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. In this paper, we introduce a method for the analysis of users' computer usage evolution. Our algorithm, Orion, segments the application-level usage of different users into a sequence of prototypical usage patterns shared among users, referred to as protos. Following an iterative process, protos are automatically derived from the segmentation, and an optimal segmentation is determined from the protos by a dynamic programming algorithm. To ensure that the segmentation is robust, constraints on the length and the number of segments are utilized. We show the validity of our method by analyzing a dataset consisting of over 28K users whose PC usage covers approximately 1M weeks. Our results show that different groups of users exhibit different usage patterns, the usage patterns of nearly 50% of the users change over time, and more than 20% of the users undergo multiple changes. Moreover, many of the differences in the usage patterns and their changes appear to correlate with various userspecific information, such as their geographic location and/or the type of computer system that they have. To show the versatility of Orion, we present additional results from an analysis of 57K grocery store orders of nearly 1000 users.
|Original language||English (US)|
|Title of host publication||2015 IEEE 31st International Conference on Data Engineering, ICDE 2015|
|Publisher||IEEE Computer Society|
|Number of pages||12|
|State||Published - May 26 2015|
|Event||2015 31st IEEE International Conference on Data Engineering, ICDE 2015 - Seoul, Korea, Republic of|
Duration: Apr 13 2015 → Apr 17 2015
|Name||Proceedings - International Conference on Data Engineering|
|Other||2015 31st IEEE International Conference on Data Engineering, ICDE 2015|
|Country||Korea, Republic of|
|Period||4/13/15 → 4/17/15|
Bibliographical notePublisher Copyright:
© 2015 IEEE.