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31st International Conference on Database and Expert Systems Applications (DEXA)

作者： Grall, Arnaud Skaf-Molli, Hala Molli, Pascal Perrin, Matthieu Univ Nantes LS2N Nantes France GFI Informat IS CIE Nantes France

ISBN: (纸本)9783030590031;9783030590024

Decentralization allows users to regain freedom and control over their digital life. As a global shared data space, the Linked Data already supports decentralization. Data providers are free to publish their data on their web domains and users can execute decentralized sparql queries over multiple data sources. However, decentralization makes query processing challenging, raising well-known problems of source discovery, answer completeness and performance. Existing approaches for decentralized sparql query processing raise issues related to autonomy and answer completeness. In this paper, we propose Qasino, an original approach for querying decentralized RDF data that targets both answer completeness, and source autonomy. Qasino is based on a decentralized random service that allows for discovering all relevant data sources. To speed up query processing, sources executing similar queries cooperate by sharing their intermediate results. Our experimental results demonstrate that collaborative query processing can significantly speedup query processing in a decentralized setup.

关键词： Decentralized data management sparql query processing Sources discovery

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On Integrating Knowledge Graph Embedding into sparql query processing 25

On Integrating Knowledge Graph Embedding into SPARQL Query P...

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25th IEEE International Conference on Web Services (IEEE ICWS) Part of the IEEE World Congress on Services

作者： Kang, Hyunjoong Hong, Sanghyun Lee, Kookjin Park, Noseong Kwon, Soonhyun Elect & Telecommun Res Inst Daejeon South Korea Univ Maryland College Pk MD 20742 USA Univ N Carolina Charlotte NC 28223 USA

ISBN: (纸本)9781538672471

sparql is a standard query language for knowledge graphs (KGs). However, it is hard to find correct answer if KGs are incomplete or incorrect. Knowledge graph embedding (KGE) enables answering queries on such KGs by inferring unknown knowledge and removing incorrect knowledge. Hence, our long-term goal in this line of research is to propose a new framework that integrates KGE and sparql, which opens various research problems to be addressed. In this paper, we solve one of the most critical problems, that is, optimizing the performance of nearest neighbor (NN) search. In our evaluations, we demonstrate that the search time of state-of-the-art NN search algorithms is improved by 40% without sacrificing answer accuracy.

关键词： sparql query processing Knowledge Graph Embedding Nearest Neighbor Searching

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DNA-Based Storage of RDF Graph Data: A Futuristic Approach to Data Analytics

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IEEE ACCESS 2023年 11卷 129931-129944页

作者： Usmani, Asad Wiese, Lena Goethe Univ Frankfurt Dept Comp Sci D-60325 Frankfurt Germany

Future data analytics will require enormous storage space for data-driven decisions, necessitating alternative storage sources for massive data archives. Storage solutions have always been in demand due to the limitations of existing media. Deoxyribonucleic Acid (DNA) is an emergent storage medium suitable for archival storage of rapidly increasing digital volumes. Due to its longevity, DNA storage technology has led to numerous applications to store and retrieve entire data. In this way, DNA synthesis and sequencing costs can be reduced by compressing data in full before it is stored. However, prior works have not used DNA storage to retrieve partial data from complex graphs, while taking advantage of cost-effective advanced analytics. In this paper, we present an efficient DNA-based query processing system to retrieve partial information using RDF graph data. Moreover, using binary search, we fetch and decode significantly fewer DNA strands to obtain partial information about RDF graph data based on sparql queries. Specifically, the experimental analysis shows that the average data retrieval per query as output is found less than 1% for RDF graphs with more than 1MB (Megabytes) in size, which consequently reduces a significant amount of sequencing costs.

关键词： Data retrieval DNA storage RDF graph data model sparql query processing

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Optimizing Multi-query Evaluation in Federated RDF Systems

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2021年第4期33卷 1692-1707页

作者： Peng, Peng Ge, Qi Zou, Lei Ozsu, M. Tamer Xu, Zhiwei Zhao, Dongyan Hunan Univ Changsha 410082 Hunan Peoples R China Peking Univ Haidian 100871 Peoples R China Univ Waterloo Waterloo ON N2L 3G1 Canada

This paper revisits the classical problem of multiple query optimization in federated RDF systems. We propose a heuristic query rewriting-based approach to optimize the evaluation of multiple queries. This approach can take advantage of sparql 1.1 to share the common computation of multiple queries while considering the cost of both query evaluation and data shipment. Although we prove that finding the optimal rewriting for multiple queries is NP-complete, we propose a heuristic rewriting algorithm with a bounded approximation ratio. Furthermore, we propose an efficient method to use the interconnection topology between RDF sources to filter out irrelevant sources, and utilize some characteristics of sparql 1.1 to optimize multiple joins of intermediate matches. The extensive experimental studies show that the proposed techniques are effective, efficient and scalable.

