Dynamic adaptive data structures for monitoring data streams

Aguilar-Saborit, Josep; Trancoso, Pedro; Muntés-Mulero, Víctor; Larriba-Pey, Josep Lluís

doi:10.1016/j.datak.2007.12.006

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…Bloom filter [1] is a space‐efficient data structure that can be used to process queries by answering whether an arbitrary element belongs to a certain set. Recently, thousands of real‐time network applications and services [2–9] (such as DDoS attack detection and protection [2–4], multicast routing [6], cache filtering in CDN networks [7], high‐speed flow measurement [8], and data‐stream monitoring [9]) are found taking Bloom filter as a fundamental component. The above applications make up just a tip of the entire iceberg.…”

Section: Introductionmentioning

confidence: 99%

Efficient Bloom filter for network protocols using AES instruction set

Zhang¹,

Zheng

Zhang

2017

IET Communications

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Section: Introductionmentioning

confidence: 99%

Efficient Bloom filter for network protocols using AES instruction set

Zhang¹,

Zheng

Zhang

2017

IET Communications

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“…Therefore, we need ways whereby frequent item sets could be found by one-time data scanning. This problem was solved through presenting a demonstration to store summarized received data [13][14][15]. This algorithm mines frequent item sets by keeping part of the latest received data and storing them as bit-based representation.…”

Section: Introductionmentioning

confidence: 99%

Proposing a Algorithm for Finding Repetitive Patterns in Web Dataflow

Rostami¹,

Ehteshami²,

Yaghoobi³

et al. 2015

IJSEIA

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Today, searching repetitive patterns on data flows is very important. By data flow we mean a type of data which is constantly produced in a very fast and unlimited manner. As a kind of these data we can name the report of clicks in computer networks. A repetitive pattern is a pattern which is available in a significant number of transactions. Finding repetitive patterns in data flows is a new and arguable issue in data mining as data is received in form of fast and continuous flow. Unlike static databases, flow mining faces a lot of problems including single review, requiring unlimited memory and high rate of input data. A common way of searching repetitive patterns is the excess check of data which requires to be saved in memory. In addition, according to the features of data flows i.e. unlimited and fast production, it is not possible to save them in memory and hence techniques are needed which are able to process them online and find repetitive patterns. One of the most popular relative techniques is using sliding windows. It's advantage is reduction of the consumed memory and increase in search speed. In this paper, a new vertical display and an algorithm based on pins, called DBP-BA, are proposed to find repetitive patters in data flows. Since this new display without any additional task has a compact form, the proposed algorithm has a better performance than similar ones in terms of consumed memory and processing time. On the other hand, experiments support this matter.

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“…We briefly examine some key cases in this domain, for example detection of heavy flows, Iceberg queries, packet attribution, and approximate state machines. Key functions for monitoring include flow classification [96], [97] and approximate counting and summarization of flows and packets [98], [99].…”

Section: Monitoring and Measurementmentioning

confidence: 99%

Theory and Practice of Bloom Filters for Distributed Systems

Tarkoma

Rothenberg

Lagerspetz

2012

IEEE Commun. Surv. Tutorials

443

244

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Abstract-Many network solutions and overlay networks utilize probabilistic techniques to reduce information processing and networking costs. This survey article presents a number of frequently used and useful probabilistic techniques. Bloom filters and their variants are of prime importance, and they are heavily used in various distributed systems. This has been reflected in recent research and many new algorithms have been proposed for distributed systems that are either directly or indirectly based on Bloom filters. In this survey, we give an overview of the basic and advanced techniques, reviewing over 20 variants and discussing their application in distributed systems, in particular for caching, peer-to-peer systems, routing and forwarding, and measurement data summarization.

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Dynamic adaptive data structures for monitoring data streams

Cited by 7 publications

References 30 publications

Efficient Bloom filter for network protocols using AES instruction set

Efficient Bloom filter for network protocols using AES instruction set

Proposing a Algorithm for Finding Repetitive Patterns in Web Dataflow

Theory and Practice of Bloom Filters for Distributed Systems

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