Abstract: A method and system for automatically consolidating images and generating associated predictions is provided. The method includes periodically monitoring email addresses for emails stored on an email server and subsequently locating an email comprising an image automatically captured from a remotely located video retrieval device locally storing the image. The image is automatically downloaded and extracted for the email. A date and time associated with the image is validated and the image is stored within a server. Metadata associated with the image is updated and the updated metadata is associated with external attributes associated with a location of the remotely located video retrieval device.

Abstract: A method and system for automatically consolidating images and generating associated predictions is provided. The method includes periodically monitoring email addresses for emails stored on an email server and subsequently locating an email comprising an image automatically captured from a remotely located video retrieval device locally storing the image. The image is automatically downloaded and extracted for the email. A date and time associated with the image is validated and the image is stored within a server. Metadata associated with the image is updated and the updated metadata is associated with external attributes associated with a location of the remotely located video retrieval device.

Abstract: A marketing method and system. The method includes receiving by a computing system total population data. A peer group is selected from the total population data. The peer group is associated with a first marketing action. A list of attributes characterizing the peer group is generated. The attributes are associated with the marketing action, demographic data, and additional purchasing habits of the peer group. A target group of individuals of the total population data is selected. The attributes are associated with the individuals of the target group. Scores associated with the individuals of the target group are generated. The individuals of the target group are ranked based on said scores. A ranking list based is generated. The ranking list includes the individuals of the target group in a ranked order. The ranking list is stored.

Abstract: A marketing method and system. The method includes receiving by a computing system total population data. A peer group is selected from the total population data. The peer group is associated with a first marketing action. A list of attributes characterizing the peer group is generated. The attributes are associated with the marketing action, demographic data, and additional purchasing habits of the peer group. A target group of individuals of the total population data is selected. The attributes are associated with the individuals of the target group. Scores associated with the individuals of the target group are generated. The individuals of the target group are ranked based on said scores. A ranking list based is generated. The ranking list includes the individuals of the target group in a ranked order. The ranking list is stored.

Abstract: A marketing method and system. The method includes receiving by a computing system total population data. A peer group is selected from the total population data. The peer group is associated with a first marketing action. A list of attributes characterizing the peer group is generated. The attributes are associated with the marketing action, demographic data, and additional purchasing habits of the peer group. A target group of individuals of the total population data is selected. The attributes are associated with the individuals of the target group. Scores associated with the individuals of the target group are generated. The individuals of the target group are ranked based on said scores. A ranking list based is generated. The ranking list includes the individuals of the target group in a ranked order. The ranking list is stored.

Abstract: A marketing method and system. The method includes receiving by a computing system total population data. A peer group is selected from the total population data. The peer group is associated with a first marketing action. A list of attributes characterizing the peer group is generated. The attributes are associated with the marketing action, demographic data, and additional purchasing habits of the peer group. A target group of individuals of the total population data is selected. The attributes are associated with the individuals of the target group. Scores associated with the individuals of the target group are generated. The individuals of the target group are ranked based on said scores. A ranking list based is generated. The ranking list includes the individuals of the target group in a ranked order. The ranking list is stored.

Abstract: A method in a data processing system for managing access to a resource. A user is validated to use the resource based on data provided in an act by the user. A set of user behavioral attributes from acts generated by the user are identified while the user is using the resource. The set of user behavioral attributes are compared to a set of historical user behavioral attributes to form a comparison. Whether to accept a request from the user to access the resource is determined based on the comparison.

Abstract: Application of a scalar function across a varying number of records within an RDBMS using the RDBMS's SQL capabilities. This is accomplished by determining a last row in a group of rows, sorting the rows and passing the sorted rows to a scalar function, where the scalar function accepts each row and stores the accepted rows in a temporary file space. When the last row is encountered, the aggregate value is calculated using all of the rows in the group, and then a filter is applied on the rows so that only a row with the aggregate value is returned.

Abstract: A technique for enhanced data mining of large scale relational databases is described. User defined functions (UDF) are created by a user and distributed by a managing node of a database to each node of the database. Upon the issuance of a prespecified SQL command, the UDF is executed by each node relative to the data controlled by each node. Specifically, targeted tuples in the data controlled by each node are scored based on criteria contained in the UDF. A new data field is added to each target tuple, and the score is placed therein. The score is then used to determine whether, for example, a customer which is represented by the tuple should be included in an advertising campaign or to tailor a mailing to a customer based on the score contained in the new data field.