Zhiwen Pan, Wen Ji, Yiqiang Chen, L. Dai, Jun Zhang
{"title":"残疾数据集的大数据管理和分析","authors":"Zhiwen Pan, Wen Ji, Yiqiang Chen, L. Dai, Jun Zhang","doi":"10.1145/3265689.3265721","DOIUrl":null,"url":null,"abstract":"The disability datasets is the datasets which contains the information of disabled populations. By analyzing these datasets, professionals who work with disabled populations can have a better understanding of how to make working plans and policies, so that they support the populations in a better way. In this paper, we proposed a big data management and mining approach for disability datasets. The contributions of this paper are follows: 1) our proposed approach can improve the quality of disability data by estimating miss attribute values and detecting anomaly and low-quality data instances. 2) Our proposed approach can explore useful patterns which reflect the correlation, association and interactional between the disability data attributes. Experiments are conducted at the end to evaluate the performance of our approach.","PeriodicalId":370356,"journal":{"name":"International Conference on Crowd Science and Engineering","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Big Data Management and Analytics for Disability Datasets\",\"authors\":\"Zhiwen Pan, Wen Ji, Yiqiang Chen, L. Dai, Jun Zhang\",\"doi\":\"10.1145/3265689.3265721\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The disability datasets is the datasets which contains the information of disabled populations. By analyzing these datasets, professionals who work with disabled populations can have a better understanding of how to make working plans and policies, so that they support the populations in a better way. In this paper, we proposed a big data management and mining approach for disability datasets. The contributions of this paper are follows: 1) our proposed approach can improve the quality of disability data by estimating miss attribute values and detecting anomaly and low-quality data instances. 2) Our proposed approach can explore useful patterns which reflect the correlation, association and interactional between the disability data attributes. Experiments are conducted at the end to evaluate the performance of our approach.\",\"PeriodicalId\":370356,\"journal\":{\"name\":\"International Conference on Crowd Science and Engineering\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Crowd Science and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3265689.3265721\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Crowd Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3265689.3265721","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Big Data Management and Analytics for Disability Datasets
The disability datasets is the datasets which contains the information of disabled populations. By analyzing these datasets, professionals who work with disabled populations can have a better understanding of how to make working plans and policies, so that they support the populations in a better way. In this paper, we proposed a big data management and mining approach for disability datasets. The contributions of this paper are follows: 1) our proposed approach can improve the quality of disability data by estimating miss attribute values and detecting anomaly and low-quality data instances. 2) Our proposed approach can explore useful patterns which reflect the correlation, association and interactional between the disability data attributes. Experiments are conducted at the end to evaluate the performance of our approach.