IP Library Granted Patent US 9,704,136
Granted Patent B2
US 9,704,136 · App. 13/755,836 · Granted Jul 11, 2017

Identifying subsets of signifiers to analyze

Inventors: Mehmet Kivanc Ozonat (San Jose, CA); Claudio Bartolini (Palo Alto, CA)
Assignee: Hewlett Packard Enterprise Development LP
G06Q10/101
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Quick Facts
Patent No.
US 9,704,136
App. No.
13/755,836
Filed
Jan 31, 2013
Granted
Jul 11, 2017
Kind
B2
Art Unit
2459
USPC
709/224
Abstract

Identifying a subset of signifiers to analyze can include determining a set of distance metrics between a first signifier and each of a plurality of second signifiers, identifying a subset of the plurality of second signifiers to analyze based on the set of distance metrics using a computing device, and determining a relation between the subset of the plurality of second signifiers and the first signifier based a subset of the set of distance metrics.

Claims (46)

1. A method comprising:

determining a set of distance metrics between a first signifier and each of a plurality of second signifiers acquired from unstructured content residing on different domains in an enterprise communications network;

identifying a subset of the plurality of second signifiers to analyze based on the set of distance metrics using a computing device, wherein identifying the subset of the second signifiers comprises:

utilizing a data tree model;

growing a number of trees of relevant signifiers;

splitting the number of trees into a number of subtrees; and

pruning the number of subtrees to include the subset of the second signifiers to analyze with a cost function that is an increasing convex function satisfying Jensen's inequality;

analyzing just the subset of the second signifiers of the existing signifiers, including determining a relation between the second subset of the existing signifiers and the first signifier based on a subset of the plurality of distance metrics, wherein analysis of just the subset of the second signifiers reduces analysis time in determining the relation of the first signifier; and

identifying content in the enterprise communication network based upon the determining of the relation between the subset of the plurality of second signifiers and the first signifier.

2. The method of claim 1 , wherein determining the relation between the subset of the plurality of second signifiers and the first signifier comprises calculating an average of the distance metric between the first signifier and each of the subset of the second signifiers.

3. The method of claim 1 , wherein pruning the number of subtrees comprises determining to terminate pruning based on a ratio of a cost function.

4. The method of claim 1 , comprising crawling an enterprise network to identify the first signifier.

5. A non-transitory computer-readable medium storing a set of instructions executable by a processing resource, wherein the set of instructions can executed by the processing resource to:

determine a set of distance metrics between a new signifier and each of a plurality of existing signifiers acquired from unstructured content residing on different domains in an enterprise communications network;

determine a cost function to analyze a relation between the plurality of existing signifiers and the new signifier;

identify a first subset of the existing signifiers utilizing a data tree model;

identify a second subset of the existing signifiers to analyze based on the set of distance metrics and the cost function, wherein the second subset is a subset of the first subset, wherein identifying the subset of the second signifiers comprises:

utilizing a data tree model;

growing a number of trees of relevant signifiers;

splitting the number of trees into a number of subtrees; and

pruning the number of subtrees to include the subset of the second signifiers to analyze with a cost function that is an increasing convex function satisfying Jensen's inequality; and

analyze just the subset of the second signifiers of the existing signifiers, including determining a relation between the second subset of the existing signifiers and the new signifier based on a subset of the plurality of distance metrics, wherein analysis of just the subset of the second signifiers reduces analysis time in determining the relation of the new signifier;

identify content in an enterprise communication network based upon the determining of the relation between the subset of the plurality of existing signifiers and the new signifier.

6. The medium of claim 5 , wherein the second subset of the existing signifiers comprises a cluster of signifiers including the identified new signifier.

7. The medium of claim 5 , wherein the instructions executable to identify the first subset comprise instructions executable to:

utilize the data tree model to split a single node data tree into subtrees; and

compare subtrees to one another utilizing a Lloyd model.

8. The medium of claim 5 , wherein the instructions executable to identify the second subset comprise instructions executable to utilize the data tree model to prune the subtrees of irrelevant content utilizing a Breiman, Friedman, Olshen, and Stone (BFOS) model.

9. The medium of claim 5 , wherein the instructions executable to determine a relation between the second subset of the plurality of existing signifiers and the new signifier comprise instructions executable to approximate a measurement of a relation of related phrases in a cluster, wherein the cluster includes the second subset of the plurality of existing signifiers and the new signifier.

