Abstract: A system may include first sensor to monitor first information relating a volume of information searched by a user, a second sensor to monitor second information relating to a number of requests, made by the user, to access a resource, a third sensor to monitor third information relating to a number of requests, made by the user, from different geographic locations, and a device to receive the first information, the second information, and the third information, and process the first information, the second information, and the third information in connection with the resource.

Abstract: According to an example, a method for data trend analysis may include retrieving data from data sources, associating the data with a time, and identifying co-occurrences of terms and concepts within the data. In response to determining that co-occurrences of term and concept pairs reach a predefined threshold, the method may include adding the term and concept pairs to an ontology. The method may include logging occurrences of terms in the ontology within the data with respect to associated data times, identifying a plurality of time periods, and for one of the plurality of time periods and for the logged terms, determining a first score indicative of a weighted term frequency metric for a logged term within the data during the one time period, and determining a second score indicative of a commonality of a presence of the logged term within the data among the plurality of time periods.

Abstract: According to an example, a method for data trend analysis may include retrieving data from data sources, associating the data with a time, and identifying co-occurrences of terms and concepts within the data. In response to determining that co-occurrences of term and concept pairs reach a predefined threshold, the method may include adding the term and concept pairs to an ontology. The method may include logging occurrences of terms in the ontology within the data with respect to associated data times, identifying a plurality of time periods, and for one of the plurality of time periods and for the logged terms, determining a first score indicative of a weighted term frequency metric for a logged term within the data during the one time period, and determining a second score indicative of a commonality of a presence of the logged term within the data among the plurality of time periods.

Abstract: According to an example, a method for data trend analysis may include retrieving data from data sources, associating the data with a time, and identifying co-occurrences of terms and concepts within the data. In response to determining that co-occurrences of term and concept pairs reach a predefined threshold, the method may include adding the term and concept pairs to an ontology. The method may include logging occurrences of terms in the ontology within the data with respect to associated data times, identifying a plurality of time periods, and for one of the plurality of time periods and for the logged terms, determining a first score indicative of a weighted term frequency metric for a logged term within the data during the one time period, and determining a second score indicative of a commonality of a presence of the logged term within the data among the plurality of time periods.

Abstract: A system may include first sensor to monitor first information relating a volume of information searched by a user, a second sensor to monitor second information relating to a number of requests, made by the user, to access a resource, a third sensor to monitor third information relating to a number of requests, made by the user, from different geographic locations, and a device to receive the first information, the second information, and the third information, and process the first information, the second information, and the third information in connection with the resource.

Abstract: According to an example, a method for data trend analysis may include retrieving data from data sources, associating the data with a time, and identifying co-occurrences of terms and concepts within the data. In response to determining that co-occurrences of term and concept pairs reach a predefined threshold, the method may include adding the term and concept pairs to an ontology. The method may include logging occurrences of terms in the ontology within the data with respect to associated data times, identifying a plurality of time periods, and for one of the plurality of time periods and for the logged terms, determining a first score indicative of a weighted term frequency metric for a logged term within the data during the one time period, and determining a second score indicative of a commonality of a presence of the logged term within the data among the plurality of time periods.

Abstract: A Scenario Generation Framework is described. The Framework provides the organizational infrastructure through which scenario data—a shared set of data supporting enterprise application coordination and interoperation—can effectively be managed from a centralized location. From this location, the framework provides capabilities whereby scenario data, described using a published data representation format, can be copied, viewed, formally compared, modified and combined with other segments of scenario data. Also from this location, suitably transformed data can be communicated to applications for use in initializing their execution in concert with other applications. In the preferred embodiment of the framework, workflow management capabilities are included to allow distribution and control of data preparation activities and an integrated workstation is made available to facilitate data manipulation and modification.