Emerging multidisciplinary research across database management systems

The database community is exploring more and more multidisciplinary avenues: Data semantics overlaps with ontology management; reasoning tasks venture into the domain of artificial intelligence; and data stream management and information retrieval shake hands, e.g., when processing Web click-streams. These new research avenues become evident, for example, in the topics that doctoral students choose for their dissertations. This paper surveys the emerging multidisciplinary research by doctoral students in database systems and related areas. It is based on the PIKM 2010, which is the 3rd Ph.D. workshop at the International Conference on Information and Knowledge Management (CIKM). The topics addressed include ontology development, data streams, natural language processing, medical databases, green energy, cloud computing, and exploratory search. In addition to core ideas from the workshop, we list some open research questions in these multidisciplinary areas

Emerging multidisciplinary research across database management systems

The database community is exploring more and more multidisciplinary avenues: Data semantics overlaps with ontology management; reasoning tasks venture into the domain of artificial intelligence; and data stream management and information retrieval shake hands, e.g., when processing Web click-streams. These new research avenues become evident, for example, in the topics that doctoral students choose for their dissertations. This paper surveys the emerging multidisciplinary research by doctoral students in database systems and related areas. It is based on the PIKM 2010, which is the 3rd Ph.D. workshop at the International Conference on Information and Knowledge Management (CIKM). The topics addressed include ontology development, data streams, natural language processing, medical databases, green energy, cloud computing, and exploratory search. In addition to core ideas from the workshop, we list some open research questions in these multidisciplinary areas

Identifying the Challenges for Optimizing the Process to Achieve Reproducible Results in E-science Applications

One of the major requirements for e-science applications\ud handling sensor data, is reproducibility of results. Several\ud optimization and scalability problems exist where the reproducibility of results remains guaranteed. Firstly, various data streams need to be coordinated to optimize the accuracy and processing of the results. Secondly, because of the high volume of streaming data and a series of processing steps to be performed on that data, demand for disk space may grow unacceptably high. Lastly, reproducibility in a decentralized scenario may be difficult to achieve because of data replication. This paper introduces and addresses these challenges which arise for optimizing the process of achieving reproducibility of results