David Andresic, Petr Šaloun, Ioannis Anagnostopoulos
{"title":"使用apache spark和自组织地图库在单机上进行高效的大数据分析","authors":"David Andresic, Petr Šaloun, Ioannis Anagnostopoulos","doi":"10.1109/SMAP.2017.8022657","DOIUrl":null,"url":null,"abstract":"Apache Spark is commonly used as a big data analytical platform on powerful computer clusters, as it primarily employ the main computer memory for the evaluation. Our attempt adds self-organizing map software libraries onto a single big data analytical stack and is efficient and fast enough even on a standard single computer. This innovative approach brings the big data analysis to researchers with limited resources. Our genuine idea was experimentally confirmed and is described here. As a case study for our method we we used the available #Brexit data and the sentiment analysis of corresponding tweets and the correlation with the stock exchange data.","PeriodicalId":441461,"journal":{"name":"2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Efficient big data analysis on a single machine using apache spark and self-organizing map libraries\",\"authors\":\"David Andresic, Petr Šaloun, Ioannis Anagnostopoulos\",\"doi\":\"10.1109/SMAP.2017.8022657\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Apache Spark is commonly used as a big data analytical platform on powerful computer clusters, as it primarily employ the main computer memory for the evaluation. Our attempt adds self-organizing map software libraries onto a single big data analytical stack and is efficient and fast enough even on a standard single computer. This innovative approach brings the big data analysis to researchers with limited resources. Our genuine idea was experimentally confirmed and is described here. As a case study for our method we we used the available #Brexit data and the sentiment analysis of corresponding tweets and the correlation with the stock exchange data.\",\"PeriodicalId\":441461,\"journal\":{\"name\":\"2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)\",\"volume\":\"86 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SMAP.2017.8022657\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMAP.2017.8022657","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Efficient big data analysis on a single machine using apache spark and self-organizing map libraries
Apache Spark is commonly used as a big data analytical platform on powerful computer clusters, as it primarily employ the main computer memory for the evaluation. Our attempt adds self-organizing map software libraries onto a single big data analytical stack and is efficient and fast enough even on a standard single computer. This innovative approach brings the big data analysis to researchers with limited resources. Our genuine idea was experimentally confirmed and is described here. As a case study for our method we we used the available #Brexit data and the sentiment analysis of corresponding tweets and the correlation with the stock exchange data.