Constraint-based Interoperability of Spatiotemporal Databases*

We propose constraint databases as an intermediate level facilitating the interoperability of spatiotemporal data models. Constraint query languages are used to express translations between different data models. We illustrate our approach in the context of a number of temporal, spatial, and spatiotemporal data models.     

Experimental Analysis of Numeric and Symbolic Constraint Satisfaction Techniques for Temporal Reasoning

Many temporal applications like planning and scheduling can be viewed as special cases of the numeric and symbolic temporal constraint satisfaction problem. Thus we have developed a temporal model, TemPro, based on the interval Algebra, to express such applications in term of qualitative and quantitative temporal constraints. TemPro extends the interval algebra relations of Allen to handle numeric information. To solve a constraint satisfaction problem, different approaches have been developed. These approaches generally use constraint propagation to simplify the original problem and backtracking to directly search for possible solutions. The constraint propagation can also be used during the backtracking to improve the performance of the search. The objective of this paper is to assess different policies for finding if a TemPro network is consistent. The main question we want to answer here is how much constraint propagation is useful for finding a single solution for a TemPro constraint graph. For this purpose, we have experimented by randomly generating large consistent networks for which either arc and/or path consistency algorithms (AC-3, AC-7 and PC-2) were applied. The main result of this study is an optimal policy combining these algorithms either at the symbolic (Allen relation propagation) or at the numerical level.     

The CCUBE Constraint Object-Oriented Database System

Constraints provide a flexible and uniform way to represent diverse data capturing spatio-temporal behavior, complex modeling requirements, partial and incomplete information etc, and have been used in a wide variety of application domains. Constraint databases have recently emerged to deeply integrate data captured by constraints in databases. This paper reports on the development of the first constraint object-oriented database system, CCUBE, and describes its specification, design and implementation. The CCUBE system is designed to be used for the implementation and optimization of high-level constraint object-oriented query languages as well as for directly building software systems requiring extensible use of constraint database features. The CCUBE data manipulation language, Constraint Comprehension Calculus, is an integration of a constraint calculus for extensible constraint domains within monoid comprehensions, which serve as an optimization-level language for object-oriented queries. The data model for the constraint calculus is based on constraint spatio-temporal (CST) objects that may hold spatial, temporal or constraint data, conceptually represented by constraints. New CST objects are constructed, manipulated and queried by means of the constraint calculus. The model for the monoid comprehensions, in turn, is based on the notion of monoids, which is a generalization of collection and aggregation types. The focal point of our work is achieving the right balance between the expressiveness, complexity and representation usefulness, without which the practical use of the system would not be possible. To that end, CCUBE constraint calculus guarantees polynomial time data complexity, and, furthermore, is tightly integrated with the monoid comprehensions to allow deeply interleaved global optimization.     

基于时态约束的关联规则挖掘算法

分析时态约束关联规则挖掘的TCAR算法,针对其在挖掘频繁时态项集时效率较低的问题,提出一种基于时态约束的关联规则挖掘算法。该算法对源数据库进行处理,缩减存储空间,并在更新挖掘最大频繁项集算法基础上,挖掘最大频繁时态项集。实例分析结果表明,该算法能提高整体挖掘效率。     

Exact and approximate reasoning about temporal relations1

Allen gives an algebra for representing qualitative temporal information about the relationships between pairs of intervals. In this paper, we address a fundamental reasoning task that arises in applications of the algebra: Given (possibly indefinite) knowledge about the relationships between intervals, find all feasible relationships between two intervals. We call this the minimal labels problem. Finding the minimal labels can be viewed as computing the deductive consequences of our knowledge. Determining exact solutions to this problem has been shown to be (almost assuredly) intractable. Allen gives an approximation algorithm based on constraint propagation. We present new approximation algorithms; determine analytically under what conditions the algorithms are exact; and examine, through some computational experiments, the quality of the approximate solutions produced by the algorithms. We also give a simple test for predicting when the approximation algorithms will and will not produce good quality approximations. Finally, we survey three example applications of the interval algebra chosen from the literature to show where the results of this paper could be useful.     

