Measuring and Querying Process Performance in Supply Chains: An Approach for Mining Big-Data Cloud Storages

AbstractSurvival in today's global environment means continuously improving processes, identifying and eliminating inefficiencies wherever they occur. With so many companies operating as part or all of complex distributed supply chain, gathering, collating and analyzing the necessary data to identify such improvement opportunities is extremely complex and costly. Although few solutions exist to correlate the data, it continues to be generated in vast quantities, rendering the use of highly scalable, cloud-based solutions for process analysis a necessity. In this paper we present an overview of an analytical framework for business activity monitoring and analysis, which has been realized using extremely scalable, cloud-based technologies. It provides a low-latency solution for entire supply chains or individual nodes in such chains to query process data stores in order to deliver business insight. A custom query language has been implemented which allows business analysts to design custom queries on processes and activities based on a standard set of process metrics. Ongoing developments are focused on testing and improving the scalability and latency of the system, as well as extending the query engine to increase its flexibility and performance

Search and Result Presentation in Scientific Workflow Repositories

We study the problem of searching a repository of complex hierarchical workflows whose component modules, both composite and atomic, have been annotated with keywords. Since keyword search does not use the graph structure of a workflow, we develop a model of workflows using context-free bag grammars. We then give efficient polynomial-time algorithms that, given a workflow and a keyword query, determine whether some execution of the workflow matches the query. Based on these algorithms we develop a search and ranking solution that efficiently retrieves the top-k grammars from a repository. Finally, we propose a novel result presentation method for grammars matching a keyword query, based on representative parse-trees. The effectiveness of our approach is validated through an extensive experimental evaluation

Service Querying to Support Process Variant Development

International audienceDeveloping process variants enables enterprises to effectively adapt their business models to different markets. Existing approaches focus on business process models to support the variant development. The assignment of services in a business process, which ensures the process variability, has not been widely examined. In this paper, we present an innovative approach that focuses on component services instead of process models. We target to recommend services to a selected position in a business process. We define the service composition context as the relationships between a service and its neighbors. We compute the similarity between services based on the matching of their composition contexts. Then, we propose a query language that considers the composition context matching for service querying. We developed an application to demonstrate our approach and performed different experiments on a public dataset of real process models. Experimental results show that our approach is feasible and efficient