[Research] proposal talk.

[Paul Komarek]

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)
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