{"title":"Kraken.me: multi-device user tracking suite","authors":"Immanuel Schweizer, Benedikt Schmidt","doi":"10.1145/2638728.2641307","DOIUrl":null,"url":null,"abstract":"An in-depths understanding of human activity is a relevant contribution to the design of interactive systems to support human activity. This is of explicit relevance for assistance systems building on prediction and recommendation. However, the understanding of human activity is limited. Albeit the omnispresence of smart phones and computers, the actual execution of complex activities with those devices in relation to context factors is not completely understood. One possible reason is the limited amount of activity related data to perform actual research. In this paper, we present the Kraken.me framework to address this lack of information. Kraken.me is the first tracking suite to offer integrated tools for mobile, social, and desktop tracking. It is also, to our knowledge, the first tool to emphasize the collection of data from both physical and soft sensors. In this paper, we will introduce the overall architecture, system components, and future research ideas for Kraken.me.","PeriodicalId":20496,"journal":{"name":"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2638728.2641307","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15
Abstract
An in-depths understanding of human activity is a relevant contribution to the design of interactive systems to support human activity. This is of explicit relevance for assistance systems building on prediction and recommendation. However, the understanding of human activity is limited. Albeit the omnispresence of smart phones and computers, the actual execution of complex activities with those devices in relation to context factors is not completely understood. One possible reason is the limited amount of activity related data to perform actual research. In this paper, we present the Kraken.me framework to address this lack of information. Kraken.me is the first tracking suite to offer integrated tools for mobile, social, and desktop tracking. It is also, to our knowledge, the first tool to emphasize the collection of data from both physical and soft sensors. In this paper, we will introduce the overall architecture, system components, and future research ideas for Kraken.me.