Browse wiki

Jump to: navigation, search
Modeling events in time using cascades of Poisson processes
Abstract For many applications, the data of interesFor many applications, the data of interest can be best thought of as events--entities that occur at a particular moment in time, have features and may in turn trigger the occurrence of other events. This thesis presents techniques for modeling the temporal dynamics of events by making each event induce an inhomogeneous Poisson process of others following it. The collection of all events observed is taken to be a draw from the superposition of the induced Poisson processes, as well as a baseline process for some of the initial triggers. The magnitude and shape of the induced Poisson processes controls the number, timing and features of the triggered events. We provide techniques for parameterizing these processes and present efficient, scalable techniques for inference. The framework is then applied to three different domains that demonstrate the power of the approach. First, we consider the problem of identifying dependencies in a computer network through passive observation and provide a technique based on hypothesis testing for accurately discovering interactions between machines. Then, we look at the relationships between Twitter messages about stocks, using the application as a test-bed to experiment with different parameterizations of induced processes. Finally, we apply these tools to build a model of the revision history of Wikipedia, identifying how the community propagates edits from a page to its neighbors and demonstrating the scalability of our approach to very large datasets.ty of our approach to very large datasets.
Added by wikilit team Added on initial load  +
Collected data time dimension Cross-sectional  +
Conclusion The model presented in this work is concepThe model presented in this work is conceptually relatively simple but flexible, due to the wide range of delay, transition and fertility functions that can be used within the framework. It captures a key aspect of event data – one event may trigger other ones – that has been insufficiently addressed by existing tools and so, has applications in various real-life situations. The resulting models can be used to extract dependencies and properties of a process’ dynamics. Furthermore, the approaches’ scaling properties allow the analysis of very large amounts of data, enabling the analysis of massive datasets.enabling the analysis of massive datasets.
Data source Experiment responses  + , Wikipedia pages  +
Google scholar url http://scholar.google.com/scholar?ie=UTF-8&q=%22Modeling%2Bevents%2Bin%2Btime%2Busing%2Bcascades%2Bof%2BPoisson%2Bprocesses%22  +
Has author Aleksandr Simma +
Has domain Computer science +
Has topic Other information retrieval topics +
Peer reviewed Yes  +
Publication type Thesis  +
Published in University of California, Berkeley +
Research design Experiment  + , Mathematical modeling  +
Research questions This thesis presents techniques for modeliThis thesis presents techniques for modeling the temporal dynamics of events by making each event induce an inhomogeneous Poisson process of others following it. The collection of all events observed is taken to be a draw from the superposition of the induced Poisson processes, as well as a baseline process for some of the initial triggers. The magnitude and shape of the induced Poisson processes controls the number, timing and features of the triggered events. We provide techniques for parameterizing these processes and present efficient, scalable techniques for inference. we apply these tools to build a model of the revision history of Wikipedia, identifying how the community propagates edits from a page to its neighbors and demonstrating the scalability of our approach to very large datasets.ty of our approach to very large datasets.
Revid 10,875  +
Theories This thesis has employed poisson models and cox processes for modeling events
Theory type Design and action  +
Title Modeling events in time using cascades of Poisson processes
Unit of analysis Edit  +
Url http://proquest.umi.com/pqdweb?did=2128789941&Fmt=7&clientId=10306&RQT=309&VName=PQD  +
Wikipedia coverage Sample data  +
Wikipedia data extraction Live Wikipedia  +
Wikipedia language Not specified  +
Wikipedia page type Article  +
Year 2010  +
Creation dateThis property is a special property in this wiki. 15 March 2012 20:29:43  +
Categories Other information retrieval topics  + , Computer science  + , Publications with missing comments  + , Publications  +
Modification dateThis property is a special property in this wiki. 30 January 2014 20:29:50  +
hide properties that link here 
  No properties link to this page.
 

 

Enter the name of the page to start browsing from.