Activity based analytics

• US 9,396,253 B2
• Filed: 09/27/2013
• Issued: 07/19/2016
• Est. Priority Date: 09/27/2013
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Porpora, Gregory A., Spann, Janet L.
• Primary Examiner(s): Reyes, Mariela
• Assistant Examiner(s): Harmon, Courtney
• Application Number:
  US14/039,718
• Publication Number:
  US 20150095333A1
• Time in Patent Office: 1,026 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/24568 Data stream processing; Continuous queries
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06Q 50/26 Government or public services business processes related to the transportation industry G06Q50/40

Abstract

An approach for filtering data into a geo-activity zone cell is presented. An area of interest specifying an individual, organization, or entity is selected. Data is extracted from streaming data and from data at rest. Metadata of the extracted data is determined. The metadata includes time and date stamp(s) and contextual information specifying the area of interest. A first portion of the metadata includes geospatial tag(s) specifying the area of interest, and a second portion of the metadata is initially missing geospatial tag(s). The missing geospatial tag(s) are determined and added to the second portion of the metadata by extracting a location from profile data and/or inferring the location based on a region-based geo-topic model. The extracted data is filtered into a geo-activity zone cell based on the first and second portions of metadata being within metadata boundaries.

Claims

1. A method of filtering data, the method comprising the steps of:

a computer selecting a person specified by a first area of interest and selecting a vehicle specified by a second area of interest;

the computer receiving an ontology map that associates key words and concepts to a domain of knowledge associated with law enforcement, the domain of knowledge including an activity;

the computer extracting data from streaming data and from data at rest;

the computer obtaining first and second groups of metadata from the extracted data;

based in part on the ontology map, the computer determining a first portion of the first group of metadata includes a first geospatial tag, a first time and date stamp, and first contextual information specifying the activity;

based in part on the ontology map, the computer determining a second portion of the first group of metadata includes a second time and date stamp and second contextual information specifying the activity, but does not include a second geospatial tag;

based on the second portion of first group of metadata not including the second geospatial tag, the computer extracting first profile data which describes the person and includes first location information about the person;

the computer setting the first location information as a first value of the second geospatial tag and converting the first value of the second geospatial tag into a first geo-hash;

based on the second portion of first group of metadata not including the second geospatial tag, the computer inferring second location information about the person by employing a model which is trained by historical data and which uses a k-nearest neighbor distance calculator;

the computer setting the second location information as a second value of the second geospatial tag and converting the second value of the second geospatial tag into a second geo-hash;

the computer determining whether the first geo-hash has more characters than the second geo-hash;

if the first geo-hash has more characters than the second geo-hash, the computer selecting the first geo-hash as an optimal geo-hash that specifies the second geospatial tag or if the second geo-hash has more characters than the first geo-hash, the computer selecting the second geo-hash as the optimal geo-hash that specifies the second geospatial tag;

the computer using entity resolution and disambiguation on a first data element specified by the first portion of the first group of metadata and a second data element specified by the second portion of the first group of metadata, and in response, determining an interrelationship between the first and second data elements;

based on the interrelationship between the first and second data elements, the computer generating a first entity-metadata element that includes the first and second data elements, the first entity-metadata element specifying the person;

based in part on the ontology map, the computer determining the second group of metadata includes a third geospatial tag, a third time and date stamp, and third contextual information specifying the activity;

the computer generating a second entity-metadata element that includes a third data element specified by the second group of metadata, the second entity-metadata element specifying the vehicle;

based in part on the optimal geo-hash determined by whether the first or second geo-hash has more characters, the computer determining a first correlation between (1) the first and second geospatial tags and (2) the third geospatial tag;

the computer determining a second correlation between (1) the first and second time and data stamps and (2) the third time and date stamp;

the computer determining a third correlation between (1) the first and second contextual information and (2) the third contextual information;

based on the first, second, and third correlations, the computer determining a relationship between the first and second entity-metadata elements and between the person and the vehicle;

the computer receiving geographic coordinates of a center point and a distance from the center point, the geographic coordinates and distance specifying a zone;

