Even though the design of business processes most often has to consolidate the knowledge of several process stakeholders, this fact is utilized only to a limited extent by existing modeling methodologies. We address this shortcoming in this paper by building an analogy between database schema design by view integration on the one hand and process modeling on the other hand. In particular, we specify a method for business process design by view integration starting from two views of a process as input. We identify formal semantic relationships between elements of the two process views which are then used to calculate the integrated process model applying the merge operator. Finally, the integrated model is optimized using reduction rules. A case study with two EPC business process models from the SAP reference model demonstrates the applicability of our approach.

Abstract. Schema integration is the activity of providing a unified representation of multiple data sources. The core problems in schema integration are: schema matching, i.e. the identification of correspondences, or mappings, between schema objects, and schema merging, i.e. the creation of a unified schema based on the identified mappings. Existing schema matching approaches attempt to identify a single mapping between each pair of objects, for which they are 100 % certain of its correctness. However, this is impossible in general, thus a human expert always has to validate or modify it. In this paper, we propose a new schema integration approach where the uncertainty in the identified mappings that is inherent in the schema matching process is explicitly represented, and that uncertainty propagates to the schema merging process, and finally it is depicted in the resulting integrated schema. 1

Abstract. Data integration is frequently performed between heterogeneous data sources, requiring that not only a schema, but also the data modelling language in which that schema is represented must be transformed between one data source and another. This paper describes an extension to the hypergraph data model (HDM), used in the AutoMed data integration approach, that allows constraint constructs found in static data modelling languages to be represented by a small set of primitive constraint operators in the HDM. In addition, a set of five equivalence preserving transformation rules are defined that operate over this extended HDM. These transformation rules are shown to allow a bidirectional mapping to be defined between equivalent relational, ER, UML and ORM schemas. The approach we propose provides a precise framework in which to compare data modelling languages, and precisely identifies what semantics of a particular domain one data model may express that another data model may not express. The approach also forms the platform for further work in automating the process of transforming between different data modelling languages. The use of the both-as-view approach to data integration means that a bidirectional association is produced between schemas in the data modelling language. Hence a further advantage of the approach is that composition of data mappings may be performed such that mapping two schemas to one common schema will produce a bidirectional mapping between the original two data sources.

Abstract Heterogeneous Business Process Modeling (BPM) schemas have been a problem for business process management throughout the last couple of years. Methodological guidance is needed in order to consolidate concurrent schema proposals especially in the BPM area. This paper discusses the applicability of schema integration for this purpose. We use the case of integrating XPDL 2.0 and BPEL 2.0 to highlight that schema integration is not able to cope with heterogeneous control flow representation of BPM schemas. We introduce a schema refactoring step that leads to integrated BPM schemas with less constructs. 1

Abstract. Model Management (MM) is a way of raising the level of abstraction in metadata intensive application areas. The key idea behind Model Management is to develop a set of generic algorithmic operators that work on schemas and mappings between schemas, rather than individual schema elements. In this demonstration we present a new approach to the implementation of MM operators based on schema transformation that provides some important advantages over existing methods. 1

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Abstract. In model management, the Merge operator takes as input a pair of schemas, together with a set of mappings between their objects, and returns an integrated schema. In this paper we present a new approach to implementing the Merge operator based on semantic mappings between objects. Our approach improves upon previous work by (1) using formal low-level transformation rules that can be translated into higher-level rules and (2) specifying precise BAV mappings, which merge schemas without any information loss or gain. 1

ABSTRACT: This paper focuses on business process engineering especially on alignment between business analysis and implementation. Through a Business Process Management approach, different transformations interfere on process models in order to make them executable. To keep the process model consistency from the business model to the IT one, we propose a pivotal metamodel-centric methodology. It aims at keeping or giving all requisite structural and semantic data needed to perform such transformations without loss of information. By this way we can ensure the alignment between business and IT. This article describes the concept of pivotal metamodel and proposes a methodology using such approach. In addition we present an example and the resulting benefits. KEYWORDS: Business Process Engineering, Metamodeling, Transformation, Alignment.

Abstract. The wide range of data sources available today means that the integration of heterogeneous data sources is now a common and important problem. It is even more challenging in a P2P environment where peers often do not know in advance which schemas of other peers will suit their information needs and there is potentially a greater diversity of data modelling languages in use. In this paper, we propose a new approach to P2P inter model data integration which supports multiple data models whilst allowing peers the flexibility of choosing how to integrate their schemas. 1