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Discovering and Exploiting Causal Dependencies for Robust Mobile Context-Aware Recommenders 总被引:2,自引:0,他引:2
Hao-Ping Hung Kun-Ta Chuang Ming-Syan Chen 《Knowledge and Data Engineering, IEEE Transactions on》2007,19(7):977-992
Due to the resource limitation in the data stream environments, it has been reported that answering user queries according to the wavelet synopsis of a stream is an essential ability of a data stream management system (DSMS). In the literature, recent research has been elaborated upon minimizing the local error metric of an individual stream. However, many emergent applications such as stock marketing and sensor detection also call for the need of recording multiple streams in a commercial DSMS. As shown in our thorough analysis and experimental studies, minimizing global error in multiple-stream environments leads to good reliability for DSMS to answer the queries. In contrast, only minimizing local error may lead to a significant loss of query accuracy. As such, we first study in this paper the problem of maintaining the wavelet coefficients of multiple streams within collective memory so that the predetermined global error metric is minimized. Moreover, we also examine a promising application in the multistream environment, that is, the queries for top-k range sum. We resolve the problem of efficient top-k query processing with minimized global error by developing a general framework. For the purposes of maintaining the wavelet coefficients and processing top-k queries, several well- designed algorithms are utilized to optimize the performance of each primary component of this general framework. We also evaluate the proposed algorithms empirically on real and simulated data streams and show that our framework can process top-k queries accurately and efficiently. 相似文献
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Due to the recent massive data generation, preference queries are becoming an increasingly important for users because such queries retrieve only a small number of preferable data objects from a huge multi-dimensional dataset. A top-k dominating query, which retrieves the k data objects dominating the highest number of data objects in a given dataset, is particularly important in supporting multi-criteria decision making because this query can find interesting data objects in an intuitive way exploiting the advantages of top-k and skyline queries. Although efficient algorithms for top-k dominating queries have been studied over centralized databases, there are no studies which deal with top-k dominating queries in distributed environments. The recent data management is becoming increasingly distributed, so it is necessary to support processing of top-k dominating queries in distributed environments. In this paper, we address, for the first time, the challenging problem of processing top-k dominating queries in distributed networks and propose a method for efficient top-k dominating data retrieval, which avoids redundant communication cost and latency. Furthermore, we also propose an approximate version of our proposed method, which further reduces communication cost. Extensive experiments on both synthetic and real data have demonstrated the efficiency and effectiveness of our proposed methods. 相似文献
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Preference query processing is important for a wide range of applications involving distributed databases, such as network monitoring, web-based systems, and market analysis. In such applications, data objects are generated frequently and massively, which presents an important and challenging problem of continuous query processing over distributed data stream environments. A top-k dominating query, which has been receiving much research attention recently, returns the k data objects that dominate the highest number of data objects in a given dataset, and due to its dominance-based ranking function, we can easily obtain superior data objects. An emerging requirement in distributed stream environments is an efficient technique for continuously monitoring top-k dominating data objects. Despite of this fact, no study has addressed this problem. In this paper, therefore, we address the problem of continuous top-k dominating query processing over distributed data stream environments. We present two algorithms that monitor the exact top-k dominating data and efficiently eliminate unqualified data objects for the result, which reduces both communication and computation costs. In addition to these algorithms, we present an approximate algorithm that further reduces both communication and computation costs. Extensive experiments on both synthetic and real data have demonstrated the efficiency and scalability of our algorithms. 相似文献
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网格索引构造简单,常用于数据流系统计算top-k和skyline。但是,网格索引结构粗略,查询过程可能访问大量非top-k结点。为了提高网格索引计算top-k查询的精确度,本文提出基于数据点逆支配点集性质的网格索引方法,将查询访问集缩小到网格索引的"k-最大运算区域区域k-MCA"中,有效地减少了网格索引存储量和查询计算开销。同时,给出了k-MCA索引结构及适应于数据流计算的k-MCA维护更新算法。理论分析和实验结果均验证了上述方法的有效性。 相似文献
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Wenceslao Palma Reza Akbarinia Esther Pacitti Patrick Valduriez 《Distributed and Parallel Databases》2009,26(2-3):291-317
Continuous query processing in data stream management systems (DSMS) has received considerable attention recently. Many applications share the same need for processing data streams in a continuous fashion. For most distributed streaming applications, the centralized processing of continuous queries over distributed data is simply not viable. This paper addresses the problem of computing approximate answers to continuous join queries over distributed data streams. We present a new method, called DHTJoin, which combines hash-based placement of tuples in a Distributed Hash Table (DHT) and dissemination of queries by exploiting the embedded trees in the underlying DHT, thereby incurring little overhead. DHTJoin also deals with join attribute value skew which may hurt load balancing and result completeness. We provide a performance evaluation of DHTJoin which shows that it can achieve significant performance gains in terms of network traffic. 相似文献
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Arvind Arasu Shivnath Babu Jennifer Widom 《The VLDB Journal The International Journal on Very Large Data Bases》2006,15(2):121-142
CQL, a continuous query language, is supported by the STREAM prototype data stream management system (DSMS) at Stanford. CQL is an expressive SQL-based declarative
language for registering continuous queries against streams and stored relations. We begin by presenting an abstract semantics
that relies only on “black-box” mappings among streams and relations. From these mappings we define a precise and general
interpretation for continuous queries. CQL is an instantiation of our abstract semantics using SQL to map from relations to
relations, window specifications derived from SQL-99 to map from streams to relations, and three new operators to map from
relations to streams. Most of the CQL language is operational in the STREAM system. We present the structure of CQL's query
execution plans as well as details of the most important components: operators, interoperator queues, synopses, and sharing
of components among multiple operators and queries. Examples throughout the paper are drawn from the Linear Road benchmark recently proposed for DSMSs. We also curate a public repository of data stream applications that includes a wide
variety of queries expressed in CQL. The relative ease of capturing these applications in CQL is one indicator that the language
contains an appropriate set of constructs for data stream processing.
