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Realizing a feature-based framework for scientific data mining

Mehta, Sameep
http://rave.ohiolink.edu/etdc/view?acc_num=osu1155650127

Abstract Details

2006, Doctor of Philosophy, Ohio State University, Computer and Information Science.
The focus in the computational sciences has been on developing algorithms and tools to facilitate large scale realistic simulations of physical processes. These tools can also simulate the physical processes at very fine temporal and spatial resolutions, resulting in huge time-varying datasets. These datasets, if analyzed properly, hold a great potential for knowledge discovery. In this dissertation, a feature-based framework for analyzing scientific datasets is realized. The main components of the framework are: 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 interactions with other features and finally, understanding and deriving various spatio-temporal relationships among features. A rule based feature detection algorithm is presented to extract the defect structures from molecular dynamics datasets. A two-step shape-based classifier assigns label to the extracted feature. To distinguish actual features from the spurious ones, visualization techniques are employed. The feature extraction algorithm is robust in presence of noise and detects the same features in noisy and noise free datasets. Moreover, the algorithm is capable of in vivo processing of the data. Next, we describe an algorithm for extracting meaningful representation of object trajectories. We take into account the shape and the size of the object. The trajectory of a feature is represented by using physically meaningful parameters: linear velocity, angular velocity and scale parameters. Next, we present a scheme to discover critical events like merging, creation etc. The results are again presented on molecular dynamics and fluid flow datasets. Finally, a visual toolkit is developed to aid the user in establishing various spatial and spatial-temporal relationships. The visual component is interactive and the user can select the spatial and temporal extents. The analysis component derives various relationships. The toolkit achieves real time performance. The usefulness of the toolkit is shown on datasets originating from 2D fluid flow datasets
Srinivasan Parthasarathy Raghu Machiraju (Advisor)
197 p.
Computer Science
Scientific Data Mining; Scientific Visualization

Recommended Citations

Citations

  • Mehta, S. (2006). Realizing a feature-based framework for scientific data mining [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1155650127

    APA Style (7th edition)

  • Mehta, Sameep. Realizing a feature-based framework for scientific data mining. 2006. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1155650127.

    MLA Style (8th edition)

  • Mehta, Sameep. "Realizing a feature-based framework for scientific data mining." Doctoral dissertation, Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=osu1155650127

    Chicago Manual of Style (17th edition)

osu1155650127
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© 2006, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.