[auton-users] [Research] proposal talk.

[Paul Komarek]

I have a nail appointment that day.

On Fri, Aug 27, 2010 at 8:49 AM, Artur Dubrawski <awd at cs.cmu.edu> wrote:
> 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
>>>
>>
>