A STRENGTHENED ALGORITHM FOR TEMPORAL REASONING ABOUT PLANS

Allen's interval algebra has been shown to be useful for representing plans. We present a strengthened algorithm for temporal reasoning about plans, which improves on straightforward applications of the existing reasoning algorithms for the algebra. This is made possible by viewing plans as both temporal networks and hierarchical structures. The temporal network view allows us to check for inconsistencies as well as propagate the effects of new temporal constraints, whereas the hierarchical view helps us to get the strengthened results by taking into account the dependency relationships between actions.
We further apply our algorithm to the process of plan recognition through the analysis of natural language input. We show that such an application has two useful effects: the temporal relations derived from the natural language input can be used as constraints to reduce the number of candidate plans, and the derived constraints can be made more specific by combining them with the prestored constraints in the plans being recognized.     

一种加权时态关联规则挖掘算法

许多现实数据库都存在时态语义问题,因此在挖掘关联规则时附加上时态约束会使规则更具有实际意义。但目前提出的大多数时态关联规则挖掘算法,一般都认为每个数据项的重要性相同,而从决策者角度出发,往往会优先考虑利润较高的项目。提出了一种加权时态关联规则挖掘算法,以项目的生命周期作为时间特征,允许用户设定不同的项目权重。实验结果证明,该算法不仅能有效地发现加权时态关联规则,而且挖掘出的规则更有价值。     

基于时序一致的工作流费用优化方法

针对效用网格下的工作流时间约束-费用优化问题,分层算法将工作流进行分层并逐层进行优化调度,取得了良好效果.然而,这类分层算法由于缺乏更有效的截止时间确定策略来保证时间约束而使得算法的适用性受限.在已有算法截止期约束的逆向分层算法(deadline bottom level,DBL)的基础上,研究工作流的时序特征,并基于任务的一致性状态对费用进行优化,提出了基于时序一致的截止期约束逆向分层算法(temporal consistency based deadline bottom level,TCDBL).TCDBL通过一致性时间点来保证时间约束,解决了DBL的适用性受限问题;同时基于各层并行度分配冗余时间,基于宽松时间约束策略进行费用优化,达到了进一步减少工作流执行费用的目标.实验结果表明TCDBL的费用优化效果比DBL改进了约14%.     

Constraint graph-based frequent pattern updating from temporal databases

There have been many kinds of association rule mining (ARM) algorithms, e.g., Apriori and FP-tree, to discover meaningful frequent patterns from a large dataset. Particularly, it is more difficult for such ARM algorithms to be applied for temporal databases which are continuously changing over time. Such algorithms are generally based on repeating time-consuming tasks, e.g., scanning databases. To deal with this problem, in this paper, we propose a constraint graph-based method for maintaining frequent patterns (FP) discovered from the temporal databases. Particularly, the constraint graph, which is represented as a set of constraint between two items, can be established by temporal persistency of the patterns. It means that some patterns can be used to build the constraint graph, when the patterns have been shown in a set of the FP. Two types of constraints can be generated by users and adaptation. Based on our scheme, we find that a large number of dataset has been efficiently reduced during mining process and the gathering information while updating.     

Reasoning with Numeric and Symbolic Time Information

Representing and reasoning about time is fundamental in many applications of Artificial Intelligence as well as of other disciplines in computer science, such as scheduling, planning, computational linguistics, database design and molecular biology. The development of a domain-independent temporal reasoning system is then practically important. An important issue when designing such systems is the efficient handling of qualitative and metric time information. We have developed a temporal model, TemPro, based on the Allen interval algebra, to express and manage such information in terms of qualitative and quantitative temporal constraints. TemPro translates an application involving temporal information into a Constraint Satisfaction Problem (CSP). Constraint satisfaction techniques are then used to manage the different time information by solving the CSP. In order for the system to deal with real time applications or those applications where it is impossible or impractical to solve these problems completely, we have studied different methods capable of trading search time for solution quality when solving the temporal CSP. These methods are exact and approximation algorithms based respectively on constraint satisfaction techniques and local search. Experimental tests were performed on randomly generated temporal constraint problems as well as on scheduling problems in order to compare and evaluate the performance of the different methods we propose. The results demonstrate the efficiency of the MCRW approximation method to deal with under constrained and middle constrained problems while Tabu Search and SDRW are the methods of choice for over constrained problems.