Business Processes: A Database Perspective
While classic data management focuses on the data itself, research on Business Processes also considers the context in which this data is generated and manipulated, namely the processes, users, and goals that this data serves. This provides the analysts a better perspective of the organizational needs centered around the data. As such, this research is of fundamental importance. Much of the success of database systems in the last decade is due to the beauty and elegance of the relational model and its declarative query languages, combined with a rich spectrum of underlying evaluation and optimization techniques, and efficient implementations. Much like the case for traditional database research, elegant modeling and rich underlying technology are likely to be highly beneficiary for the Business Process owners and their users; both can benefit from easy formulation and analysis of the processes. While there have been many important advances in this research in recent years, there is still much to be desired: specifically, there have been many works that focus on the processes behavior (flow), and many that focus on its data, but only very few works have dealt with both the state-of-the-art in a database approach to Business Process modeling and analysis, the progress towards a holistic flow-and-data framework for these tasks, and highlight the current gaps and research directions. Table of Contents: Introduction / Modeling / Querying Business Processes / Other Issues / Conclusion
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Abiteboul activity name activity pair aggr Algorithm TOP-K allow analysis atomic activity boolean query bounded-history BPEL BPQL Business Process model business processes capture chooseTravel compound activity node computation consider context corresponding cWeight function data mining defined denote embedding Etessami and Yannakakis EX-pattern EX-traces Example execution flow exists exponential EXPTIME Finite State Machines flights flow and data flow rooted flows(s FTable full EX-flow guarding formulas history-independent input interface intuitive k-captures least fixed point Lemma likelihood likelihoodb logs manipulated matched modules nodes and edges nodes labeled observe obtained ofthe output particular Parts(p path polynomial possible implementation probabilistic probability process mining process model process specification projection queries PTIME query complexity query language query q recursive invocation Recursive State Machines root activity samples selection queries sub-flow rooted sub-queries sum semantics Theorem top-1 flow top-k top-k EX-flows transitive edge variables weighted BP specification workflow