[auton-users] [Research] proposal talk.

[Artur Dubrawski]

You're not coming Paul???


On 8/27/2010 11:48 AM, Paul Komarek wrote:
> good luck Robin!
>
> On Fri, Aug 27, 2010 at 7:15 AM, Robin Sabhnani<sabhnani+ at cs.cmu.edu>  wrote:
>> Hi all,
>>
>> I am giving my thesis proposal talk this afternoon. You are welcome to
>> attend it. See announcement below.
>>
>> ####################
>>
>> Date: 8/27/10
>> Time: 3:00pm
>> Place: 4405 GHC
>>
>> PhD Candidate: Maheshkumar (Robin) Sabhnani
>>
>> Title: Disjunctive Anomaly Detection: Identifying Complex Anomalous
>> Patterns
>>
>> Abstract:
>>
>> The problem of anomaly detection in multivariate time series data is
>> common to many applications of practical interest. A few examples
>> include network intrusion detection systems, manufacturing processes,
>> climate studies, syndromic surveillance, video stream processing, etc.
>> Our motivating application is syndromic surveillance that aims to detect
>> potential disease outbreaks in pre-diagnosis data to facilitate timely
>> public health response. To achieve this goal, efficient data structures
>> and smart algorithms are needed to analyze highly multivariate temporal
>> data.
>>
>> In this thesis work, we introduce Disjunctive Anomaly Detection (DAD),an
>> algorithm for detecting complex anomalous clusters in multivariate
>> datasets with categorical dimensions. Our proposed algorithm assumes
>> that an anomalous cluster can affect any subset data dimensions (using
>> conjunctions) and any subset of values (using disjunctions) along each
>> data dimension. We believe that such a cluster definition is more
>> informative of the real outbreaks as compared to the current approaches.
>> In addition, the DAD algorithm models multiple anomalous clusters
>> simultaneously, hence promising better detection power in the presence
>> of multiple overlapping anomalous events. So far, we have compared DAD
>> algorithm against the relevant powerful alternatives on two important
>> tasks: finding sample-variable associations in cancer microarray data,
>> and searching for the emerging disease outbreaks in public health data.
>> Experimental results indicate that DAD is able to detect and explain
>> complex anomalous clusters better than the alternative approaches such
>> as the Large Average Submatrix (LAS) algorithm and the What's Strange
>> About Recent Events (WSARE) algorithm.
>>
>> To assist in the development of future complex multidimensional and
>> multivariate algorithms (including extensions to DAD),we also introduce
>> the T-Cube data structure that efficiently represents any time series
>> data with multiple categorical dimensions (typical in many fields of
>> application including surveillance). The T-Cube data structure (inspired
>> from AD-Trees for categorical count data) acts as a cache and quickly
>> responds to any ad-hoc queries during an investigation. It enables
>> processing of millions of time series during massive data mining
>> operations.We have successfully applied T-Cube to mine interesting
>> patterns in diverse projects involving temporal event data.
>>
>> Thesis Committee:
>> Artur Dubrawski (Co-chair)
>> Jeff Schneider (Co-chair)
>> Aarti Singh
>> Greg Cooper (University of Pittsburgh)
>> _______________________________________________
>> Research mailing list
>> Research at autonlab.org
>> https://www.autonlab.org/mailman/listinfo/research
>>
>