The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform
Fernando Saenzperez - One of the best experts on this subject based on the ideXlab platform.
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a fuzzy datalog Deductive Database system
IEEE Transactions on Fuzzy Systems, 2018Co-Authors: Pascual Julianiranzo, Fernando SaenzperezAbstract:This paper describes a proposal for a Deductive Database system with fuzzy ${{\small \mathsf{Datalog}}}$ as its query language. Concepts supporting the fuzzy logic programming system ${\small{\mathsf{Bousi}}}{\sim} {{\small \mathsf{Prolog}}}$ are tailored to the needs of the Deductive Database system ${{\small \mathsf{DES}}}$ . We develop a version of fuzzy ${{\small \mathsf{Datalog}}}$ where programs and queries are compiled to the ${{\small \mathsf{DES}}}$ core ${{\small \mathsf{Datalog}}}$ language. Weak unification and weak SLD resolution are adapted for this setting, and extended to allow rules with truth degree annotations. We provide a public implementation in ${{\small \mathsf{Prolog}}}$ , which is open source, multiplatform, portable, and in-memory, featuring a graphical user interface. A distinctive feature of this system is that, unlike others, we have formally demonstrated that our implementation techniques fit the proposed operational semantics. We also study the efficiency of these implementation techniques through a series of detailed experiments. Moreover, a Database example for a recommender system is used to illustrate some of the features of the system and its usefulness.
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an extended constraint Deductive Database theory and implementation
The Journal of Logic and Algebraic Programming, 2014Co-Authors: Gabriel Arandalopez, Fernando Saenzperez, Susana Nieva, Jaime SanchezhernandezAbstract:Abstract The scheme of Hereditary Harrop formulas with constraints, HH ( C ) , has been proposed as a basis for constraint logic programming languages. In the same way that Datalog emerges from logic programming as a Deductive Database language, such formulas can support a very expressive framework for constraint Deductive Databases, allowing hypothetical queries and universal quantifications. As negation is needed in the Database field, HH ( C ) is extended with negation to get HH ¬ ( C ) . This work presents the theoretical foundations of HH ¬ ( C ) and an implementation that shows the viability and expressive power of the proposal. Moreover, the language is designed in a flexible way in order to support different constraint domains. The implementation includes several domain instances, and it also supports aggregates as usual in Database languages. The formal semantics of the language is defined by a proof-theoretic calculus, and for the operational mechanism we use a stratified fixpoint semantics, which is proved to be sound and complete w.r.t. the former. Hypothetical queries and aggregates require a more involved stratification than the common one used in Datalog. The resulting fixpoint semantics constitutes a suitable foundation for the system implementation.
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tabling with support for relational features in a Deductive Database
Electronic Communication of The European Association of Software Science and Technology, 2013Co-Authors: Fernando SaenzperezAbstract:Tabling has been acknowledged as a useful technique in the logic programming arena for enhancing both performance and declarative properties of programs. As well, Deductive Database implementations benefit from this technique for implementing query solving engines. In this paper, we show how unusual operations in Deductive systems can be integrated with tabling. Such operations come from relational Database systems in the form of null-related (outer) joins, duplicate support and duplicate elimination. The proposal has been implemented as a proof of concept rather than an efficient system in the Datalog Educational System (DES) using Prolog as a development language and its dynamic Database.
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outer joins in a Deductive Database system
Electronic Notes in Theoretical Computer Science, 2012Co-Authors: Fernando SaenzperezAbstract:Outer joins are extended relational algebra operations intended to deal with unknown information represented with null values. This work shows an approach to embed both null values and outer join operations in the Deductive Database system DES (Datalog Educational System), which uses Datalog as a query language. This system also supports SQL, where views and queries are compiled to Datalog programs. So, as SQL statements are ultimately solved by a Datalog engine, it became a need to integrate null-related operations into Datalog in order to support a wider set of SQL. Since DES implements a top-down-driven bottom-up stratified fixpoint computation based on tabling for solving Datalog queries, we show how to compute outer joins in such a context by means of source-to-source transformations applied to Datalog programs.
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a Deductive Database with datalog and sql query languages
Asian Symposium on Programming Languages and Systems, 2011Co-Authors: Fernando Saenzperez, Rafael Caballero, Yolanda GarciaruizAbstract:This paper introduces Datalog Educational System (DES), a Deductive Database which supports both Datalog and SQL as query languages. Since its inception, this system is targeted to educational purposes rather to develop an efficient, competitive system with respect to other existing systems. As distinguishing features, it is free, open-source, multiplatform, interactive, portable, GUI-enabled, implemented following ISO-Prolog and supports extensions to pure Datalog in the form of stratified negation, strong constraints, types, metapredicates, and duplicates. Also, test case generation for SQL views and declarative debugging for Datalog programs and SQL views are supported. SQL statements, following ISO standard, are compiled to Datalog programs and solved by its inference engine. Nonetheless, ODBC connections are also supported, which enables access to external DBMSs and benefit from their solving performance, persistency and scalability.
Carlo Zaniolo - One of the best experts on this subject based on the ideXlab platform.
