1,991 research outputs found
Statistical structures for internet-scale data management
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
Distributed top-k aggregation queries at large
Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network
Fault-Tolerant Aggregation: Flow-Updating Meets Mass-Distribution
Flow-Updating (FU) is a fault-tolerant technique that has proved to be
efficient in practice for the distributed computation of aggregate functions in
communication networks where individual processors do not have access to global
information. Previous distributed aggregation protocols, based on repeated
sharing of input values (or mass) among processors, sometimes called
Mass-Distribution (MD) protocols, are not resilient to communication failures
(or message loss) because such failures yield a loss of mass. In this paper, we
present a protocol which we call Mass-Distribution with Flow-Updating (MDFU).
We obtain MDFU by applying FU techniques to classic MD. We analyze the
convergence time of MDFU showing that stochastic message loss produces low
overhead. This is the first convergence proof of an FU-based algorithm. We
evaluate MDFU experimentally, comparing it with previous MD and FU protocols,
and verifying the behavior predicted by the analysis. Finally, given that MDFU
incurs a fixed deviation proportional to the message-loss rate, we adjust the
accuracy of MDFU heuristically in a new protocol called MDFU with Linear
Prediction (MDFU-LP). The evaluation shows that both MDFU and MDFU-LP behave
very well in practice, even under high rates of message loss and even changing
the input values dynamically.Comment: 18 pages, 5 figures, To appear in OPODIS 201
Histograms and Wavelets on Probabilistic Data
There is a growing realization that uncertain information is a first-class
citizen in modern database management. As such, we need techniques to correctly
and efficiently process uncertain data in database systems. In particular, data
reduction techniques that can produce concise, accurate synopses of large
probabilistic relations are crucial. Similar to their deterministic relation
counterparts, such compact probabilistic data synopses can form the foundation
for human understanding and interactive data exploration, probabilistic query
planning and optimization, and fast approximate query processing in
probabilistic database systems.
In this paper, we introduce definitions and algorithms for building
histogram- and wavelet-based synopses on probabilistic data. The core problem
is to choose a set of histogram bucket boundaries or wavelet coefficients to
optimize the accuracy of the approximate representation of a collection of
probabilistic tuples under a given error metric. For a variety of different
error metrics, we devise efficient algorithms that construct optimal or near
optimal B-term histogram and wavelet synopses. This requires careful analysis
of the structure of the probability distributions, and novel extensions of
known dynamic-programming-based techniques for the deterministic domain. Our
experiments show that this approach clearly outperforms simple ideas, such as
building summaries for samples drawn from the data distribution, while taking
equal or less time
BlinkDB: queries with bounded errors and bounded response times on very large data
In this paper, we present BlinkDB, a massively parallel, approximate query engine for running interactive SQL queries on large volumes of data. BlinkDB allows users to trade-off query accuracy for response time, enabling interactive queries over massive data by running queries on data samples and presenting results annotated with meaningful error bars. To achieve this, BlinkDB uses two key ideas: (1) an adaptive optimization framework that builds and maintains a set of multi-dimensional stratified samples from original data over time, and (2) a dynamic sample selection strategy that selects an appropriately sized sample based on a query's accuracy or response time requirements. We evaluate BlinkDB against the well-known TPC-H benchmarks and a real-world analytic workload derived from Conviva Inc., a company that manages video distribution over the Internet. Our experiments on a 100 node cluster show that BlinkDB can answer queries on up to 17 TBs of data in less than 2 seconds (over 200 x faster than Hive), within an error of 2-10%.National Science Foundation (U.S.) (CISE Expeditions Award CCF-1139158)United States. Defense Advanced Research Projects Agency (XData Award FA8750-12-2-0331)
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