Challenges in Scientific Data Management | What's New in Scientific Data to DBMS: Challenges, Features, and Optimizations

Mohamed Eltabakh

ISBN 10: 3659256862 / ISBN 13: 9783659256868

Published by LAP LAMBERT Academic Publishing, 2012

About this Item

Challenges in Scientific Data Management | What's New in Scientific Data to DBMS: Challenges, Features, and Optimizations | Mohamed Eltabakh | Taschenbuch | 180 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659256868 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 106200955

Bibliographic Details

• Title: Challenges in Scientific Data Management | What's New in Scientific Data to DBMS: Challenges, Features, and Optimizations
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2012
• Language: English
• ISBN 10: 3659256862
• ISBN 13: 9783659256868
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 12D millimeters 220 millimeters width by 150 millimeters height by 12 millimeters depth
• Weight: 286 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.

About the Author

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