JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 76100, Rehovot, Israel", 
          "id": "http://www.grid.ac/institutes/grid.13992.30", 
          "name": [
            "Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 76100, Rehovot, Israel"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Naor", 
        "givenName": "Moni", 
        "id": "sg:person.07776170271.83", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07776170271.83"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 76100, Rehovot, Israel", 
          "id": "http://www.grid.ac/institutes/grid.13992.30", 
          "name": [
            "Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 76100, Rehovot, Israel"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yogev", 
        "givenName": "Eylon", 
        "id": "sg:person.015120037757.44", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015120037757.44"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2013", 
    "datePublishedReg": "2013-01-01", 
    "description": "A Bloom filter is a method for reducing the space (memory) required for representing a set by allowing a small error probability. In this paper we consider a Sliding Bloom Filter: a data structure that, given a stream of elements, supports membership queries of the set of the last n elements (a sliding window), while allowing a small error probability and a slackness parameter. The problem of sliding Bloom filters has appeared in the literature in several communities, but this work is the first theoretical investigation of it.We formally define the data structure and its relevant parameters and analyze the time and memory requirements needed to achieve them. We give a low space construction that runs in O(1) time per update with high probability (that is, for all sequences with high probability all operations take constant time) and provide an almost matching lower bound on the space that shows that our construction has the best possible space consumption up to an additive lower order term.", 
    "editor": [
      {
        "familyName": "Cai", 
        "givenName": "Leizhen", 
        "type": "Person"
      }, 
      {
        "familyName": "Cheng", 
        "givenName": "Siu-Wing", 
        "type": "Person"
      }, 
      {
        "familyName": "Lam", 
        "givenName": "Tak-Wah", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-642-45030-3_48", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-642-45029-7", 
        "978-3-642-45030-3"
      ], 
      "name": "Algorithms and Computation", 
      "type": "Book"
    }, 
    "keywords": [
      "Bloom filter", 
      "data structure", 
      "small error probability", 
      "stream of elements", 
      "error probability", 
      "memory requirements", 
      "space consumption", 
      "membership queries", 
      "space construction", 
      "queries", 
      "filter", 
      "set", 
      "high probability", 
      "space", 
      "requirements", 
      "update", 
      "probability", 
      "streams", 
      "construction", 
      "relevant parameters", 
      "time", 
      "work", 
      "method", 
      "elements", 
      "consumption", 
      "parameters", 
      "terms", 
      "structure", 
      "community", 
      "lower order terms", 
      "literature", 
      "order terms", 
      "investigation", 
      "theoretical investigation", 
      "first theoretical investigation", 
      "paper", 
      "problem", 
      "Sliding Bloom Filter", 
      "slackness parameter", 
      "low space construction", 
      "best possible space consumption", 
      "possible space consumption", 
      "additive lower order term"
    ], 
    "name": "Sliding Bloom Filters", 
    "pagination": "513-523", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1020480472"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-642-45030-3_48"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-642-45030-3_48", 
      "https://app.dimensions.ai/details/publication/pub.1020480472"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-01-01T19:09", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/chapter/chapter_157.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-642-45030-3_48"
  }
]

Download the RDF metadata as:  json-ld nt turtle xml License info

This table displays all metadata directly associated to this object as RDF triples.

120 TRIPLES      23 PREDICATES      69 URIs      62 LITERALS      7 BLANK NODES