Results 1  10
of
82
Data Streams: Algorithms and Applications
, 2005
"... In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerg ..."
Abstract

Cited by 538 (22 self)
 Add to MetaCart
In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerged for reasoning about algorithms that work within these constraints on space, time, and number of passes. Some of the methods rely on metric embeddings, pseudorandom computations, sparse approximation theory and communication complexity. The applications for this scenario include IP network traffic analysis, mining text message streams and processing massive data sets in general. Researchers in Theoretical Computer Science, Databases, IP Networking and Computer Systems are working on the data stream challenges. This article is an overview and survey of data stream algorithmics and is an updated version of [175].1
Approximate aggregation techniques for sensor databases
 In ICDE
, 2004
"... In the emerging area of sensorbased systems, a significant challenge is to develop scalable, faulttolerant methods to extract useful information from the data the sensors collect. An approach to this data management problem is the use of sensor database systems, exemplified by TinyDB and Cougar, w ..."
Abstract

Cited by 300 (6 self)
 Add to MetaCart
(Show Context)
In the emerging area of sensorbased systems, a significant challenge is to develop scalable, faulttolerant methods to extract useful information from the data the sensors collect. An approach to this data management problem is the use of sensor database systems, exemplified by TinyDB and Cougar, which allow users to perform aggregation queries such as MIN, COUNT and AVG on a sensor network. Due to power and range constraints, centralized approaches are generally impractical, so most systems use innetwork aggregation to reduce network traffic. Also, aggregation strategies must provide faulttolerance to address the issues of packet loss and node failures inherent in such a system. An unfortunate consequence of standard methods is that they typically introduce duplicate values, which must be accounted for to compute aggregates correctly. Another consequence of loss in the network is that exact aggregation is not possible in general. With this in mind, we investigate the use of approximate innetwork aggregation using small sketches. Our contributions are as follows: 1) we generalize well known duplicateinsensitive sketches for approximating COUNT to handle SUM (and by extension, AVG and other aggregates), 2) we present and analyze methods for using sketches to produce accurate results with low communication and computation overhead (even on lowpowered CPUs with little storage and no floating point operations), and 3) we present an extensive experimental validation of our methods. 1
Issues in Data Stream Management
, 2003
"... Traditional databases store sets of relatively static records with no predefined notion of time, unless timestamp attributes are explicitly added. While this model adequately represents commercial catalogues or repositories of personal information, many current and emerging applications require sup ..."
Abstract

Cited by 159 (6 self)
 Add to MetaCart
Traditional databases store sets of relatively static records with no predefined notion of time, unless timestamp attributes are explicitly added. While this model adequately represents commercial catalogues or repositories of personal information, many current and emerging applications require support for online analysis of rapidly changing data streams. Limitations of traditional DBMSs in supporting streaming applications have been recognized, prompting research to augment existing technologies and build new systems to manage streaming data. The purpose of this paper is to review recent work in data stream management systems, with an emphasis on application requirements, data models, continuous query languages, and query evaluation.
Streaming Pattern Discovery in Multiple TimeSeries
 In VLDB
, 2005
"... In this paper, we introduce SPIRIT (Streaming Pattern dIscoveRy in multIple Timeseries) . Given n numerical data streams, all of whose values we observe at each time tick t, SPIRIT can incrementally find correlations and hidden variables, which summarise the key trends in the entire stream col ..."
Abstract

Cited by 105 (18 self)
 Add to MetaCart
(Show Context)
In this paper, we introduce SPIRIT (Streaming Pattern dIscoveRy in multIple Timeseries) . Given n numerical data streams, all of whose values we observe at each time tick t, SPIRIT can incrementally find correlations and hidden variables, which summarise the key trends in the entire stream collection.
An Optimal Algorithm for the Distinct Elements Problem
"... We give the first optimal algorithm for estimating the number of distinct elements in a data stream, closing a long line of theoretical research on this problem begun by Flajolet and Martin in their seminal paper in FOCS 1983. This problem has applications to query optimization, Internet routing, ne ..."
Abstract

