This volume represents a valuable collective contribution to the research and development of database systems. It contains papers in a variety of topics such as data models, distributed databases, multimedia databases, concurrency control, hypermedia and document processing, user interface, query processing and database applications.

This volume brings together papers by experts in different areas of computer science, who have a common interest in the design and management of visual interfaces. Since cognitive science and metaphor analysis prove useful for understanding the basic mechanisms which allow visual interfaces to be easy to learn and use, these topics are also featured. Other areas focused on are: visual languages, visual database systems, intelligent agents for system interaction, graphical and pictorial communication tools, multimedia environments and specific technological developments.

As its title suggests, "Uncertainty Management in Information Systems" is a book about how information systems can be made to manage information permeated with uncertainty. This subject is at the intersection of two areas of knowledge: information systems is an area that concentrates on the design of practical systems that can store and retrieve information; uncertainty modeling is an area in artificial intelligence concerned with accurate representation of uncertain information and with inference and decision-making under conditions infused with uncertainty. New applications of information systems require stronger capabilities in the area of uncertainty management. Our hope is that lasting interaction between these two areas would facilitate a new generation of information systems that will be capable of servicing these applications. Although there are researchers in information systems who have addressed themselves to issues of uncertainty, as well as researchers in uncertainty modeling who have considered the pragmatic demands and constraints of information systems, to a large extent there has been only limited interaction between these two areas. As the subtitle, "From Needs to Solutions," indicates, this book presents view points of information systems experts on the needs that challenge the uncer tainty capabilities of present information systems, and it provides a forum to researchers in uncertainty modeling to describe models and systems that can address these needs.

Computing the Brain provides readers with an integrated view of current informatics research related to the field of neuroscience. This book clearly defines the new work being done in neuroinformatics and offers information on resources available on the Web to researchers using this new technology. It contains chapters that should appeal to a multidisciplinary audience with introductory chapters for the nonexpert reader. Neuroscientists will find this book an excellent introduction to informatics technologies and the use of these technologies in their research. Computer scientists will be interested in exploring how these technologies might benefit the neuroscience community. - An integrated view of neuroinformatics for a multidisciplinary audience - Explores and explains new work being done in neuroinformatics - Cross-disciplinary with chapters for computer scientists and neuroscientists - An excellent tool for graduate students coming to neuroinformatics research from diverse disciplines and for neuroscientists seeking a comprehensive introduction to the subject - Discusses, in-depth, the structuring of masses of data by a variety of computational models - Clearly defines computational neuroscience - the use of computational techniques and metaphors to investigate relations between neural structure and function - Offers a guide to resources and algorithms that can be found on the Web - Written by internationally renowned experts in the field

Time is ubiquitous in information systems. Almost every enterprise faces the problem of its data becoming out of date. However, such data is often valu able, so it should be archived and some means to access it should be provided. Also, some data may be inherently historical, e.g., medical, cadastral, or ju dicial records. Temporal databases provide a uniform and systematic way of dealing with historical data. Many languages have been proposed for tem poral databases, among others temporal logic. Temporal logic combines ab stract, formal semantics with the amenability to efficient implementation. This chapter shows how temporal logic can be used in temporal database applica tions. Rather than presenting new results, we report on recent developments and survey the field in a systematic way using a unified formal framework [GHR94; Ch094]. The handbook [GHR94] is a comprehensive reference on mathematical foundations of temporal logic. In this chapter we study how temporal logic is used as a query and integrity constraint language. Consequently, model-theoretic notions, particularly for mula satisfaction, are of primary interest. Axiomatic systems and proof meth ods for temporal logic [GHR94] have found so far relatively few applications in the context of information systems. Moreover, one needs to bear in mind that for the standard linearly-ordered time domains temporal logic is not re cursively axiomatizable [GHR94]' so recursive axiomatizations are by necessity incomplete.