2,203 research outputs found
Generalized quantifiers in distributed databases.
Optimizing queries in a distributed database is quite difficult. This work proposes defining new generalized quantifiers which operate on sets rather than tuples. These quantifiers would allow for easier optimization in a horizontally distributed database. These operators are scalable with respect to both the number of hosts in the environment and the size of the data used
Formulas as Programs
We provide here a computational interpretation of first-order logic based on
a constructive interpretation of satisfiability w.r.t. a fixed but arbitrary
interpretation. In this approach the formulas themselves are programs. This
contrasts with the so-called formulas as types approach in which the proofs of
the formulas are typed terms that can be taken as programs. This view of
computing is inspired by logic programming and constraint logic programming but
differs from them in a number of crucial aspects.
Formulas as programs is argued to yield a realistic approach to programming
that has been realized in the implemented programming language ALMA-0 (Apt et
al.) that combines the advantages of imperative and logic programming. The work
here reported can also be used to reason about the correctness of non-recursive
ALMA-0 programs that do not include destructive assignment.Comment: 34 pages, appears in: The Logic Programming Paradigm: a 25 Years
Perspective, K.R. Apt, V. Marek, M. Truszczynski and D.S. Warren (eds),
Springer-Verlag, Artificial Intelligence Serie
Towards an Efficient Evaluation of General Queries
Database applications often require to
evaluate queries containing quantifiers or disjunctions,
e.g., for handling general integrity constraints. Existing
efficient methods for processing quantifiers depart from the
relational model as they rely on non-algebraic procedures.
Looking at quantified query evaluation from a new angle,
we propose an approach to process quantifiers that makes
use of relational algebra operators only. Our approach
performs in two phases. The first phase normalizes the
queries producing a canonical form. This form permits to
improve the translation into relational algebra performed
during the second phase. The improved translation relies
on a new operator - the complement-join - that generalizes
the set difference, on algebraic expressions of universal
quantifiers that avoid the expensive division operator in
many cases, and on a special processing of disjunctions by
means of constrained outer-joins. Our method achieves an
efficiency at least comparable with that of previous
proposals, better in most cases. Furthermore, it is considerably
simpler to implement as it completely relies on
relational data structures and operators
Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses
A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses
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