Abstract: A system and method for characterizing textual data by generating a first data abstraction based on a set of textual data. The first data abstraction can be presented to a user, and the user can provide instructions to make changes to the first data abstraction to generate a second data abstraction. The textual data can be extracted and characterized from the set of textual data using the second data abstraction.

Abstract: The present disclosure provides a system that allows for the real-time and online monitoring of the exchanges between customers and a CRM team over social media. While crawling all messages exchanged over the social media by customers and CRM team, the system aggregates related messages exchanged between a given customer and the CRM team into a conversation. The system includes a linguistic framework for the analysis of conversations (based on the two linguistic theories of dialog acts and conversation analysis) to label the nature of the messages in a conversation or thread.

Abstract: A system and method for characterizing textual data by generating a first data abstraction based on a set of textual data. The first data abstraction can be presented to a user, and the user can provide instructions to make changes to the first data abstraction to generate a second data abstraction. The textual data can be extracted and characterized from the set of textual data using the second data abstraction.

Abstract: A process discovery system that includes an offline system training module configured to cluster similar process log traces using Non-negative Matrix Factorization (NMF) with each cluster representing a process model, and learn a Conditional Random Field (CRF) model for each process model and an online system usage module configured to decode new incoming log traces and construct a process graph in which transitions are shown or hidden according to a tuning parameter.

Abstract: A process discovery system that includes an offline system training module configured to cluster similar process log traces using Non-negative Matrix Factorization (NMF) with each cluster representing a process model, and learn a Conditional Random Field (CRF) model for each process model and an online system usage module configured to decode new incoming log traces and construct a process graph in which transitions are shown or hidden according to a tuning parameter.

Abstract: A system and method for predicting the class of future customer calls to a call center. Saved call data is analyzed using a robust tokenizer of a computerized device. The tokenizer transforms a sequence of characters in a call summary field of the saved call data into a sequence of tokens. Tokenized call data is produced. Multiple maximum entropy (MaxEnt) models are created based on the tokenized call data, using the computerized device. The MaxEnt models produce a probability distribution of all classes for a next call to a call center. A conditional random field (CRF) classifier is trained with the MaxEnt models and information from the saved call data, using the computerized device. The CRF classifier uses chronologically ordered sequences of prior calls to the call center and predicts a class for a new call to the call center based on the saved call data. A call class prediction is produced for the new call received from a returning customer based on the CRF classifier and the MaxEnt model.

Abstract: The present disclosure provides a system that allows for the real-time and online monitoring of the exchanges between customers and a CRM team over social media. While crawling all messages exchanged over the social media by customers and CRM team, the system aggregates related messages exchanged between a given customer and the CRM team into a conversation. The system includes a linguistic framework for the analysis of conversations (based on the two linguistic theories of dialog acts and conversation analysis) to label the nature of the messages in a conversation or thread.

Abstract: Disclosed is a method and system for optimizing a workflow planning via the selection of service providers and data centers according to various QoS (Quality of Service) metrics. The algorithm handles multiple QoS parameters and does not require an a priori weighting of their importance, a typical requirement of other approaches to this problem. This is accomplished by using an algorithm that will guarantee that no alternative solution will be strictly better in all QoS criteria than the chosen solution. A variant of the algorithm to specify the preference order among the QoS parameters is also disclosed.