Understanding computer usage evolution

The proliferation of computing devices in recent years has dramatically changed the way people work, play, communicate, and access information. The personal computer (PC) now has to compete with smartphones, tablets, and other devices for tasks it used to be the default device for. Understanding how PC usage evolves over time can help provide the best overall user experience for current customers, can help determine when they need brand new systems vs. upgraded components, and can inform future product design to better anticipate user needs.

[1]  Catherine Garbay,et al.  Learning recurrent behaviors from heterogeneous multivariate time-series , 2007, Artif. Intell. Medicine.

[2]  Faicel Chamroukhi,et al.  Joint segmentation of multivariate time series with hidden process regression for human activity recognition , 2013, Neurocomputing.

[3]  János Abonyi,et al.  Modified Gath-Geva clustering for fuzzy segmentation of multivariate time-series , 2005, Fuzzy Sets Syst..

[4]  Dana Kulic,et al.  Scaffolding on-line segmentation of full body human motion patterns , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Cyrus Shahabi,et al.  An efficient k nearest neighbor search for multivariate time series , 2007, Inf. Comput..

[6]  Zoran Nikoloski,et al.  Network-Based Segmentation of Biological Multivariate Time Series , 2013, PloS one.

[7]  Xiaodong Liu,et al.  Dynamic programming approach for segmentation of multivariate time series , 2014, Stochastic Environmental Research and Risk Assessment.

[8]  Xiaodong Liu,et al.  Improved Gath–Geva clustering for fuzzy segmentation of hydrometeorological time series , 2011, Stochastic Environmental Research and Risk Assessment.

[9]  Jeffrey D. Scargle,et al.  An algorithm for optimal partitioning of data on an interval , 2003, IEEE Signal Processing Letters.

[10]  Emilie Lebarbier,et al.  Detecting multiple change-points in the mean of Gaussian process by model selection , 2005, Signal Process..

[11]  Evimaria Terzi,et al.  Efficient Algorithms for Sequence Segmentation , 2006, SDM.

[12]  Vanja Josifovski,et al.  Web-scale user modeling for targeting , 2012, WWW.

[13]  R. Bellman,et al.  Curve Fitting by Segmented Straight Lines , 1969 .

[14]  Paul R. Cohen,et al.  Bayesian Clustering by Dynamics Contents 1 Introduction 1 2 Clustering Markov Chains 2 , 2022 .

[15]  A. Kehagias,et al.  A dynamic programming segmentation procedure for hydrological and environmental time series , 2006 .

[16]  Jean-Yves Tourneret,et al.  Joint Segmentation of Multivariate Astronomical Time Series: Bayesian Sampling With a Hierarchical Model , 2007, IEEE Transactions on Signal Processing.

[17]  C. Lawrence,et al.  Algorithms for the optimal identification of segment neighborhoods , 1989 .

[18]  Faicel Chamroukhi,et al.  Activity recognition using hidden Markov models , 2011, IEEE/RJS International Conference on Intelligent RObots and Systems.

[19]  Heikki Mannila,et al.  Time series segmentation for context recognition in mobile devices , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[20]  Vanja Josifovski,et al.  Towards a robust modeling of temporal interest change patterns for behavioral targeting , 2013, WWW '13.

[21]  Evgeniy Gabrilovich,et al.  Retrieval models for audience selection in display advertising , 2011, CIKM '11.