponds to a physical component of the environment; and

receiving information about one of (i) a periodicity and (ii) a delay of the time series in the set of time series.

4. The computer implemented method of claim 3, wherein an output time series of the analytic function instance is a time series in the set of time series, and wherein the output time series comprises data produced by the analytic function instance.

5. The computer implemented method of claim 1, further comprising:

analyzing a dependency between a first analytic function instance and a second analytic function instance in the set of analytic function instances.

6. The computer implemented method of claim 1, wherein the analytics clustering rule comprises:

grouping a plurality of time series from a source into a group, wherein the source corresponds to a physical component of the environment.

7. The computer implemented method of claim 1, wherein the analytics clustering rule comprises:

determining, forming a grouping determination, whether all time series in the set of input time series are members of a group; and

grouping, responsive to the grouping determination being true, an output time series of the analytic function instance in the group, wherein the output time series comprises data produced by the analytic function instance.

8. The computer implemented method of claim 1, wherein the analytics clustering rule comprises:

determining, forming a grouping determination, whether all time series in the set of input time series are members of a group; and

grouping, responsive to the grouping determination being false, an output time series of the analytic function instance in a second group, wherein all members of the second group share a common input group configuration, and wherein the output time series comprises data produced by the analytic function instance.

9. A computer implemented method for clustering analytic functions, the computer implemented method comprising:

receiving information about a set of analytic function instances;

receiving information about a set of time series, the set of time series comprising data produced by a set of physical components in an environment, a physical component being a data source, a time series in the set of time series being associated with a data source in a set of data sources, and a first subset of the set of time series being a set of input time series received over a data network in an analytic function instance in the set of analytic function instances;

applying an analytics clustering rule to the information about the set of analytic function instances and the information about the set of time series; and

co-locating, in a data processing system, the analytic function instance and a subset of data sources in the set of data sources responsive to applying the analytics clustering rule.

10. The computer implemented method of claim 9, wherein receiving the information about the set of analytic function instances further comprises:

receiving an information about an input binding of the analytic function instance;

receiving information about a temporal semantics of the analytic function instance; and

receiving information about an output time series of the analytic function instance, wherein the output time series comprises data produced by the analytic function instance; and

wherein receiving the information about the set of time series further comprises;

receiving information about a source of a time series in the set of time series, the information about the source including information about a location of the source, wherein the source corresponds to a physical component of the environment; and

receiving information about one of (i) a periodicity and (ii) a delay of the time series in the set of time series.

11. The computer implemented method of claim 9, wherein a second analytic function instance in the set of analytic function instances corresponds to a data source in the set of data sources, and wherein an output time series of the second analytic function instance is a time series in the set of time series, and wherein the output time series comprises data produced by the analytic function instance.

12. The computer implemented method of claim 11, further comprising:

analyzing a dependency between the analytic function instance and the second analytic function instance.

13. The computer implemented method of claim 9, wherein the analytics clustering rule comprises:

determining, forming a co-location determination, if co-locating the analytic function instance and the subset of data sources reduces a data traffic in the data network; and

grouping the analytic function instance and the subset of data sources in a group, responsive to the co-location determination being true.

14. A computer usable program product comprising a computer usable medium including computer usable code for clustering analytic functions, the computer usable code comprising:

computer usable code for receiving information about a set of analytic function instances;

computer usable code for receiving information about a set of time series, the set of time series comprising data produced by a set of physical components in an environment, a first subset of the set of time series being a set of input time series received over a data network in an analytic function instance in the set of analytic function instances;

computer usable code for analyzing a dependency between the analytic function instance and a second analytic function instance in the set of analytic function instances;

computer usable code for applying an analytics clustering rule to the information about the set of analytic function instances and the information about the set of time series; and

computer usable code for clustering a second subset of time series in a group responsive to applying the analytics clustering rule.

