Bücher von Kevin Shaw

We are facing a rapidly growing capability to collect more and more data regarding our environment. With that, we must have the ability to extract more insightful knowledge about the environmental processes at work on the earth. Spatio-Temporal Information Systems (STIS) will especially prove beneficial in producing useful knowledge about changes in our world from these ever burgeoning collections of environment data.STIS provide the ability to store, analyze and represent the dynamic properties of the environment, that is, geographic information in space and time. An STIS, for example, can produce a weather map, but more importantly, it can present a user with information in map or report form indicating how precipitation progresses in space over time to affect a watershed. Other uses include forestry and even electrical systems management. Forestry experts using an STIS are able to examine the rates of movements of forest fires, how they evolve over time, and their impact on forest growth over long periods of time. A large electrical network system manager uses an STIS to track the failures and repairs of electrical transformers. Use of an STIS in this case allows the reconstruction of the status of the network at any given past time. Mining Spatio-Temporal Information Systems, an edited volume is composed of chapters from leading experts in the field of Spatial-Temporal Information Systems and addresses the many issues in support of modeling, creation, querying, visualizing and mining. Mining Spatio-Temporal Information Systems is intended to bring together a coherent body of recent knowledge relating to STIS data modeling, design, implementation and STIS in knowledge discovery. In particular, the reader is exposed to the latest techniques for the practical design of STIS, essential for complex query processing. Mining Spatio-Temporal Information Systems is structured to meet the needs of practitioners and researchers in industry and graduate-level students in Computer Science.

Researchers in data management have recently recognized the importance of a new class of data-intensive applications that requires managing data streams, i.e., data composed of continuous, real-time sequence of items. Streaming applications pose new and interesting challenges for data management systems. Such application domains require queries to be evaluated continuously as opposed to the one time evaluation of a query for traditional applications. Streaming data sets grow continuously and queries must be evaluated on such unbounded data sets. These, as well as other challenges, require a major rethink of almost all aspects of traditional database management systems to support streaming applications. Stream Data Management comprises eight invited chapters by researchers active in stream data management. The collected chapters provide exposition of algorithms, languages, as well as systems proposed and implemented for managing streaming data. Stream Data Management is designed to appeal to researchers or practitioners already involved in stream data management, as well as to those starting out in this area. This book is also suitable for graduate students in computer science interested in learning about stream data management.