Showing papers by "Sameep Mehta published in 2006"

Robust periodicity detection algorithms

Abstract: Periodicity detection is an important pre-processing step for many time series algorithms. It provides important information about the structural properties of a time series. Feature vectors based on periodicity can be used for clustering, classification, abnormality detection, and human motion understanding. The periodicity detection task is not difficult in case of simple and uncontaminated signal. Unfortunately, most of the real datasets exhibit one or more of the following properties: i) non-stationarity, ii) interlaced cyclic patterns and iii) data contamination, which makes the period detection extremely challenging. A seemingly straightforward solution is to develop individual specialized algorithms for handling each case separately. However, determining if a time series is non-stationary or is contaminated in itself is an extremely difficult task. In this article, we propose generic algorithms which can detect periods in complex, noisy and incomplete datasets. The algorithm leverages the frequency characterization and autocorrelation structure inherent in a time series to estimate its periodicity. We extend the methods to handle non-stationary time series by tracking the candidate periods using a Kalman filter. We also address the interesting problem of finding multiple interlaced periodicities.

Visual Exploration of Spatio-temporal Relationships for Scientific Data

Abstract: Spatio-temporal relationships among features extracted from temporally-varying scientific datasets can provide useful information about the evolution of an individual feature and its interactions with other features. However, extracting such useful relationships without user guidance is cumbersome and often an error prone process. In this paper, we present a visual analysis system that interactively discovers such relationships from the trajectories of derived features. We describe analysis algorithms to derive various spatial and spatio-temporal relationships. A visual interface is presented using which the user can interactively select spatial and temporal extents to guide the knowledge discovery process. We show the usefulness of our proposed algorithms on datasets originating from computational fluid dynamics. We also demonstrate how the derived relationships can help in explaining the occurrence of critical events like merging and bifurcation of the vortices.

On Trajectory Representation for Scientific Features

Abstract: In this article, we present trajectory representation algorithms for tangible features found in temporally varying scientific datasets. Rather than modeling the features as points, we take attributes like shape and extent of the feature into account. Our contention is that these attributes play an important role in understanding the temporal evolution and interactions among features. The proposed representation scheme is based on motion and shape parameters including linear velocity, angular velocity, etc. We use these parameters to segment the trajectory instead of relying on the geometry of the trajectory. We evaluate our algorithms on real datasets originating from different domains. We show the accuracy of the motion and shape parameter estimation by reconstructing the trajectories with high accuracy. Finally, we present performance and scalability results.

Realizing a feature-based framework for scientific data mining

Abstract: This dissertation presents an efficient realization of a feature based framework for analyzing scientific data. The main components of the framework include: feature detection, feature classification, feature verification, and modeling the evolutionary behavior of the features. The usefulness of first three steps is shown on datasets originating from computational molecular dynamics. Modeling the evolutionary behavior of the features involves: (i) understanding the trajectory of an individual feature; (ii) discovering the change which features undergo due to various interactions; and (iii) understanding and deriving various spatio-temporal relationships among features. A rule-based feature detection algorithm extracts the features. These rules are developed by making use of the domain specific properties. The algorithm is highly robust in the presence of noise. The features detected from noisy datasets are consistent with the features detected from noise-free data. The trajectory of a feature is represented by using physically meaningful parameters: linear velocity, angular velocity and scale parameters. Most of the existing techniques abstract the feature to a single point and only take into account the change in the position. The proposed representation scheme accounts for change in position, orientation and size of the feature. The representation also aids in establishing various spatial and spatio-temporal relationships among the features. The usefulness of the scheme is evaluated on datasets originating from molecular dynamics and fluid flows. The interactions among co-existing features is captured by a set of critical events: continuation, merging, bifurcation, creation and dissipation. The algorithms establish correspondence among features based on the degree of overlap between the features in consecutive time steps. Finally, a visual toolkit is developed which aids the user in establishing various spatial and spatial-temporal relationships. The toolkit achieves real time performance. The usefulness of the toolkit is shown on datasets originating from 2D fluid-flow datasets. Prior to the developed algorithms, manual analysis of a very small dataset of 100 MB used to take around 6 weeks. However, now feature extraction and classification tasks for a 10 GB molecular dynamics dataset can be performed in 25 hours which is faster than data generation time of 35 hours. (Abstract shortened by UMI.)

Containment Policies for Transmissible Diseases

Abstract: 1. ABSTRACT Till date, there have been several cases of H5N1 inuenza virus outbreak in avian populations. It is speculated that the mutations in highly unstable inuenza virus represents a serious transmissible pandemic threat. Therefore, it is essential to be prepared for such a sudden and fatal transmissible disease outbreak. In this paper, we propose several strategies to curb such transmissible diseases from spreading. Our policies identies individuals and locations which play a vital role in spreading the disease. Our analysis is based on simulation data generated by Episims system. We model this data as People-People Contact Network and PeopleLocations Activity Graph. We also evaluate our proposed strategies under two practical constraints viz. limited number of anti-viral drugs and the delay in implementation of containment policies.