Finding progression stages in time-evolving event sequences (2014)
| Venue: | In WWW |
| Citations: | 6 - 0 self |
BibTeX
@INPROCEEDINGS{Yang14findingprogression,
author = {Jaewon Yang and Julian Mcauley and Jure Leskovec and Paea Lependu and Nigam Shah},
title = {Finding progression stages in time-evolving event sequences},
booktitle = {In WWW},
year = {2014},
pages = {783--794}
}
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Abstract
Event sequences, such as patients ’ medical histories or users ’ se-quences of product reviews, trace how individuals progress over time. Identifying common patterns, or progression stages, in such event sequences is a challenging task because not every individual follows the same evolutionary pattern, stages may have very differ-ent lengths, and individuals may progress at different rates. In this paper, we develop a model-based method for discover-ing common progression stages in general event sequences. We develop a generative model in which each sequence belongs to a class, and sequences from a given class pass through a common set of stages, where each sequence evolves at its own rate. We then de-velop a scalable algorithm to infer classes of sequences, while also segmenting each sequence into a set of stages. We evaluate our method on event sequences, ranging from patients ’ medical his-tories to online news and navigational traces from the Web. The evaluation shows that our methodology can predict future events in a sequence, while also accurately inferring meaningful progression stages, and effectively grouping sequences based on common pro-gression patterns. More generally, our methodology allows us to reason about how event sequences progress over time, by discov-ering patterns and categories of temporal evolution in large-scale datasets of events.
Keyphrases
progression stage event sequence time-evolving event sequence navigational trace general event sequence common pro-gression pattern meaningful progression stage generative model temporal evolution future event patient medical history model-based method patient medical his-tories different rate discov-ering pattern discover-ing common progression stage user se-quences scalable algorithm common set common pattern class pas differ-ent length large-scale datasets product review challenging task evolutionary pattern
