We investigate the relationship between programming with constraints and database query languages. We show that efficient, declarative database programming can be combined with efficient constraint solving. The key intuition is that the generalization of a ground fact, or tuple, is a conjunction of constraints over a small number of variables. We describe the basic Constraint Query Language design principles and illustrate them with four classes of constraints: real polynomial inequalities, dense linear order inequalities, equalities over an infinite domain, and boolean equalities. For the analysis, we use quantifier elimination techniques from logic and the concept of data complexity from database theory. This framework is applicable to managing spatial data and can be combined with existing multidimensional searching algorithms and data structures.

Constraints are a natural mechanism for the specification of similarity queries on time-series data. However, to realize the expressive power of constraint programming in this context, one must provide the matching implementation technology for efficient indexing of very large data sets. In this paper, we formalize the intuitive notions of exact and approximate similarity between time-series patterns and data. Our definition of similarity extends the distance metric used in [2, 7] with invariance under a group of transformations. Our main observation is that the resulting, more expressive, set of constraint queries can be supported by a new indexing technique, which preserves all the desirable properties of the indexing scheme proposed in [2, 7].

We define formal notions of temporal domain and temporal database, and use them to survey a wide spectrum of temporal query languages. We distinguish between an abstract temporal database and its concrete representations, and accordingly between abstract and concrete temporal query languages. We also address the issue of incomplete temporal information. 1 Introduction A temporal database is a repository of temporal information. A temporal query language is any query language for temporal databases. In this paper we propose a formal notion of temporal database and use this notion in surveying a wide spectrum of temporal query languages. The need to store temporal information arises in many computer applications. Consider, for example, records of various kinds: financial [37], personnel, medical [98], or judicial. Also, monitoring data, e.g., in telecommunications network management [4] or process control, has often a temporal dimension. There has been a lot of research in temporal dat...

We survey a number of approaches to the problem of finite representation of infinite temporal extensions. Two of them, Datalog 1S and Templog, are syntactical extensions of Datalog; the third is based on repetition and arithmetic constraints. We provide precise characterizations of the expressiveness and the computational complexity of these languages. We also describe query evaluation methods.

. The declarative programming paradigms used in constraint languages can lead to powerful extensions of Codd's relational data model. The development of constraint database query languages from logical database query languages has many similarities with the development of constraint logic programming from logic programming, but with the additional requirements of data efficient, set-at-a-time, and bottomup evaluation. In this overview of constraint query languages (CQLs) we first present the framework of [41]. The principal idea is that: "the k-tuple (or record) data type can be generalized by a conjunction of quantifier-free constraints over k variables". The generalization must preserve various language properties of the relational data model, e.g., the calculus/algebra equivalence, and have time complexity polynomial in the size of the data. We next present an algebra for dense order constraints that is simpler to evaluate than the calculus described in [41], and we sharpen some of...

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. 1 Introduction Very large temporal and spatial databases are a common occurrence nowadays. Although they are usually created with a specific application in mind, they often contain data of potentially broader interest, e.g., historical records or geographical data. By database interoperability we mean the problem of making the data from one database usable to the users of another. Data sharing between different applications and different sites is often An early version of some of the results in this paper appeared in [CR97]. The work of the first author was supported by NSF grant IRI-9632870. The work of the second author was supported by NSF grants IRI-9632871 and ...

Integrating data and knowledge from multiple heterogeneous sources --- like databases, knowledge bases or specific software packages --- is often required for answering certain queries. Recently, a powerful framework for defining mediated views spanning multiple knowledge bases by a set of constrained rules (cf. work of Kanellakis et. al. [27]) was proposed [39, 5, 26]. Within this paper, we investigate the materialization of these views by unfolding the view definition and the efficient maintenance of the resulting materialized mediated view in case of updates. Thereby, we consider two kinds of updates: updates to the view and updates to the underlying sources. For each of these two cases several efficient algorithms maintaining materialized mediated views are given. We improve on previous algorithms like the DRed algorithm [22] and introduce a new fixpoint operator WP which --- opposed to the standard fixpoint operator TP [19] --- allows us to correctly capture the update'...

this paper, we present a procedure that generates and propagates minimum QRP-constraints (if it terminates), based on the definition and uses of program predicates (Section 4). By propagating minimum QRP-constraints to the original program, we obtain a program that fully utilizes the constraint information present in the original program. This procedure is based on two sub-procedures: 1. Procedure Gen Prop predicate-constraints, which generates and propagates constraints that are satisfied by program predicates based on their definitions.

Traditionally, dependency theory has been developed for uninterpreted data. Specifically, the only assumption that is made about the data domains is that data values can be compared for equality. However, data is often interpreted and there can be advantages in considering it as such, for instance obtaining more compact representations as done in constraint databases. This paper considers dependency theory in the context of interpreted data. Specifically, it studies constraint-generating dependencies. These are a generalization of equality-generating dependencies where equality requirements are replaced by constraints on an interpreted domain. The main technical results in the paper are a general decision procedure for the implication and consistency problems for constraint-generating dependencies, and complexity results for specific classes of such dependencies over given domains. The decision procedure proceeds by reducing the dependency problem to a decision problem for the constraint theory of interest, and is applicable as soon as the underlying constraint theory is decidable. The complexity results are, in some cases, directly lifted from the constraint theory; in other cases, optimal complexity bounds are obtained by taking into account the specific form of the constraint decision problem obtained by reducing the dependency implication problem.

In recent years, much work has been directed towards evaluating logic programs and queries on deductive databases by using an iterative bottom-up fixpoint computation. The resulting techniques offer an attractive alternative to Prolog-style top-down evaluation in several situations. They are sound and complete for positive Horn clause programs, are well-suited to applications with large volumes of data (facts), and can support a variety of extensions to the standard logic programming paradigm. We present the basics of database query evaluation and logic programming evaluation, and then discuss bottom-up fixpoint evaluation. We discuss an approach based upon using a program transformation ("Magic Templates") to restrict search, followed by fixpoint computation using a technique ("Semi-naive evaluation") that avoids repeated inferences. The program transformation technique focuses the fixpoint evaluation, which is a forward-chaining strategy, by propagating bindings in the goal in a ma...