%0 Journal Article %1 DavidsonGilpinWalker12dmkd %A Davidson, Ian %A Gilpin, Sean %A Walker, Peter %D 2012 %J Data Mining and Knowledge Discovery %K v1500 springer paper ai information retrieval action data pattern recognition analysis processing algorithm zzz.big zzz.sfit %N 3 %P 635-653 %R 10.1007/s10618-012-0269-7 %T Behavioral Event Data and their Analysis %V 25 %X Social science is broadly defined as the analysis of human behavior whether it be at an individual or a group level. In this work, we explore the analysis of human behavior encoded as a trail of their events over time and space, which we refer to as behavioral event data. We show that such data offers challenges to data mining algorithm designers as the data to analyze is naturally multi-way, involves complex patterns that form/reform over time, and has complex interactions between groups in the population. Though the data naturally lends itself to be represented as graphs and tensors we show how existing techniques are limited in their usefulness and outline our own algorithms to overcome these challenges. In this paper, using the adversarial event behavior of blue and red forces, we show three core problems and solutions in event behavior analysis: (1) Decomposing behavior to identify areas of intense activity, (2) Predicting what groups of events are likely to occur, and (3) Analysis to identify interacting behavior given a known template.

@article{DavidsonGilpinWalker12dmkd, abstract = {Social science is broadly defined as the analysis of human behavior whether it be at an individual or a group level. In this work, we explore the analysis of human behavior encoded as a trail of their events over time and space, which we refer to as behavioral event data. We show that such data offers challenges to data mining algorithm designers as the data to analyze is naturally multi-way, involves complex patterns that form/reform over time, and has complex interactions between groups in the population. Though the data naturally lends itself to be represented as graphs and tensors we show how existing techniques are limited in their usefulness and outline our own algorithms to overcome these challenges. In this paper, using the adversarial event behavior of blue and red forces, we show three core problems and solutions in event behavior analysis: (1) Decomposing behavior to identify areas of intense activity, (2) Predicting what groups of events are likely to occur, and (3) Analysis to identify interacting behavior given a known template.}, added-at = {2012-09-07T14:34:46.000+0200}, author = {Davidson, Ian and Gilpin, Sean and Walker, Peter}, biburl = {https://www.bibsonomy.org/bibtex/2000bee05c04671709d7b164e933bb4b7/flint63}, doi = {10.1007/s10618-012-0269-7}, file = {SpringerLink:2012/DavidsonGilpinWalker12dmkd.pdf:PDF}, groups = {public}, interhash = {f2e2093a21f75823c5e109ad55c8f392}, intrahash = {000bee05c04671709d7b164e933bb4b7}, issn = {1384-5810}, journal = {Data Mining and Knowledge Discovery}, keywords = {v1500 springer paper ai information retrieval action data pattern recognition analysis processing algorithm zzz.big zzz.sfit}, month = {#nov#}, number = 3, pages = {635-653}, timestamp = {2018-04-16T12:00:37.000+0200}, title = {Behavioral Event Data and their Analysis}, username = {flint63}, volume = 25, year = 2012 }