A Rule-Based Approach to Analyzing Database Schema Objects with Datalog

Database schema elements such as tables, views, triggers and functions are typically defined with many interrelationships. In order to support database users in understanding a given schema, a rule-based approach for analyzing the respective dependencies is proposed using Datalog expressions. We show that many interesting properties of schema elements can be systematically determined this way. The expressiveness of the proposed analysis is exemplarily shown with the problem of computing induced functional dependencies for derived relations. The propagation of functional dependencies plays an important role in data integration and query optimization but represents an undecidable problem in general. And yet, our rule-based analysis covers all relational operators as well as linear recursive expressions in a systematic way showing the depth of analysis possible by our proposal. The analysis of functional dependencies is well-integrated in a uniform approach to analyzing dependencies between schema elements in general.Comment: Pre-proceedings paper presented at the 27th International Symposium on Logic-Based Program Synthesis and Transformation (LOPSTR 2017), Namur, Belgium, 10-12 October 2017 (arXiv:1708.07854

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks

Flux de l'information en programmation logique

Cette thèse est développée dans le but d'aborder la question du flux de l'information en programmation logique. Les contributions de cette thèse peuvent être divisées en trois parties: 1. Flux de l'information en programmation logique: Nous proposons une base théorique de ce que pourrait être un flux de l'information en programmation logique. Plusieurs définitions de flux d'information (basées sur la réussite / échec, les substitutions réponses, bisimulation entre les arbres de résolution des buts logiques) sont évaluées et comparées. Des problèmes de décision sont donnés pour chaque définition et la complexité est étudiée pour certaines catégories de programmes logiques. 2. Bisimulation de buts logiques: Nous introduisons la notion de bisimulation entre les buts Datalog: deux buts Datalog sont bisimilaires par rapport à un programme Datalog donné lorsque leurs SLD-arbres, considérés comme des structures relationnelles, sont bisimilaires. Nous abordons le problème de décider si deux buts donnés sont bisimilaires à l'égard d'un programme donné. Lorsque les programmes sont hiérarchiques ou restricted, ce problème est décidable en 2EXPTIME. 3. Contrôle préventif de l'inférence dans les bases de données déductives: Nous proposons un mécanisme de sécurité sûr et précis pour les bases de données déductives basé sur la notion de flux de l'information dans la programmation logique.This thesis is developed in order to tackle the issue of information flow in logic programming. The contributions of this thesis can be split into three mains parts: 1. Information flow in logic programming: we propose a theoretical foundation of what could be an information flow in logic programming. Several information flow definitions (based on success/failure, substitution answers, bisimulation between resolution trees of goals) are stated and compared. Decision procedures are given for each definition and complexity is studied for specific classes of logic programs. 2. Bisimulation of logic goals: We introduce the concept of bisimulation between Datalog goals: two Datalog goals are bisimilar with respect to a given Datalog program when their SLD-trees, considered as relational structures, are bisimilar. We address the problem of deciding whether two given goals are bisimilar with respect to given programs. When the given programs are hierarchical or restricted, this problem is decidable in 2EXPTIME. 3. Preventive inference control for deductive databases: We propose a secure and a precise security mechanism for deductive databases based on the notion of information flow in logic programming

Schema Independent Relational Learning

Learning novel concepts and relations from relational databases is an important problem with many applications in database systems and machine learning. Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. Nevertheless, the same data set may be represented under different schemas for various reasons, such as efficiency, data quality, and usability. Unfortunately, the output of current relational learning algorithms tends to vary quite substantially over the choice of schema, both in terms of learning accuracy and efficiency. This variation complicates their off-the-shelf application. In this paper, we introduce and formalize the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations. We study both sample-based learning algorithms, which learn from sets of labeled examples, and query-based algorithms, which learn by asking queries to an oracle. We prove that current relational learning algorithms are generally not schema independent. For query-based learning algorithms we show that the (de) composition transformations influence their query complexity. We propose Castor, a sample-based relational learning algorithm that achieves schema independence by leveraging data dependencies. We support the theoretical results with an empirical study that demonstrates the schema dependence/independence of several algorithms on existing benchmark and real-world datasets under (de) compositions

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks

Living with inconsistencies in a multidatabase system

Integration of autonomous sources of information is one of the most important problems in implementation of the global information systems. This paper considers multidatabase systems as one of the typical architectures of global information services and addresses a problem of storing and processing inconsistent information in such systems. A new data model proposed in the paper separates sure from inconsistent information and introduces a system of elementary operations on the containers with sure and inconsistent information. A review of the implementation aspects in an environment of a typical relational database management system concludes the paper