Abstract: An approach for filtering data is presented. A relationship between first and second entity-metadata elements specifying a person and a vehicle, respectively, and between the person and the vehicle is determined. Representations of the first and second entity-metadata elements are displayed within a regular polygon that includes locations indicated by a geospatial tag that includes location information about the person extracted from profile information describing the person and by other geospatial tags included in metadata obtained from data extracted from streaming data and data at rest. The metadata includes contextual information that specifies an activity included in a domain of knowledge associated with law enforcement. Based on hidden Markov and support vector machine models, a frequent pattern growth algorithm, and a Kohonen map, another activity of the person is predicted.

Abstract: An approach for filtering data is presented. A relationship between first and second entity-metadata elements specifying a person and a vehicle, respectively, and between the person and the vehicle is determined. Representations of the first and second entity-metadata elements are displayed within a regular polygon that includes locations indicated by a geospatial tag that includes location information about the person extracted from profile information describing the person and by other geospatial tags included in metadata obtained from data extracted from streaming data and data at rest. The metadata includes contextual information that specifies an activity included in a domain of knowledge associated with law enforcement. Based on hidden Markov and support vector machine models, a frequent pattern growth algorithm, and a Kohonen map, another activity of the person is predicted.

Abstract: An approach for filtering data is presented. A first geo-hash indicating location information of a person based on profile data or a second geo-hash indicating location information of the person based on an inference is determined to have more characters and is selected as an optimal geo-hash specifying a first geospatial tag. Based on correlations between geospatial tags, time/date stamps, and contextual information, a relationship between first and second entity-metadata elements specifying the person and a vehicle, respectively, and between the person and the vehicle is determined. Representations of the first and second entity-metadata elements are displayed within a regular polygon that includes locations indicated by the geospatial tags. Based on hidden Markov and support vector machine models, a frequent pattern growth algorithm, and a Kohonen map, another activity of the person is predicted.

Abstract: An approach for filtering data is presented. A first geo-hash indicating location information of a person based on profile data or a second geo-hash indicating location information of the person based on an inference is determined to have more characters and is selected as an optimal geo-hash specifying a first geospatial tag. Based on correlations between geospatial tags, time/date stamps, and contextual information, a relationship between first and second entity-metadata elements specifying the person and a vehicle, respectively, and between the person and the vehicle is determined. Representations of the first and second entity-metadata elements are displayed within a regular polygon that includes locations indicated by the geospatial tags. Based on hidden Markov and support vector machine models, a frequent pattern growth algorithm, and a Kohonen map, another activity of the person is predicted.

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.

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.

Abstract: A method and system for implementing a project for a customer. A computer readable Requirements Traceability and Verification Matrix (RTVM). The RTVM is stored in a computer readable storage device. A verification is implemented, which verifies: an acceptability of business requirements of the project due to business requirements review (BRR) exit criteria having been satisfied, an acceptability of system requirements of the project due to system requirements review (SRR) exit criteria having been satisfied, an acceptability of component requirements of the project due to component requirements review (PDR) exit criteria having been satisfied. The business requirements are decomposed into the system requirements. The system requirements are decomposed into the component requirements.

Abstract: A method for implementing a project for a customer. Business requirements are developed for the project and are reviewed for acceptability in accordance with Business Requirements Review (BRR) exit criteria. System requirements are developed for the project and are reviewed for acceptability in accordance with system requirements review (SRR) exit criteria. Component requirements are developed for the project and are reviewed for acceptability in accordance with Preliminary Design Review (PDR) exit criteria. The business requirements are decomposed into the system requirements. The system requirements are decomposed into the component requirements. A Requirements Traceability and Verification Matrix (RTVM) is generated when the business requirements are established. The RTVM is updated throughout the life of the project. The RTVM includes verification information relating to the business requirements, the system requirements, and the component requirements.

Abstract: A method for implementing a project for a customer. Business requirements are developed for the project and are reviewed for acceptability in accordance with Business Requirements Review (BRR) exit criteria. System requirements are developed for the project and are reviewed for acceptability in accordance with system requirements review (SRR) exit criteria. Component requirements are developed for the project and are reviewed for acceptability in accordance with Preliminary Design Review (PDR) exit criteria. The business requirements are decomposed into the system requirements. The system requirements are decomposed into the component requirements. A Requirements Traceability and Verification Matrix (RTVM) is generated when the business requirements are established. The RTVM is updated throughout the life of the project. The RTVM includes verification information relating to the business requirements, the system requirements, and the component requirements.

Abstract: A method for implementing a project for a customer. Business requirements are developed for the project and are reviewed for acceptability in accordance with Business Requirements Review (BRR) exit criteria. System requirements are developed for the project and are reviewed for acceptability in accordance with system requirements review (SRR) exit criteria. Component requirements are developed for the project and are reviewed for acceptability in accordance with Preliminary Design Review (PDR) exit criteria. The business requirements are decomposed into the system requirements. The system requirements are decomposed into the component requirements. A Requirements Traceability and Verification Matrix (RTVM) is generated when the business requirements are established. The RTVM is updated throughout the life of the project. The RTVM includes verification information relating to the business requirements, the system requirements, and the component requirements.