10. A system for identifying a subset of signifiers to analyze comprising:

a processing resource; and

a memory resource communicatively coupled to the processing resource containing instructions executable by the processing resource to:

identify a new signifier associated with content on an enterprise network;

determine a set of distance metrics between the new signifier and each of a plurality of existing signifiers acquired from unstructured content residing on different domains in an enterprise communications network;

identify a cost function to analyze a relation between the plurality of existing signifiers and the new signifier;

identify a subset of the plurality of existing signifiers to analyze based on the set of distance metrics and the cost function utilizing a data tree model;

analyze just the subset of the existing signifiers, including determining a relation between the subset of the plurality of existing signifiers and the new signifier based on a distance metric between each, wherein analysis of just the subset of the existing signifiers reduces analysis time in determining the relation of the new signifier; and

identify content in an enterprise communication network based upon the determining of the relation between the subset of the plurality of existing signifiers and the new signifier, wherein the instructions executable to identify the cost function comprise instructions to identify a first component of the cost function that is minimized using a Lloyd function and a second component of the cost function is an increasing convex function that satisfies Jensen's inequality.

11. The system of claim 10 , wherein the instructions executable to identify the subset of the plurality of existing signifiers to analyze comprise instructions to group the plurality of existing signifiers and the new signifier into a plurality of clusters based on the cost function and the set of distance metrics utilizing a data tree model.

12. The system of claim 10 , wherein the instructions executable to identify the subset of the plurality of existing signifiers to analyze comprise instructions to identify a terminal node in a data tree structure that the new signifier belongs to.

13. The system of claim 10 , wherein the instructions executable to determine the relations between the subset of the plurality of existing signifiers and the new signifier comprise instructions to approximate the relation of the new signifier with the existing signifiers in the subset.

14. The method of claim 1 , wherein the set of distance metrics comprise a frequency of co-occurrence of the first signifier and the second signifier.

15. The method of claim 1 , wherein the set of distance metrics comprise a metric based upon a proximity of the first signifier to the second signifier.

16. The method of claim 1 , wherein the content comprises unstructured content.

17. The method of claim 1 , wherein the set of distance metrics comprises a metric calculated by calculating a weighted Euclidean distance including constructing in an-dimensional feature vector, the Euclidean distance comprising an ordinary distance between two points.

18. The medium of claim 5 , wherein the enterprise communication network comprises unstructured content from which the content is identified and wherein the set of distance metrics comprise a metric selected from a group of metrics consisting of: a frequency of co-occurrence of each of the existing signifiers and the new signifier; and a proximity of each of the existing signifiers to the new signifier.

Assignments (8)
RELEASE OF SECURITY INTEREST REEL/FRAME 044183/0577 Recorded Feb 2, 2023
From: JPMORGAN CHASE BANK, N.A.
To: MICRO FOCUS LLC (F/K/A ENTIT SOFTWARE LLC)
Reel/Frame 063560/0001 →
RELEASE OF SECURITY INTEREST REEL/FRAME 044183/0718 Recorded Feb 2, 2023
From: JPMORGAN CHASE BANK, N.A.
To: MICRO FOCUS LLC (F/K/A ENTIT SOFTWARE LLC); BORLAND SOFTWARE CORPORATION; MICRO FOCUS (US), INC.; SERENA SOFTWARE, INC; ATTACHMATE CORPORATION; MICRO FOCUS SOFTWARE INC. (F/K/A NOVELL, INC.); NETIQ CORPORATION
Reel/Frame 062746/0399 →
CHANGE OF NAME Recorded Aug 8, 2019
From: ENTIT SOFTWARE LLC
To: MICRO FOCUS LLC
Reel/Frame 050004/0001 →
SECURITY INTEREST Recorded Oct 11, 2017
From: ATTACHMATE CORPORATION; BORLAND SOFTWARE CORPORATION; NETIQ CORPORATION; MICRO FOCUS (US), INC.; MICRO FOCUS SOFTWARE, INC.; ENTIT SOFTWARE LLC; ARCSIGHT, LLC; SERENA SOFTWARE, INC.
To: JPMORGAN CHASE BANK, N.A.
Reel/Frame 044183/0718 →
SECURITY INTEREST Recorded Oct 11, 2017
From: ENTIT SOFTWARE LLC; ARCSIGHT, LLC
To: JPMORGAN CHASE BANK, N.A.
Reel/Frame 044183/0577 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 9, 2017
From: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
To: ENTIT SOFTWARE LLC
Reel/Frame 042746/0130 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 9, 2015
From: HEWLETT-PACKARD DEVELOPMENT COMPANY, L.P.
To: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Reel/Frame 037079/0001 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 1, 2013
From: OZONAT, MEHMET KIVANC; BARTOLINI, CLAUDIO
To: HEWLETT-PACKARD DEVELOPMENT COMPANY, L.P.
Reel/Frame 029737/0120 →
Continuity (1)
Related Publication 20140215054A1 · Jul 31, 2014