A temporal database contains time-varying data. In a real-time database transactions have deadlines or timing constraints. In this paper we review the substantial research in these two heretofore separate research areas. We first characterize the time domain, then investigate temporal and real-time data models. We evaluate temporal and real-time query languages along several dimensions. Temporal and real-time DBMS implementation is examined. We conclude with a summary of the major accomplishments of the research to date, and list several research questions that should be addressed next. Keywords: object-oriented database, relational databases, query language, temporal data model, time-constrained database, transaction time, user-defined time, valid time 1 Introduction Time is an important aspect of all real-world phenomena. Events occur at specific points in time; objects and the relationships among objects exist over time. The ability to model this temporal dimension of the real worl...

A new domain-independent knowledge-based inference structure is presented, specific to the task of abstracting higher-level concepts from time-stamped data. The framework includes a model of time, parameters, events, and contexts. A formal specification of a domains temporal-abstraction knowledge supports acquisition, maintenance, reuse, and sharing of that knowledge.

Problem-solving methods for knowledge-based systems establish the behavior of such systems by defining the roles in which domain knowledge is used and the ordering of inferences. Developers can compose problem-solving methods that accomplish complex application tasks from primitive, reusable methods. The key steps in this development approach are task analysis, method selection "from a library", and method configuration.

We have defined a knowledge-based framework for solving the task of creating abstract, intervalbased concepts from time-stamped clinical datathe knowledge-based temporal-abstraction (KBTA) method. The KBTA method decomposes the temporal-abstraction task into five tasks; a formal mechanism is proposed for solving each subtask. The KBTA framework emphasizes the explicit representation of the knowledge required for abstraction of time-oriented clinical data, and facilitates its acquisition, maintenance, reuse, and sharing. The RSUM system implements the KBTA method. We tested RSUM in several clinical domains in which the task of monitoring patients is prominent. In particular, we tested the KBTA framework in the domain of monitoring patients who have insulin-dependent diabetes. We acquired from a diabetes-therapy expert a diabetes-therapy temporal-abstraction knowledge base. Two diabetes-therapy experts (including the first one) created temporal abstractions relevant to the therapy-monitoring task from about 800 points of data from cases of diabetic patients. The RSUM system generated about 80% of the abstractions agreed by both experts; about 97% of the overall generated abstractions were valid. We discuss the advantages and limitations of the current architecture.

Provision of automated support for planning protocol-directed therapy requires a computer program to take as input clinical data stored in an electronic patient-record system, and to generate as output recommendations for therapeutic interventions and laboratory testing that are defined by applicable protocols. This paper presents a synthesis of research carried out at Stanford University to model the therapy-planning task, and to demonstrate a component-based architecture for building protocol-based decision-support systems. We have constructed general-purpose software components that (1) interpret abstract protocol specifications to construct appropriate patient-specific treatment plans; (2) infer from time-stamped patient data higher-level, interval-based, abstract concepts; (3) perform time-oriented queries on a timeoriented patient database; and (4) allow acquisition and maintenance of protocol knowledge in a manner that facilitates efficient processing both by humans and by computers. We have implemented these components in a computer system known as EON. Each of the components has been developed and evaluated independently. We have evaluated the integration of the components as a composite architecture by implementing T-HELPER, a computer-based patient-record system that uses EON to offer advice regarding the management of patients who have AIDS. A test of the reuse of the software components in a different clinical domain demonstrated rapid development of a prototype application to support protocol-based care of patients who have breast cancer.

PROTG-II is a suite of tools and a methodology for building knowledge-based systems and domain-specific knowledge-acquisition tools. In this paper, we show how PROTG-II can be applied to the task of providing protocol-based decision support in the domain of treating HIVinfected patients. For this task, we use a problem-solving method called episodic skeletal-plan refinement. This method is decomposable; we construct it from a set of reusable components. In addition, we build an application ontology that consists of the terms and relations in the domain, plus terms that supply method-specific knowledge requirements. From this ontology, we automatically generate a domain-specific knowledge-acquisition tool. The general goal of the PROTG-II approach is to produce systems and components that are easily maintained and reusable. This is the rationale for constructing a problem-solving method from a set of smaller-grained methods and mechanisms. This is also why our knowledge-acquisition tools are domain-specific and generated automatically from ontologies. Although our evaluation is still preliminary, for the application task of providing protocol-based decision support, we show that these goals of reusability and easy maintenance can be achieved. We discuss design decisions and the tradeoffs that have to be made in the development of the system. Keywords. Decision support; expert systems; knowledge acquisition.

Intensive care is becoming increasingly complex. If mistakes are to be avoided, there is a need for the large amount of clinical data to be presented effectively to the medical staff. Although the most common approach is to present the data graphically, it has been shown that textual summarisation can lead to improved decision making. As the first step in the BabyTalk project, a prototype is being developed which will generate a textual summary of 45 minutes of continuous physiological signals and discrete events (e.g.: equipment settings and drug administration). Its architecture brings together techniques from the different areas of signal analysis, medical reasoning, and natural language generation. Although the current system is still being improved, it is powerful enough to generate meaningful texts containing the most relevant information. This prototype will be extended to summarize several hours of data and to include clinical interpretation. 1

Temporal abstraction is the task of abstracting higher-level concepts from time-stamped data in a contextsensitive manner. We have developed and implemented a formal knowledge-based framework for decomposing and solving that task that supports acquisition, maintenance, reuse, and sharing of temporalabstraction knowledge.

Abstract: Medical diagnosis and therapy planning at modern intensive care units (ICUs) have been refined by the technical improvement of their equipment. However, the bulk of continuous data arising from complex monitoring systems in combination with discontinuously assessed numerical and qualitative data creates a rising information management problem at neonatal ICUs (NICUs). We developed methods for data validation and therapy planning which incorporate knowledge about point and interval data, as well as expected qualitative trend descriptions to arrive at unified qualitative descriptions of parameters (temporal data abstraction). Our methods are based on schemata for data-point transformation and curve fitting which express the dynamics of and the reactions to different degrees of parameters ' abnormalities as well as on smoothing and adjustment mechanisms to keep the qualitative descriptions stable. We show their applicability in detecting anomalous system behavior early, in recommending therapeutic actions, and in assessing the effectiveness of these actions within a certain period. We implemented our methods in VIE-VENT, an open-loop knowledge-based monitoring and therapy planning system for artificially ventilated newborn infants. The applicability and usefulness of our approach are illustrated by examples of VIE-VENT. Finally, we present our first experiences with using VIE-VENT in

ion Methods for Resource Allocation Berthe Y. Choueiry , Boi Faltings and Guevara Noubir Artificial Intelligence Laboratory Industrial Computing Laboratory Swiss Federal Institute of Technology (EPFL) IN-Ecublens, CH-1015 Lausanne, Switzerland E-mail: fchoueiry j faltings j noubir g@di.epfl.ch Abstract. The main difficulty in resource allocation is the enormous number of tasks and resources which have to be handled. This makes resource allocation a good candidate for proving the usefulness of abstraction methods, which summarize large amount of details and simplify problem solving. In this paper, we give a heuristic for finding groups of interchangeable tasks and resources, and operators for grouping them into common abstractions. We show how these abstractions simplify problem solving to the point where the user can intervene in the solution of the problem. These techniques have been implemented, but this paper is restricted to describing the methodology. 1. Introduction ...