Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and, typically, there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we have developed techniques for discovering workflow models. The starting point for such techniques is a so-called "workflow log" containing information about the workflow process as it is actually being executed. We present a new algorithm to extract a process model from such a log and represent it in terms of a Petri net. However, we also demonstrate that it is not possible to discover arbitrary workflow processes. We explore a class of workflow processes that can be discovered. We show that the /spl alpha/-algorithm can successfully mine any workflow represented by a so-called SWF-net.

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Workflow mining: discovering process models from event logs

Many of today's information systems are driven by explicit process models. Workflow management systems, but also ERP, CRM, SCM, and B2B, are configured on the basis of a workflow model specifying the order in which tasks need to be executed. Creating a workflow design is a complicated time-consuming process and typically there are discrepancies between the actual workflow processes and the processes as perceived by the management. To support the design of workflows, we propose the use of workflow mining. Starting point for workflow mining is a so-called "workflow log" containing information about the workflow process as it is actually being executed. In this paper, we introduce the concept of workflow mining and present a common format for workflow logs. Then we discuss the most challenging problems and present some of the workflow mining approaches available today.

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Workflow mining: a survey of issues and approaches

Conformance checking of processes based on monitoring real behavior

http://www.hpl.hp.com/techreports/2002/HPL-2002-119.pdf

Business process intelligence

The push toward business process automation has generated the need for integrating different enterprise applications involved in such processes. The typical approach to integration and to process automation is based on the use of adapters and message brokers. The need for adapters in Web services mainly comes from two sources: one is the heterogeneity at the higher levels of the interoperability stack, and the other is the high number of clients, each of which can support different interfaces and protocols, thereby generating the need for providing multiple interfaces to the same service. In this paper, we characterize the problem of adaptation of web services by identifying and classifying different kinds of adaptation requirements. Then, we focus on business protocol adapters, and we classify the different ways in which two protocols may differ. Next, we propose a methodology for developing adapters in Web services, based on the use of mismatch patterns and service composition technologies.

/pdf/developing-adapters-for-web-services-integration-kjd0313x0f.pdf

Developing adapters for web services integration

Business process automation technologies are being increasingly used by many companies to improve the efficiency of both internal processes as well as of e-services offered to customers. In order to satisfy customers and employees, business processes need to be executed with a high and predictable quality. In particular, it is crucial for organizations to meet the Service Level Agreements (SLAs) stipulated with the customers and to foresee as early as possible the risk of missing SLAs, in order to set the right expectations and to allow for corrective actions. In this paper we focus on a critical issue in business process quality: that of analyzing, predicting and preventing the occurrence of exceptions, i.e., of deviations from the desired or acceptable behavior. We characterize the problem and propose a solution, based on data warehousing and mining t We then describe the architecture and implementation of a tool suite that enables exception analysis, prediction, and prevention. Finally, we show experimental results obtained by using the tool suite to analyze internal HP processes.

/pdf/improving-business-process-quality-through-exception-3q9y6zfpr9.pdf

Improving Business Process Quality through Exception Understanding, Prediction, and Prevention

Interaction System based on Internet of Things as Support for Education

Finding useful services is a challenging and important task in several applications. Current approaches for services retrieval are mostly limited to the matching of their inputs/outputs. In this paper, we argue that in several applications (services having multiple and dependent operations and scientific workflows), the service discovery should be based on the specification of service behavior. The idea behind is to develop matching techniques that operate on behavior models and allow delivery of approximate matches and evaluation of semantic distance between these matches and the user requirements. To do so, we reduce the problem of behavioral matching to a graph matching problem and adapt existing algorithms for this purpose. To validate our approach, we developed a BPEL ranking platform that allows to find in a service repository, a set of service candidates satisfying user requirements, and then, to rank these candidates using a behavioral-based similarity measure.

Daniela Grigori

Papers

Business process intelligence

Developing adapters for web services integration

Improving Business Process Quality through Exception Understanding, Prediction, and Prevention

Interaction System based on Internet of Things as Support for Education

Ranking BPEL Processes for Service Discovery