322,061 research outputs found
Pattern based processing of XPath queries
As the popularity of areas including document storage and
distributed systems continues to grow, the demand for high
performance XML databases is increasingly evident. This
has led to a number of research eorts aimed at exploiting
the maturity of relational database systems in order to in-
crease XML query performance. In our approach, we use an
index structure based on a metamodel for XML databases
combined with relational database technology to facilitate
fast access to XML document elements. The query process
involves transforming XPath expressions to SQL which can
be executed over our optimised query engine. As there are
many dierent types of XPath queries, varying processing
logic may be applied to boost performance not only to indi-
vidual XPath axes, but across multiple axes simultaneously.
This paper describes a pattern based approach to XPath
query processing, which permits the execution of a group of
XPath location steps in parallel
Using Visualization to Support Data Mining of Large Existing Databases
In this paper. we present ideas how visualization technology can be used to improve the difficult process of querying very large databases. With our VisDB system, we try to provide visual support not only for the query specification process. but also for evaluating query results and. thereafter, refining the query accordingly. The main idea of our system is to represent as many data items as possible by the pixels of the display device. By arranging and coloring the pixels according to the relevance for the query, the user gets a visual impression of the resulting data set and of its relevance for the query. Using an interactive query interface, the user may change the query dynamically and receives immediate feedback by the visual representation of the resulting data set. By using multiple windows for different parts of the query, the user gets visual feedback for each part of the query and, therefore, may easier understand the overall result. To support complex queries, we introduce the notion of approximate joins which allow the user to find data items that only approximately fulfill join conditions. We also present ideas how our technique may be extended to support the interoperation of heterogeneous databases. Finally, we discuss the performance problems that are caused by interfacing to existing database systems and present ideas to solve these problems by using data structures supporting a multidimensional search of the database
The relationship between IR and multimedia databases
Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud
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Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud
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Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud
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First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud
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Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud
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Third, we add the functionality to process the users' relevance feedback.\ud
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We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud
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We conclude with an outline for implementation of miRRor on top of the Monet extensible database system
First-Class Functions for First-Order Database Engines
We describe Query Defunctionalization which enables off-the-shelf first-order
database engines to process queries over first-class functions. Support for
first-class functions is characterized by the ability to treat functions like
regular data items that can be constructed at query runtime, passed to or
returned from other (higher-order) functions, assigned to variables, and stored
in persistent data structures. Query defunctionalization is a non-invasive
approach that transforms such function-centric queries into the data-centric
operations implemented by common query processors. Experiments with XQuery and
PL/SQL database systems demonstrate that first-order database engines can
faithfully and efficiently support the expressive "functions as data" paradigm.Comment: Proceedings of the 14th International Symposium on Database
Programming Languages (DBPL 2013), August 30, 2013, Riva del Garda, Trento,
Ital
Knowledge Engineering and Intelligence Gathering
A process of intelligence gathering begins when a user enters a query into the system. Several objects can match the result of a query with different degrees of relevance. Most systems estimate a numeric value about how well each object matches the query and classifies objects according to this value. Many researches have focused on practices of intelligence gathering. In knowledge engineering, knowledge gathering consists in fiding it from structured and unstructured sources in a way that must represent knowledge in a way that facilitates inference.
DOI: 10.13140/RG.2.2.32191.1552
Constrained Query Answering
Traditional answering methods evaluate queries only against positive
and definite knowledge expressed by means of facts and deduction rules. They do
not make use of negative, disjunctive or existential information. Negative or indefinite
knowledge is however often available in knowledge base systems, either as
design requirements, or as observed properties. Such knowledge can serve to rule out
unproductive subexpressions during query answering. In this article, we propose an
approach for constraining any conventional query answering procedure with general,
possibly negative or indefinite formulas, so as to discard impossible cases and to
avoid redundant evaluations. This approach does not impose additional conditions
on the positive and definite knowledge, nor does it assume any particular semantics
for negation. It adopts that of the conventional query answering procedure it
constrains. This is achieved by relying on meta-interpretation for specifying the
constraining process. The soundness, completeness, and termination of the underlying
query answering procedure are not compromised. Constrained query answering
can be applied for answering queries more efficiently as well as for generating more
informative, intensional answers
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