No abstract

No abstract

We formalize and study business process systems that are centered around "business artifacts", or simply "artifacts". Artifacts are used to represent (real or conceptual) key business entities, including both their data schema and lifecycles. The lifecycle of an artifact type specifies the possible sequencings of services that can be applied to an artifact of this type as it progresses through the business process. The artifact-centric approach was introduced by IBM, and has been used to achieve substantial savings when performing business transformations.

Systems for managing and querying semistructured-data sources often store data in proprietary object repositories or in a tagged-text format. We describe a technique that can use relational database management systems to store and manage semistructured data. Our technique relies on a mapping between the semistructured data model and the relational data model, expressed in a query language called STORED. When a semistrcutured data instance is given, a STORED mapping can be generated automatically using data-mining techniques. We are interested in applying STORED to XML data, which is an instance of semistructured data. We show how a document-type-descriptor (DTD), when present, can be exploited to further improve performance.

We state and solve the query reformulation problem for XML publishing in a general setting that allows mixed (XML and relational) storage for the proprietary data and exploits redundancies (materialized views, indexes and caches) to enhance performance. The correspondence between published and proprietary schemas is specified by views in both directions, and the same algorithm performs rewriting-with-views, compositionwith-views, or the combined effect of both, unifying the Global-As-View and Local-As-View approaches to data integration. We prove a completeness theorem which guarantees that under certain conditions, our algorithm will and a minimal reformulation if one exists. Moreover, we identify conditions when this algorithm achieves optimal complexity bounds. We solve the reformulation problem for constraints by exploiting a reduction to the problem of query reformulation.