11,477 research outputs found
Federated Query Processing
Big data plays a relevant role in promoting both manufacturing and scientific development through industrial digitization and emerging interdisciplinary research. Semantic web technologies have also experienced great progress, and scientific communities and practitioners have contributed to the problem of big data management with ontological models, controlled vocabularies, linked datasets, data models, query languages, as well as tools for transforming big data into knowledge from which decisions can be made. Despite the significant impact of big data and semantic web technologies, we are entering into a new era where domains like genomics are projected to grow very rapidly in the next decade. In this next era, integrating big data demands novel and scalable tools for enabling not only big data ingestion and curation but also efficient large-scale exploration and discovery. Federated query processing techniques provide a solution to scale up to large volumes of data distributed across multiple data sources. Federated query processing techniques resort to source descriptions to identify relevant data sources for a query, as well as to find efficient execution plans that minimize the total execution time of a query and maximize the completeness of the answers. This chapter summarizes the main characteristics of a federated query engine, reviews the current state of the field, and outlines the problems that still remain open and represent grand challenges for the area
Schema architecture and their relationships to transaction processing in distributed database systems
We discuss the different types of schema architectures which could be supported by distributed database systems, making a clear distinction between logical, physical, and federated distribution. We elaborate on the additional mapping information required in architecture based on logical distribution in order to support retrieval as well as update operations. We illustrate the problems in schema integration and data integration in multidatabase systems and discuss their impact on query processing. Finally, we discuss different issues relevant to the cooperation (or noncooperation) of local database systems in a heterogeneous multidatabase system and their relationship to the schema architecture and transaction processing
Developing a Benchmark Suite for Semantic Web Data from Existing Workflows
This paper presents work in progress towards developing a new benchmark for federated query processing systems. Unlike other popular benchmarks, our queryset is not driven by technical evaluation, but is derived from workflows established by the pharmacology community. The value of this queryset is that it is realistic but at the same time it comprises complex queries that test all features of modern query processing systems
Tracking Federated Queries in the Linked Data
Federated query engines allow data consumers to execute queries over the
federation of Linked Data (LD). However, as federated queries are decomposed
into potentially thousands of subqueries distributed among SPARQL endpoints,
data providers do not know federated queries, they only know subqueries they
process. Consequently, unlike warehousing approaches, LD data providers have no
access to secondary data. In this paper, we propose FETA (FEderated query
TrAcking), a query tracking algorithm that infers Basic Graph Patterns (BGPs)
processed by a federation from a shared log maintained by data providers.
Concurrent execution of thousand subqueries generated by multiple federated
query engines makes the query tracking process challenging and uncertain.
Experiments with Anapsid show that FETA is able to extract BGPs which, even in
a worst case scenario, contain BGPs of original queries
The Odyssey Approach for Optimizing Federated SPARQL Queries
Answering queries over a federation of SPARQL endpoints requires combining
data from more than one data source. Optimizing queries in such scenarios is
particularly challenging not only because of (i) the large variety of possible
query execution plans that correctly answer the query but also because (ii)
there is only limited access to statistics about schema and instance data of
remote sources. To overcome these challenges, most federated query engines rely
on heuristics to reduce the space of possible query execution plans or on
dynamic programming strategies to produce optimal plans. Nevertheless, these
plans may still exhibit a high number of intermediate results or high execution
times because of heuristics and inaccurate cost estimations. In this paper, we
present Odyssey, an approach that uses statistics that allow for a more
accurate cost estimation for federated queries and therefore enables Odyssey to
produce better query execution plans. Our experimental results show that
Odyssey produces query execution plans that are better in terms of data
transfer and execution time than state-of-the-art optimizers. Our experiments
using the FedBench benchmark show execution time gains of at least 25 times on
average.Comment: 16 pages, 10 figure
DAW: Duplicate-AWare Federated Query Processing over the Web of Data
Abstract. Over the last years the Web of Data has developed into a large compendium of interlinked data sets from multiple domains. Due to the decentralised architecture of this compendium, several of these datasets contain duplicated data. Yet, so far, only little attention has been paid to the effect of duplicated data on federated querying. This work presents DAW, a novel duplicate-aware approach to feder-ated querying over the Web of Data. DAW is based on a combination of min-wise independent permutations and compact data summaries. It can be directly combined with existing federated query engines in or-der to achieve the same query recall values while querying fewer data sources. We extend three well-known federated query processing engines – DARQ, SPLENDID, and FedX – with DAW and compare our exten-sions with the original approaches. The comparison shows that DAW can greatly reduce the number of queries sent to the endpoints, while keeping high query recall values. Therefore, it can significantly improve the performance of federated query processing engines. Moreover, DAW provides a source selection mechanism that maximises the query recall, when the query processing is limited to a subset of the sources
Hypermedia-based discovery for source selection using low-cost linked data interfaces
Evaluating federated Linked Data queries requires consulting multiple sources on the Web. Before a client can execute queries, it must discover data sources, and determine which ones are relevant. Federated query execution research focuses on the actual execution, while data source discovery is often marginally discussed-even though it has a strong impact on selecting sources that contribute to the query results. Therefore, the authors introduce a discovery approach for Linked Data interfaces based on hypermedia links and controls, and apply it to federated query execution with Triple Pattern Fragments. In addition, the authors identify quantitative metrics to evaluate this discovery approach. This article describes generic evaluation measures and results for their concrete approach. With low-cost data summaries as seed, interfaces to eight large real-world datasets can discover each other within 7 minutes. Hypermedia-based client-side querying shows a promising gain of up to 50% in execution time, but demands algorithms that visit a higher number of interfaces to improve result completeness
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