{"title":"Analysis of user mobility data sources for multi-user context modeling","authors":"Paras Mehta, A. Voisard","doi":"10.1145/2442952.2442955","DOIUrl":null,"url":null,"abstract":"Finding the right data source for research is a challenge that many of us face. Although we live in times where 'Open Data' and 'Big Data' have become buzzwords, getting hold of a reasonable size and quality dataset is often hard. When it comes to user data such as mobility data, this becomes even tougher due to privacy-related concerns. This paper briefly explains our research in the area of multi-user context modeling and presents some criteria that we believe are important while selecting a dataset for testing different approaches in this domain. To find the right dataset, some relevant publicly available human mobility datasets are examined using these criteria. The following are the datasets that have been analyzed: Microsoft Research GeoLife Trajectory Dataset, Tracking Delft I Pedestrian Trajectory Dataset, MIT Media Lab Reality Mining Dataset and LifeMap Dataset. Besides these, some other useful data sources for researchers have been cited.","PeriodicalId":132038,"journal":{"name":"GEOCROWD '12","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"GEOCROWD '12","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2442952.2442955","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Finding the right data source for research is a challenge that many of us face. Although we live in times where 'Open Data' and 'Big Data' have become buzzwords, getting hold of a reasonable size and quality dataset is often hard. When it comes to user data such as mobility data, this becomes even tougher due to privacy-related concerns. This paper briefly explains our research in the area of multi-user context modeling and presents some criteria that we believe are important while selecting a dataset for testing different approaches in this domain. To find the right dataset, some relevant publicly available human mobility datasets are examined using these criteria. The following are the datasets that have been analyzed: Microsoft Research GeoLife Trajectory Dataset, Tracking Delft I Pedestrian Trajectory Dataset, MIT Media Lab Reality Mining Dataset and LifeMap Dataset. Besides these, some other useful data sources for researchers have been cited.
为研究寻找合适的数据来源是我们许多人面临的挑战。虽然我们生活在一个“开放数据”和“大数据”已经成为流行语的时代,但获得一个合理规模和质量的数据集往往是困难的。当涉及到移动数据等用户数据时,由于与隐私相关的担忧,这变得更加困难。本文简要解释了我们在多用户上下文建模领域的研究,并提出了一些我们认为在选择用于测试该领域不同方法的数据集时很重要的标准。为了找到正确的数据集,使用这些标准检查了一些相关的公开可用的人类流动性数据集。以下是已分析的数据集:Microsoft Research GeoLife轨迹数据集,Tracking Delft I行人轨迹数据集,MIT Media Lab现实挖掘数据集和LifeMap数据集。除此之外,还引用了其他一些对研究人员有用的数据来源。