DEC 31, 1969

Web Seminars

Turning the IoT into opportunities
January 31, 2017

Advances in Distributed and Parallel Knowledge Discovery

Print
Reprints
Email

Knowledge discovery and data mining (KDD) deals with the problem of extracting interesting associations, classifiers, clusters, and other patterns from data. The emergence of network-based distributed computing environments has introduced an important new dimension to this problem--distributed sources of data. Traditional centralized KDD typically requires central aggregation of distributed data, which may not always be feasible because of limited network bandwidth, security concerns, scalability problems, and other practical issues. Distributed knowledge discovery (DKD) works with the merger of communication and computation by analyzing data in a distributed fashion. This technology is particularly useful for large heterogeneous distributed environments such as the Internet, intranets, mobile computing environments, and sensor-networks.

Click here to buy this book

Please note you must now log in with your email address and password.