data stream research papers 2011-2012





 A GENETIC ALGORITHM FOR MANAGEMENT DATA STREAM IN VANET
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Information has become, in our era, very important in the business, in the home and on the
road in the car or vehicle in general. In addition to being a significant information generator,
the car is also used as a means of disseminating this information. One of the problems yet 

Towards incremental learning of nonstationary imbalanced data stream: a multiple selectively recursive approach
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S Chen, H He ,Evolving Systems, 2011 ,Springer
Abstract Difficulties of learning from nonstationary data stream are generally twofold. First,
dynamically structured learning framework is required to catch up with the evolution of
unstable class concepts, ie, concept drifts. Second, imbalanced class distribution over 

 TOWARDS A HIGH RESOLUTION LONG-TERM SOLAR RESOURCE DATABASE: APPLYING THE SUNY MODEL TO ISCCP B1U DATA STREAM
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R Perez, J Schlemmer, K Hemker Jr, PW Stackhouse ,2012 ,ases.conference-services.net
ABSTRACT This article presents an operational evaluation of the SUNY satellite irradiance
prediction model when using the ISCCP B1U data as an input and compares its
performance against the current Unidata-driven operational version of the same 

 Detecting Hidden Anomalies Using Sketch for High-speed Network Data Stream Monitoring
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A Li, Y Han, B Zhou, W Han, Y Jia ,Appl. Math, 2012
Abstract: Monitoring network data streams in real-time to check security event become more
and more important along with the rapid growth of Internet applications. The detection
typically treats the traffic as a collection of flows that need to be examined for significant 

 Using GNUsmail to Compare Data Stream Mining Methods for On-line Email Classification
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Abstract Real-time classification of emails is a challenging task because of its online nature,
and also because email streams are subject to concept drift. Identifying email spam, where
only two different labels or classes are defined (spam or not spam), has received great 

Inferring fine-grained data provenance in stream data processing: reduced storage cost, high accuracy
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Fine-grained data provenance ensures reproducibility of results in decision making, process
control and e-science applications. However, maintaining this provenance is challenging in
stream data processing because of its massive storage consumption, especially with large 

DS-Means: Distributed Data Stream Clustering
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This paper proposes DS-means, a novel algorithm for clustering distributed data streams.
Given a network of computing nodes, each of them receiving its share of a distributed data
stream, our goal is to obtain a common clustering under the following restrictions (i) the 

Facing the reality of data stream classification: Coping with scarcity of labeled data
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MM Masud, C Woolam, J Gao, L Khan, J Han , and Information Systems, 2011 ,Springer
Abstract Recent approaches for classifying data streams are mostly based on supervised
learning algorithms, which can only be trained with labeled data. Manual labeling of data is
both costly and time consuming. Therefore, in a real streaming environment where large 

Linked Stream Data Processing Engines: Facts and Figures
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Linked Stream Data, ie, the RDF data model extended for representing stream data
generated from sensors social network applications, is gaining popularity. This has
motivated considerable work on developing corresponding data models associated with 

Data Provenance and Management in Radio Astronomy: A Stream Computing Approach
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M Mahmoud, A Ensor, A Biem, B Elmegreen  Data Provenance and , 2011 ,Springer
New approaches for data provenance and data management (DPDM) are required for mega
science projects like the Square Kilometer Array, characterized by extremely large data
volume and intense data rates, therefore demanding innovative and highly efficient 

 CluSandra: A Framework and Algorithm for Data Stream Cluster Analysis
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JR Fernandez, EM El-Sheikh ,International Journal, 2011
Abstract—The clustering or partitioning of a dataset’s records into groups of similar records
is an important aspect of knowledge discovery from datasets. A considerable amount of
research has been applied to the identification of clusters in very large multi-dimensional 

Distributed processing of continuous sliding-window k-NN queries for data stream filtering
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Abstract A sliding-window k-NN query (k-NN/w query) continuously monitors incoming data
stream objects within a sliding window to identify k closest objects to a query. It enables
effective filtering of data objects streaming in at high rates from potentially distributed 

Mobile activity recognition using ubiquitous data stream mining
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Mobile activity recognition focuses on inferring the current activities of a mobile user by
leveraging the rich sensory data that is available on today’s smart phones and other
wearable sensors. The state of the art in mobile activity recognition research has focused 

 NEIGHBOR-dmin PRECODER FOR THREE DATA-STREAM MIMO SYSTEMS
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ABSTRACT A new precoding strategy, which considers not only the minimum Euclidean
distance but also the number of neighbors providing the distance dmin, is proposed. Firstly,
a new parameterized form of Neighbor-dmin precoder is presented, where all angles 

 StreamSqueeze: a dynamic stream visualization for monitoring of event data
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ABSTRACT While in clear-cut situations automated analytical solution for data streams are
already in place, only few visual approaches have been proposed in the literature for
exploratory analysis tasks on dynamic information. However, due to the competitive or 

 Stability Analysis of Wireless Sensor Network Service via Data Stream Methods
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R Hu ,Appl. Math, 2012
Revised 16 May 2012; Accepted 01 Jul. 2012 Received: Jul 8,011; Revised Oct. 4, 2011;
Accepted Jan. 6, 2012 Abstract: Providing stable compositions of Wireless Sensor Network
Service (WSNS) is a challenging issue since the data stream architect often has only a 

 Aerial root classifiers for predicting missing values in data stream decision tree classification
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Abstract Data Stream Mining (DSM) is a new breed of data mining algorithms that handles
continuous data streams and predicts (or classifies) a target value on the fly. Such data
streams are inevitably prone to have missing values. Some common examples include 

 Space-efficient tracking of persistent items in a massive data stream
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B Lahiri, J Chandrashekar ,Proceedings of the 5th , 2011 ,eng.iastate.edu
ABSTRACT Motivated by scenarios in network anomaly detection, we consider the problem
of detecting persistent items in a data stream, which are items that occur regularly in the
stream. In contrast with heavy-hitters, persistent items do not necessarily contribute 

Hierarchical clustering for real-time stream data with noise
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P Kranen, F Reidl, F Sanchez Villaamil ,Scientific and Statistical , 2011 ,Springer
In stream data mining, stream clustering algorithms provide summaries of the relevant data
objects that arrived in the stream. The model size of the clustering, ie the granularity, is
usually determined by the speed (data per time) of the data stream. For varying streams, 

Securing advanced metering infrastructure using intrusion detection system with data streammining
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Advanced metering infrastructure (AMI) is an imperative component of the smart grid, as it is
responsible for collecting, measuring, analyzing energy usage data, and transmitting these
data to the data concentrator and then to a central system in the utility side. Therefore, the 

Hybridizing data stream mining and technical indicators in automated trading systems
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M Mayo ,Modeling Decision for Artificial Intelligence, 2011 ,Springer
Automated trading systems for financial markets can use data mining techniques for future
price movement prediction. However, classifier accuracy is only one important component in
such a system: the other is a decision procedure utilizing the prediction in order to be long, 

 Design considerations of a flexible data stream processing middleware
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N Cipriani, M Grossmann ,Proceedings of the , 2011 ,informatik.rwth-aachen.de
Abstract. Techniques for efficient and distributed processing of huge, unbound data streams
have made some impact in the database community. Distributed data stream processing
systems have emerged providing a distributed environment to process these potentially 



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