Preference queries in deductive databases

The treatment of abstract query languages such as Relational Algebra, Relational Calculus, and Datalog in introductory database courses varies considerably across the various computer science departments around the world. We propose an integrated approach in which students learn how to express a natural language query in Domain Relational Calculus (DRC), apply a query-transforming algorithm to the DRC expression tree to produce Datalog Rules (DLG), and finally apply another query-transforming algorithm to the DLG rules to produce an equivalent Relational Algebra (RA) expression. This systematic approach works very well for forall-queries in which the forall-quantifier is necessary in the DRC expression. To make this approach even more effective, the students are required to use DRC, DLG, and RA interpreters that allow them to execute query expressions as well as produce equivalent expressions. The students are also involved in implementing these interpreters to get a deeper understanding of querying in relational systems.

Skyline query processing and the more general preference queries become reality in current database systems. Preference queries select those tuples from a database that are optimal with respect to a set of designated preference attributes. In a Skyline query these preferences only refer to minimum and maximum, whereas the more general approach of preference queries allow a more granular specification of user wishes as well as the specification of the relative importance of individual preferences. The incorporation of preferences into practical relational database engines necessitates an efficient and effective selectivity estimation module: A better understanding of the preference selectivity is useful for better design of algorithms and necessary to extend a database query optimizer's cost model to accommodate preference queries. This paper presents a survey on selectivity and cardinality estimation for arbitrary preference queries. The paper presents current approaches and discusses their advantages and disadvantages, such that one could decide which model should be used in a database engine to estimate optimization costs.

In this paper, we present a method for bottom-up evaluation of Datalog programs in deductive databases that “pushes” derived facts immediately to other rules where they are used for deriving more facts. In this way, the materialization of derived relations is avoided as far as possible. Derived facts are represented by values in variables and a location in the program, and not as explicitly constructed tuples. This helps to avoid copying operations and to keep the actively used memory small to make better use of modern processors. The method can be quite easily explained by translating Datalog to functions in a standard programming language like C++ and then using optimizations of the C++ compiler like inlining. First performance tests with benchmarks from the OpenRuleBench collection give good results. This is interesting because systems based on SLD-resolution with tabling such as XSB have beaten older deductive database implementations based on bottom-up evaluation. Now it seems that bottom-up evaluation can be done in a very competitive way.

RDBI is an educational tool that provides students with the capability to test their understanding of the formal relational query languages (relational algebra, domain relational calculus and tuple relational calculus) and the industry standard query language SQL. Although RDBI is an integral part of the database management courses at a number of universities, it is unavailable to those universities that do not have a license for the software product in which RDBI is implemented. WinRDBI, a version of RDBI for Windows, removes this limitation by increasing the availability of the educational tool to a standard Windows platform. Another advantage of WinRDBI is its graphical user interface, providing the student with a more intuitive interface than the command line interface of RDBI. This paper describes the architecture and user interface of WinRDBI. The features of WinRDBI are also illustrated using nontrivial examples from a popular database text. Although formal relational query languages do not provide inherent support for aggregation, these examples illustrate how to write queries in the formal languages to support (a limited form of) counting and minimum/maximum queries.

Bottom-up evaluation and query optimization of well-founded models

We show how the language Constraint Handling Rules (CHR), a high-level logic language for the implementation of constraint solvers, can be slightly extended to become a general-purpose logic programming language with an expressive power subsuming the expressive ower of Horn clause programs with SLD resolution. The extended language, called “CHR∀”, retains however the extra features of CHR, e.g., committed choice and matching, which axe important for other purposes, especially for efficiently solving constraints. CHR∀ turns out to be a very flexible query language in the sense that it supports several (constraint) logic programming paradigms and allows to mix them in a single program. In particular, it supports top-down query evaluation and also bottom-up evaluation as it is frequently used in (disjunctive) deductive databases.

We introduce the notion of conditional facts in deductive databases. The language used to express the conditions in the basic facts does not involve constructs used to describe the database and is motivated by its potential usefulness in expressing disjunctive facts and answering queries in disjunctive deductive databases. We present a bottom-up procedure that uses tabular structures to represent the conditional facts and computes the intended meaning of conditional deductive databases. We show how this procedure can be used to answer queries in a subclass of disjunctive deductive databases which involves disjunctive facts but only definite rules.

