US 9,069,880 B2Granted

Prediction and isolation of patterns across datasets

AssigneeMicrosoft Technology Licensing, LLC

InventorsSamuel T. Trim, Brad Snow

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Abstract

Various embodiments pertain to techniques for predicting and isolating patterns or trends across datasets. In various embodiments, one or more Q-entities are extracted from a data seed, associated with one or more dimensions, and classified into one or more clusters for each dimension with which it is associated. In some embodiments, a Q-entity can exist in more than one dimension and/or more than one cluster within a dimension. Once information from the data seed is associated with a dimension and cluster, frequency analysis can be utilized to ascertain a pattern or trend in the data. In various embodiments, additional data can be processed, added to the dimensions and clusters, and frequency analysis can be performed on the updated dataset to provide additional information on the pattern or trend.

Claims17

Dependent claims

A method comprising:

receiving a data seed for use in identifying a trend across data in a network-based environment;
extracting a Q-entity from the data seed;
obtaining additional Q-entities based on the Q-entity extracted from the data seed, wherein a subsequent data seed is utilized to obtain the additional Q-entities, the subsequent data seed comprising a web page to which each of a plurality of hyperlinks redirect a web browser;
analyzing each Q-entity to determine when the Q-entity belongs to more than one dimension;
analyzing, using a relational graph model having at least one Q-dimension having at least one Q-cluster, the Q-entity effective to associate one of the Q-entities with the at least one Q-cluster and the at least one Q-dimension of the relational graph;
and
identifying, based on a frequency analysis of the Q-cluster in the relational graph model to determine clustering patterns that signify relational ties between Q-entities that are used to identify the trend, wherein the trend is identified according to an expression:
_
⁢
trend
=
∑
t
=
-
∞
∞
⁢
(
(
∑
d
=
1
3
⁢
(
∑
k
=
1
3
⁢
(
F
⁢
⁢
C
⁢
⁢
M
)
)
)
Δ
⁢
⁢
t
)
where t is a time ranging from negative infinity to infinity with 0 being a time of origin, d is a value assigned to the at least one Q-dimension with which the Q-entity is associated, and k is a value assigned to the at least one Q-cluster with which the Q-entity is associated, and
wherein the value of d is a number from one to three assigned to the at least one Q-dimension, and wherein the value of k is a number from one to three assigned to the at least one Q-cluster.

48 more elements

One or more computer-readable storage media comprising instructions that are executable to cause a device to perform a process comprising:

extracting, from a data seed, one or more Q-entities;
obtaining additional Q-entities based on the one or more Q-entities extracted from the data seed, wherein a subsequent data seed is utilized to obtain the additional Q-entities, the subsequent data seed comprising a web page to which each of a plurality of hyperlinks redirect a web browser;
analyzing the Q-entity to determine when the Q-entity belongs to more than one dimension;
analyzing using a relational graph model having a plurality of Q-dimensions each having a plurality of Q-clusters, each of the Q-entities effective to associate each Q-entity with a Q-cluster and Q-dimension;
and
identifying, based on frequency analysis of the Q-clusters in the relational graph model to determine clustering patterns that signify relational ties between Q-entities that are used to identify a trend in a web environment to which the data seed pertains, wherein the trend is identified according to an expression:
_
⁢
trend
=
∑
t
=
-
∞
∞
⁢
(
(
∑
d
=
1
3
⁢
(
∑
k
=
1
3
⁢
(
F
⁢
⁢
C
⁢
⁢
M
)
)
)
Δ
⁢
⁢
t
)
where t is a time ranging from negative infinity to infinity with 0 being a time of origin, d is a value assigned to the at least one Q-dimension with which the Q-entity is associated, and k is a value assigned to the at least one Q-cluster with which the Q-entity is associated, and
wherein the value of d is a number from one to three assigned to the at least one Q-dimension, and wherein the value of k is a number from one to three assigned to the at least one Q-cluster.

47 more elements

A device comprising:

one or more processors;
one or more computer-readable storage media;
one or more modules embodied on the one or more computer-readable storage media and executable under the influence of the one or more processors, the one or more modules configured to:
extract, from a data seed from a web environment, Q-entities;
obtain additional Q-entities based on the Q-entity extracted from the data seed, wherein a subsequent data seed is utilized to obtain the additional Q-entities, the subsequent data seed comprising a web page to which each of a plurality of hyperlinks redirect a web browser;
analyze each Q-entity to determine when the Q-entity belongs to more than one dimension;
analyze each Q-entity using a relational graph model having three Q-dimensions, each Q-dimension having three Q-clusters;
and
identify a trend present in the web environment according to related concepts in one or more of the Q-clusters by determining clustering patterns that signify relational ties between the Q-entities, wherein the trend is identified according to an expression:
_
⁢
trend
=
∑
t
=
-
∞
∞
⁢
(
(
∑
d
=
1
3
⁢
(
∑
k
=
1
3
⁢
(
F
⁢
⁢
C
⁢
⁢
M
)
)
)
Δ
⁢
⁢
t
)
where t is a time ranging from negative infinity to infinity with 0 being a time of origin, d is a value assigned to the at least one Q-dimension with which the Q-entity is associated, and k is a value assigned to the at least one Q-cluster with which the Q-entity is associated, and
wherein the value of d is a number from one to three assigned to the at least one Q-dimension, and wherein the value of k is a number from one to three assigned to the at least one Q-cluster.

50 more elements

Dependencies are inferred from the claim text.

Facts

Application: 13/421,905Filed 16 Mar 2012
Published: US 2013/0246463 A119 Sep 2013
Granted: 30 Jun 2015
Term ends: Not estimatedThe source records don’t support an estimate.

Classification12 codes

G06F 16/9024
Indexing; Data structures therefor; Storage structures: graphs; Linked lists
G06Q 10/44
Business processes related to social networking or social networking services: identification of trends within social networks, e.g.…
G06Q 30/02
Commerce: marketing; Price estimation or determination; Fundraising
H04L 41/147
Network analysis or design: for predicting network behaviour