Proceedings Ninth International Symposium on Temporal Representation and Reasoning

Event notification services are used in various applications such as digital libraries, stock tickers, traffic control, or facility management. However to our knowledge, a common semantics of events in event notification services has not been defined so far. We propose a parameterized event algebra which describes the semantics of composite events for event notification systems. The parameters serve as a basis for flexible handling of duplicates in both primitive and composite events.

Chúng tôi giới thiệu một logic thời gian để lý luận về các ứng dụng toàn cầu. Trước tiên, chúng tôi định nghĩa một logic mô-đun cho địa phương tính, nhúng các lý thuyết địa phương của mỗi thành phần vào một lý thuyết về các trạng thái phân tán của hệ thống. Chúng tôi cung cấp cho logic một hệ thống tiên đề đầy đủ và chuyên sâu. Sau đó, chúng tôi mở rộng logic với một toán tử thời gian. Đóng góp của chúng tôi là khả năng lý luận về các tính chất liên quan đến nhiều thành phần theo cách tự nhiên, ngay cả khi không có đồng hồ toàn cầu, như được yêu cầu trong một môi trường bất đồng bộ.

Events employed in natural language semantics are characterized in terms of regular languages, each string in which can be regarded as a motion picture. The relevant finite automata then amount to movie cameras/projectors, or more formally, to finite Kripke structures with partial valuations. The usual regular constructs (concatenation, choice, etc) are supplemented with superposition of strings/automata/languages, realized model-theoretically as conjunction.

The disjunctive fuzzy temporal constraint network (DFTCN) model is a framework that allows reasoning with fuzzy qualitative and quantitative complex temporal constraints. However, its general complexity is exponential. In this work, we review methods that can reduce this complexity, based on graph topology and preprocessing. The final goal is to integrate all these methods into a general tool for temporal reasoning with DFTCN.

In this paper, we focus on an application and extension of artificial intelligence temporal reasoning techniques in order to represent and reason with temporal constraints in clinical guidelines. Particular attention is dedicated to the treatment of repeated (periodic) events, which play a major role in clinical therapies. We also discuss some limitations of our current approach, highlighting possible future enhancements. The work in this paper has been developed in the GLARE project, meant to realize a prototype of a domain-independent manager of clinical guidelines. The GLARE system has been built in cooperation with Azienda Ospedaliera S. Giovanni Battista of Turin, and has been successfully tested on different clinical domains.

Many natural languages use prepositions to mark relations between entities of various kinds-between physical entities and their spatial locations, between temporal entities and their temporal locations, between abstract entities of various kinds (e.g. between ideas and their 'mental locations'). I show that the consequences of using prepositions to relate temporal entities emerge naturally from the basic interpretations of the prepositions themselves together with a very weak logic of events, where I take it that prepositions denote abstract relations whose significance only emerges when properties of the related items are taken into consideration.

Two classical semantical approaches to studying logics which combine time and modality are the T /spl times/ W-frames and Kamp-frames (Thomason (1984)). In this paper we study a new kind of frame that extends the one introduced in Burrieza et al. (2002). The motivation is twofold: theoretical, i.e., representing properties of the basic theory of functions (definability); and practical, their use in computational applications (considering time-flows as memory of computers connected in a net, each computer with its own clock). Specifically, we present a temporal /spl times/ modal (labelled) logic, whose semantics are given by ind-functional frames in which accessibility functions are used in order to interconnect time-flows. This way, we can: (i) specify to what time-flow we want to go; (ii) carry out different comparisons among worlds with different time measures; and (iii) define properties of certain kinds of functions (in particular, of total, injective, surjective, constant, increasing and decreasing functions), without the need to resort to second-order theories. In addition, we define a minimal axiomatic system and give the completeness theorem (Henkin-style).

We evaluate a model for temporal data utilizing the benefits of object-relational database systems (ORDBS). In particular we show how attribute timestamping can be efficiently implemented in state-of-the-art ORDBS. The attribute timestamping concept is based on introducing user-specific types for temporal versions of datatypes. Moreover on the physical level we make use of user-defined index structures; in particular adapted spatial indexes based on generalized search trees are applied. These index structures greatly improve performance of important operators on both valid and bitemporal datatypes and show the effectiveness of this approach.

Local search techniques have attracted considerable interest in the artificial intelligence (AI) community since the development of GSAT (Selman et al., 1992) and the min-conflicts heuristic (Minton et al., 1992) for solving large propositional satisfiability (SAT) problems and binary constraint satisfaction problems (CSPs) respectively. Newer SAT techniques, such as the Discrete Langrangian Method (DLM) (Shang and Wah, 1998), have significantly improved on GSAT and can also be applied to general constraint satisfaction and optimisation. However, local search has yet to be successfully employed in solving temporal constraint satisfaction problems (TCSPs). We argue that current formalisms for representing TCSPs are inappropriate for a local search approach, and we propose an alternative CSP-based end-point ordering model for temporal reasoning. In particular we look at modelling and solving problems formulated using Allen's (1983) interval algebra (IA) and propose a new constraint weighting algorithm derived from DLM. Using a set of randomly generated IA problems, we show that our local search outperforms Nebel's (1997) backtracking algorithm on larger and more difficult consistent problems.

Summary form only given. Data expiration is an essential component of data warehousing solutions: whenever large amounts of data are repeatedly collected over a period of time, it is essential to have a clear approach to identifying parts of the data no-longer needed and a policy that allows disposing and/or archiving these parts of the data. Such policies are necessary even if adding storage to accommodate an ever-growing collection of data were possible, since the growing amount of data needs to be examined during querying and in turn leads to deterioration of query performance over time. The approaches to data expiration range from adhoc administrative policies or regulations to sophisticated data analysis-based techniques. The approaches have, however, one thing in common: intuitively, they try to identify the parts of the data collection that are not needed in the future. The key to deciding if a piece of information will be needed in the future lies in identifying what queries can be asked over the collection of data and how the collection can evolve from its current state. The various techniques proposed in the literature differ in the way they identify no longer needed parts of data. The author formalizes the notion of data expiration in terms of how the data is used to answer queries. He surveys existing approaches to the problem in a unified framework and discusses their features and limits and the limits of data expiration based techniques in general. Particular focus is on comparing the performance of various data expiration methods.