Abstract: An electronic communication is received from a first communication device by a multi-tasking operating system. For example, a customer has entered a service request at a web site, which is received by the multi-tasking operating system. The electronic communication comprises a plurality of items of information associated with a service request. An accuracy level for the plurality of items of information is determined. The accuracy level for the plurality of items is based on a history of prior electronic communications. In response to determining the accuracy level for the plurality of items of information associated with the service request, a communication system associated with a contact center and/or a contact center agent is identified. In response to identifying the communication system associated with the contact center and/or contact center agent, the electronic communication is routed to the communication system associated with the contact center and/or contact center agent.

Abstract: An electronic communication is received from a first communication device by a multi-tasking operating system. For example, a customer has entered a service request at a web site, which is received by the multi-tasking operating system. The electronic communication comprises a plurality of items of information associated with a service request. An accuracy level for the plurality of items of information is determined. The accuracy level for the plurality of items is based on a history of prior electronic communications. In response to determining the accuracy level for the plurality of items of information associated with the service request, a communication system associated with a contact center and/or a contact center agent is identified. In response to identifying the communication system associated with the contact center and/or contact center agent, the electronic communication is routed to the communication system associated with the contact center and/or contact center agent.

Abstract: Disclosed herein are systems, methods, and computer-readable storage media for identifying and remediating risky source files. An example system configured to practice the method can gather data describing each file in a source code repository, and generate, using a weighted algorithm based on empirical relationships between the data and customer-found defects, a risk score for each file in the source code repository, wherein the weighted algorithm prioritizes factors based on predictiveness of defects. Then the system can generate a list of files having risk scores above a threshold, and make risk-mitigation recommendations based on the risk scores. A file can include a single file or a collection of files such as a module. The system can identify, for each file in the list of files having risk scores above the threshold, a respective risk type, and make the risk-mitigation recommendation for each file based on the respective risk type.

Abstract: Systems, methods, and computer-readable storage media for predicting escalation risks in customer support contexts. A system implementing the example method can identify instances of customer escalation from a corpus of customer support sales, customer support, and product software development data, and determine, for the instances of customer escalation, sets of trigger conditions that were at least partially responsible for respective instances of customer escalation. Then the system can classify each of the instances of customer escalation by problem type based on the set of trigger conditions to yield classifications, and add the classifications, the sets of trigger conditions, and the instances of customer escalation to a database. Then the system can compare the database to support data to identify customers or support tasks that are likely to be escalated, and the system or a user can allocate additional support resources to proactively prevent escalations.

Abstract: Disclosed herein are systems, methods, and computer-readable storage media for identifying and remediating risky source files. An example system configured to practice the method can gather data describing each file in a source code repository, and generate, using a weighted algorithm based on empirical relationships between the data and customer-found defects, a risk score for each file in the source code repository, wherein the weighted algorithm prioritizes factors based on predictiveness of defects. Then the system can generate a list of files having risk scores above a threshold, and make risk-mitigation recommendations based on the risk scores. A file can include a single file or a collection of files such as a module. The system can identify, for each file in the list of files having risk scores above the threshold, a respective risk type, and make the risk-mitigation recommendation for each file based on the respective risk type.

Abstract: A data visualization system provides data visualization objects to a live document author for incorporation into a live document. The system is responsive to selection of one or more objects by incorporating the object in a hypertext markup. Objects may include view objects, such as bar charts and dynamic tables, text objects, which permit an author to guide a reader through the live document, and interaction objects which permit a reader to interact with a live document. The system also permits views to be linked by an author and interaction sessions to be saved by a reader.

Abstract: A data visualization system provides data visualization objects to a live document author for incorporation into a live document. The system is responsive to selection of one or more objects by incorporating the object in a hypertext markup. Objects may include view objects, such as bar charts and dynamic tables, text objects, which permit an author to guide a reader through the live document, and interaction objects which permit a reader to interact with a live document. The system also permits views to be linked by an author and interaction sessions to be saved by a reader.

Abstract: Real time data summaries from a common data base are produced within a network. A line document program is stored within the network enabling selective visual analysis and displays of the data when the data when the data summaries are downloaded at computer terminals at a plurality of sites.