Amit Sheth – författare

"As organizations have become more sophisticated, pressure to provide information sharing across dissimilar platforms has mounted. In addition, advances in distributed computing and networking combined with the affordable high level of connectivity, are making information sharing across databases closer to being accomplished...With the advent of the internet, intranets, and affordable network connectivity, business reengineering has become a necessity for modern corporations to stay competitive in the global market...An end-user in a heterogeneous computing environment should be able to not only invoke multiple exiting software systems and hardware devices, but also coordinate their interactions."--From the Introduction

Seventeen leaders in the field contributed chapters specifically for this unique book, together providing the most comprehensive resource on managing multidatabase systems involving heterogeneous and autonomous databases available today. The book covers virtually all fundamental issues, concepts, and major research topics.* Presents implications of distribution, autonomy, and heterogeneity on system architectures and management techniques* Discusses logical-level issues, including representation, semantics, schema integration, and languages* Covers system-level issues, with an emphasis on data consistency, recovery, and transaction management

The 37 revised full papers presented together with 40 short papers were carefully reviewed and selected from 166 submissions. The papers discuss a range of topics including: Semantic Web and Semantics; Graph Matching; Data Modeling, Data Abstraction, and Uncertainty; Preferences and Query Optimization; Data Integration and RDF Matching; Security and Privacy; Web Search; Data Clustering; Top-K and Skyline Queries; Data Mining and Big Data; Service Computing; Continuous and Temporal Data, and Continuous Query Language; Text Processing and Semantic Search; Indexing and Concurrency Control Methods; Data Warehouse and Data Stream Warehouse; Data Mining and Machine Learning; Recommender Systems and Query Recommendation; Graph Algorithms; Semantic Clustering and Data Classification.

The 37 revised full papers presented together with 40 short papers were carefully reviewed and selected from 166 submissions. The papers discuss a range of topics including: Semantic Web and Semantics; Graph Matching; Data Modeling, Data Abstraction, and Uncertainty; Preferences and Query Optimization; Data Integration and RDF Matching; Security and Privacy; Web Search; Data Clustering; Top-K and Skyline Queries; Data Mining and Big Data; Service Computing; Continuous and Temporal Data, and Continuous Query Language; Text Processing and Semantic Search; Indexing and Concurrency Control Methods; Data Warehouse and Data Stream Warehouse; Data Mining and Machine Learning; Recommender Systems and Query Recommendation; Graph Algorithms; Semantic Clustering and Data Classific