15. The computer usable program product of claim 14, wherein the computer usable code for receiving the information about the set of analytic function instances further comprises:

computer usable code for receiving an information about an input binding of the analytic function instance;

computer usable code for receiving information about a temporal semantics of the analytic function instance; and

computer usable code for receiving information about an output time series of the analytic function instance, wherein the output time series comprises data produced by the analytic function instance; and

wherein the computer usable code for receiving the information about the set of time series further comprises;

computer usable code for receiving information about a source of a time series in the set of time series, the information about the source including information about a location of the source, wherein the source corresponds to a physical component of the environment; and

computer usable code for receiving information about one of (i) a periodicity and (ii) a delay of the time series in the set of time series.

16. The computer usable program product of claim 14, wherein an output time series of the analytic function instance is a time series in the set of time series, and wherein the output time series comprises data produced by the analytic function instance.

17. The computer usable program product of claim 14, wherein the analytics clustering rule comprises:

computer usable code for grouping a plurality of time series from a source into a group, wherein the source corresponds to a physical component of the environment.

18. The computer usable program product of claim 14, wherein the analytics clustering rule comprises:

computer usable code for determining, forming a grouping determination, whether all time series in the set of input time series are members of a group; and

computer usable code for grouping, responsive to the grouping determination being true, an output time series of the analytic function instance in the group, wherein the output time series comprises data produced by the analytic function instance.

19. The computer usable program product of claim 14, wherein the analytics clustering rule comprises:

computer usable code for determining, forming a grouping determination, whether all time series in the set of input time series are members of a group; and

computer usable code for grouping, responsive to the grouping determination being false, an output time series of the analytic function instance in a second group, wherein all members of the second group share a common input group configuration, and wherein the output time series comprises data produced by the analytic function instance.

20. A data processing system for clustering analytic functions, the data processing system comprising:

a storage device including a storage medium, wherein the storage device stores computer usable program code; and

a processor, wherein the processor executes the computer usable program code, and wherein the computer usable program code comprises;

computer usable code for receiving information about a set of analytic function instances;

computer usable code for receiving information about a set of time series, the set of time series comprising data produced by a set of physical components in an environment, a first subset of the set of time series being a set of input time series received over a data network in an analytic function instance in the set of analytic function instances;

computer usable code for analyzing a dependency between the analytic function instance and a second analytic function instance in the set of analytic function instances;

computer usable code for applying an analytics clustering rule to the information about the set of analytic function instances and the information about the set of time series; and

computer usable code for clustering a second subset of time series in a group responsive to applying the analytics clustering rule.

21. The computer usable program product of claim 20, wherein the computer usable code for receiving the information about the set of analytic function instances further comprises:

computer usable code for receiving an information about an input binding of the analytic function instance;

computer usable code for receiving information about a temporal semantics of the analytic function instance; and

computer usable code for receiving information about an output time series of the analytic function instance, wherein the output time series comprises data produced by the analytic function instance; and

wherein the computer usable code for receiving the information about the set of time series further comprises;

computer usable code for receiving information about a source of a time series in the set of time series, the information about the source including information about a location of the source, wherein the source corresponds to a physical component of the environment; and

computer usable code for receiving information about one of (i) a periodicity and (ii) a delay of the time series in the set of time series.

22. The computer usable program product of claim 20, wherein an output time series of the analytic function instance is a time series in the set of time series, and wherein the output time series comprises data produced by the analytic function instance.

23. The computer usable program product of claim 20, wherein the analytics clustering rule comprises:

computer usable code for grouping a plurality of time series from a source into a group, wherein the source corresponds to a physical component of the environment.

24. The computer usable program product of claim 20, wherein the analytics clustering rule comprises:

computer usable code for determining, forming a grouping determination, whether all time series in the set of input time series are members of a group; and

computer usable code for grouping, responsive to the grouping determination being true, an output time series of the analytic function instance in the group, wherein the output time series comprises data produced by the analytic function instance.

25. The computer usable program product of claim 20, wherein the analytics clustering rule comprises:

computer usable code for determining, forming a grouping determination, whether all time series in the set of input time series are members of a group; and

computer usable code for grouping, responsive to the grouping determination being false, an output time series of the analytic function instance in a second group, wherein all members of the second group share a common input group configuration, and wherein the output time series comprises data produced by the analytic function instance.