Papers query3

Recommend papers, including paper title, authors and publication year, which address the problem of "SPARQL Query Federation" and being published in ISWC conference.

We recommend the following papers:

Paper	Venue	Authors	year	abstract	conclusion	future work
FedX: Optimization Techniques for Federated Query Processing on Linked Data	ISWC	Andreas Schwarte Peter Haase Katja Hose Ralf Schenkel Michael Schmidt	2011	Motivated by the ongoing success of Linked Data and the growing amount of semantic data sources available on the Web, new challenges to query processing are emerging. Especially in distributed settings that require joining data provided by multiple sources, sophisticated optimization techniques are necessary for efficient query processing. We propose novel join processing and grouping techniques to minimize the number of remote requests, and develop an effective solution for source selection in the absence of preprocessed metadata. We present FedX, a practical framework that enables efficient SPARQL query processing on heterogeneous, virtually integrated Linked Data sources. In experiments, we demonstrate the practicability and efficiency of our framework on a set of real-world queries and data sources from the Linked Open Data cloud. With FedX we achieve a significant improvement in query performance over state-of-the-art federated query engines.	In this paper, we proposed novel optimization techniques for efficient SPARQL query processing in the federated setting. As revealed by our benchmarks, bound joins combined with our grouping and source selection approaches are effective in terms of performance. By minimizing the number of intermediate requests, we are able to improve query performance significantly compared to state-of-the-art systems. We presented FedX, a practical solution that allows for querying multiple distributed Linked Data sources as if the data resides in a virtually integrated RDF graph. Compatible with the SPARQL 1.0 query language, our framework allows clients to integrate available SPARQL endpoints on-demand into a federation without any local preprocessing. While we focused on optimization techniques for conjunctive queries, namely basic graph patterns (BGPs), there is additional potential in developing novel, operator-specific optimization techniques for distributed settings (in particular for OPTIONAL queries), which we are planning to address in future work. As our experiments confirm, the optimization of BGPs alone (combined with common equivalent rewritings) already yields significant performance gains. Important features for federated query processing are the federation extensions proposed for the upcoming SPARQL 1.1 language definition. These allow to specify data sources directly within the query using the SERVICE operator, and moreover to attach mappings to the query as data using the BINDINGS operator. When implementing the SPARQL 1.1 federation extensions for our next release,FedX can exploit these language features to further improve performance. In fact, the SPARQL 1.1 SERVICE keyword is a trivial extension, which enhances our source selection approach with possibilities for manual specification of new sources and gives the query designer more control. Statistics can in uence performance tremendously in a distributed setting. Currently, FedX does not use any local statistics since we follow the design goal of on-demand federation setup. We aim at providing a federation framework, in which data sources can be integrated ad-hoc, and used immediately for query processing. In a future release, (remote) statistics (e.g., using VoID ) can be incorporated for source selection and to further improve our join order algorithm.	While we focused on optimization techniques for conjunctive queries, namely basic graph patterns (BGPs), there is additional potential in developing novel, operator-specific optimization techniques for distributed settings (in particular for OPTIONAL queries), which we are planning to address in future work. In a future release, (remote) statistics (e.g., using VoID) can be incorporated for source selection and to further improve our join order algorithm.
Optimizing SPARQL Queries over Disparate RDF Data Sources through Distributed Semi-joins	ISWC	Jan Zemánek Simon Schenk Vojtěch Svátek Abraham Bernstein	2008	With the ever-increasing amount of data on the Web available at SPARQL endpoints the need for an integrated and transparent way of accessing the data has arisen. It is highly desirable to have a way of asking SPARQL queries that make use of data residing in disparate data sources served by multiple SPARQL endpoints. We aim at providing such a capability and thus enabling an integrated way of querying the whole Semantic Web at a time.	We briefly presented our Sesame extension Distributed SPARQL which aims at providing an integrated way of querying data sources scattered across multiple SPARQL endpoints. We shortly described its implementation and optimization used so far and outlined the direction for its future development. Distributed SPARQL is a part of Networked Graphs project and is publicly available at https://launchpad.net/networkedgraphs.	We would like to further improve the query evaluation performance by introducing a distributed join-aware join reordering. We will make use of the current Sesame optimization techniques for local queries and add our own component which will be re-ordering joins according to their relative costs. The costs will be based on statistics taking into account a sub-query selectivity combined with the distinction whether a triple pattern is supposed to be evaluated locally or at a remote SPARQL endpoint. In addition to join re-ordering we would like to make use of statistics about SPARQL endpoints in order to optimize queries even further. Hopefully the recent initiative called Vocabulary of Interlinked Datasets (http://community.linkeddata.org/MediaWiki/index.php?VoiD) will get to a point where it could be used for this purpose.
ANAPSID: An Adaptive Query Processing Engine for SPARQL Endpoints	ISWC	Maribel Acosta Maria-Esther Vidal Tomas Lampo Julio Castillo	2011	Following the design rules of Linked Data, the number of available SPARQL endpoints that support remote query processing is quickly growing; however, because of the lack of adaptivity, query executions may frequently be unsuccessful. First, fixed plans identified following the traditional optimize-then execute paradigm, may timeout as a consequence of endpoint availability. Second, because blocking operators are usually implemented, endpoint query engines are not able to incrementally produce results, and may become blocked if data sources stop sending data. We present ANAPSID, an adaptive query engine for SPARQL endpoints that adapts query execution schedulers to data availability and run-time conditions. ANAPSID provides physical SPARQL operators that detect when a source becomes blocked or data traÆc is bursty, and opportunistically, the operators produce results as quickly as data arrives from the sources. Additionally, ANAPSID operators implement main memory replacement policies to move previously computed matches to secondary memory avoiding duplicates. We compared ANAPSID performance with respect to RDF stores and endpoints, and observed that ANAPSID speeds up execution time, in some cases, in more than one order of magnitude.	