We develop a new scheme for evaluating different molecular integrals using Gaussian type orbitals. In this new scheme, the evaluation of integrals is performed in two steps during runtime. The first step is a top-down procedure that maps each recurrence relation into a jagged array (array of arrays), where each element of a member array represents either the final results or some intermediate integrals that are stored in our developed data structure "coarse-grained circular buffer". This step is the same for all different one- and two-electron operators so that the same algorithm and source codes can be used. In the second step, a bottom-up procedure is carried out that computes all the intermediate and the final molecular integrals by backtracking elements from the last member array of each jagged array. Different source codes should in principle be used for different electron operators in the second step, but which can be generated automatically by our developed recurrence-relation compiler. The currently proposed general recurrence-relation generation scheme provides a new, generic and automatic programming way for various one- and two-electron integrals needed in computational chemistry. Users can even introduce new electron operators and evaluate their integrals during runtime by combining the implementation of the proposed new scheme and the just-in-time compilation technique.

Crowd-sourcing is a powerful solution for finding correct answers to expensive and unanswered queries in databases, including those with uncertain and incomplete data. Attempts to use crowd-sourcing to exploit human abilities to process these expensive queries using human workers have helped to provide accurate results by utilising the available data in the crowd. Crowd-sourcing database systems (CSDBs) combine the knowledge of the crowd with a relational database by using some variant of a relational database with minor changes. This paper surveys the leading studies conducted in the area of query processing with regard to both traditional and preference queries in CSDBs. The focus of this work is on highlighting the strengths and the weakness of each approach. A detailed discussion of current and future trends research associated with query processing in the area of CSDBs is also presented.

A Cognitive map is a graphical semantic model that represents influences between concepts. A cognitive map is easy to design and use, but the only query a user can make on it is to infer the propagated influence from a concept to another. This paper (This work is made in the context of “Analyse Cognitive de Savoirs”, a computer science project, granted by French region Pays de la Loire.) proposes CMQL, a general query language for cognitive maps. This language provides a way to query the different items of the model and not only the propagated influence. The language is declarative and is inspired of the domain relational calculus.

Declarative database query languages have recently been criticized by the research community. Proponents of database programming languages and object-oriented databases recommended doing away with declarative means for specifying retrieval, except for perhaps ad-hoc querying. Supporters of deductive databases and logic programming want to extend declarative query languages into full programming languages while retaining declarativity. It is our conviction that neither approach will lead to a satisfactory solution. We claim instead that languages offering both paradigms in full extension are desirable and can be achieved without suffering from the drawbacks encountered when simply combining existing query and programming languages. In this paper we outline the basic design decisions of an experimental language aimed at a knowledge-based style of persistent programming.KeywordsLogic ProgrammingQuery LanguageImpedance MismatchDeductive DatabaseDeclarative LanguageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this paper a set-oriented bottom-up parsing system based on a deductive database is presented. The system allows the purely declarative specification of Definite Clause Grammar rules as a logic program. The parser applies a set-oriented bottom-up evaluation strategy. It is therefore particularly wellsuited for the analysis of language ambiguities. Furthermore the system can easily be connected to a relational database. The impedance mismatch between a tuple-oriented PROLOG system and a set-oriented relational database system is avoided. Thus a number of drawbacks which PROLOG based parsers suffer from have been overcome.

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Semi-Formal Query Language Interface (SFQI) is a user interface for interaction with database systems. SFQI is based on a Semi-Formal Query Language (SFQL) which is an enhancement of SQL. SFQI provides facilities for guiding users in performing database querying, such as formulating a valid database query, refining user-misconceptions and modifying database query misconstructions. The syntax of SFQL is not as rigid as in most database query languages. The system provides logical database navigation and users are not required to know, in detail, the structure of the database being queried.

Handling spatial information is required by many database applications, and each poses different requirements on query languages. In many cases the precise size of the regions is important, while in other applications we may only be interested in the TOPOLOGICAL relations- hips between regions — intuitively, those that pertain to adjacency and connectivity properties of the regions, and are therefore invariant under homeomorphisms. Such differences in scope and emphasis are crucial, as they affect the data model, the query language, and performance. This talk focuses on queries targeted towards topological information for two- dimensional spatial databases, where regions are specified by polynomial inequalities with integer coeficients. We focus on two main aspects: (i) languages for expressing topological queries, and (ii) the representation of topological information. In regard to (i), we study several languages geared towards topological queries, building upon well-known topologi- cal relationships between pairs of planar regions proposed by Egenhofer. In regard to (ii), we show that the topological information in a spatial database can be precisely summarized by a finite relational database which can be viewed as a topological annotation to the raw spatial data. All topological queries can be answered using this annotation, called to- pological invariant. This yields a potentially more economical evaluation strategy for such queries, since the topological invariant is generally much smaller than the raw data. We examine in detail the problem of transla- ting topological queries against the spatial database into queries against the topological invariant. The languages considered are first-order on the spatial database side, and fixpoint and first-order on the topological in- variant side. In particular, it is shown that fixpoint expresses precisely the PTIME queries on topological invariants. This suggests that topolo- gical invariants are particularly well-behaved with respect to descriptive complexity. (Based on joint work with C.H.Papadimitriou, D. Suciu and L. Segoufin.)