Efficient parallel processing of range queries through replicated declustering

A common technique used to minimize I/O in data intensive applications is data declustering over parallel servers. This technique involves distributing data among several disks so as to parallelize query retrieval and thus, improve performance. We focus on optimizing access to large spatial data, and the most common type of queries on such data, i.e., range queries. An optimal declustering scheme is one in which the processing for all range queries is balanced uniformly among the available disks. It has been shown that single copy based declustering schemes are non-optimal for range queries. In this paper, we integrate replication in conjunction with parallel disk declustering for efficient processing of range queries. We note that replication is largely used in database applications for several purposes like load balancing, fault tolerance and availability of data. We propose theoretical foundations for replicated declustering and propose a class of replicated declustering schemes, periodic allocations, which are shown to be strictly optimal for a number of disks. We propose a framework for replicated declustering, using a limited amount of replication and provide extensions to apply it on real data, which include arbitrary grids and a large number of disks. Our framework also provides an effective indexing scheme that enables fast identification of data of interest in parallel servers. In addition to optimal processing of single queries, we show that this framework is effective for parallel processing of multiple queries. We present experimental results comparing the proposed replication scheme to other techniques for both single queries and multiple queries, on synthetic and real data sets. Recommended by: Ahmed Elmagarmid Supported by U.S. Department of Energy (DOE) Award No. DE-FG02-03ER25573, and National Science Foundation (NSF) grant CNS-0403342.     

Visual spatio-temporal function-based querying

Visual interfaces are very important for human interactions in cyberworlds. Visual spatio-temporal querying should be one of the basic tools for data mining and retrieval in cyberworlds. In this paper, we propose a novel function-based query model for arbitrary shape spatio-temporal querying. The queries are defined as geometric shapes changing over time. In our model, data are interpreted geometrically as multidimensional points with time dimension or as moving points. The queries are formulated with geometric objects and operations over them to form the query solid changing over time. The proposed query model allows us to pose arbitrary shape spatio-temporal range queries. With the uniform geometric model we integrate visual mining and querying of time-dependent data employing 3D visualization tools. It allows for creating an intuitive visual interface using 2D projections of 3D query shapes. Our approach combines visualization of spatio-temporal data with visualization of the range query formulation employing very compact function-based query model. The implemented visual query system and its visual interface are proposed and described. An example of application of the system in analysis of simulation results in molecular dynamics is considered.     

Database querying under changing preferences

We present here a formal foundation for an iterative and incremental approach to constructing and evaluating preference queries. Our main focus is query modification: a query transformation approach which works by revising the preference relation in the query. We provide a detailed analysis of the cases where the order-theoretic properties of the preference relation are preserved in the revision. We consider a number of different revision operators: union, prioritized and Pareto composition. We also formulate algebraic laws that enable incremental evaluation of preference queries. Finally, we consider two variations of the basic framework: finite restrictions of preference relations and weak-order extensions of strict partial order preference relations.      

Adaptive Versus Nonadaptive Attribute-Efficient Learning

We study the complexity of learning arbitrary Boolean functions of n variables by membership queries, if at most r variables are relevant. Problems of this type have important applications in fault searching, e.g. logical circuit testing and generalized group testing. Previous literature concentrates on special classes of such Boolean functions and considers only adaptive strategies. First we give a straightforward adaptive algorithm using O(r2 ^r log n) queries, but actually, most queries are asked nonadaptively. This leads to the problem of purely nonadaptive learning. We give a graph-theoretic characterization of nonadaptive learning families, called r-wise bipartite connected families. By the probabilistic method we show the existence of such families of size O(r2 ^r log n + r ²2 ^r ). This implies that nonadaptive attribute-efficient learning is not essentially more expensive than adaptive learning. We also sketch an explicit pseudopolynomial construction, though with a slightly worse bound. It uses the common derandomization technique of small-biased k-independent sample spaces. For the special case r = 2, we get roughly 2.275 log n adaptive queries, which is fairly close to the obvious lower bound of 2 log n. For the class of monotone functions, we prove that the optimal query number O(2 ^r + r log n) can be already achieved in O(r) stages. On the other hand, (2 ^r log n) is a lower bound on nonadaptive queries.     

Adaptive searching in succinctly encoded binary relations and tree-structured documents

The methods most heavily used by search engines to answer conjunctive queries on binary relations (such as one associating keywords with web-pages) are based on computing the intersection of postings lists stored as sorted arrays and using variants of binary search. We show that a succinct representation of the binary relation permits much better results, while using less space than traditional methods. We apply our results not only to conjunctive queries on binary relations, but also to queries on semi-structured documents such as XML documents or file-system indexes, using a variant of an adaptive algorithm used to solve conjunctive queries on binary relations.     

Organizing distributed data bases for parallel searching

Abstract

This paper is mainly concerned with the problem of distributing a data base (i.e., a set of segments) in a computer network system so as to facilitate parallel searching. In our distributed data base model, we assume that all segments are stored in nodes. Each time a query occurs, all nodes are searched concurrently. For convenience, we define the time required to access a segment from any node as a time unit. For a network with d nodes, the response time of a query is then identical to the maximum (n ₁ , n ₂, …, n_d ), where n_i , is the number of segments that satisfies the query and is stored in node i. Unfortunately, the solution for finding an optimal way to organize a distributed data base for parallel searching is still at large. In other words, given a data base, there is no efficient polynomial time algorithm for finding an optimal arrangement of segments onto nodes. In this article, we shall present a “heuristic algorithm” based upon a multivariant analysis method in statistics to distribute a data base in a network system. Some experimental results will show that our method is indeed feasible and effective.     

一种基于超节点和能量优先的无线传感器网络的高效查询算法

文中提出一种基于超节点和能量优先的无线传感器网络的高效查询算法.该算法包括传感器节点的层次聚类算法及基于能量代价模型等支撑技术,主要解决了以下两个问题:(1)数据如何从传感器节点传送到汇聚节点;(2)通过对传感器节点进行聚类,形成超节点,使得在查询过程中减少对无关节点的访问.实验表明该算法在提高无线传感器网络查询效率的情况下,延长网络的使用寿命.     

数据保护技术研究与应用

首先指出了数据保护的重要性,随后介绍了数据保护的各种技术方法和具体要求,对数据保护技术进行了比较分析,最后结合数据保护的具体应用,提出了一种数据保护技术的选择模型,并对企业数据保护的实施过程进行了划分。     

数据保护技术研究与应用

首先指出了数据保护的重要性,随后介绍了数据保护的各种技术方法和具体要求,对数据保护技术进行了比较分析,最后结合数据保护的具体应用,提出了一种数据保护技术的选择模型,并对企业数据保护的实施过程进行了划分。