6 Access Methods and Query Processing Techniques

The performance of a database management system (DBMS) is fundamentally dependent on the access methods and query processing techniques available to the system. Traditionally, relational DBMSs have relied on well-known access methods, such as the ubiquitous B +-tree, hashing with chaining, and, in som

Authenticity Preservation with Histogram-Based Reversible Data Hiding and Quadtree Concepts

With the widespread use of identification systems, establishing authenticity with sensors has become an important research issue. Among the schemes for making authenticity verification based on information security possible, reversible data hiding has attracted much attention during the past few years. With its characteristics of reversibility, the scheme is required to fulfill the goals from two aspects. On the one hand, at the encoder, the secret information needs to be embedded into the original image by some algorithms, such that the output image will resemble the input one as much as possible. On the other hand, at the decoder, both the secret information and the original image must be correctly extracted and recovered, and they should be identical to their embedding counterparts. Under the requirement of reversibility, for evaluating the performance of the data hiding algorithm, the output image quality, named imperceptibility, and the number of bits for embedding, called capacity, are the two key factors to access the effectiveness of the algorithm. Besides, the size of side information for making decoding possible should also be evaluated. Here we consider using the characteristics of original images for developing our method with better performance. In this paper, we propose an algorithm that has the ability to provide more capacity than conventional algorithms, with similar output image quality after embedding, and comparable side information produced. Simulation results demonstrate the applicability and better performance of our algorithm

Space-Efficient Representations of Raster Time Series

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Raster time series, a.k.a. temporal rasters, are collections of rasters covering the same region at consecutive timestamps. These data have been used in many different applications ranging from weather forecast systems to monitoring of forest degradation or soil contamination. Many different sensors are generating this type of data, which makes such analyses possible, but also challenges the technological capacity to store and retrieve the data. In this work, we propose a space-efficient representation of raster time series that is based on Compact Data Structures (CDS). Our method uses a strategy of snapshots and logs to represent the data, in which both components are represented using CDS. We study two variants of this strategy, one with regular sampling and another one based on a heuristic that determines at which timestamps should the snapshots be created to reduce the space redundancy. We perform a comprehensive experimental evaluation using real datasets. The results show that the proposed strategy is competitive in space with alternatives based on pure data compression, while providing much more efficient query times for different types of queries.The data used in this study were acquired as part of the mission of NASA’s Earth Science Division and archived and distributed by the Goddard Earth Sciences (GES) Data and Information Services Center (DISC). Funding: CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by “Secretaría Xeral de Universidades” (Grant ED431G 2019/01). This work was also supported by Xunta de Galicia/FEDER-UE under Grants [IG240.2020.1.185; IN852A 2018/14]; Ministerio de Ciencia, Innovación y Universidades under Grants [TIN2016-78011-C4-1-R; RTC-2017-5908-7; PID2019- 105221RB-C41/AEI/10.13039/501100011033]; ANID - Millennium Science Initiative Program - Code ICN17_002; Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo (CYTED) [Grant No. 519RT0579]Xunta de Galicia; ED431G 2019/01Xunta de Galicia; IG240.2020.1.185Xunta de Galicia; IN852A 2018/14Chile. Agencia Nacional de Investigación y Desarrollo; ICN17_00

A quadtree approach to parallel image processing

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 93-95).by Hany S. Saleeb.M.Eng

Symbolic and Deep Learning Based Data Representation Methods for Activity Recognition and Image Understanding at Pixel Level

