Designing data marts for data warehouses
ACM Transactions on Software Engineering and Methodology (TOSEM)
Visual Web Information Extraction with Lixto
Proceedings of the 27th International Conference on Very Large Data Bases
WS-Specification: Specifying Web Services Using UDDI Improvements
Revised Papers from the NODe 2002 Web and Database-Related Workshops on Web, Web-Services, and Database Systems
The Lixto data extraction project: back and forth between theory and practice
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Using the web service modeling ontology to enable semantic e-business
Communications of the ACM - The semantic e-business vision
Event-driven Dynamic Web Services Composition and Automation of Business Processes
SCC '06 Proceedings of the IEEE International Conference on Services Computing
Designing data-intensive web applications for content accessibility using web marts
Communications of the ACM
Towards context-aware semantic web service discovery through conceptual situation spaces
Proceedings of the 2008 international workshop on Context enabled source and service selection, integration and adaptation: organized with the 17th International World Wide Web Conference (WWW 2008)
Investigating web services on the world wide web
Proceedings of the 17th international conference on World Wide Web
Implementing Semantic Web Services: The SESA Framework
Implementing Semantic Web Services: The SESA Framework
Applied Ontology
URBE: Web Service Retrieval Based on Similarity Evaluation
IEEE Transactions on Knowledge and Data Engineering
Web Services: Concepts, Architectures and Applications
Web Services: Concepts, Architectures and Applications
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The use of patterns in data management is not new: in data warehousing, data marts are simple conceptual schemas with exactly one core entity, describing facts, surrounded by multiple entities, describing data analysis dimensions; data marts support special analysis operations, such as roll up, drill down, and cube. Similarly, service marts are simple schemas which match "Web objects" by hiding the underlying data source structures and presenting a simple interface, consisting of input, output, and rank attributes; attributes may have multiple values and be clustered within repeating group. Service marts support Search Computing operations, such as ranked access and joins. When objects are accessed through service marts, responses are ranked lists of objects, which are presented subdivided in chunks, so as to avoid receiving too many irrelevant objects – cutting results and showing only the best ones is typical of search services. This chapter includes a survey of service definition standards (discussing the standards for service description and the current state-of-the-art for service registration and discovery), then introduces a formal definition of service marts and of connection patterns at the conceptual, logical, and physical levels. Then, we show how service marts can be implemented, by taking into account different kinds of data sources, and taking advantage of components (written in Java and SQL) and tools (such as a materialize specifically developed to help service mart implementation). We use such components and tools to build a collection of services used in a running example throughout the chapters of this part.