13,398 research outputs found
Completing Queries: Rewriting of IncompleteWeb Queries under Schema Constraints
Reactive Web systems, Web services, and Web-based publish/
subscribe systems communicate events as XML messages, and in
many cases require composite event detection: it is not sufficient to react
to single event messages, but events have to be considered in relation to
other events that are received over time.
Emphasizing language design and formal semantics, we describe the
rule-based query language XChangeEQ for detecting composite events.
XChangeEQ is designed to completely cover and integrate the four complementary
querying dimensions: event data, event composition, temporal
relationships, and event accumulation. Semantics are provided as
model and fixpoint theories; while this is an established approach for rule
languages, it has not been applied for event queries before
Algebraic optimization of recursive queries
Over the past few years, much attention has been paid to deductive databases. They offer a logic-based interface, and allow formulation of complex recursive queries. However, they do not offer appropriate update facilities, and do not support existing applications. To overcome these problems an SQL-like interface is required besides a logic-based interface.\ud
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In the PRISMA project we have developed a tightly-coupled distributed database, on a multiprocessor machine, with two user interfaces: SQL and PRISMAlog. Query optimization is localized in one component: the relational query optimizer. Therefore, we have defined an eXtended Relational Algebra that allows recursive query formulation and can also be used for expressing executable schedules, and we have developed algebraic optimization strategies for recursive queries. In this paper we describe an optimization strategy that rewrites regular (in the context of formal grammars) mutually recursive queries into standard Relational Algebra and transitive closure operations. We also describe how to push selections into the resulting transitive closure operations.\ud
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The reason we focus on algebraic optimization is that, in our opinion, the new generation of advanced database systems will be built starting from existing state-of-the-art relational technology, instead of building a completely new class of systems
A survey of parallel execution strategies for transitive closure and logic programs
An important feature of database technology of the nineties is the use of parallelism for speeding up the execution of complex queries. This technology is being tested in several experimental database architectures and a few commercial systems for conventional select-project-join queries. In particular, hash-based fragmentation is used to distribute data to disks under the control of different processors in order to perform selections and joins in parallel. With the development of new query languages, and in particular with the definition of transitive closure queries and of more general logic programming queries, the new dimension of recursion has been added to query processing. Recursive queries are complex; at the same time, their regular structure is particularly suited for parallel execution, and parallelism may give a high efficiency gain. We survey the approaches to parallel execution of recursive queries that have been presented in the recent literature. We observe that research on parallel execution of recursive queries is separated into two distinct subareas, one focused on the transitive closure of Relational Algebra expressions, the other one focused on optimization of more general Datalog queries. Though the subareas seem radically different because of the approach and formalism used, they have many common features. This is not surprising, because most typical Datalog queries can be solved by means of the transitive closure of simple algebraic expressions. We first analyze the relationship between the transitive closure of expressions in Relational Algebra and Datalog programs. We then review sequential methods for evaluating transitive closure, distinguishing iterative and direct methods. We address the parallelization of these methods, by discussing various forms of parallelization. Data fragmentation plays an important role in obtaining parallel execution; we describe hash-based and semantic fragmentation. Finally, we consider Datalog queries, and present general methods for parallel rule execution; we recognize the similarities between these methods and the methods reviewed previously, when the former are applied to linear Datalog queries. We also provide a quantitative analysis that shows the impact of the initial data distribution on the performance of methods
Relational Approach to Logical Query Optimization of XPath
To be able to handle the ever growing volumes of XML documents, effective and efficient data management solutions are needed. Managing XML data in a relational DBMS has great potential. Recently, effective relational storage schemes and index structures have been proposed as well as special-purpose join operators to speed up querying of XML data using XPath/XQuery. In this paper, we address the topic of query plan construction and logical query optimization. The claim of this paper is that standard relational algebra extended with special-purpose join operators suffices for logical query optimization. We focus on the XPath accelerator storage scheme and associated staircase join operators, but the approach can be generalized easily
Distributed Processing of Generalized Graph-Pattern Queries in SPARQL 1.1
We propose an efficient and scalable architecture for processing generalized
graph-pattern queries as they are specified by the current W3C recommendation
of the SPARQL 1.1 "Query Language" component. Specifically, the class of
queries we consider consists of sets of SPARQL triple patterns with labeled
property paths. From a relational perspective, this class resolves to
conjunctive queries of relational joins with additional graph-reachability
predicates. For the scalable, i.e., distributed, processing of this kind of
queries over very large RDF collections, we develop a suitable partitioning and
indexing scheme, which allows us to shard the RDF triples over an entire
cluster of compute nodes and to process an incoming SPARQL query over all of
the relevant graph partitions (and thus compute nodes) in parallel. Unlike most
prior works in this field, we specifically aim at the unified optimization and
distributed processing of queries consisting of both relational joins and
graph-reachability predicates. All communication among the compute nodes is
established via a proprietary, asynchronous communication protocol based on the
Message Passing Interface
Web and Semantic Web Query Languages
A number of techniques have been developed to facilitate
powerful data retrieval on the Web and Semantic Web. Three categories
of Web query languages can be distinguished, according to the format
of the data they can retrieve: XML, RDF and Topic Maps. This article
introduces the spectrum of languages falling into these categories
and summarises their salient aspects. The languages are introduced using
common sample data and query types. Key aspects of the query
languages considered are stressed in a conclusion
Reasoning & Querying ā State of the Art
Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF
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An effective data placement strategy for XML documents
As XML is increasingly being used in Web applications, new
technologies need to be investigated for processing XML documents with high
performance. Parallelism is a promising solution for structured document
processing and data placement is a major factor for system performance
improvement in parallel processing. This paper describes an effective XML
document data placement strategy. The new strategy is based on a multilevel
graph partitioning algorithm with the consideration of the unique features of
XML documents and query distributions. A new algorithm, which is based on
XML query schemas to derive the weighted graph from the labelled directed
graph presentation of XML documents, is also proposed. Performance analysis
on the algorithm presented in the paper shows that the new data placement
strategy exhibits low workload skew and a high degree of parallelism
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