The Application of Stream Data Time-Series Pattern Reliance Mining in Stock Market Analysis

With the rapid development of computer information technology, a new data model-the stream data emerged. The stream data is coming continuously, quickly, changing with time, and may be unpredictable and unlimited in the way. The emergence of stream data has brought new challenges from the nature ofthe data applied to static database technology and data mining technology. At present, the stream data has been widely used in telecommunications, financial ....etc

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Abstract.
We consider updates to an n-dimensional frequency vector of a data stream, that is, the vector f is updated coordinate-wise by means of insertions or deletions in any arbitrary order. A fundamental problem in this model is to recall the vector approximately, that is to return an estimate ^ f of f such that j ^ fi ....etc

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Abstract
Data stream computations in domains such as internet applications are often performed in a highly distributed fashion in order to save time. An example is the class of applications that use the Google Mapreduce framework of scalable distributed processing as presented by (Dean & Ghemawat 2004). A basic question here is: what kind of data stream computations admit scalable and efficient distributed algorithms? We show that the class of data stream computations that approximate functions of the frequency vector of the stream ca ....etc

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what is necessity of this project? why the project has been taken up? has this become successful? ....etc

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can you please give idea of future enhancement for online intrusion alert aggregation with generative data stream modeling ....etc

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Abstract.
We present a simple way of designing deterministic algorithms for problems in the data stream model via lossless expander graphs.We illustrate this by considering two problems, namely, k-sparsity testing and estimating frequency of items.
1 Introduction
We say that an n-dimensional vector f from Zn is k-sparse if it has at most k non-zero entries. The problem is to test whether f is k-sparse or no after it has been subject to a sequence of coordinate wise updates in arbitrary order, that is, f is the frequency vec ....etc

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all uml diag ,including class activity,sequence,deployment ,component,,object,collaboration,state transition,dfd,













rashid bhaldar ....etc

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An embedded system is a special-purpose system in which the computer is completely encapsulated by the device it controls. Unlike a general-purpose computer, such as a personal computer, an embedded system performs one or a few pre-defined tasks, usually with very specific requirements. Since the system is dedicated to specific tasks, design engineers can optimize it, reducing the size and cost of the product.

http://ziddudownload/5571379/Automotivebasedmanag.rar.html ....etc

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Aurora: A New Model And Architecture For Data Stream Management
B.Tech Seminar Report
by
Shafeek T K
Department of Computer Science And Engineering
Government Engineering College, Thrissur
December 2010



Abstract
This paper introduces a new class of application called monitoring applications, These
applications show dier substantially from conventional data processing. The fact that
a software system should process and react to, inputs in continuous manner f ....etc

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DESCRIPTION:
A data stream is a massive, open-ended sequence of data elements continuously generated at a rapid rate. Mining data streams is more difficult than mining static databases because the huge, high-speed and continuous characteristics of streaming data. In this paper, we propose a new one-pass algorithm called DSM-MFI (stands for Data Stream Mining for Maximal Frequent Itemsets), which mines the set of all maximal frequent itemsets in landmark windows over data streams.
The proposed algorithm DSM-MFI ....etc

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