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Paper details
Number 1 - March 2014
Volume 24 - 2014
Survival analysis on data streams: Analyzing temporal events in dynamically changing environments
Ammar Shaker, Eyke Hüllermeier
Abstract
In this paper, we introduce a method for survival analysis on data streams. Survival analysis (also known as event history
analysis) is an established statistical method for the study of temporal “events” or, more specifically, questions regarding
the temporal distribution of the occurrence of events and their dependence on covariates of the data sources. To make this
method applicable in the setting of data streams, we propose an adaptive variant of a model that is closely related to the
well-known Cox proportional hazard model. Adopting a sliding window approach, our method continuously updates its
parameters based on the event data in the current time window. As a proof of concept, we present two case studies in which
our method is used for different types of spatio-temporal data analysis, namely, the analysis of earthquake data and Twitter
data. In an attempt to explain the frequency of events by the spatial location of the data source, both studies use the location
as covariates of the sources.
Keywords
data streams, survival analysis, event history analysis, earthquake data, Twitter data