Similarity Search and Analysis Techniques for Uncertain Time Series Data

Emerging applications, such as wireless sensor networks and location-based services, require the ability to analyze large quantities of uncertain time series, where the exact value at each timestamp is unavailable or unknown. Traditional similarity search techniques used for standard time series are not always effective for uncertain time series data analysis. This motivates our work in this dissertation. We investigate new, efficient solution techniques for similarity search and analysis of both uncertain time series models, i.e., PDF-based uncertain time series (having probability density function) and multiset-based uncertain time series (having multiset of observed values) in general, as well as correlation queries in particular. In our research, we first formalize the notion of normalization. This notion is used to introduce the idea of correlation for uncertain time series data. We model uncertain correlation as a random variable that is a basis to develop techniques for similarity search and analysis of uncertain time series. We consider a class of probabilistic, threshold-based correlation queries over such data. Moreover, we propose a few query optimization and query quality improvement techniques. Finally, we demonstrate experimentally how the proposed techniques can improve similarity search in uncertain time series. We believe that our results provide a theoretical baseline for uncertain time series management and analysis tools that will be required to support many existing and emerging applications

Introducing Geometry in Active Learning for Image Segmentation

We propose an Active Learning approach to training a segmentation classifier that exploits geometric priors to streamline the annotation process in 3D image volumes. To this end, we use these priors not only to select voxels most in need of annotation but to guarantee that they lie on 2D planar patch, which makes it much easier to annotate than if they were randomly distributed in the volume. A simplified version of this approach is effective in natural 2D images. We evaluated our approach on Electron Microscopy and Magnetic Resonance image volumes, as well as on natural images. Comparing our approach against several accepted baselines demonstrates a marked performance increase

Chemical structure matching using correlation matrix memories

This paper describes the application of the Relaxation By Elimination (RBE) method to matching the 3D structure of molecules in chemical databases within the frame work of binary correlation matrix memories. The paper illustrates that, when combined with distributed representations, the method maps well onto these networks, allowing high performance implementation in parallel systems. It outlines the motivation, the neural architecture, the RBE method and presents some results of matching small molecules against a database of 100,000 models