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A Deductive Database Approach to A.I. Planning
Journal of Intelligent Information Systems, 2003Co-Authors: Antonio Brogi, V.s. Subrahmanian, Carlo ZanioloAbstract:In this paper, we show that the classical A.I. planning problem can be modelled using simple Database constructs with logic-based semantics. The approach is similar to that used to model updates and nondeterminism in active Database rules. We begin by showing that planning problems can be automatically converted to Datalog_1 S programs with nondeterministic choice constructs, for which we provide a formal semantics using the concept of stable models. The resulting programs are characterized by a syntactic structure ( XY -stratification) that makes them amenable to efficient implementation using compilation and fixpoint computation techniques developed for Deductive Database systems. We first develop the approach for sequential plans, and then we illustrate its flexibility and expressiveness by formalizing a model for parallel plans, where several actions can be executed simultaneously. The characterization of parallel plans as partially ordered plans allows us to develop (parallel) versions of partially ordered plans that can often be executed faster than the original partially ordered plans.
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the Deductive Database system ldl
arXiv: Databases, 2002Co-Authors: Faiz Arni, Kayliang Ong, Haixun Wang, Shalom Tsur, Carlo ZanioloAbstract:This paper describes the LDL++ system and the research advances that have enabled its design and development. We begin by discussing the new nonmonotonic and nondeterministic constructs that extend the functionality of the LDL++ language, while preserving its model-theoretic and fixpoint semantics. Then, we describe the execution model and the open architecture designed to support these new constructs and to facilitate the integration with existing DBMSs and applications. Finally, we describe the lessons learned by using LDL++ on various tested applications, such as middleware and datamining.
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Data and knowledge in Database systems: Deductive Databases
2002Co-Authors: Carlo ZanioloAbstract:The objective of Deductive Databases is to provide efficient support for sophisticated queries and reasoning on large Databases; toward this goal, they combine the technology of logic programming with that of relational Databases. Deductive Database research has produced methods and techniques for implementing the declarative semantics of logical rules via efficient computation of fixpoints. Also, advances in language design and nonmonotonic semantics were made to allow the use of negation and set-aggregates in recursive programs; these yield greater expressive power while retaining polynomial data complexity and semantic well-formedness. Deductive Database systems have been used in data mining and other advanced applications, and their techniques have been incorporated into a new generation of commercial Databases.
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nonmonotonic reasoning in ldl
Logic-based artificial intelligence, 2000Co-Authors: Haixun Wang, Carlo ZanioloAbstract:Deductive Database systems have made major advances on efficient support for nonmonotonic reasoning. A first generation of Deductive Database systems supported the notion of stratification for programs with negation and set aggregates. Stratification is simple to understand and efficient to implement but it is too restrictive; therefore, a second generation of systems seeks efficient support for more powerful semantics based on notions such as well-founded models and stable models. In this respect, a particularly powerful set of constructs is provided by the recently enhanced LDL++ system that supports (i) monotonic user-defined aggregates, (ii) XY-stratified programs, and (iii) the nondeterministic choice constructs under stable model semantics. This integrated set of primitives supports a terse formulation and efficient implementation for complex computations, such as greedy algorithms and data mining functions, yielding levels of expressive power unmatched by other Deductive Database systems.
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the logic of totally and partially ordered plans a Deductive Database approach
Annals of Mathematics and Artificial Intelligence, 1997Co-Authors: Antonio Brogi, V.s. Subrahmanian, Carlo ZanioloAbstract:The problem of finding effective logic-based formalizations for problems involving actions remains one of the main application challenges of non-monotonic knowledge representation. In this paper, we show that complex planning strategies find natural logic-based formulations and efficient implementations in the framework of Deductive Database languages. We begin by modeling classical STRIPS-like totally ordered plans by means of Datalog_{1S} programs, and show that these programs have a stable model semantics that is also amenable to efficient computation. We then show that the proposed approach is quite expressive and flexible, and can also model partially ordered plans, which are abstract plans whereby each plan stands for a whole class of totally ordered plans. This results in a reduction of the search space and a subsequent improvement in efficiency.
Satoru Kuhara - One of the best experts on this subject based on the ideXlab platform.
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application of a Deductive Database system to search for topological and similar three dimensional structures in protein
Bioinformatics, 1997Co-Authors: Yukiko Tsukamoto, Kenji Satou, Emiko Furuichi, T Takagi, Kyoko Takiguchi, Satoru KuharaAbstract:A Deductive Database system PACADE (Protein Atomic Coordinate Analyzer with Deductive Engine) has been developed for protein structure analysis. With this system, super-secondary structures described in logical and declarative rules can be retrieved effectively. For protein structure analysis, comparison of local structures in different proteins is a necessary mean. A function to search for similar structures has, therefore, been added to the PACADE system. We describe herein the result of searches for the same topological structures and three-dimensionally similar ones. A user of PACADE can select these two levels of similarity by changing parameters. This function enables the inference system to retrieve similar structures, according to the restraints of variables defined by the user. Similar supersecondary structures among proteins can be searched for automatically, which is useful for protein structure analysis. The retrieved similar super-secondary structures can serve as criteria for protein spatial alignment.
