Jianwen Su is a Professor of Computer Science at the University of California, Santa Barbara. He received his BS/MS degrees from Fudan University and his PhD degree from the University of Southern California. He held visiting positions at INRIA and Bell Labs, was/is an adjunct professor at Fudan, Peking, and Donghua Universities in China. His research concerns data modeling and query languages, scientific databases, formal verification, web services, and business process/workflow management. His current work focuses on modeling and analysis of business processes concerning compositions and management with an emphasis on fully incorporation of data in logical models. His work on data with nested structures, incremental query evaluation, constraint databases, web services, and data-centric workflow is widely known and cited. Dr. Su received two IBM Faculty Awards, was a keynote speaker at several international workshops/conferences including ICSOC 2012 and WS-FM 2013. He served/is serving on program committees of many conferences in databases (PODS, ICDT, VLDB, ICDE, EDBT, etc.) and services computing (ICSOC, BPM, WS-FM, ICWE, ICWS, etc.). He was a general co-chair of ICSOC 2013, the general chair of SIGMOD 2001, the PC chair of PODS 2009, and a program co-chair of a few other database/services computing conferences. He served on the Executive Committees of ACM SIGMOD and PODS, editorial boards for IJFCS and JCST, and is an associate editor of IJCIS.
The conceptual elevation of data in business workflow modeling was first proposed by Nigam and Caswell in their 2003 paper. The research community responded to this new idea enthusiastically. In the past decade, there have been numerous research activities concerning the interactions between business workflow/activities and data in many aspects of business workflow management. Earlier work on workflow with data focused on development of workflow modeling languages that incorporate modeling of data needed for workflow, workflow modeling tools concerning formal verification and collaborative support for workflow, monitoring of KPIs, mining process models in presence of data, runtime workflow data management, business-process-as-a-service, etc. Many of these advancements have or will have impact on design/modeling, analysis, implementation of individual business workflow models in the development of workflow management (software) systems.
However, there is still a lack of techniques for developing a suite of “interrelated” workflows, an important challenge for the second decade. Interrelated workflows are a cluster of workflows (models and/or enactments) that share data and other resources, are collectively constrained by regulations and policies, influence KPIs as a group, and likely execute in the same administrative organizations. Many current practical applications of workflow systems are in urgent need for tools and techniques for workflow clusters. In this talk, we formulate a broad “Enterprise Process Framework” (EPF) to address this need and outline a few initial technical details. An EPF combines techniques developed in data integration and data-centric workflow research, elevates data modeling from individual workflow models to a cluster of interrelated workflow models, considers runtime data management issues for all workflows in the group. We discuss several interesting research challenges arising from EPF. (This is a joint work with Lijie Wen and Jian Yang.)