{"id":"https://openalex.org/W4376852250","doi":"https://doi.org/10.1145/3564121.3564129","title":"Modeling Email Server I/O Events As Multi-temporal Point Processes","display_name":"Modeling Email Server I/O Events As Multi-temporal Point Processes","publication_year":2022,"publication_date":"2022-10-12","ids":{"openalex":"https://openalex.org/W4376852250","doi":"https://doi.org/10.1145/3564121.3564129"},"language":"en","primary_location":{"id":"doi:10.1145/3564121.3564129","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3564121.3564129","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Second International Conference on AI-ML Systems","raw_type":"proceedings-article"},"type":"conference-paper","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/A5083951969","display_name":"Vinayaka Kamath","orcid":"https://orcid.org/0000-0002-9338-4968"},"institutions":[{"id":"https://openalex.org/I4210124949","display_name":"Microsoft Research (India)","ror":"https://ror.org/02w7f3w92","country_code":"IN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210124949"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vinayaka Kamath","raw_affiliation_strings":["Microsoft Research, India"],"raw_orcid":"https://orcid.org/0000-0002-9338-4968","affiliations":[{"raw_affiliation_string":"Microsoft Research, India","institution_ids":["https://openalex.org/I4210124949"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110976425","display_name":"Eva Sinclair","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eva Sinclair","raw_affiliation_strings":["Microsoft, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, United States","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091958538","display_name":"Damon Gilkerson","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Damon Gilkerson","raw_affiliation_strings":["Microsoft, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, United States","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014775697","display_name":"Venkat Padmanabhan","orcid":"https://orcid.org/0000-0002-5973-8742"},"institutions":[{"id":"https://openalex.org/I4210124949","display_name":"Microsoft Research (India)","ror":"https://ror.org/02w7f3w92","country_code":"IN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210124949"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Venkat Padmanabhan","raw_affiliation_strings":["Microsoft Research, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, India","institution_ids":["https://openalex.org/I4210124949"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108351571","display_name":"Sreangsu Acharyya","orcid":null},"institutions":[{"id":"https://openalex.org/I4210124949","display_name":"Microsoft Research (India)","ror":"https://ror.org/02w7f3w92","country_code":"IN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210124949"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sreangsu Acharyya","raw_affiliation_strings":["Microsoft Research, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, India","institution_ids":["https://openalex.org/I4210124949"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11830","display_name":"Point processes and geometric inequalities","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11830","display_name":"Point processes and geometric inequalities","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9635000228881836,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9054999947547913,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.79496169090271},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.48917463421821594},{"id":"https://openalex.org/keywords/server","display_name":"Server","score":0.4100649356842041},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.3206164836883545}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.79496169090271},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.48917463421821594},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.4100649356842041},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.3206164836883545},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3564121.3564129","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3564121.3564129","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Second International Conference on AI-ML Systems","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":16,"referenced_works":["https://openalex.org/W84856470","https://openalex.org/W138372711","https://openalex.org/W1535106372","https://openalex.org/W2034767804","https://openalex.org/W2052197092","https://openalex.org/W2507058334","https://openalex.org/W2548087597","https://openalex.org/W2595897327","https://openalex.org/W2807474590","https://openalex.org/W2903784647","https://openalex.org/W2914874661","https://openalex.org/W2936792392","https://openalex.org/W3006080578","https://openalex.org/W3027795164","https://openalex.org/W3080332655","https://openalex.org/W4242680622"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2092530219","https://openalex.org/W2388464034","https://openalex.org/W2533125852","https://openalex.org/W2140460949","https://openalex.org/W2105580438","https://openalex.org/W2057435755","https://openalex.org/W2390279801","https://openalex.org/W2358668433"],"abstract_inverted_index":{"We":[0,169],"model":[1,102,190],"the":[2,44,50,53,58,67,71,77,80,104,109,114,123,129,135,141,150,185],"read-workload":[3],"experienced":[4],"by":[5,15,43,91,108,128,140],"an":[6,37,47,119],"email":[7,38,48,54],"server":[8],"as":[9,24],"a":[10,25,32,92,97,145,155,180],"superposition":[11],"of":[12,31,46,52,63,87,116,118,122],"reads":[13,127],"performed":[14],"different":[16,81,110,132],"software":[17],"clients":[18],"at":[19],"non-deterministic":[20],"times,":[21],"each":[22],"modeled":[23],"dependent":[26],"point":[27,64,93,174],"process.":[28],"The":[29,100],"probability":[30],"read":[33],"event":[34],"occurring":[35],"on":[36,149,165],"is":[39,89,137,144,154],"affected,":[40],"among":[41],"others,":[42],"age":[45,84,117],"and":[49,85],"time":[51,86,121,186],"recipient\u2019s":[55],"day.":[56],"Unlike":[57],"more":[59],"commonly":[60],"encountered":[61],"variants":[62],"processes":[65],"\u2013":[66,76],"one-dimensional":[68],"temporal,":[69],"or":[70,74,131,143,153],"multi-dimensional":[72],"spatial":[73],"spatio-temporal":[75],"dependence":[78],"between":[79],"temporal":[82],"axes,":[83],"day,":[88,125],"incorporated":[90],"process":[94,175],"defined":[95],"over":[96],"non-Euclidean":[98],"manifold.":[99],"used":[101],"captures":[103],"diverse":[105],"patterns":[106],"exhibited":[107],"clients,":[111,133],"for":[112],"example,":[113],"influence":[115],"email,":[120],"user\u2019s":[124,151,166],"recent":[126],"same":[130],"whether":[134],"client":[136],"controlled":[138],"directly":[139],"user,":[142],"software-agent":[146],"acting":[147],"semi-autonomously":[148],"behalf":[152],"server-side":[156],"batch":[157],"job":[158],"that":[159],"attempts":[160],"to":[161,179,187],"avoid":[162],"adverse":[163],"impact":[164],"latency":[167],"experience.":[168],"show":[170],"how":[171],"estimating":[172],"this":[173],"can":[176],"be":[177],"mapped":[178],"Poisson":[181],"regression,":[182],"thereby":[183],"saving":[184],"implement":[188],"custom":[189],"training":[191],"software.":[192]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}