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a Deductive Database system pacade for analyzing 3 d and secondary structures of protein
Bioinformatics, 1993Co-Authors: Kenji Satou, Emiko Furuichi, Kyoko Takiguchi, Toshihisa Takagi, Satoru KuharaAbstract:We have developed a Deductive Database system PACADE for analyzing 3-D and secondary structures of protein. The PACADE system consists of a relational Database created from Protein Data Bank and a Deductive engine DEE based on logic programming. It has the following features: (1) The system has an inference mechanism. This means by which users can easily write and check biological hypotheses using logical and declarative rules instead of procedural programs. (2) The relational Database of the PACADE system stores data on both 3-D and secondary structures of protein. The integration of this two level structure makes feasible an abstract representation of the protein structure. We describe herein the design, functions, and implementation of this PACADE system.
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a Deductive Database system pacade for the three dimensional structure of protein
Hawaii International Conference on System Sciences, 1991Co-Authors: Satoru Kuhara, Kenji Satou, Emiko Furuichi, T Takagi, H Takehara, Y SakakiAbstract:Describes a Deductive Database system PACADE (Protein Atomic Coordinate Analyzer with Deductive Engine) which facilitates a search of three dimensional data kept at the Brookhaven's Protein Data Bank. This system has the following features. Recursive relations can be handled. Long range connectivities between amino acids in proteins can be searched from short range connectivities. The system involves deduction from the approach of bottom up evaluation. Unlike Prolog, it always computes all answers without a description of the control by users and always terminates. This system makes use of query processing techniques proposed for computer science, hence queries are processed efficiently. >
Y Sakaki - One of the best experts on this subject based on the ideXlab platform.
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a Deductive Database system pacade for the three dimensional structure of protein
Hawaii International Conference on System Sciences, 1991Co-Authors: Satoru Kuhara, Kenji Satou, Emiko Furuichi, T Takagi, H Takehara, Y SakakiAbstract:Describes a Deductive Database system PACADE (Protein Atomic Coordinate Analyzer with Deductive Engine) which facilitates a search of three dimensional data kept at the Brookhaven's Protein Data Bank. This system has the following features. Recursive relations can be handled. Long range connectivities between amino acids in proteins can be searched from short range connectivities. The system involves deduction from the approach of bottom up evaluation. Unlike Prolog, it always computes all answers without a description of the control by users and always terminates. This system makes use of query processing techniques proposed for computer science, hence queries are processed efficiently. >
Kenji Satou - One of the best experts on this subject based on the ideXlab platform.
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application of a Deductive Database system to search for topological and similar three dimensional structures in protein
Bioinformatics, 1997Co-Authors: Yukiko Tsukamoto, Kenji Satou, Emiko Furuichi, T Takagi, Kyoko Takiguchi, Satoru KuharaAbstract:A Deductive Database system PACADE (Protein Atomic Coordinate Analyzer with Deductive Engine) has been developed for protein structure analysis. With this system, super-secondary structures described in logical and declarative rules can be retrieved effectively. For protein structure analysis, comparison of local structures in different proteins is a necessary mean. A function to search for similar structures has, therefore, been added to the PACADE system. We describe herein the result of searches for the same topological structures and three-dimensionally similar ones. A user of PACADE can select these two levels of similarity by changing parameters. This function enables the inference system to retrieve similar structures, according to the restraints of variables defined by the user. Similar supersecondary structures among proteins can be searched for automatically, which is useful for protein structure analysis. The retrieved similar super-secondary structures can serve as criteria for protein spatial alignment.
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a Deductive Database system pacade for analyzing 3 d and secondary structures of protein
Bioinformatics, 1993Co-Authors: Kenji Satou, Emiko Furuichi, Kyoko Takiguchi, Toshihisa Takagi, Satoru KuharaAbstract:We have developed a Deductive Database system PACADE for analyzing 3-D and secondary structures of protein. The PACADE system consists of a relational Database created from Protein Data Bank and a Deductive engine DEE based on logic programming. It has the following features: (1) The system has an inference mechanism. This means by which users can easily write and check biological hypotheses using logical and declarative rules instead of procedural programs. (2) The relational Database of the PACADE system stores data on both 3-D and secondary structures of protein. The integration of this two level structure makes feasible an abstract representation of the protein structure. We describe herein the design, functions, and implementation of this PACADE system.
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a Deductive Database system pacade for the three dimensional structure of protein
Hawaii International Conference on System Sciences, 1991Co-Authors: Satoru Kuhara, Kenji Satou, Emiko Furuichi, T Takagi, H Takehara, Y SakakiAbstract:Describes a Deductive Database system PACADE (Protein Atomic Coordinate Analyzer with Deductive Engine) which facilitates a search of three dimensional data kept at the Brookhaven's Protein Data Bank. This system has the following features. Recursive relations can be handled. Long range connectivities between amino acids in proteins can be searched from short range connectivities. The system involves deduction from the approach of bottom up evaluation. Unlike Prolog, it always computes all answers without a description of the control by users and always terminates. This system makes use of query processing techniques proposed for computer science, hence queries are processed efficiently. >
