492 research outputs found
A Survey Paper on Implementing Service Oriented Architecture for Data Mining
Web service is working with the web with an object or component to achieve the communication between the distributed applications and between the different platforms through a series of protocols. Web Service provides a set of standard types systems, rules, techniques and internet service-oriented applications for communication between the different platforms, different programming languages and different types of systems to achieve interoperability. This survey paper gives the application of web service for data mining also we build a data mining model based on Web services and going forward it is possible to build a new data mining solution for security according to the prototype of a dynamic web service based data mining process system.
DOI: 10.17762/ijritcc2321-8169.15079
Implementing Service Oriented Architecture for Data Mining
With Web technology, data on internet has become increasingly large and complex. No matter users or internet users needs all this data. Also the data which is available on web not all the time useful information or it is knowledgeable. Hence web data mining is necessary to fulfill this demand. Web data mining can extract unstructured, undiscovered data which is possibly useful information and knowledge, from much incomplete, noisy, ambiguous, random, practical application related data from WWW network. It is a new emerging commercial information/data mining technology. Its main characteristic is to extract key data to support business for decision making from business database through the use of extraction, conversion, analysis and other transaction models. Web service is deployed on the web with an object or component to achieve distributed application software platform through a series of protocols. Web Service platform provides a set of standard types systems, rules, techniques and internet service-oriented applications for communication between the different platforms, different programming languages and different types of systems to achieve interoperability. This paper gives the actual and practical application of web services for data mining, we build a data mining model based on Web services and going forward it is possible to implement the new data mining solution for security configuration. This has been achieved with the use of prototypes of a dynamic web service based data mining systems.
DOI: 10.17762/ijritcc2321-8169.15079
The Family of MapReduce and Large Scale Data Processing Systems
In the last two decades, the continuous increase of computational power has
produced an overwhelming flow of data which has called for a paradigm shift in
the computing architecture and large scale data processing mechanisms.
MapReduce is a simple and powerful programming model that enables easy
development of scalable parallel applications to process vast amounts of data
on large clusters of commodity machines. It isolates the application from the
details of running a distributed program such as issues on data distribution,
scheduling and fault tolerance. However, the original implementation of the
MapReduce framework had some limitations that have been tackled by many
research efforts in several followup works after its introduction. This article
provides a comprehensive survey for a family of approaches and mechanisms of
large scale data processing mechanisms that have been implemented based on the
original idea of the MapReduce framework and are currently gaining a lot of
momentum in both research and industrial communities. We also cover a set of
introduced systems that have been implemented to provide declarative
programming interfaces on top of the MapReduce framework. In addition, we
review several large scale data processing systems that resemble some of the
ideas of the MapReduce framework for different purposes and application
scenarios. Finally, we discuss some of the future research directions for
implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author
Internet Traffic Flow Analysis using Hadoop
The internet traffic analysis elucidates the network administrator for monitoring the ongoing operation in the network and to understand the network so that the behavior could be examined and large problem can be examined. Flow analysis assists in traffic management, allocation of resources and fault tolerance. Due to the fast increase in internet user simultaneously the network usage has also escalated rapidly. The major problem of this fast growth in network is the traffic management, storing of traffic data and analysis this enormous amount of data in a single machine. To resolve this issue hadoop has been implemented to scan multiple input data and produce output for traffic identification and clustering flow. In this paper internet traffic flow analysis has been done using hadoop. In this proposed method system accepts packet data as input from network and this input is appended to hadoop distributed file system (HDFS) and at last processing is done through MapReduce. Once the output has been generated the network administrator analyses the internet traffic and troubleshoot any problem if necessary
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