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Home / programming language / Online Java Projects / Scalable Daily Human Behavioral Pattern Mining from Multivariate Temporal Data
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Scalable Daily Human Behavioral Pattern Mining from Multivariate Temporal Data

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SKU: PROJ7497 Categories: 2017 Projects, Datamining Projects, Final Year Projects, Online Java Projects, programming language Tags: Android Project, Asp.Net Project, C# Project, Computer Engineering Final Year Projects, Computer Science Final Year Projects, Electronics Projects Engineering Students Final Year, Engineering Student Project Ideas, Final Semester Projects, Final Year Project Center, Final Year Projects, ieee ECE Projects, ieee EEE Projects, ieee Final Year Projects, IEEE Projects, ieee Projects Networking, Image Processing Projects, J2EE Project, Java Project, Java Project Code, Matlab Project, PHP Project, Students Projects
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Scalable Daily Human Behavioral Pattern Mining from Multivariate Temporal Data

Abstract— This work introduces a set of scalable algorithms to identify patterns of human daily behaviors. These patterns are extracted from multivariate temporal data that have been collected from smartphones. We have exploited sensors that are available on these devices, and have identified frequent behavioral patterns with a temporal granularity, which has been inspired by the way individuals segment time into events. These patterns are helpful to both end-users and third parties who provide services based on this information. We have demonstrated our approach on two real-world datasets and showed that our pattern identification algorithms are scalable. This scalability makes analysis on resource constrained and small devices such as smartwatches feasible. Traditional data analysis systems are usually operated in a remote system outside the device. This is largely due to the lack of scalability originating from software and hardware restrictions of mobile/wearable devices. By analyzing the data on the device, the user has the control over the data, i.e., privacy, and the network costs will also be removed. Therefore, it is essential to have flexibility in temporal analysis. We have implemented this dynamic of human behavior by introducing a simple but novel human-centric temporal granularity method. < final year projects >

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