. Bazoubandi, H., de Rooij , S., Urbani, J., ten Teije , A., van Harmelen , F., & Bal, H. Volume 9088. A Compact In-Memory Dictionary for RDF data, pages 205–220. Springer/Verlag, 2015. Proceedings title: Proceedings of the twelfth European Semantic Web Conference Publisher: Springer Place of publication: Berlin
doi  abstract   bibtex   
While almost all dictionary compression techniques focus on static RDF data, we present a compact in-memory RDF dictionary for dynamic and streaming data. To do so, we analysed the structure of terms in real-world datasets and observed a high degree of common prefixes. We studied the applicability of Trie data structures on RDF data to reduce the memory occupied by common prefixes and discovered that all existing Trie implementations lead to either poor performance, or an excessive memory wastage. In our approach, we address the existing limitations of Tries for RDF data, and propose a new variant of Trie which contains some optimizations explicitly designed to improve the performance on RDF data. Furthermore, we show how we use this Trie as an in-memory dictionary by using as numerical ID a memory address instead of an integer counter. This design removes the need for an additional decoding data structure, and further reduces the occupied memory. An empirical analysis on realworld datasets shows that with a reasonable overhead our technique uses 50–59% less memory than a conventional uncompressed dictionary.
@inbook{85421a7bb476400da0040eb31b196374,
  title     = "A Compact In-Memory Dictionary for RDF data",
  abstract  = "While almost all dictionary compression techniques focus on static RDF data, we present a compact in-memory RDF dictionary for dynamic and streaming data. To do so, we analysed the structure of terms in real-world datasets and observed a high degree of common prefixes. We studied the applicability of Trie data structures on RDF data to reduce the memory occupied by common prefixes and discovered that all existing Trie implementations lead to either poor performance, or an excessive memory wastage. In our approach, we address the existing limitations of Tries for RDF data, and propose a new variant of Trie which contains some optimizations explicitly designed to improve the performance on RDF data. Furthermore, we show how we use this Trie as an in-memory dictionary by using as numerical ID a memory address instead of an integer counter. This design removes the need for an additional decoding data structure, and further reduces the occupied memory. An empirical analysis on realworld datasets shows that with a reasonable overhead our technique uses 50–59% less memory than a conventional uncompressed dictionary.",
  author    = "Bazoubandi, {Hamid R.} and {de Rooij}, Steven and Jacopo Urbani and {ten Teije}, Annette and {van Harmelen}, Frank and Henri Bal",
  note      = "Proceedings title: Proceedings of the twelfth European Semantic Web Conference Publisher: Springer Place of publication: Berlin",
  year      = "2015",
  doi       = "10.1007/978-3-319-18818-8_13",
  volume    = "9088",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "205--220",
  booktitle = "The Semantic Web: Latest Advances and New Domains - 12th European Semantic Web Conference, ESWC 2015, Proceedings",
}

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