Cited by 70 (6 self)
 Add to MetaCart
(Show Context)
We give the first optimal algorithm for estimating the number of distinct elements in a data stream, closing a long line of theoretical research on this problem begun by Flajolet and Martin in their seminal paper in FOCS 1983. This problem has applications to query optimization, Internet routing, network topology, and data mining. For a stream of indices in {1,..., n}, our algorithm computes a (1 ± ε)approximation using an optimal O(ε −2 +log(n)) bits of space with 2/3 success probability, where 0 < ε < 1 is given. This probability can be amplified by independent repetition. Furthermore, our algorithm processes each stream update in O(1) worstcase time, and can report an estimate at any point midstream in O(1) worstcase time, thus settling both the space and time complexities simultaneously.
Distributed streams algorithms for sliding windows
 In Proc. ACM Symp. on Parallel Algorithms and Architectures (SPAA
, 2002
"... Massive data sets often arise as physically distributed, parallel data streams, and it is important to estimate various aggregates and statistics on the union of these streams. This paper presents algorithms for estimating aggregate functions over a “sliding window ” of the N most recent data items ..."
Abstract

Cited by 62 (11 self)
 Add to MetaCart
(Show Context)
Massive data sets often arise as physically distributed, parallel data streams, and it is important to estimate various aggregates and statistics on the union of these streams. This paper presents algorithms for estimating aggregate functions over a “sliding window ” of the N most recent data items in one or more streams. Our results include: 1. For a single stream, we present the first ɛapproximation scheme for the number of 1’s in a sliding window that is optimal in both worst case time and space. We also present the first ɛapproximation scheme for the sum of integers in [0..R] in a sliding window that is optimal in both worst case time and space (assuming R is at most polynomial in N). Both algorithms are deterministic and use only logarithmic memory words. 2. In contrast, we show that any deterministic algorithm that estimates, to within a small constant relative error, the number of 1’s (or the sum of integers) in a sliding window on the union of distributed streams requires Ω(N) space.
New Streaming Algorithms for Fast Detection of Superspreaders
 in Proceedings of Network and Distributed System Security Symposium (NDSS
, 2005
"... Highspeed monitoring of Internet traffic is an important and challenging problem, with applications to realtime attack detection and mitigation, traffic engineering, etc. However, packetlevel monitoring requires fast streaming algorithms that use very little memory and little communication among c ..."
Abstract

Cited by 56 (2 self)
 Add to MetaCart
Highspeed monitoring of Internet traffic is an important and challenging problem, with applications to realtime attack detection and mitigation, traffic engineering, etc. However, packetlevel monitoring requires fast streaming algorithms that use very little memory and little communication among collaborating network monitoring points. In this paper, we consider the problem of detecting superspreaders, which are sources that connect to a large number of distinct destinations. We propose new streaming algorithms for detecting superspreaders and prove guarantees on their accuracy and memory requirements. We also show experimental results on real network traces. Our algorithms are substantially more efficient (both theoretically and experimentally) than previous approaches. We also extend our algorithms to identify superspreaders in a distributed setting, with sliding windows, and when deletions are allowed in the stream (which lets us identify sources that make a large number of failed connections to distinct destinations). More generally, our algorithms are applicable to any problem that can be formulated as follows: given a stream of (x, y) pairs, find all the x’s that are paired with a large number of distinct y’s. We call this the heavy distincthitters problem. There are many network security applications of this general problem. This paper discusses these applications and, for concreteness, focuses on the superspreader problem. 1
The pipelined set cover problem
, 2003
"... A classical problem in query optimization is to find the optimal ordering of a set of possibly correlated selections. We provide an abstraction of this problem as a generalization of set cover called pipelined set cover, where the sets are applied sequentially to the elements to be covered and the ..."
Abstract

