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Approximate continuous querying over distributed streams

ACM TRANSACTIONS ON DATABASE SYSTEMS 2008年第2期33卷 1–39页

作者： Cormode, Graham Garofalakis, Minos AT&T Labs Res Florham Pk NJ USA Univ Calif Berkeley Berkeley CA 94720 USA

While traditional database systems optimize for performance on one-shot query processing, emerging large-scale monitoring applications require continuous tracking of complex data-analysis queries over collections of physically distributed streams. Thus, effective solutions have to be simultaneously space/time efficient (at each remote monitor site), communication efficient (across the underlying communication network), and provide continuous, guaranteed-quality approximate query answers. In this paper, we propose novel algorithmic solutions for the problem of continuously tracking a broad class of complex aggregate queries in such a distributed-streams setting. Our tracking schemes maintain approximate query answers with provable error guarantees, while simultaneously optimizing the storage space and processing time at each remote site, and the communication cost across the network. In a nutshell, our algorithms rely on tracking general-purpose randomized sketch summaries of local streams at remote sites along with concise prediction models of local site behavior in order to produce highly communication-and space/time-efficient solutions. The end result is a powerful approximate query tracking framework that readily incorporates several complex analysis queries (including distributed join and multi-join aggregates, and approximate wavelet representations), thus giving the first known low-overhead tracking solution for such queries in the distributed-streams model. Experiments with real data validate our approach, revealing significant savings over naive solutions as well as our analytical worst-case guarantees.

关键词： algorithms experimentation performance continuous distributed monitoring data stream algorithms data synopses approximate query processing

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Lightweight monitoring of distributed Streams

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ACM TRANSACTIONS ON DATABASE SYSTEMS 2018年第2期43卷 1–37页

作者： Lazerson, Arnon Keren, Daniel Schuster, Assaf Technion Israel Inst Technol IL-32000 Haifa Israel Univ Haifa IL-31905 Haifa Israel

As data becomes dynamic, large, and distributed, there is increasing demand for what have become known as distributed stream algorithms. Since continuously collecting the data to a central server and processing it there is infeasible, a common approach is to define local conditions at the distributed nodes, such that-as long as they are maintained-some desirable global condition holds. Previous methods derived local conditions focusing on communication efficiency. While proving very useful for reducing the communication volume, these local conditions often suffer from heavy computational burden at the nodes. The computational complexity of the local conditions affects both the runtime and the energy consumption. These are especially critical for resource-limited devices like smartphones and sensor nodes. Such devices are becoming more ubiquitous due to the recent trend toward smart cities and the Internet of Things. To accommodate for high data rates and limited resources of these devices, it is crucial that the local conditions be quickly and efficiently evaluated. Here we propose a novel approach, designated CB (for Convex/Concave Bounds). CB defines local conditions using suitably chosen convex and concave functions. Lightweight and simple, these local conditions can be rapidly checked on the fly. CB's superiority over the state-of-the-art is demonstrated in its reduced runtime and power consumption, by up to six orders of magnitude in some cases. As an added bonus, CB also reduced communication overhead in all the tested application scenarios.

关键词： Disributed stream mining continuous distributed monitoring

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distributed Geometric Query monitoring Using Prediction Models

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ACM TRANSACTIONS ON DATABASE SYSTEMS 2014年第2期39卷 1–42页

作者： Giatrakos, Nikos Deligiannakis, Antonios Garofalakis, Minos Sharfman, Izchak Schuster, Assaf Tech Univ Crete Sch Elect & Comp Engn Khania 73100 Greece Technion Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel

Many modern streaming applications, such as online analysis of financial, network, sensor, and other forms of data, are inherently distributed in nature. An important query type that is the focal point in such application scenarios regards actuation queries, where proper action is dictated based on a trigger condition placed upon the current value that a monitored function receives. Recent work [Sharfman et al. 2006, 2007b, 2008] studies the problem of (nonlinear) sophisticated function tracking in a distributive manner. The main concept behind the geometric monitoring approach proposed there is for each distributed site to perform the function monitoring over an appropriate subset of the input domain. In the current work, we examine whether the distributed monitoring mechanism can become more efficient, in terms of the number of communicated messages, by extending the geometric monitoring framework to utilize prediction models. We initially describe a number of local estimators (predictors) that are useful for the applications that we consider and which have already been shown particularly useful in past work. We then demonstrate the feasibility of incorporating predictors in the geometric monitoring framework and show that prediction-based geometric monitoring in fact generalizes the original geometric monitoring framework. We propose a large variety of different prediction-based monitoring models for the distributed threshold monitoring of complex functions. Our extensive experimentation with a variety of real datasets, functions, and parameter settings indicates that our approaches can provide significant communication savings ranging between two times and up to three orders of magnitude, compared to the transmission cost of the original monitoring framework.

关键词： Algorithms Experimentation Performance continuous distributed monitoring data streams prediction models

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