{"title":"大数据的分类模型","authors":"Laurent Thiry, Heng Zhao, M. Hassenforder","doi":"10.1109/BigDataCongress.2018.00049","DOIUrl":null,"url":null,"abstract":"This paper shows how concepts coming from category theory associated to a functional programming language can help to formalize and reason about data and get efficient programs in a BigData context. More precisely, it shows how data structures can be modeled by functors related by natural transformations (and isomorphisms). The transformation functions can then serve to shift a data structure and then get another program (eventually educing time complexity). The paper then explains the main concepts of the theory, how to apply them and gives an application to a concrete database and the performances obtained.","PeriodicalId":177250,"journal":{"name":"2018 IEEE International Congress on Big Data (BigData Congress)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Categorical Models for BigData\",\"authors\":\"Laurent Thiry, Heng Zhao, M. Hassenforder\",\"doi\":\"10.1109/BigDataCongress.2018.00049\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper shows how concepts coming from category theory associated to a functional programming language can help to formalize and reason about data and get efficient programs in a BigData context. More precisely, it shows how data structures can be modeled by functors related by natural transformations (and isomorphisms). The transformation functions can then serve to shift a data structure and then get another program (eventually educing time complexity). The paper then explains the main concepts of the theory, how to apply them and gives an application to a concrete database and the performances obtained.\",\"PeriodicalId\":177250,\"journal\":{\"name\":\"2018 IEEE International Congress on Big Data (BigData Congress)\",\"volume\":\"81 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE International Congress on Big Data (BigData Congress)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BigDataCongress.2018.00049\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Congress on Big Data (BigData Congress)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BigDataCongress.2018.00049","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper shows how concepts coming from category theory associated to a functional programming language can help to formalize and reason about data and get efficient programs in a BigData context. More precisely, it shows how data structures can be modeled by functors related by natural transformations (and isomorphisms). The transformation functions can then serve to shift a data structure and then get another program (eventually educing time complexity). The paper then explains the main concepts of the theory, how to apply them and gives an application to a concrete database and the performances obtained.
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