Tree-Encoded Bitmaps

We propose a novel method to represent compressed bitmaps. Similarly to existing bitmap compression schemes, we exploit the compression potential of bitmaps populated with consecutive identical bits, i.e., 0-runs and 1-runs. But in contrast to prior work, our approach employs a binary tree structure to represent runs of various lengths. Leaf nodes in the upper tree levels thereby represent longer runs, and vice versa. The tree-based representation results in high compression ratios and enables efficient random access, which in turn allows for the fast intersection of bitmaps. Our experimental analysis with randomly generated bitmaps shows that our approach significantly improves over state-of-the-art compression techniques when bitmaps are dense and/or only barely clustered. Further, we evaluate our approach with real-world data sets, showing that our tree-encoded bitmaps can save up to one third of the space over existing techniques

Neuroinformatics in Functional Neuroimaging

This Ph.D. thesis proposes methods for information retrieval in functional neuroimaging through automatic computerized authority identification, and searching and cleaning in a neuroscience database. Authorities are found through cocitation analysis of the citation pattern among scientific articles. Based on data from a single scientific journal it is shown that multivariate analyses are able to determine group structure that is interpretable as particular “known ” subgroups in functional neuroimaging. Methods for text analysis are suggested that use a combination of content and links, in the form of the terms in scientific documents and scientific citations, respectively. These included context sensitive author ranking and automatic labeling of axes and groups in connection with multivariate analyses of link data. Talairach foci from the BrainMap ™ database are modeled with conditional probability density models useful for exploratory functional volumes modeling. A further application is shown with conditional outlier detection where abnormal entries in the BrainMap ™ database are spotted using kernel density modeling and the redundancy between anatomical labels and spatial Talairach coordinates. This represents a combination of simple term and spatial modeling. The specific outliers that were found in the BrainMap ™ database constituted among others: Entry errors, errors in the article and unusual terminology