@TechReport{LDBC-SemanticPublishing-2014,
  author       = {Venelin Kotsev and Atanas Kiryakov and Irini Fundulaki and Vladimir Alexiev},
  title        = {{{LDBC Semantic Publishing Benchmark}} ({{SPB}}) v2.0},
  institution  = {Linked Data Benchmarking Council project},
  year         = 2014,
  number       = {v2.0 First Public Draft Release},
  url          = {https://ldbcouncil.org/publication/ldbc-spc-specification/},
  keywords     = {LDBC, benchmark, semantic publishing, graph databases},
  abstract     = {The Semantic Publishing Benchmark (SPB) is a LDBC benchmark for RDF database engines inspired by the Media/Publishing industry, particularly by the BBC’s Dynamic Semantic Publishing approach. As of June 2014 the benchmark has reached the state of draft publication. This document describes the current state of the Semantic Publishing Benchmark software. The application scenario behind the benchmark considers a media or a publishing organisation that deals with large volume of streaming content, namely articles and other “creative works” and “media assets”. This content is enriched with metadata that describes it and links it to reference knowledge – taxonomies and databases that include relevant concepts, entities and factual information. This metadata allows publishers to efficiently retrieve relevant content, according to their various business models. From a technology standpoint, the benchmark assumes that an RDF database is used to store both the reference knowledge and the metadata. The main interactions with the repository are (i) updates, that add new metadata or alter the repository, and (ii) aggregation queries, that retrieve content according to various criteria. The engine should handle instantly large number of updates in parallel with massive amount of aggregation queries. This document describes all features of the SPB : data (reference data-sets, ontologies, data generation), query workloads (descriptions of queries used, choke point descriptions), validation of query results and instructions (how to configure and use the benchmark driver, execution, auditing and disclosure rules)},
}

{"_id":"5gPAcCAdoJSE8HhS4","bibbaseid":"kotsev-kiryakov-fundulaki-alexiev-ldbcsemanticpublishingbenchmarkspbv20-2014","author_short":["Kotsev, V.","Kiryakov, A.","Fundulaki, I.","Alexiev, V."],"bibdata":{"bibtype":"techreport","type":"techreport","author":[{"firstnames":["Venelin"],"propositions":[],"lastnames":["Kotsev"],"suffixes":[]},{"firstnames":["Atanas"],"propositions":[],"lastnames":["Kiryakov"],"suffixes":[]},{"firstnames":["Irini"],"propositions":[],"lastnames":["Fundulaki"],"suffixes":[]},{"firstnames":["Vladimir"],"propositions":[],"lastnames":["Alexiev"],"suffixes":[]}],"title":"LDBC Semantic Publishing Benchmark (SPB) v2.0","institution":"Linked Data Benchmarking Council project","year":"2014","number":"v2.0 First Public Draft Release","url":"https://ldbcouncil.org/publication/ldbc-spc-specification/","keywords":"LDBC, benchmark, semantic publishing, graph databases","abstract":"The Semantic Publishing Benchmark (SPB) is a LDBC benchmark for RDF database engines inspired by the Media/Publishing industry, particularly by the BBC’s Dynamic Semantic Publishing approach. As of June 2014 the benchmark has reached the state of draft publication. This document describes the current state of the Semantic Publishing Benchmark software. The application scenario behind the benchmark considers a media or a publishing organisation that deals with large volume of streaming content, namely articles and other “creative works” and “media assets”. This content is enriched with metadata that describes it and links it to reference knowledge – taxonomies and databases that include relevant concepts, entities and factual information. This metadata allows publishers to efficiently retrieve relevant content, according to their various business models. From a technology standpoint, the benchmark assumes that an RDF database is used to store both the reference knowledge and the metadata. The main interactions with the repository are (i) updates, that add new metadata or alter the repository, and (ii) aggregation queries, that retrieve content according to various criteria. The engine should handle instantly large number of updates in parallel with massive amount of aggregation queries. This document describes all features of the SPB : data (reference data-sets, ontologies, data generation), query workloads (descriptions of queries used, choke point descriptions), validation of query results and instructions (how to configure and use the benchmark driver, execution, auditing and disclosure rules)","bibtex":"@TechReport{LDBC-SemanticPublishing-2014,\n author = {Venelin Kotsev and Atanas Kiryakov and Irini Fundulaki and Vladimir Alexiev},\n title = {{{LDBC Semantic Publishing Benchmark}} ({{SPB}}) v2.0},\n institution = {Linked Data Benchmarking Council project},\n year = 2014,\n number = {v2.0 First Public Draft Release},\n url = {https://ldbcouncil.org/publication/ldbc-spc-specification/},\n keywords = {LDBC, benchmark, semantic publishing, graph databases},\n abstract = {The Semantic Publishing Benchmark (SPB) is a LDBC benchmark for RDF database engines inspired by the Media/Publishing industry, particularly by the BBC’s Dynamic Semantic Publishing approach. As of June 2014 the benchmark has reached the state of draft publication. This document describes the current state of the Semantic Publishing Benchmark software. The application scenario behind the benchmark considers a media or a publishing organisation that deals with large volume of streaming content, namely articles and other “creative works” and “media assets”. This content is enriched with metadata that describes it and links it to reference knowledge – taxonomies and databases that include relevant concepts, entities and factual information. This metadata allows publishers to efficiently retrieve relevant content, according to their various business models. From a technology standpoint, the benchmark assumes that an RDF database is used to store both the reference knowledge and the metadata. The main interactions with the repository are (i) updates, that add new metadata or alter the repository, and (ii) aggregation queries, that retrieve content according to various criteria. The engine should handle instantly large number of updates in parallel with massive amount of aggregation queries. This document describes all features of the SPB : data (reference data-sets, ontologies, data generation), query workloads (descriptions of queries used, choke point descriptions), validation of query results and instructions (how to configure and use the benchmark driver, execution, auditing and disclosure rules)},\n}\n\n","author_short":["Kotsev, V.","Kiryakov, A.","Fundulaki, I.","Alexiev, V."],"key":"LDBC-SemanticPublishing-2014","id":"LDBC-SemanticPublishing-2014","bibbaseid":"kotsev-kiryakov-fundulaki-alexiev-ldbcsemanticpublishingbenchmarkspbv20-2014","role":"author","urls":{"Paper":"https://ldbcouncil.org/publication/ldbc-spc-specification/"},"keyword":["LDBC","benchmark","semantic publishing","graph databases"],"metadata":{"authorlinks":{}}},"bibtype":"techreport","biburl":"https://vladimiralexiev.github.io/my/Alexiev-bibliography.bib","dataSources":["qQ4QyF9WbfwAyRcSb"],"keywords":["ldbc","benchmark","semantic publishing","graph databases"],"search_terms":["ldbc","semantic","publishing","benchmark","spb","kotsev","kiryakov","fundulaki","alexiev"],"title":"LDBC Semantic Publishing Benchmark (SPB) v2.0","year":2014}