We have defined ANAPSID, an adaptive query processing engine for RDF Linked Data accessible through SPARQL endpoints. ANAPSID provides a set of physical operators and an execution engine able to adapt the query execution to the availability of the endpoints and to hide delays from users. Reported experimental results suggest that our proposed techniques reduce execution times and are able to produce answers when other engines fail. Also, depending on the selectivity of the join operator and the data transfer delays, ANAPSID operators may overcome state-of-the-art Symmetric Hash Join operators. In the future, we plan to extend ANAPSID with more powerful and lightweight operators like Eddy and MJoin, which are able to route received responses through different operators and adapt the execution to unpredictable delays by changing the order in which each data item is routed.	In the future we plan to extend ANAPSID with more powerful and lightweight operators like Eddy and MJoin, which are able to route received responses through different operators, and adapt the execution to unpredictable delays by changing the order in which each data item is routed.
Avalanche: Putting the Spirit of the Web back into Semantic Web Querying	ISWC	Cosmin Basca Abraham Bernstein	2010	Traditionally Semantic Web applications either included a web crawler or relied on external services to gain access to the Web of Data. Recent efforts have enabled applications to query the entire Semantic Web for up-to-date results. Such approaches are based on either centralized indexing of semantically annotated metadata or link traversal and URI dereferencing as in the case of Linked Open Data. By making limiting assumptions about the information space, they violate the openness principle of the Web – a key factor for its ongoing success. In this article we propose a technique called Avalanche, designed to allow a data surfer to query the Semantic Web transparently without making any prior assumptions about the distribution of the data – thus adhering to the openness criteria. Specifically, Avalanche can perform “live” (SPARQL) queries over the Web of Data. First, it gets on-line statistical information about the data distribution, as well as bandwidth availability. Then, it plans and executes the query in a distributed manner trying to quickly provide first answers. The main contribution of this paper is the presentation of this open and distributed SPARQL querying approach. Furthermore, we propose to extend the query planning algorithm with qualitative statistical information. We empirically evaluate Avalanche using a realistic dataset, show its strengths but also point out the challenges that still exist.	In this paper we presented Avalanche , a novel approach for querying the Web of Data that (1) makes no assumptions about data distribution, availability, or partitioning, (2) provides up-to-date results, and (3) is flexible since it assumes nothing about the structure of participating triple stores. Specifically, we showed that Avalanche is able to execute non-trivial queries over distributed data-sources with an ex-ante unknown data-distribution. We showed two possible utility functions to guide the planning and execution over the distributed data-sources—the basic simple model and an extended model exploiting join estimation. We found that whilst the simple model found some results faster it did find less results than the extended model using the same stopping criteria. We believe that if we were to query huge information spaces the overhead of badly selected plans will be subdued by the better but slower plans of the extended utility function. To our knowledge, Avalanche is the first Semantic Web query system that makes no assumptions about the data distribution whatsoever. Whilst it is only a first implementation with a number of drawbacks it represents a first important towards bringing the spirit of the web back to triple-stores—a major condition to fulfill the vision of a truly global and open Semantic Web.	The Avalanche system has shown how a completely heterogeneous distributed query engine that makes no assumptions about data distribution could be implemented. The current approach does have a number of limitations. In particular, we need to better understand the employed objective functions for the planner, investigate if the requirements put on participating triple-stores are reasonable, explore if Avalanche can be changed to a stateless model, and empirically evaluate if the approach truly scales to large number of hosts. Here we discuss each of these issues in turn. The core optimization of the Avalanche system lies in its cost and utility function. The basic utility function only considers possible joins with no information regarding the probability of the respective join. The proposed utility extension UE estimates the join probability of two highly selective molecules. Although this improves the accuracy of the objective function, its limitation to highly selective molecules is often impractical, as many queries (such as our example query) combine highly selective molecules with non-selective ones. Hence, we need to find a probabilistic distributed join cardinality estimation for low selectivity molecules. One approach might be the usage of bloom-filter caches to store precomputed, “popular” estimates. Another might be investigating sampling techniques for distributed join estimation. In order to support Avalanche existing triple-stores should be able to: – report statistics: cardinalities, bloom filters, other future extensions – support the execution of distributed joins (common in distributed databases), which could be delegated to an intermediary but would be inefficient – share the same key space (can be URIs but would result in bandwidth intensive joins and merges) Whilst these requirements seem simple we need to investigate how complex these extensions of triple-stores are in practice. Even better would be an extension of the SPARQL standard with the above-mentioned operations, which we will attempt to propose. The current Avalanche process assumes that hosts keep partial results throughout plan execution to reduce the cost of local database operations and that result-views are kept for the duration of a query. This limits the number of queries a host can handle. We intend to investigate if a stateless approach is feasible. Note that the simple approach—the use of REST-full services—may not be applicable as the size of the state (i.e., the partial results) may be huge and overburden the available bandwidth. We designed Avalanche with the need for high scalability in mind. The core idea follows the principle of decentralization. It also supports asynchrony using asynchronous HTTP requests to avoid blocking, autonomy by delegating the coordination and execution of the distributed join/update/merge operations to the hosts, concurrency through the pipeline shown in Figure 1, symmetry by allowing each endpoint to act as the initiating Avalanche node for a query caller, and fault tolerance through a number of time-outs and stopping conditions. Nonetheless, an empirical evaluation of Avalanche with a large number of hosts is still missing—a non-trivial shortcoming (due to the lack of suitable, partitioned datasets and the significant experimental complexity) we intend to address in the near future.

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