关键词： Resource description framework query processing Metadata Optimization Bioinformatics Germanium Federated RDF systems sparql query processing multiple query optimization

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Map-Side Join processing of sparql Queries Based on Abstract RDF Data Filtering

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JOURNAL OF DATABASE MANAGEMENT 2019年第1期30卷 22-40页

作者： Song, Minjae Oh, Hyunsuk Seo, Seungmin Lee, Kyong-Ho Yonsei Univ Dept Comp Sci Seoul South Korea Yonsei Univ Seoul South Korea

The amount of RDF data being published on the Web is increasing at a massive rate. MapReduce-based distributed frameworks have become the general trend in processing sparql queries against RDF data. Currently, query processing systems that use MapReduce have not been able to keep up with the increase of semantic annotated data, resulting in non-interactive sparql query processing. The principal reason is that intermediate query results from join operations in a MapReduce framework are so massive that they consume all available network bandwidth. In this article, the authors present an efficient sparql processing system that uses MapReduce and HBase. The system runs a job optimized query plan using their proposed abstract RDF data to decrease the number of jobs and also decrease the amount of input data. The authors also present an efficient algorithm of using Map-side joins while also using the abstract RDF data to filter out unneeded RDF data. Experimental results show that the proposed approach demonstrates better performance when processing queries with a large amount of input data than those found in previous works.

关键词： Abstract RDF Data HBase MapReduce Map-Side Join sparql query processing

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An Extension of sparql for Expressing Qualitative Preferences 16th

An Extension of SPARQL for Expressing Qualitative Preference...

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16th International Semantic Web Conference (ISWC)

作者： Troumpoukis, Antonis Konstantopoulos, Stasinos Charalambidis, Angelos NCSR Demokritos Inst & Informat & Telecommun Athens 15310 Greece Univ Athens Dept Informat & Telecommun Athens Greece

ISBN: (纸本)9783319682884;9783319682877

In this paper we present SPREFQL, an extension of the sparql language that allows appending a "PREFER" clause that expresses 'soft' preferences over the query results obtained by the main body of the query. The extension does not add expressivity and any SPREFQL query can be transformed to an equivalent standard sparql query. However, clearly separating preferences from the 'hard' patterns and filters in the "WHERE" clause gives queries where the intention of the client is more cleanly expressed, an advantage for both human readability and machine optimization. In the paper we formally define the syntax and the semantics of the extension and we also provide empirical evidence that optimizations specific to SPREFQL improve run-time efficiency by comparison to the usually applied optimizations on the equivalent standard sparql query.

关键词： sparql query processing Expressing preferences query execution optimization

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Efficient processing of sparql Queries Over GraphFrames 17

Efficient Processing of SPARQL Queries Over GraphFrames

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IEEE/WIC/ACM International Conference on Web Intelligence (WI)

作者： Bahrami, Ramazan Ali Gulati, Jayati Abulaish, Muhammad South Asian Univ Dept Comp Sci Delhi India

ISBN: (纸本)9781450349512

With the advent of huge data management systems storing voluminous data, there arises a need to develop efficient data analytics techniques for knowledge discovery at different levels of granularity. Resource Description Framework (RDF), mainly developed for Semantic Web, is presumably a good option when considering graph databases dealing with huge real-world data. RDF models information in the form of triples , and is considered as a useful tool to store graph data (aka linked data) where each edge can be stored as a triple. Due to existence of huge amount of linked data, mostly in the form of graphs, graph mining has been successful in attracting researchers from different research fields for efficient handling (storage, indexing, retrieval, etc.) of graph data. As a result, various APIs like GraphX and GraphFrames are developed to facilitate relational queries over graph data. Though GraphX is older than GraphFrames and processing sparql queries over GraphX has been explored by some researchers, to the best of our knowledge, sparql query processing over GraphFrames has not been explored yet. In this paper, we present an initial study on query-specific search space pruning and query optimization approach to process sparql queries over GraphFrames in an efficient manner. The experimental results, in terms of low response time for query execution, are encouraging, and give way to invest more research efforts in this direction.

关键词： Graph mining Linked data mining sparql query processing GraphFrames GraphX

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Accelerating sparql queries by exploiting hash-based locality and adaptive partitioning

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VLDB JOURNAL 2016年第3期25卷 355-380页

作者： Harbi, Razen Abdelaziz, Ibrahim Kalnis, Panos Mamoulis, Nikos Ebrahim, Yasser Sahli, Majed King Abdullah Univ Sci & Technol Thuwal Saudi Arabia Univ Ioannina GR-45110 Ioannina Greece Microsoft Corp Redmond WA 98052 USA

State-of-the-art distributed RDF systems partition data across multiple computer nodes (workers). Some systems perform cheap hash partitioning, which may result in expensive query evaluation. Others try to minimize inter-node communication, which requires an expensive data preprocessing phase, leading to a high startup cost. Apriori knowledge of the query workload has also been used to create partitions, which, however, are static and do not adapt to workload changes. In this paper, we propose AdPart, a distributed RDF system, which addresses the shortcomings of previous work. First, AdPart applies lightweight partitioning on the initial data, which distributes triples by hashing on their subjects;this renders its startup overhead low. At the same time, the locality-aware query optimizer of AdPart takes full advantage of the partitioning to (1) support the fully parallel processing of join patterns on subjects and (2) minimize data communication for general queries by applying hash distribution of intermediate results instead of broadcasting, wherever possible. Second, AdPart monitors the data access patterns and dynamically redistributes and replicates the instances of the most frequent ones among workers. As a result, the communication cost for future queries is drastically reduced or even eliminated. To control replication, AdPart implements an eviction policy for the redistributed patterns. Our experiments with synthetic and real data verify that AdPart: (1) starts faster than all existing systems;(2) processes thousands of queries before other systems become online;and (3) gracefully adapts to the query load, being able to evaluate queries on billion-scale RDF data in subseconds.

关键词： Parallel and distributed RDF systems sparql query processing Main memory engines

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