Ontology-based end-user visual query formulation: Why, what, who, how, and which?

Value creation in an organisation is a time-sensitive and data-intensive process, yet it is often delayed and bounded by the reliance on IT experts extracting data for domain experts. Hence, there is a need for providing people who are not professional developers with the flexibility to pose relatively complex and ad hoc queries in an easy and intuitive way. In this respect, visual methods for query formulation undertake the challenge of making querying independent of users’ technical skills and the knowledge of the underlying textual query language and the structure of data. An ontology is more promising than the logical schema of the underlying data for guiding users in formulating queries, since it provides a richer vocabulary closer to the users’ understanding. However, on the one hand, today the most of world’s enterprise data reside in relational databases rather than triple stores, and on the other, visual query formulation has become more compelling due to ever-increasing data size and complexity—known as Big Data. This article presents and argues for ontology-based visual query formulation for end-users; discusses its feasibility in terms of ontology-based data access, which virtualises legacy relational databases as RDF, and the dimensions of Big Data; presents key conceptual aspects and dimensions, challenges, and requirements; and reviews, categorises, and discusses notable approaches and systems

A Friendly and Intelligent Approach to Data Retrieval in a Multimedia DBMS

Manipulation of multimedia data is not straightforward as in conventional database. One main problem is the retrieval of multimedia data from the database with the need to match the contents of multimedia data to a user query. In order to achieve a content based retrieval in our approach, we use natural language captions which allow the user to describe the contents of multimedia data. In a similar manner, users will specify their queries on multimedia data contents in natural language form. A problem is that different or even the same user describe the same thing differently at different times which results in the descriptions of the contents of multimedia data to rarely exactly match the descriptions of the user queries. Hence, partial or approximate match between descriptions of multimedia data and user queries is generally required during multimedia data retrieval. We propose an intelligent approach to approximate match by integrating both object-oriented and natural language understanding techniques. In order to make the query specification process easier we also develop a graphical user interface supporting incremental query specification and a natural way of expressing joins. The Multimedia Database Management System (MDBMS) described in this paper incorporates the capabilities as mentioned aboveNaval Ocean Systems Center, San Diego, CAhttp://archive.org/details/friendlyintellig00keimOM&N Direct FundingNAApproved for public release; distribution is unlimited

Concept Membership Modeling Using a Choquet Integral

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