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| Title | Efficiently and Effectively Mining Time-Constrained Sequential Patterns of Smartphone Application Usage |
| Creator | Hsu, Kuo-Wei 徐國偉 |
| Contributor | 資訊科學系 |
| Key Words | Data mining; Signal encoding; Technology transfer; Application developers; Daily lives; Mobile communication and computing; Pattern mining algorithms; Sequential patterns; Sequential-pattern mining; Smart-phone applications; Time interval; Smartphones |
| Date | 2017 |
| Date Issued | 27-Jul-2017 12:52:18 (UTC+8) |
| Summary | Today, we have the freedom to install and use all kinds of applications on smartphones, thanks to the development of mobile communication and computing technologies. Undoubtedly, the system and application developers are eager to know how we use the applications on our smartphones in our daily life and so are the researchers. In this paper, we present our work on developing a pattern mining algorithm and applying it to smartphone application usage log collected from tens of smartphone users for several years. Our goal is to mine the sequential patterns each of which presents a series of application uses and satisfies a constraint on the maximum time interval between two application uses. However, we cannot mine such patterns by general algorithms and will miss some patterns by using the widely used implementation of the advanced algorithm specifically designed for time-constrained sequential pattern mining. We not only present an algorithm that can efficiently and effectively mine the patterns in which we are interested but also discuss and visualize the mined patterns. Our work could potentially support the related studies. © 2017 Kuo-Wei Hsu. |
| Relation | Mobile Information Systems, Volume 2017 (2017), Article ID 3689309, 18 pages |
| Type | article |
| DOI | http://dx.doi.org/10.1155/2017/3689309 |
| dc.contributor | 資訊科學系 | |
| dc.creator (作者) | Hsu, Kuo-Wei | en-US |
| dc.creator (作者) | 徐國偉 | zh-tw |
| dc.date (日期) | 2017 | |
| dc.date.accessioned | 27-Jul-2017 12:52:18 (UTC+8) | - |
| dc.date.available | 27-Jul-2017 12:52:18 (UTC+8) | - |
| dc.date.issued (上傳時間) | 27-Jul-2017 12:52:18 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/111425 | - |
| dc.description.abstract (摘要) | Today, we have the freedom to install and use all kinds of applications on smartphones, thanks to the development of mobile communication and computing technologies. Undoubtedly, the system and application developers are eager to know how we use the applications on our smartphones in our daily life and so are the researchers. In this paper, we present our work on developing a pattern mining algorithm and applying it to smartphone application usage log collected from tens of smartphone users for several years. Our goal is to mine the sequential patterns each of which presents a series of application uses and satisfies a constraint on the maximum time interval between two application uses. However, we cannot mine such patterns by general algorithms and will miss some patterns by using the widely used implementation of the advanced algorithm specifically designed for time-constrained sequential pattern mining. We not only present an algorithm that can efficiently and effectively mine the patterns in which we are interested but also discuss and visualize the mined patterns. Our work could potentially support the related studies. © 2017 Kuo-Wei Hsu. | |
| dc.format.extent | 3104941 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | Mobile Information Systems, Volume 2017 (2017), Article ID 3689309, 18 pages | |
| dc.subject (關鍵詞) | Data mining; Signal encoding; Technology transfer; Application developers; Daily lives; Mobile communication and computing; Pattern mining algorithms; Sequential patterns; Sequential-pattern mining; Smart-phone applications; Time interval; Smartphones | |
| dc.title (題名) | Efficiently and Effectively Mining Time-Constrained Sequential Patterns of Smartphone Application Usage | en-US |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1155/2017/3689309 | |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1155/2017/3689309 |