ACM Journal of Data and Information Quality (JDIQ)最新文献

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Business Intelligence Framework Design and Implementation: A Real-estate Market Case Study 商业智能框架设计与实现:房地产市场案例研究
ACM Journal of Data and Information Quality (JDIQ) Pub Date : 2021-06-30 DOI: 10.1145/3422669
Salam Fraihat, W. Salameh, Ammar Elhassan, Bushra Abu Tahoun, Maisa Asasfeh
{"title":"Business Intelligence Framework Design and Implementation: A Real-estate Market Case Study","authors":"Salam Fraihat, W. Salameh, Ammar Elhassan, Bushra Abu Tahoun, Maisa Asasfeh","doi":"10.1145/3422669","DOIUrl":"https://doi.org/10.1145/3422669","url":null,"abstract":"This article builds on previous work in the area of real-world applications of Business Intelligence (BI) technology. It illustrates the analysis, modeling, and framework design of a BI solution with high data quality to provide reliable analytics and decision support in the Jordanian real estate market. The motivation is to provide analytics dashboards to potential investors about specific segments or units in the market. The article ekxplains the design of a BI solution, including background market and technology investigation, problem domain requirements, solution architecture modeling, design and testing, and the usability of descriptive and predictive features. The resulting framework provides an effective BI solution with user-friendly market insights for investors with little or no market knowledge. The solution features predictive analytics based on established Machine Learning modeling techniques, analyzed and contrasted to select the optimum methodology and model combination for predicting market behavior to empower inexperienced users.","PeriodicalId":299504,"journal":{"name":"ACM Journal of Data and Information Quality (JDIQ)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130724402","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}
引用次数: 4
Achieving Transparency Report Privacy in Linear Time 在线性时间内实现透明度报告隐私
ACM Journal of Data and Information Quality (JDIQ) Pub Date : 2021-03-31 DOI: 10.1145/3460001
Chien-Lun Chen, L. Golubchik, R. Pal
{"title":"Achieving Transparency Report Privacy in Linear Time","authors":"Chien-Lun Chen, L. Golubchik, R. Pal","doi":"10.1145/3460001","DOIUrl":"https://doi.org/10.1145/3460001","url":null,"abstract":"An accountable algorithmic transparency report (ATR) should ideally investigate (a) transparency of the underlying algorithm, and (b) fairness of the algorithmic decisions, and at the same time preserve data subjects’ privacy. However, a provably formal study of the impact to data subjects’ privacy caused by the utility of releasing an ATR (that investigates transparency and fairness), has yet to be addressed in the literature. The far-fetched benefit of such a study lies in the methodical characterization of privacy-utility trade-offs for release of ATRs in public, and their consequential application-specific impact on the dimensions of society, politics, and economics. In this paper, we first investigate and demonstrate potential privacy hazards brought on by the deployment of transparency and fairness measures in released ATRs. To preserve data subjects’ privacy, we then propose a linear-time optimal-privacy scheme, built upon standard linear fractional programming (LFP) theory, for announcing ATRs, subject to constraints controlling the tolerance of privacy perturbation on the utility of transparency schemes. Subsequently, we quantify the privacy-utility trade-offs induced by our scheme, and analyze the impact of privacy perturbation on fairness measures in ATRs. To the best of our knowledge, this is the first analytical work that simultaneously addresses trade-offs between the triad of privacy, utility, and fairness, applicable to algorithmic transparency reports.","PeriodicalId":299504,"journal":{"name":"ACM Journal of Data and Information Quality (JDIQ)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114335428","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}
引用次数: 1
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