2,410 research outputs found

    Statistical structures for internet-scale data management

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    Efficient query processing in traditional database management systems relies on statistics on base data. For centralized systems, there is a rich body of research results on such statistics, from simple aggregates to more elaborate synopses such as sketches and histograms. For Internet-scale distributed systems, on the other hand, statistics management still poses major challenges. With the work in this paper we aim to endow peer-to-peer data management over structured overlays with the power associated with such statistical information, with emphasis on meeting the scalability challenge. To this end, we first contribute efficient, accurate, and decentralized algorithms that can compute key aggregates such as Count, CountDistinct, Sum, and Average. We show how to construct several types of histograms, such as simple Equi-Width, Average-Shifted Equi-Width, and Equi-Depth histograms. We present a full-fledged open-source implementation of these tools for distributed statistical synopses, and report on a comprehensive experimental performance evaluation, evaluating our contributions in terms of efficiency, accuracy, and scalability

    Constrained Querying of Multimedia Databases

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    Copyright 2001 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. http://dx.doi.org/10.1117/12.410976This paper investigates the problem of high-level querying of multimedia data by imposing arbitrary domain-specific constraints among multimedia objects. We argue that the current structured query mode, and the query-by-content model, are insufficient for many important applications, and we propose an alternative query framework that unifies and extends the previous two models. The proposed framework is based on the querying-by-concept paradigm, where the query is expressed simply in terms of concepts, regardless of the complexity of the underlying multimedia search engines. The query-by-concept paradigm was previously illustrated by the CAMEL system. The present paper builds upon and extends that work by adding arbitrary constraints and multiple levels of hierarchy in the concept representation model. We consider queries simply as descriptions of virtual data set, and that allows us to use the same unifying concept representation for query specification, as well as for data annotation purposes. We also identify some key issues and challenges presented by the new framework, and we outline possible approaches for overcoming them. In particular, we study the problems of concept representation, extraction, refinement, storage, and matching

    Optimisation of partitioned temporal joins

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    Forward Scan based Plane Sweep Algorithm for Parallel Interval Joins

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    The interval join is a basic operation that finds application in temporal, spatial, and uncertain databases. Although a number of centralized and distributed algorithms have been proposed for the efficient evaluation of interval joins, classic plane sweep approaches have not been considered at their full potential. A recent piece of related work proposes an optimized approach based on plane sweep (PS) for modern hardware, showing that it greatly outperforms previous work. However, this approach depends on the development of a complex data structure and its parallelization has not been adequately studied. In this paper, we explore the applicability of a largely ignored forward scan (FS) based plane sweep algorithm, which is extremely simple to implement. We propose two optimizations of FS that greatly reduce its cost, making it competitive to the state-of-the-art single-threaded PS algorithm while achieving a lower memory footprint. In addition, we show the drawbacks of a previously proposed hash-based partitioning approach for parallel join processing and suggest a domain-based partitioning approach that does not produce duplicate results. Within our approach we propose a novel breakdown of the partition join jobs into a small number of independent mini-join jobs with varying cost and manage to avoid redundant comparisons. Finally, we show how these mini-joins can be scheduled in multiple CPU cores and propose an adaptive domain partitioning, aiming at load balancing. We include an experimental study that demonstrates the efficiency of our optimized FS and the scalability of our parallelization framework.published_or_final_versio
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