presented by:
Chedy Ra¨ıssi,Pascal Poncelet,Maguelonne Teisseire

ABSTRACT
Many recent real-world applications, such as network traf-_c monitoring, intrusion detection systems, sensor networkdata analysis, click stream mining and dynamic tracing of_nancial transactions, call for studying a new kind of data.Called stream data, this model is, in fact, a continuous, potentiallyin_nite ow of information as opposed to _nite,statically stored data sets extensively studied by researchersof the data mining community. An important appl ....etc

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Abstract.
The problem of estimating the kth frequency moment Fk, for any
non-negative integral value of k, over a data stream by looking at the items exactly
once as they arrive, was considered in a seminal paper by Alon, Matias and
Szegedy . They present a sampling based algorithm to estimate Fk where,
k  2, using space ˜O(n1−1/k)). Coppersmith and Kumar and , using
different methods, present algorithms for estimating Fk with space complexity
˜O
(n1−1/(k−1)). In this paper, we present an algorithm for es ....etc

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ABSTRACT
There is growing interest in algorithms for processing and query- ing continuous data streams (i.e., data that is seen only once in a _xed order) with limited memory resources. Providing (perhaps approximate) answers to queries over such streams is a crucial re- quirement for many application environments; examples include large IP network installations where performance data from dif- ferent parts of the network needs to be continuously collected and analyzed. The ability to estimate the number of distinct (sub)tuples in the ....etc

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Abstract.
We present a randomized procedure named Hierarchical Sampling from Sketches (HSS) that can be used for estimating a class of functions over the frequency vector f of update streams of the form (S) = Pn i=1 (|fi|).We illustrate this by applying the HSS technique to design nearly space-optimal algorithms for estimating the pth moment of the frequency vector, for real p _ 2 and for estimating the entropy of a data stream. 3
1 Introduction
A variety of applications in diverse areas, such as, networking, database sys ....etc

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My name is keerthana mtech student.
I want coding on ieee project efficient and dynamic routing topology inference from end to end measurements
if any one have that please post to my mail id [email protected] ....etc

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Abstract—Despite its promise, RFID technology presents numerous challenges, including incomplete data, lack of location and containment information, and very high volumes. In this work, we present a novel data inference and compression substrate over RFID streams to address these challenges. Our substrate employs a time-varying graph model to efficiently capture possible object locations and interobject relationships such as containment from raw RFID streams. It then employs a probabilistic algorithm to estimate the most likely location and c ....etc

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i am final year student.because i search for project titles,so,kindly
request for this..... ....etc

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Domain
NETWORKING

Technology
JAVA

Abstract:
EFFICIENT AND DYNAMIC ROUTING TOPOLOGY INFERENCE FROM END-TO-END MEASUREMENTS

Inferring the routing topology and page link performance from a node to a set of other nodes is an important component in network monitoring and application design. In this paper we propose a general framework for designing topology inference algorithms based on additive metrics. The framework can flexibly fuse information from multiple measurements to achieve better estimation accuracy. We develop computa ....etc

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ABSTRACT
There is growing interest in algorithms for processing and querying continuous data streams (i.e., data that is seen only once in a _xed order) with limited memory resources. In its most general form, a data stream is actually an update stream, i.e., comprising data-item deletions as well as inser- tions. Such massive update streams arise naturally in several application domains (e.g., monitoring of large IP network installations, or processing of retail-chain transactions). Estimating the cardinality of set expressions de_ne ....etc

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Abstract.
We present an algorithm for estimating entropy of data streams consisting of insertion and deletion operations using ~O(1) space.
1 1 Introduction
Recently, there has been an emergence of monitoring applications in diverse areas, including, network traf_c monitoring, network topology monitoring, sensor networks, _nancial market monitoring, web-log monitoring, etc.. In these applications, data is generated rapidly and continuously, and must be analyzed very ef_ciently, in realtime, to identify large trends, anomalies ....etc

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