<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Survival analysis on data streams: Analyzing temporal events in dynamically changing environments

Department of Mathematics and Computer Science University of Marburg, Hans-Meerwein-Straße, 35032 Marburg, Germany

International Journal of Applied Mathematics and Computer Science

In this paper, we introduce a method for survival analysis on data streams. Survival analysis (also known as event history analysis) is an established...

Survival analysis on data streams: Analyzing temporal events in dynamically changing environments

Published Online: Mar 25, 2014

Page range: 199 - 212

DOI: https://doi.org/10.2478/amcs-2014-0015

Keywords
data streams, survival analysis, event history analysis, earthquake data, Twitter data.

This content is open access.

Survival analysis on data streams: Analyzing temporal events in dynamically changing environments

Published Online: Mar 25, 2014

Page range: 199 - 212

DOI: https://doi.org/10.2478/amcs-2014-0015

Keywordsdata streams, survival analysis, event history analysis, earthquake data, Twitter data.

This content is open access.

Keywords
data streams, survival analysis, event history analysis, earthquake data, Twitter data.