{"id":"https://openalex.org/W2613827279","doi":"https://doi.org/10.1145/3035918.3056446","title":"Demonstration","display_name":"Demonstration","publication_year":2017,"publication_date":"2017-05-09","ids":{"openalex":"https://openalex.org/W2613827279","doi":"https://doi.org/10.1145/3035918.3056446","mag":"2613827279"},"language":"en","primary_location":{"id":"doi:10.1145/3035918.3056446","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3035918.3056446","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM International Conference on Management of Data","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002538469","display_name":"Peter Bailis","orcid":"https://orcid.org/0000-0003-1166-7823"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Peter Bailis","raw_affiliation_strings":["Stanford University, Stanford, CA, USA"],"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, CA, USA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068366265","display_name":"Edward Gan","orcid":"https://orcid.org/0000-0002-3237-6657"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Edward Gan","raw_affiliation_strings":["Stanford University, Stanford , CA, USA"],"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford , CA, USA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001368248","display_name":"Kexin Rong","orcid":"https://orcid.org/0000-0002-3282-5360"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kexin Rong","raw_affiliation_strings":["Stanford University, Stanford, CA, USA"],"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, CA, USA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057132920","display_name":"Sahaana Suri","orcid":null},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sahaana Suri","raw_affiliation_strings":["Stanford University, Stanford , CA, USA"],"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford , CA, USA","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5002538469"],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":0.5851,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.74728205,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1699","last_page":"1702"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8490087985992432},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.6926969885826111},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.5859375596046448},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5645214319229126},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5620403289794922},{"id":"https://openalex.org/keywords/pipeline-transport","display_name":"Pipeline transport","score":0.492544025182724},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.47450894117355347},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4739168584346771},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.43961861729621887},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4149523377418518},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.4126589000225067},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.34561994671821594},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.15621736645698547},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.09326627850532532}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8490087985992432},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.6926969885826111},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.5859375596046448},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5645214319229126},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5620403289794922},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.492544025182724},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.47450894117355347},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4739168584346771},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43961861729621887},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4149523377418518},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.4126589000225067},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.34561994671821594},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.15621736645698547},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.09326627850532532},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C87717796","wikidata":"https://www.wikidata.org/wiki/Q146326","display_name":"Environmental engineering","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3035918.3056446","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3035918.3056446","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM International Conference on Management of Data","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W2296760620","https://openalex.org/W2605094798","https://openalex.org/W2613751718"],"related_works":["https://openalex.org/W4380433113","https://openalex.org/W4386072068","https://openalex.org/W252339960","https://openalex.org/W2390529043","https://openalex.org/W2378320433","https://openalex.org/W2358343511","https://openalex.org/W2051877971","https://openalex.org/W1970117064","https://openalex.org/W1787170397","https://openalex.org/W4292347844"],"abstract_inverted_index":{"Data":[0],"volumes":[1],"are":[2,24],"rising":[3],"at":[4],"an":[5,121],"increasing":[6],"rate,":[7],"stressing":[8],"the":[9,105,117],"limits":[10],"of":[11,33,37,72,120],"human":[12],"attention.":[13],"Current":[14],"techniques":[15],"for":[16,76,94,124],"prioritizing":[17,125],"user":[18],"attention":[19,126],"in":[20,127],"this":[21,47,99],"fast":[22,54,65],"data":[23,55,66,130],"characterized":[25],"by":[26],"either":[27],"cumbersome,":[28],"ad-hoc":[29],"analysis":[30],"pipelines":[31,92],"comprised":[32],"a":[34,53,61,70],"diverse":[35],"set":[36,71],"analytics":[38,56],"tools,":[39],"or":[40],"brittle,":[41],"static":[42],"rule-based":[43],"engines.":[44],"To":[45],"address":[46],"gap,":[48],"we":[49],"have":[50,104],"developed":[51],"MacroBase,":[52],"engine":[57,63,123],"that":[58],"acts":[59],"as":[60],"search":[62],"over":[64],"streams.":[67,131],"MacroBase":[68,114],"provides":[69],"highly-optimized,":[73],"modular":[74],"operators":[75,88],"streaming":[77],"feature":[78],"transformation,":[79],"classification,":[80],"and":[81,110,115],"explanation.":[82],"Users":[83],"can":[84],"leverage":[85],"these":[86],"optimized":[87],"to":[89,107],"construct":[90],"efficient":[91],"tailored":[93],"their":[95],"use":[96],"case.":[97],"In":[98],"demonstration,":[100],"SIGMOD":[101],"attendees":[102],"will":[103],"opportunity":[106],"interactively":[108],"answer":[109],"refine":[111],"queries":[112],"using":[113],"discover":[116],"potential":[118],"benefits":[119],"advanced":[122],"high-volume,":[128],"real-world":[129]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2017-05-19T00:00:00"}