Cited by 36 (6 self)
 Add to MetaCart
(Show Context)
A classical problem in query optimization is to find the optimal ordering of a set of possibly correlated selections. We provide an abstraction of this problem as a generalization of set cover called pipelined set cover, where the sets are applied sequentially to the elements to be covered and the elements covered at each stage are discarded. We show that several natural heuristics for this NPhard problem, such as the greedy setcover heuristic and a localsearch heuristic, can be analyzed using a linearprogramming framework. These heuristics lead to efficient algorithms for pipelined set cover that can be applied to order possibly correlated selections in conventional database systems as well as datastream processing systems. We use our linearprogramming framework to show that the greedy and localsearch algorithms are 4approximations for pipelined set cover. We extend our analysis to minimize the lpnorm of the costs paid by the sets, where p ≥ 2 is an integer, to examine the improvement in performance when the total cost has increasing contribution from initial sets in the pipeline. Finally, we consider the online version of pipelined set cover and present a competitive algorithm with a logarithmic performance guarantee. Our analysis framework may be applicable to other problems in query optimization where it is important to account for correlations.
Approximately detecting duplicates for streaming data using stable bloom filters
 In SIGMOD
, 2006
"... Traditional duplicate elimination techniques are not applicable to many data stream applications. In general, precisely eliminating duplicates in an unbounded data stream is not feasible in many streaming scenarios. Therefore, we target at approximately eliminating duplicates in streaming environmen ..."
Abstract

Cited by 34 (2 self)
 Add to MetaCart
(Show Context)
Traditional duplicate elimination techniques are not applicable to many data stream applications. In general, precisely eliminating duplicates in an unbounded data stream is not feasible in many streaming scenarios. Therefore, we target at approximately eliminating duplicates in streaming environments given a limited space. Based on a wellknown bitmap sketch, we introduce a data structure, Stable Bloom Filter, and a novel and simple algorithm. The basic idea is as follows: since there is no way to store the whole history of the stream, SBF continuously evicts the stale elements so that SBF has room for those more recent ones. After finding some properties of SBF analytically, we show that a tight upper bound of false positive rates is guaranteed. In our empirical study, we compare SBF to alternative methods. The results show that our method is superior in terms of both accuracy and time efficiency when a fixed small space and an acceptable false positive rate are given. 1.
Less is more: Compact matrix decomposition for large sparse graphs
, 2007
"... Given a large sparse graph, how can we find patterns and anomalies? Several important applications can be modeled as large sparse graphs, e.g., network traffic monitoring, research citation network analysis, social network analysis, and regulatory networks in genes. Low rank decompositions, such as ..."
Abstract

Cited by 34 (3 self)
 Add to MetaCart
(Show Context)
Given a large sparse graph, how can we find patterns and anomalies? Several important applications can be modeled as large sparse graphs, e.g., network traffic monitoring, research citation network analysis, social network analysis, and regulatory networks in genes. Low rank decompositions, such as SVD and CUR, are powerful techniques for revealing latent/hidden variables and associated patterns from high dimensional data. However, those methods often ignore the sparsity property of the graph, and hence usually incur too high memory and computational cost to be practical. We propose a novel method, the Compact Matrix Decomposition (CMD), to compute sparse low rank approximations. CMD dramatically reduces both the computation cost and the space requirements over existing decomposition methods (SVD, CUR). Using CMD as the key building block, we further propose procedures to efficiently construct and analyze dynamic graphs from realtime application data. We provide theoretical guarantee for our methods, and present results on two real, large datasets, one on network flow data (100GB trace of 22K hosts over one month) and one on DBLP (200MB over 25 years). We show that CMD is often an order of magnitude more efficient than the state of the art (SVD and CUR): it is over 10X faster, but requires less than 1/10 of the space, for the same reconstruction accuracy. Finally, we demonstrate how CMD is used for detecting anomalies and monitoring timeevolving graphs, in which it successfully detects wormlike hierarchical scanning patterns in real network data.