Efficient representation of large amount of data particularly images and video helps in the analysis, processing and overall understanding of the data. In this work, we present two frameworks that encapsulate the information present in such data. At first, we present an automated symbolic framework to recognize particular activities in real time from videos. The framework uses regular expressions for symbolically representing (possibly infinite) sets of motion characteristics obtained from a video. It is a uniform framework that handles trajectory-based and periodic articulated activities and provides polynomial time graph algorithms for fast recognition. The regular expressions representing motion characteristics can either be provided manually or learnt automatically from positive and negative examples of strings (that describe dynamic behavior) using offline automata learning frameworks. Confidence measures are associated with recognitions using Levenshtein distance between a string representing a motion signature and the regular expression describing an activity. We have used our framework to recognize trajectory-based activities like vehicle turns (U-turns, left and right turns, and K-turns), vehicle start and stop, person running and walking, and periodic articulated activities like digging, waving, boxing, and clapping in videos from the VIRAT public dataset, the KTH dataset, and a set of videos obtained from YouTube. Next, we present a core sampling framework that is able to use activation maps from several layers of a Convolutional Neural Network (CNN) as features to another neural network using transfer learning to provide an understanding of an input image. The intermediate map responses of a Convolutional Neural Network (CNN) contain information about an image that can be used to extract contextual knowledge about it. Our framework creates a representation that combines features from the test data and the contextual knowledge gained from the responses of a pretrained network, processes it and feeds it to a separate Deep Belief Network. We use this representation to extract more information from an image at the pixel level, hence gaining understanding of the whole image. We experimentally demonstrate the usefulness of our framework using a pretrained VGG-16 model to perform segmentation on the BAERI dataset of Synthetic Aperture Radar (SAR) imagery and the CAMVID dataset. Using this framework, we also reconstruct images by removing noise from noisy character images. The reconstructed images are encoded using Quadtrees. Quadtrees can be an efficient representation in learning from sparse features. When we are dealing with handwritten character images, they are quite susceptible to noise. Hence, preprocessing stages to make the raw data cleaner can improve the efficacy of their use. We improve upon the efficiency of probabilistic quadtrees by using a pixel level classifier to extract the character pixels and remove noise from the images. The pixel level denoiser uses a pretrained CNN trained on a large image dataset and uses transfer learning to aid the reconstruction of characters. In this work, we primarily deal with classification of noisy characters and create the noisy versions of handwritten Bangla Numeral and Basic Character datasets and use them and the Noisy MNIST dataset to demonstrate the usefulness of our approach

Modeling and rendering for development of a virtual bone surgery system

A virtual bone surgery system is developed to provide the potential of a realistic, safe, and controllable environment for surgical education. It can be used for training in orthopedic surgery, as well as for planning and rehearsal of bone surgery procedures...Using the developed system, the user can perform virtual bone surgery by simultaneously seeing bone material removal through a graphic display device, feeling the force via a haptic deice, and hearing the sound of tool-bone interaction --Abstract, page iii

A Survey on Spatial Indexing

Spatial information processing has been a centre of attention of research in the previous decade. In spatial databases, data related with spatial coordinates and extents are retrieved based on spatial proximity. A large number of spatial indexes have been proposed to make ease of efficient indexing of spatial objects in large databases and spatial data retrieval. The goal of this paper is to review the advance techniques of the access methods. This paper tries to classify the existing multidimensional access methods, according to the types of indexing, and their performance over spatial queries. K-d trees out performs quad tress without requiring additional memory usage

A Potential-Field-Based Multilevel Algorithm for Drawing Large Graphs

The aim of automatic graph drawing is to compute a well-readable layout of a given graph G=(V,E). One very popular class of algorithms for drawing general graphs are force-directed methods. These methods generate drawings of G in the plane so that each edge is represented by a straight line connecting its two adjacent nodes. The computation of the drawings is based on associating G with a physical model. Then, the algorithms iteratively try to find a placement of the nodes so that the total energy of the physical system is minimal. Several force-directed methods can visualize large graphs containing many thousands of vertices in reasonable time. However, only some of these methods guarantee a sub-quadratic running time in special cases or under certain assumptions, but not in general. The others are not sub-quadratic at all. We develop a new force-directed algorithm that is based on a combination of an efficient multilevel strategy and a method for approximating the repulsive forces in the system by rapidly evaluating potential fields. The worst-case running time of the new method is O(|V| log|V|+|E|) with linear memory requirements. In practice, the algorithm generates nice drawings of graphs containing up to 100000 nodes in less than five minutes. Furthermore, it clearly visualizes even the structures of those graphs that turned out to be challenging for other tested methods