Edited by M. Franklin 相似文献
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与传统关系数据库不同,数据流管理系统主要处理并发的连续查询.由于查询可能随时增删,所以其主要关注适合查询增删的并发连续查询优化,而不是单条查询优化.提出适合频繁增删查询环境下的数据流窗口连接优化算法.对于新注册的查询以类似最小生成树算法写出数据流的探测序列,然后在不更改其他查询探测序列顺序的情况下尽量合并,减少重复计算.注册或删除查询并不影响其他的查询计划,不需要执行繁琐的查询计划迁移.理论分析和实验证明,该算法简单,优化性能在可接受的范围内,尤其适合查询更新频率较高的系统. 相似文献
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Moustafa A. Hammad Walid G. Aref Ahmed K. Elmagarmid 《The VLDB Journal The International Journal on Very Large Data Bases》2008,17(3):469-488
This paper introduces a class of join algorithms, termed W-join, for joining multiple infinite data streams. W-join addresses
the infinite nature of the data streams by joining stream data items that lie within a sliding window and that match a certain
join condition. In addition to its general applicability in stream query processing, W-join can be used to track the motion
of a moving object or detect the propagation of clouds of hazardous material or pollution spills over time in a sensor network
environment. We describe two new algorithms for W-join and address variations and local/global optimizations related to specifying
the nature of the window constraints to fulfill the posed queries. The performance of the proposed algorithms is studied experimentally
in a prototype stream database system, using synthetic data streams and real time-series data. Tradeoffs of the proposed algorithms
and their advantages and disadvantages are highlighted, given variations in the aggregate arrival rates of the input data
streams and the desired response times per query.
This is an extended version of the paper published in the Proceedings of the 15th International Conference on Scientific and
Statistical Database Management, SSDBM 2003, Boston, U.S.A., pp. 75–84. 相似文献
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Tucker P.A. Maier D. Sheard T. Stephens P. 《Knowledge and Data Engineering, IEEE Transactions on》2007,19(9):1227-1240
Many systems and strategies have been proposed for processing nonterminating data streams. Each approach has advantages and disadvantages, including the kinds of queries that can be executed. We present a framework for characterizing the kinds of queries that can be executed over streams based on a notion of compact sets from topology. We first apply our framework to queries over punctuated data streams. Previous work on punctuations focused primarily on the behavior of individual query operators. We use our framework to determine if an entire query can benefit from punctuations available from stream sources. We then consider other common strategies proposed in the literature for executing queries over streams, and we discuss how our framework can characterize the kinds of queries each strategy can answer. 相似文献
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由于在经济、军事等领域的广泛应用,不确定数据的查询处理技术成为近年来数据库领域的研究热点.概率top-κ查询根据打分函数和概率两个维度来对数据进行排序,因此具有多种查询语义.作为I/O密集型查询,概率top-κ查询需要具备一定通用性的索引技术来提高查询效率.本文从分析概率top-κ查询满足的性质入手,分别基于skyline和支配频率的概念,提出两种层次索引.通过理论分析和实验证明了满足特定性质的概率top-κ查询均可以利用这两种索引来提高I/O效率,其中基于支配频率的索引具有更好的鲁棒性. 相似文献
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The answer to a top-k query is an ordered set of tuples, where the ordering is based on how closely each tuple matches the query. In the context of middleware systems, new algorithms to answer top-k queries have been recently proposed. Among these, the threshold algorithm (TA) is the most well-known instance due to its simplicity and memory requirements. TA is based on an early-termination condition and can evaluate top-k queries without examining all the tuples. This top-k query model is prevalent not only over middleware systems, but also over plain relational data. In this work, we analyze the challenges that must be addressed to adapt TA to a relational database system. We show that, depending on the available indices, many alternative TA strategies can be used to answer a given query. Choosing the best alternative requires a cost model that can be seamlessly integrated with that of current optimizers. In this work, we address these challenges and conduct an extensive experimental evaluation of the resulting techniques by characterizing which scenarios can take advantage of TA-like algorithms to answer top-k queries in relational database systems 相似文献