based on the geographic coordinates of the center point and the distance from the center point, the computer generating a regular polygon having a circumradius equal to the distance from the center point and performing a continuous query against entity-metadata elements, and in response, determining that the first, second, and third geospatial tags indicate locations within the regular polygon and determining the first entity-metadata element specifies the person and the second entity-metadata element specifies the vehicle;

based on the relationship between the first and second entity-metadata elements and between the person and the vehicle, and the first, second, and third geospatial tags, the computer displaying representations of the first and second entity-metadata elements within the regular polygon;

the computer employing a hidden Markov model, which tracks the person and the vehicle;

the computer employing a support vector machine model, which classifies the activity;

the computer employing a frequent pattern growth algorithm, which identifies associations between the activity and one or more other persons;

the computer employing a Kohonen map, which determines a previously unknown activity of the person and the vehicle; and

based on the hidden Markov model, the support vector machine model, the frequent pattern growth algorithm, and the Kohonen map, the computer predicting another activity of the person.

2. The method of claim 1, further comprising the steps of:

subsequent to the step of displaying the representations of the first and second entity-metadata elements, the computer extracting additional data from the streaming data and from the data at rest;

based on the additional extracted data, the computer determining a third entity-metadata element specifying a person of interest, a network of persons, or another vehicle, the third entity-metadata element including a geo-spatial tag that indicates a location within the zone;

based on the geo-spatial tag included in the third entity-metadata element, the computer generating and displaying a representation of the third entity-metadata element within the regular polygon, which includes the representations of the first and second entity-metadata elements;

the computer tracking changes to the zone which includes an addition of the third entity-metadata element to the zone.

3. The method of claim 2, wherein the step of determining the third entity-metadata element includes:

performing a continuous query against the extracted additional data using a master key to search for metadata that is geo-referenced based on the center point and the distance from the center point;

based on the master key, cross correlating and disambiguating the metadata that is geo-referenced into a set of groups including at least a first group associated with a person of interest, a second group associated with a network of persons, and a third group associated with another vehicle; and

designating the third entity-metadata element as including a portion of the metadata included in the first, second, or third group in the set of groups.

4. The method of claim 1, further comprising the steps of:

the computer selecting another person, another vehicle, or a network of persons;

the computer extracting other data from streaming data and from data at rest;

the computer determining other metadata of the extracted other data, the computer filtering the extracted other data into another entity-metadata element in another activity zone based on the other metadata being within other metadata boundaries of the other entity-metadata element;

the computer employing complex entity correlation which determines a relationship between the zone and the other zone based on a relationship between the first entity-metadata element and the other entity-metadata element; and

the computer generating and sending an alert about the zone and the other zone being related.

5. The method of claim 1, further comprising the step of:

providing at least one support service for at least one of creating, integrating, hosting, maintaining, and deploying computer-readable program code in the computer, the program code being executed by a processor of the computer to implement the steps of selecting the person, selecting the vehicle, receiving the ontology map, extracting the data, obtaining the first and second groups of metadata, determining the first portion of the first group of metadata includes the first geospatial tag, the first time and date stamp, and the first contextual information, determining the second portion of the first group of metadata includes the second time and date stamp and the second contextual information, extracting the first profile data, setting the first location information as the first value of the second geospatial tag, converting the first value, inferring the second location information about the person, setting the second location information as the second value of the second geospatial tag, converting the second value, determining whether the first geo-hash has more characters than the second geo-hash, selecting the first geo-hash as the optimal geo-hash if the first geo-hash has more characters than the second geo-hash or selecting the second geo-hash as the optimal geo-hash if the second geo-hash has more characters than the first geo-hash, using entity resolution and disambiguation on the first data element and the second data element, determining the interrelationship between the first and second data elements, generating the first entity-metadata element, determining the second group of metadata, generating the second entity-metadata element, determining the first correlation, determining the second correlation, determining the third correlation, determining the relationship between the first and second entity-metadata elements and between the person and the vehicle, receiving geographic coordinates of the center point and the distance from the center point, generating the regular polygon, performing the continuous query, determining that the first, second, and third geospatial tags indicate locations within the regular polygon, determining the first entity-metadata element specifies the person and the second entity-metadata element specifies the vehicle, displaying the representations of the first and second entity-metadata elements, employing the hidden Markov model, employing the support vector machine model, employing the frequent pattern growth algorithm, employing the Kohonen map, and predicting the other activity of the person.

