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Discovering places of interest in everyday life from smartphone data

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Structural cluster analysis of chemical reactions in solution

Fabio Pietrucci

We introduce a simple and general approach to the problem of clustering structures from atomic trajectories of chemical reactions in solution. By considering distance metrics which are invariant under permutation of identical atoms or molecules, we demonst ...

Amer Inst Physics2013

Where to go from here? Mobility prediction from instantaneous information

Patrick Thiran, Matthias Grossglauser, Ehsan Kazemi, Vincent Etter, Mohamed Kafsi

We present the work that allowed us to win the Next-Place Prediction task of the Nokia Mobile Data Challenge. Using data collected from the smartphones of 80 users, we explore the characteristics of their mobility traces. We then develop three families of ...

Elsevier Science Bv2013

Data Mining and Analysis on Twitter

Pulkit Goyal, Sapan Diwakar

With the tremendous growth of social networks, there has been a growth in the amount of new data that is being created every minute on these networking sites. Twitter acts as a great source of rich information for millions of users on the internet and ther ...

2012

Hypergraph Clustering for Better Network Traffic Inspection

Boi Faltings, Li Pu

Networked computing environments are subject to configuration errors, unauthorized users, undesired activities and attacks by malicious software. These can be detected by monitoring network traffic, but network administrators are overwhelmed by the amount ...

2011

Numerical Ecology with R

François Gillet, Daniel Borcard

Numerical Ecology with R provides a long-awaited bridge between a textbook in Numerical Ecology and the implementation of this discipline in the R language. After short theoretical overviews, the authors accompany the users through the exploration of the m ...

Springer2011

Finding Audio-Visual Events in Informal Social Gatherings

In this paper we address the problem of detecting and localizing objects that can be both seen and heard, e.g., people. This may be solved within the framework of data clustering. We propose a new multimodal clustering algorithm based on a Gaussian mixture ...

2011

Robust and Hierarchical Stop Discovery in Sparse and Diverse Trajectories

Quoc Viet Hung Nguyen, Zhixian Yan, Le Hung Tran, Ngoc Hoan Do

The advance of GPS tracking technique brings a large amount of trajectory data. To better understand such mobility data, semantic models like “stop/move” (or inferring “activity”, “transportation mode”) recently become a hot topic for trajectory data analy ...

2011

Smartphone usage in the wild: a large-scale analysis of applications and context

Daniel Gatica-Perez

This paper presents a large-scale analysis of contextualized smartphone usage in real life. We introduce two contextual variables that condition the use of smartphone applications, namely places and social context. Our study shows strong dependencies betwe ...

2011

Discovering Human Places of Interest from Multimodal Mobile Phone Data

Daniel Gatica-Perez

In this paper, a new framework to discover places-of-interest from multimodal mobile phone data is presented. Mobile phones have been used as sensors to obtain location information from usersâ€™ real lives. Two levels of clustering are used to obtain place ...

2010

Modeling Route Choice Behavior From Smartphone GPS data

Michel Bierlaire, Jingmin Chen, Jeffrey Newman

Smartphones have the capability of recording various kinds of data from built-in sensors such as GPS in a non-intrusive, systematic way. In order to be used as observations for route choice models, the discrete sequences of GPS data need to be associated w ...

2010