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Rajib Goswami D. K Bhattacharyya Malayananda Dutta 《Journal of Intelligent Information Systems》2017,49(3):407-433
For speeding up query processing on Big Data, frequent sub-queries or views may be materialized such that the query processing cost is minimized with optimum cost of maintaining the materialized views and/or queries. Materializing frequent sub-queries and views means that resultant data set of the views reside in the memory of one or more nodes in the cluster, so that it reduces the MapReduce cost, submission and scheduling cost of Distributed File System jobs for query processing. We have defined materialized views as resultant data of frequent sub-queries and aggregation functions of a set of Big Data warehousing queries that are saved for enhancing query performance. The problem is defined as a multi-objective optimization problem for minimizing the total query processing MapReduce cost, MapReduce cost for maintaining the materialized views and the number of views selected for materializing with maximized total size of the views selected. We applied Differential Evolution algorithm and NSGA-II to study their performances for developing a recommendation system for selecting views for materializing in Big Data warehousing. 相似文献
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top-k查询在分布式环境中引起越来越多的关注,但是现存的一些top-k算法大都只适用于集中式网络.提出了一个解决分布式网络中top-k查询的新方法—Histogram-Container算法(简称为HC算法),它不仅网络延迟小,网络带宽花费少,而且能够运行在任何结构的分布式网络中.本文将基于一个树型拓扑网络来说明如何使用本地的直方图和bloom filter信息来优化查询,以及如何在中间节点进行部分结果的合并.实验评估和性能分析表明HC算法在网络带宽消耗和查询响应时间方面要优于其他同类方法. 相似文献
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大规模数据流管理系统往往由上层的关系查询系统和下层的流处理系统组成。当用户提交查询请求时,往往需要根据数据流的流速和分布情况动态配置系统参数。然而,由于数据流的易变性,频繁改变参数配置会降低系统性能。针对该问题,提出了OrientStream+框架。设定以用户自定义查询延迟阈值为间隔片段的微批量数据流传输机制;并利用多级别管道缓存,对相同配置的数据流进行批量处理;然后按照数据流的时间戳计算出精准查询结果;引入基于异常检测的增量学习模型,用于提高OrientStream+的预测精度。最后,在Storm上实现了该资源配置框架,并进行了大量的实验。实验结果表明,OrientStream+框架可进一步降低系统的处理延迟并提高系统的吞吐率。 相似文献
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Thao N. Pham Panos K. Chrysanthis Alexandros Labrinidis 《The VLDB Journal The International Journal on Very Large Data Bases》2016,25(2):197-221
Data stream management systems (DSMSs) offer the most effective solution for processing data streams by efficiently executing continuous queries (CQs) over the incoming data. CQs inherently have different levels of criticality and hence different levels of expected quality of service (QoS) and quality of data (QoD). Adhering to such expected QoS/QoD metrics is even more important in cases of multi-tenant data stream management services. In this work, we propose DILoS, a framework that, through priority-based scheduling and load shedding, supports differentiated QoS and QoD for multiple classes of CQs. Unlike existing works that consider scheduling and load shedding separately, DILoS is a novel unified framework that exploits the synergy between scheduling and load shedding. We also propose ALoMa, a general, adaptive load manager that DILoS is built upon. By its design, ALoMa performs better than the state-of-the-art alternatives in three dimensions: (1) it automatically tunes the headroom factor, (2) it honors the delay target, (3) it is applicable to complex query networks with shared operators. We implemented DILoS and ALoMa in our real DSMS prototype system (AQSIOS) and evaluate their performance for a variety of real and synthetic workloads. Our experimental evaluation of ALoMa verified its clear superiority over the state-of-the-art approaches. Our experimental evaluation of the DILoS framework showed that it (a) allows the scheduler and load shedder to consistently honor CQs’ priorities, (b) significantly increases system capacity utilization by exploiting batch processing, and (c) enables operator sharing among query classes of different priorities while avoiding priority inversion, i.e., a lower-priority class never blocks a higher-priority one. 相似文献