6. A computer system comprising:

a central processing unit (CPU);

a memory coupled to the CPU;

a computer-readable, tangible storage device coupled to the CPU, the storage device containing instructions that are executed by the CPU via the memory to implement a method of filtering data, the method comprising the steps of;

the computer system selecting a person specified by a first area of interest and selecting a vehicle specified by a second area of interest;

the computer system receiving an ontology map that associates key words and concepts to a domain of knowledge associated with law enforcement, the domain of knowledge including an activity;

the computer system extracting data from streaming data and from data at rest;

the computer system obtaining first and second groups of metadata from the extracted data;

based in part on the ontology map, the computer system determining a first portion of the first group of metadata includes a first geospatial tag, a first time and date stamp, and first contextual information specifying the activity;

based in part on the ontology map, the computer system determining a second portion of the first group of metadata includes a second time and date stamp and second contextual information specifying the activity, but does not include a second geospatial tag;

based on the second portion of first group of metadata not including the second geospatial tag, the computer system extracting first profile data which describes the person and includes first location information about the person;

the computer system setting the first location information as a first value of the second geospatial tag and converting the first value of the second geospatial tag into a first geo-hash;

based on the second portion of first group of metadata not including the second geospatial tag, the computer system inferring second location information about the person by employing a model which is trained by historical data and which uses a k-nearest neighbor distance calculator;

the computer system setting the second location information as a second value of the second geospatial tag and converting the second value of the second geospatial tag into a second geo-hash;

the computer system determining whether the first geo-hash has more characters than the second geo-hash;

if the first geo-hash has more characters than the second geo-hash, the computer system selecting the first geo-hash as an optimal geo-hash that specifies the second geospatial tag or if the second geo-hash has more characters than the first geo-hash, the computer system selecting the second geo-hash as the optimal geo-hash that specifies the second geospatial tag;

the computer system using entity resolution and disambiguation on a first data element specified by the first portion of the first group of metadata and a second data element specified by the second portion of the first group of metadata, and in response, determining an interrelationship between the first and second data elements;

based on the interrelationship between the first and second data elements, the computer system generating a first entity-metadata element that includes the first and second data elements, the first entity-metadata element specifying the person;

based in part on the ontology map, the computer system determining the second group of metadata includes a third geospatial tag, a third time and date stamp, and third contextual information specifying the activity;

the computer system generating a second entity-metadata element that includes a third data element specified by the second group of metadata, the second entity-metadata element specifying the vehicle;

based in part on the optimal geo-hash determined by whether the first or second geo-hash has more characters, the computer system determining a first correlation between (1) the first and second geospatial tags and (2) the third geospatial tag;

the computer system determining a second correlation between (1) the first and second time and data stamps and (2) the third time and date stamp;

the computer system determining a third correlation between (1) the first and second contextual information and (2) the third contextual information;

based on the first, second, and third correlations, the computer system determining a relationship between the first and second entity-metadata elements and between the person and the vehicle;

the computer system receiving geographic coordinates of a center point and a distance from the center point, the geographic coordinates and distance specifying a zone;

based on the geographic coordinates of the center point and the distance from the center point, the computer system generating a regular polygon having a circumradius equal to the distance from the center point and performing a continuous query against entity-metadata elements, and in response, determining that the first, second, and third geospatial tags indicate locations within the regular polygon and determining the first entity-metadata element specifies the person and the second entity-metadata element specifies the vehicle;

based on the relationship between the first and second entity-metadata elements and between the person and the vehicle, and the first, second, and third geospatial tags, the computer system displaying representations of the first and second entity-metadata elements within the regular polygon;

the computer system employing a hidden Markov model, which tracks the person and the vehicle;

the computer system employing a support vector machine model, which classifies the activity;

the computer system employing a frequent pattern growth algorithm, which identifies associations between the activity and one or more other persons;

the computer system employing a Kohonen map, which determines a previously unknown activity of the person and the vehicle; and

based on the hidden Markov model, the support vector machine model, the frequent pattern growth algorithm, and the Kohonen map, the computer system predicting another activity of the person.

