Challenges in Scientific Data Management

The growth of scientific information and the increasing automation of data collection have made databases integral to many scientific disciplines including life sciences, physics, meteorology, and chemistry. These sciences pose new data management challenges to current DBMSs. This book addresses three key challenges in scientific data management: (1) Annotation Management: Annotations are important metadata that go hand-in-hand with scientific data. However, a major challenge is how to manage large volumes of annotations along with their corresponding data items. (2) Complex Dependencies Involving Real-world Activities: The processing of scientific data is complex and may involve sequences of activities external to the database system, e.g., wet-lab experiments, and manual measurements. The challenge is how to efficiently integrate these activities within the database engine. And (3) Fast Access to Scientific Data: Scientific experiments produce large volumes of data of complex types, e.g., arrays, images, and long sequences. A major challenge is how to provide fast access to these large pools of scientific data with non-traditional data types.

Autorentext

Is an Assistant Professor of computer science at Worcester Polytechnic Institute. His research interest is in the broad area of Database Management Systems and Information Management. His work is in the areas of query processing and optimization, indexing techniques, and large-scale data analytics.He received his Ph.D from Purdue University in 2010