B. Kamala

B. Kamala

Process mining is a new emerging research trend over the last decade which focuses on analyzing the processes using event log and data. The raising integration of information systems for the operation of business processes provides the basis for innovative data analysis approaches. Process mining has the strong relationship between with data mining so that it enables the bond between business intelligence approach and business process management. It focuses on end to end processes and is possible because of the growing availability of event data and new process discovery and conformance checking techniques. Process mining aims to discover, monitor and improve real processes by extracting knowledge from event logs readily available in todays information systems. The discovered process models can be used for a variety of analysis purposes. Many companies have adopted Process aware Information Systems for supporting their business processes in some form. These systems typically have their log events related to the actual business process executions. Proper analysis of Process Aware Information Systems execution logs can yield important knowledge and help organizations improve the quality of their services. This paper reviews and compares various process mining algorithms based on their input parameters, the techniques used and the output generated by them.

B. Kamala, J. M. Nandhini

Ontologies have become the effective modeling for various applications and significantly in the semantic web. The difficulty of extracting information from the web, which was created mainly for visualising information, has driven the birth of the semantic web, which will contain much more resources than the web and will attach machine-readable semantic information to these resources. Ontological bootstrapping on a set of predefined sources, such as web services, must address the problem of multiple, largely unrelated concepts. The web services consist of basically two components, Web Services Description Language (WSDL) descriptors and free text descriptors. The WSDL descriptor is evaluated using two methods, namely Term Frequency/Inverse Document Frequency (TF/IDF) and web context generation. The proposed bootstrapping ontological process integrates TF/IDF and web context generation and applies validation using the free text descriptor service, so that, it offers more accurate definition of ontologies. This paper uses ranking adaption model which predicts the rank for a collection of web service documents which leads to the automatic construction, enrichment and adaptation of ontologies.