7. The computer system of claim 6, wherein the method further comprises the steps of:

subsequent to the step of displaying the representations of the first and second entity-metadata elements, the computer system extracting additional data from the streaming data and from the data at rest;

based on the additional extracted data, the computer system determining a third entity-metadata element specifying a person of interest, a network of persons, or another vehicle, the third entity-metadata element including a geo-spatial tag that indicates a location within the zone;

based on the geo-spatial tag included in the third entity-metadata element, the computer system generating and displaying a representation of the third entity-metadata element within the regular polygon, which includes the representations of the first and second entity-metadata elements;

the computer system tracking changes to the zone, which includes an addition of the third entity-metadata element to the zone.

8. The computer system of claim 7, wherein the step of determining the third entity-metadata element includes:

performing a continuous query against the extracted additional data using a master key to search for metadata that is geo-referenced based on the center point and the distance from the center point;

based on the master key, cross correlating and disambiguating the metadata that is geo-referenced into a set of groups including at least a first group associated with a person of interest, a second group associated with a network of persons, and a third group associated with another vehicle; and

designating the third entity-metadata element as including a portion of the metadata included in the first, second, or third group in the set of groups.

9. The computer system of claim 6, wherein the method further comprises the steps of:

the computer system selecting another person, another vehicle, or a network of persons;

the computer system extracting other data from streaming data and from data at rest;

the computer system determining other metadata of the extracted other data,

the computer system filtering the extracted other data into another entity-metadata element in another zone based on the other metadata being within metadata boundaries of the other entity-metadata element;

the computer system employing complex entity correlation which determines a relationship between the zone and the other zone based on a relationship between the first entity-metadata element and the other entity-metadata element; and

the computer system generating and sending an alert about the zone and the other zone being related.

10. A computer program product, comprising:

a computer-readable, tangible storage device; and

a computer-readable program code stored in the computer-readable, tangible storage device, the computer-readable program code containing instructions that are executed by a central processing unit (CPU) of a computer system to implement a method of filtering data, the method comprising the steps of;

the computer system selecting a person specified by a first area of interest and selecting a vehicle specified by a second area of interest;

the computer system receiving an ontology map that associates key words and concepts to a domain of knowledge associated with law enforcement, the domain of knowledge including an activity;

the computer system extracting data from streaming data and from data at rest;

the computer system obtaining first and second groups of metadata from the extracted data;

based in part on the ontology map, the computer system determining a first portion of the first group of metadata includes a first geospatial tag, a first time and date stamp, and first contextual information specifying the activity;

based in part on the ontology map, the computer system determining a second portion of the first group of metadata includes a second time and date stamp and second contextual information specifying the activity, but does not include a second geospatial tag;

based on the second portion of first group of metadata not including the second geospatial tag, the computer system extracting first profile data which describes the person and includes first location information about the person;

the computer system setting the first location information as a first value of the second geospatial tag and converting the first value of the second geospatial tag into a first geo-hash;

based on the second portion of first group of metadata not including the second geospatial tag, the computer system inferring second location information about the person by employing a model which is trained by historical data and which uses a k-nearest neighbor distance calculator;

the computer system setting the second location information as a second value of the second geospatial tag and converting the second value of the second geospatial tag into a second geo-hash;

the computer system determining whether the first geo-hash has more characters than the second geo-hash;

if the first geo-hash has more characters than the second geo-hash, the computer system selecting the first geo-hash as an optimal geo-hash that specifies the second geospatial tag or if the second geo-hash has more characters than the first geo-hash, the computer system selecting the second geo-hash as the optimal geo-hash that specifies the second geospatial tag;

