Knowledge discovery in databases presents many interesting challenges within the context of providing computer tools for exploring large data archives. Electronic data repositories are growing quickly and contain data from commercial, scientific, and other domains. Much of this data is inherently temporal, such as stock prices or NASA telemetry data. Detecting patterns in such data streams or time series is an important knowledge discovery task. This paper describes some preliminary experiments with a dynamic programming approach to the problem. The pattern detection algorithm is based on the dynamic time warping technique used in the speech recognition field.

Using dynamic time warping to find patterns in time series

A large number of problems in AI and other areas of computer science can be viewed as special cases of the constraint-satisfaction problem. Some examples are machine vision, belief maintenance, scheduling, temporal reasoning, graph problems, floor plan design, the planning of genetic experiments, and the satisfiability problem. A number of different approaches have been developed for solving these problems. Some of them use constraint propagation to simplify the original problem. Others use backtracking to directly search for possible solutions. Some are a combination of these two techniques. This article overviews many of these approaches in a tutorial fashion.

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Algorithms for constraint-satisfaction problems: a survey

Metaheuristics are the most exciting development in approximate optimization techniques of the last two decades. They have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas. This bibliography provides a classification of a comprehensive list of 1380 references on the theory and application of metaheuristics. Metaheuristics include but are not limited to constraint logic programming; greedy random adaptive search procedures; natural evolutionary computation; neural networks; non-monotonic search strategies; space-search methods; simulated annealing; tabu search; threshold algorithms and their hybrids. References are presented in alphabetical order under a number of subheadings.

Metaheuristics: A bibliography

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 previously separate areas. First we characterize the time domain; then we investigate temporal and real-time data models. We evaluate temporal and real-time query languages along several dimensions. We examine temporal and real-time DBMS implementation. Finally, we summarize major research accomplishments to date and list several unanswered research questions. >

Temporal and real-time databases: a survey

: The satisfiability (SAT) problem is a core problem in mathematical logic and computing theory. In practice, SAT is fundamental in solving many problems in automated reasoning, computer aided design, computer aided manufacturing, machine vision, database, robotics, integrated circuit design, computer architecture design, and computer network design. Traditional methods treat SAT as a discrete, constrained decision problem. In recent years, many optimization methods, parallel algorithms, and practical techniques have been developed for solving SAT. In this survey, we present a general framework (an algorithm space) that integrates existing SAT algorithms into a unified perspective. We describe sequential and parallel SAT algorithms including variable splitting, resolution, local search, global optimization, mathematical programming, and practical SAT algorithms. We give performance evaluation of some existing SAT algorithms. Finally, we provide a set of practical applications of the satisfiability problems.

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Algorithms for the Satisfiability (SAT) Problem: A Survey,

Numerous proposals for extending the relational data model to incorporate the temporaldimension of data have appeared in the past several years. These proposals have differedconsiderably in the way that the temporal dimension has been incorporated both into thestructure of the extended relations of these temporal models, and consequently into theextended relational algebra or calculus that they define. Because of these differences it hasbeen difficult to compare the proposed models and to make judgments as to which of themmight in some sense be equivalent or even better. In this paper we define the notions oftemporally grouped and temporally ungrouped historical data models and proposetwo notions of historical relational completeness, analogous to Codd's notion of relationalcompleteness, one for each type of model. We show that the temporally ungroupedmodels are less powerful than the grouped models, but demonstrate a technique for extendingthe ungrouped models with a grouping mechanism to capture the additional semanticpower of temporal grouping. For the ungrouped models we define three different languages,a temporal logic, a logic with explicit reference to time, and a temporal algebra, and showthat under certain assumptions all three are equivalent in power. For the grouped modelswe define a many-sorted logic with variables over ordinary values, historical values, andtimes. Finally, we demonstrate the equivalence of this grouped calculus and the ungroupedcalculus extended with the proposed grouping mechanism. We believe the classification ofhistorical data models into grouped and ungrouped provides a useful framework for thecomparison of models in the literature, and furthermore the exposition of equivalent languagesfor each type provides reasonable standards for common, and minimal, notions ofhistorical relational completeness.

On Completeness of Historical Relational Query Languages

Numerous proposals for extending the relational data model to incorporate the temporal dimension of data have appeared in the past several years. These proposals have differed considerably in the way that the temporal dimension has been incorporated both into the structure of the extended relations of these temporal models and into the extended relational algebra or calculus that they define. Because of these differences, it has been difficult to compare the proposed models and to make judgments as to which of them might in some sense be equivalent or even better. In this paper we define temporally grouped and temporally ungrouped historical data models and propose two notions of historical relational completeness, analogous to Codd's notion of relational completeness, one for each type of model. We show that the temporally ungrouped models are less expressive than the grouped models, but demonstrate a technique for extending the ungrouped models with a grouping mechanism to capture the additional semantic power of temporal grouping. For the ungrouped models, we define three different languages, a logic with explicit reference to time, a temporal logic, and a temporal algebra, and motivate our choice for the first of these as the basis for completeness for these models. For the grouped models, we define a many-sorted logic with variables over ordinary values, historical values, and times. Finally, we demonstrate the equivalence of this grouped calculus and the ungrouped calculus extended with a grouping mechanism. We believe the classification of historical data models into grouped and ungrouped models provides a useful framework for the comparison of models in the literature, and furthermore, the exposition of equivalent languages for each type provides reasonable standards for common, and minimal, notions of historical relational completeness.

On completeness of historical relational query languages

In this paper we address some of the concerns that have been expressed regarding the practicality of the temporally-grouped, or history-oriented, data modeling approach. Specifically, we address the concern over the lack of an algebra for this paradigm, by presenting such an algebra. In addition, we examine the semantics of two notions, coalescing and restructuring, from the perspective of a comparison between the temporally grouped and temporally ungrouped paradigms.

On Temporal Grouping

The authors describe an implemented system supporting the maintenance of evolving symbolic models and the spreadsheet-like algebraic models based on them. The system's primitives are knowledge fragments that are instantiated into symbolic models; these fragments can be modified in response to changes in the task environment, and appropriate changes induced in the algebraic model to reflect changes in the symbolic model. They analyze model features for problem-solving in domains with three levels of structuredness: highly formalized problems. The authors define data structures used for representing choice sets and constraints and describe the system's procedural knowledge component, which allows knowledge represented in the structures to be used in an integrated way. >

Albert Croker

Papers

On Completeness of Historical Relational Query Languages

On completeness of historical relational query languages

On Temporal Grouping

Knowledge-based decision support in business: issues and a solution

Knowledge-based decision support in business: Issues and a solution