{"title":"Smart surveillance for public safety enabled by edge computing","authors":"","doi":"10.1049/pbpc033e_ch19","DOIUrl":"https://doi.org/10.1049/pbpc033e_ch19","url":null,"abstract":"","PeriodicalId":314870,"journal":{"name":"Edge Computing: Models, technologies and applications","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115475251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this chapter, we present and analyze some popular platforms for Big Data processing, with a focus on edge computing. We start with a review of edge computing, followed by a classification of frameworks and models for Big Data processing. In the next section, we describe some well-known platforms for Big Data processing, such as MapReduce, Spark, Flink and Google Cloud Dataflow with its open-source version under Apache Beam. In different section, we present Big Data frameworks specific to edge computing, including hybrid MapReduce and Apache Edgent. Finally, we summarize the chapter by presenting challenges and opportunities for research in this area.
{"title":"Distributed big data computing platforms for edge computing","authors":"Dumitrel Loghin, Lavanya Ramapantulu, Y. M. Teo","doi":"10.1049/pbpc033e_ch9","DOIUrl":"https://doi.org/10.1049/pbpc033e_ch9","url":null,"abstract":"In this chapter, we present and analyze some popular platforms for Big Data processing, with a focus on edge computing. We start with a review of edge computing, followed by a classification of frameworks and models for Big Data processing. In the next section, we describe some well-known platforms for Big Data processing, such as MapReduce, Spark, Flink and Google Cloud Dataflow with its open-source version under Apache Beam. In different section, we present Big Data frameworks specific to edge computing, including hybrid MapReduce and Apache Edgent. Finally, we summarize the chapter by presenting challenges and opportunities for research in this area.","PeriodicalId":314870,"journal":{"name":"Edge Computing: Models, technologies and applications","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124851736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Collaborative platforms and technologies for edge computing","authors":"","doi":"10.1049/pbpc033e_ch11","DOIUrl":"https://doi.org/10.1049/pbpc033e_ch11","url":null,"abstract":"","PeriodicalId":314870,"journal":{"name":"Edge Computing: Models, technologies and applications","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131668883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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