the computer system using entity resolution and disambiguation on a first data element specified by the first portion of the first group of metadata and a second data element specified by the second portion of the first group of metadata, and in response, determining an interrelationship between the first and second data elements;

based on the interrelationship between the first and second data elements, the computer system generating a first entity-metadata element that includes the first and second data elements, the first entity-metadata element specifying the person;

based in part on the ontology map, the computer system determining the second group of metadata includes a third geospatial tag, a third time and date stamp, and third contextual information specifying the activity;

the computer system generating a second entity-metadata element that includes a third data element specified by the second group of metadata, the second entity-metadata element specifying the vehicle;

based in part on the optimal geo-hash determined by whether the first or second geo-hash has more characters, the computer system determining a first correlation between (1) the first and second geospatial tags and (2) the third geospatial tag;

the computer system determining a second correlation between (1) the first and second time and data stamps and (2) the third time and date stamp;

the computer system determining a third correlation between (1) the first and second contextual information and (2) the third contextual information;

based on the first, second, and third correlations, the computer system determining a relationship between the first and second entity-metadata elements and between the person and the vehicle;

the computer system receiving geographic coordinates of a center point and a distance from the center point, the geographic coordinates and distance specifying a zone;

based on the geographic coordinates of the center point and the distance from the center point, the computer system generating a regular polygon having a circumradius equal to the distance from the center point and performing a continuous query against entity-metadata elements, and in response, determining that the first, second, and third geospatial tags indicate locations within the regular polygon and determining the first entity-metadata element specifies the person and the second entity-metadata element specifies the vehicle;

based on the relationship between the first and second entity-metadata elements and between the person and the vehicle, and the first, second, and third geospatial tags, the computer system displaying representations of the first and second entity-metadata elements within the regular polygon;

the computer system employing a hidden Markov model, which tracks the person and the vehicle;

the computer system employing a support vector machine model, which classifies the activity;

the computer system employing a frequent pattern growth algorithm, which identifies associations between the activity and one or more other persons;

the computer system employing a Kohonen map, which determines a previously unknown activity of the person and the vehicle; and

based on the hidden Markov model, the support vector machine model, the frequent pattern growth algorithm, and the Kohonen map, the computer system predicting another activity of the person.

11. The program product of claim 10, wherein the method further comprises the steps of:

subsequent to the step of displaying the representations of the first and second entity-metadata elements, the computer system extracting additional data from the streaming data and from the data at rest;

based on the additional extracted data, the computer system determining a third entity-metadata element specifying a person of interest, a network of persons, or another vehicle, the third entity-metadata element including a geo-spatial tag that indicates a location within the zone;

based on the geo-spatial tag included in the third entity-metadata element, the computer system generating and displaying a representation of the third entity-metadata element within the regular polygon, which includes the representations of the first and second entity-metadata elements;

the computer system tracking changes to the zone, which includes an addition of the third-entity metadata element to the zone.

12. The program product of claim 11, wherein the step of determining the third entity-metadata element includes:

performing a continuous query against the extracted additional data using a master key to search for metadata that is geo-referenced based on the center point and the distance from the center point;

based on the master key, cross correlating and disambiguating the metadata that is geo-referenced into a set of groups including at least a first group associated with a person of interest, a second group associated with a network of persons, and a third group associated with another vehicle; and

designating the third entity-metadata element as including a portion of the metadata included in the first, second, or third group in the set of groups.

13. The program product of claim 10, wherein the method further comprises the steps of:

the computer system selecting another person, another vehicle, or a network of persons;

the computer system extracting other data from streaming data and from data at rest;

the computer system determining other metadata of the extracted other data, the computer system filtering the extracted other data into another entity-metadata element in another zone based on the other metadata being within metadata boundaries of the other entity-metadata element;

the computer system employing complex entity correlation which determines a relationship between the zone and the other zone based on a relationship between the first entity-metadata element and the other entity-metadata element; and

the computer system generating and sending an alert about the zone and the other zone being related.