A Scalable Scheme for Bulk Loading Large RDF Graphs into Oracle

Das, Shantanu; Chong, Eugene Inseok; Wu, Zhe; Annamalai, Melliyal; Srinivasan, Jagannathan

doi:10.1109/icde.2008.4497539

Cited by 10 publications

(10 citation statements)

References 9 publications

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“…For the synthetic LUBM datasets, 3XL-MP uses 36% more time than BigOWLIM and 3XL-Array uses 21% more time. However, BigOWLIM is using a file-based data store, which is reported [11] to have higher loading performance than relational database-based stores in general. RDF-3X has the slowest load performance, using 83% and 180% more time than BIGOWLIM for the EIAO and LUBM datasets, respectively.…”

Section: Loading Timementioning

confidence: 96%

“…and DBMS-based (called ''db-based'') triple-stores in the table. As far as the performance of db-based triple-stores is concerned, the Virtuoso triple-store and the Oracle bulk-loading scheme (described in detail in [11]) have the best performance, with loading speeds of 12,692 and 10,750 triples/s, respectively. However, the Virtuoso scheme is run on much more powerful hardware, meaning that the Oracle scheme is in fact the fastest of the two.…”

Section: Comparison With Other Bulk-loading Systemsmentioning

confidence: 99%

“…Virtuoso's implementation has employed parallel loading techniques and storage optimization like bitmap indexes, etc., while the Oracle scheme has used the high-performance commercial DBMS Oracle and made use of its bulk-loading utility SQLnLoader. The paper [11] thus establishes the Oracle scheme as the leading DBMS-based bulk-loading scheme. It is thus natural to compare it with 3XL.…”

Section: Comparison With Other Bulk-loading Systemsmentioning

confidence: 99%

“…In order to compare with DBMS-based triple-stores, we refer to a bulk-loading study published by [7,11]. Table 2 (reproduced from [7,11]) summarizes the bulk-loading speed rates of a number of tools.…”

Section: Comparison With Other Bulk-loading Systemsmentioning

confidence: 99%

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3XL: Supporting efficient operations on very large OWL Lite triple-stores

Liu

Thomsen

Pedersen

2011

Information Systems

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a b s t r a c tAn increasing number of (semantic) web applications store a very large number of (subject, predicate, object) triples in specialized storage engines called triple-stores. Often, triple-stores are used mainly as plain data stores, i.e., for inserting and retrieving large amounts of triples, but not using more advanced features such as logical inference, etc. However, current triple-stores are not optimized for such bulk operations and/or do not support OWL Lite. Further, triple-stores can be inflexible when the data has to be integrated with other kinds of data in non-triple form, e.g., standard relational data. This paper presents 3XL, a triple-store that efficiently supports operations on very large amounts of OWL Lite triples. 3XL also provides the user with high flexibility as it stores data in an object-relational database in a schema that is easy to use and understand. It is, thus, easy to integrate 3XL data with data from other sources. The distinguishing features of 3XL include (a) flexibility as the data is stored in a database, allowing easy integration with other data, and can be queried by means of both triple queries and SQL, (b) using a specialized data-dependent schema (with intelligent partitioning) which is intuitive and efficient to use, (c) using object-relational DBMS features such as inheritance, (d) efficient loading through extensive use of bulk loading and caching, and (e) efficient triple query operations, especially in the important case when the subject and/or predicate is known. Extensive experiments with a PostgreSQL-based implementation show that 3XL performs very well for such operations and that the performance is comparable to state-of-the-art triple-stores.

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Section: Loading Timementioning

confidence: 96%

Section: Comparison With Other Bulk-loading Systemsmentioning

confidence: 99%

Section: Comparison With Other Bulk-loading Systemsmentioning

confidence: 99%

Section: Comparison With Other Bulk-loading Systemsmentioning

confidence: 99%

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3XL: Supporting efficient operations on very large OWL Lite triple-stores

Liu

Thomsen

Pedersen

2011

Information Systems

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“…Several works have tried to scale the reasoning process on larger inputs using a variety of computer architectures, like clusters [46], supercomputers [47], GPUs [48], or using state of the art database engines [49,50]. Despite these efforts, reasoning remains a complex task.…”

Section: Reasoningmentioning

confidence: 99%

Streaming the Web: Reasoning over dynamic data

Margara

Urbani

Harmelen

et al. 2014

Journal of Web Semantics

110

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a b s t r a c tIn the last few years a new research area, called stream reasoning, emerged to bridge the gap between reasoning and stream processing. While current reasoning approaches are designed to work on mainly static data, the Web is, on the other hand, extremely dynamic: information is frequently changed and updated, and new data is continuously generated from a huge number of sources, often at high rate. In other words, fresh information is constantly made available in the form of streams of new data and updates.Despite some promising investigations in the area, stream reasoning is still in its infancy, both from the perspective of models and theories development, and from the perspective of systems and tools design and implementation.The aim of this paper is threefold: (i) we identify the requirements coming from different application scenarios, and we isolate the problems they pose; (ii) we survey existing approaches and proposals in the area of stream reasoning, highlighting their strengths and limitations; (iii) we draw a research agenda to guide the future research and development of stream reasoning. In doing so, we also analyze related research fields to extract algorithms, models, techniques, and solutions that could be useful in the area of stream reasoning.

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