Zhilei Ma

Towards Measuring Key Performance Indicators of Semantic Business Processes

Business Activity Monitoring (BAM) enables continuous, real-time performance measurement of business processes based on key performance indicators (KPI). The performance information is employed by business users but prior support from IT engineers is required for setting up the BAM solution. Semantic Business Process Management (SBPM) tries to minimize the needed support from IT staff throughout the business process lifecycle. In this paper we introduce a framework for BAM as part of SBPM. We show how performance measurement related activities can be integrated into the semantic business process lifecycle. KPIs are modeled by business analysts exploiting semantic annotations of business processes. KPI models are automatically transformed to IT-level event-based models and used for real-time monitoring using reasoning technology.

Ontology Reasoning with Large Data Repositories

As reasoning with large amounts of data together with ontological knowledge is becoming an increasingly more pertinent issue, we will give in this chapter an overall introduction to some well-known ontology repositories, including native stores and database based stores, and highlight strengths and limitations of each store. We take Minerva as an example to analyze ontology storage in databases in depth, as well as to discuss efficient indexes for scaling up ontology repositories. We then discuss a scalable reasoning method for handling expressive ontologies, as well as summarize other similar approaches. We will subsequently delve into the details of one particular ontology language based on Description Logics called WSML-DL and we will show that reasoning with this language can be done by a transformation from WSML-DL to OWL DL and supports all main DL specific reasoning tasks. Finally, we want to make reasoning a bit more tangible by showing a reasoning example in a practical business context: we thus present the Semantic Business Process Repository (SBPR) for systemical management of semantic business process models. We first analyze the main requirements on a SBPR. After the comparison of different approaches for storage mechanisms, we conclude that a RDBMS with the IRIS inference engine integrated is, due to the expressiveness of the query language and the reasoning capability, a suitable solution.

BPEL Fragments for Modularized Reuse in Modeling BPEL Processes

BPEL has been established as the standard for modeling business processes by orchestrating Web services. When modeling a BPEL process, users end up in basically three approaches: from scratch, by using a process template or a reference process, and by ad hoc modifying a existing process model to meet the current requirements. There is strong demand on a flexible and modularized approach for reusing BPEL process models. As neither the BPEL standard nor the current BPEL extensions support such a feature for reuse, we present in this paper a formal definition for BPEL fragments. In this paper we first study the related work on process fragments and distinguish BPEL fragments from existing reuse approaches in service-oriented process modeling. Based on the requirement analysis we present a definition of BPEL fragments in XML schema. By adopting our definition of BPEL fragments, the flexibility of modularized reuse of BPEL process models can be significantly increased.

Query Structural Information of BPEL Processes

Reuse of proven process models increases modeling efficiency and ensure the quality of process models. To provide a better support for reuse, the retrieval mechanisms of process repositories should be able to propose similar process models that ranked according to their similarity degrees to the query request. As a process model and a query request on process structure can both be viewed as rooted, directed, and acyclic graphs, the problem of querying structural information of BPEL processes can be reduced to a graph matchmaking problem. In this paper we present a novel and efficient graph-based algorithm for querying structural information of BPEL processes based on an incomplete matchmaking semantics. Our algorithm performs in the worst case in polynomial time in the orders of the query graph and the process graph.