BACKGROUND: HMMER is a commonly used bioinformatics tool based on Hidden Markov Models (HMMs) to analyze and process biological sequences. One of its main homology engines is based on the Viterbi decoding algorithm, which was already highly parallelized and optimized using Farrar's striped processing pattern with Intel SSE2 instruction set extension. RESULTS: A new SIMD vectorization of the Viterbi decoding algorithm is proposed, based on an SSE2 inter-task parallelization approach similar to the DNA alignment algorithm proposed by Rognes. Besides this alternative vectorization scheme, the proposed implementation also introduces a new partitioning of the Markov model that allows a significantly more efficient exploitation of the cache locality. Such optimization, together with an improved loading of the emission scores, allows the achievement of a constant processing throughput, regardless of the innermost-cache size and of the dimension of the considered model. CONCLUSIONS: The proposed optimized vectorization of the Viterbi decoding algorithm was extensively evaluated and compared with the HMMER3 decoder to process DNA and protein datasets, proving to be a rather competitive alternative implementation. Being always faster than the already highly optimized ViterbiFilter implementation of HMMER3, the proposed Cache-Oblivious Parallel SIMD Viterbi (COPS) implementation provides a constant throughput and offers a processing speedup as high as two times faster, depending on the model's size.

A novel shape descriptor, which can be extracted from the major object edges automatically and used for the multimedia content-based retrieval in multimedia databases, is presented. By adopting a multiscale approach over the edge field where the scale represents the amount of simplification, the most relevant edge segments, referred to as subsegments, which eventually represent the major object boundaries, are extracted from a scale-map. Similar to the process of a walking ant with a limited line of sight over the boundary of a particular object, we traverse through each subsegment and describe a certain line of sight, whether it is a continuous branch or a corner, using individual 2-D histograms. Furthermore, the proposed method can also be tuned to be an efficient texture descriptor, which achieves a superior performance especially for directional textures. Finally, integrating the whole process as feature extraction module into MUVIS framework allows us to test the mutual performance of the proposed shape descriptor in the context of multimedia indexing and retrieval.

A new scheme for boundary-based object extraction and description of still images, through multi-scale edge detection, is proposed in this paper. Boundary-based methods try to extract closed contours from individual edge pixels through edge-linking. Our approach is based on a connected structure of edge pixels as the initial edge-linking elements. These connected structures, the sub-segments, are extracted from the Canny edge map of an image. Multiple simplification-scales are derived from applying iterations of the bilateral filter to the image, providing extra information about the relative importance of each sub-segment. Edge-linking towards contour closure is achieved through perceptually-driven minimum cost search. Furthermore, a shape-based description vector is derived from the extracted contours, and retrieval results are obtained via the integration of the whole scheme into MUVIS framework

Column-oriented database system architectures invite a re-evaluation of how and when data in databases is compressed. Storing data in a column-oriented fashion greatly increases the similarity of adjacent records on disk and thus opportunities for compression. The ability to compress many adjacent tuples at once lowers the per-tuple cost of compression, both in terms of CPU and space overheads.In this paper, we discuss how we extended C-Store (a column-oriented DBMS) with a compression sub-system. We show how compression schemes not traditionally used in row-oriented DBMSs can be applied to column-oriented systems. We then evaluate a set of compression schemes and show that the best scheme depends not only on the properties of the data but also on the nature of the query workload.

In this work, we focus on automatic extraction of object boundaries from Canny edge field for the purpose of content-based indexing and retrieval over image and video databases. A multiscale approach is adopted where each successive scale provides further simplification of the image by removing more details, such as texture and noise, while keeping major edges. At each stage of the simplification, edges are extracted from the image and gathered in a scale-map, over which a perceptual subsegment analysis is performed in order to extract true object boundaries. The analysis is mainly motivated by Gestalt laws and our experimental results suggest a promising performance for main objects extraction, even for images with crowded textural edges and objects with color, texture, and illumination variations. Finally, integrating the whole process as feature extraction module into MUVIS framework allows us to test the mutual performance of the proposed object extraction method and subsequent shape description in the context of multimedia indexing and retrieval. A promising retrieval performance is achieved, and especially in some particular examples, the experimental results show that the proposed method presents such a retrieval performance that cannot be achieved by using other features such as color or texture.