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QuWeDa 2019 : QuWeDa 2019: 3rd Workshop on Querying and Benchmarking the Web of Data


When Oct 26, 2019 - Oct 26, 2019
Where Co-located with ISWC 2019 at Auckland NZ
Submission Deadline Jul 5, 2019
Notification Due Jul 29, 2019
Final Version Due Aug 5, 2019
Categories    computer science   querying   semantics   benchmarking

Call For Papers

The constant growth of Linked Data on the Web raises new challenges for querying and integrating massive amounts of data across multiple datasets. Such datasets are available through various interfaces, such as data dumps, Linked Data Platform, SPARQL endpoints and Triple Pattern Fragments. In addition, various sources produce streaming data. Efficiently querying these sources is of central importance for the scalability of Linked Data and Semantic Web technologies. To exploit the massive amount of data to its full potential, users should be able to query and combine this data easily and effectively. This workshop at the International Semantic Web Conference (ISWC) seeks original articles describing theoretical and practical methods and techniques for fostering, querying, consuming, and benchmarking the Web of Data.
This workshop at the International Semantic Web Conference 2019 (ISWC 2019) seeks original articles describing theoretical and practical methods and techniques for fostering, querying, and consuming the Data Web. Topics relevant to this workshop include - but are not limited to - the following:

.SPARQL query processing
.Centralized, decentralized, federated, and distributed
.Demos and applications
.Optimization and source selection
.Benchmarks, ranking, measures, and performance evaluation
.Lightweight Linked Data interfaces
.Big Data techniques
.Entailment regimes
.Caching and replication
.Integrating public and private Linked Data
.Querying personal Linked Data stores
.Domain-specific query languages (e.g., temporal and spatial queries)
.Querying embedded Linked Data
.Query relaxation and rewriting
.Alternative languages for representing and querying the Web of Data

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