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Pre-Processing and Modeling Tools for Big Data

100%

Hashem H. , Ranc D.

tom Vol. 41, No. 3

151--162

Modeling tools and operators help the user / developer to identify the processing field on the top of the sequence and to send into the computing module only the data related to the requested result. The remaining data is not relevant and it will slow down the processing. The biggest challenge nowadays is to get high quality processing results with a reduced computing time and costs. To do so, we must review the processing sequence, by adding several modeling tools. The existing processing models do not take in consideration this aspect and focus on getting high calculation performances which will increase the computing time and costs. In this paper we provide a study of the main modeling tools for Big Data and a new model based on pre-processing.

High Frequency Rule Synthesis in a Large Scale Multiple Database with MapReduce

86%

Bisoyi S. S. , Mishra P. , Mishra S. N.

tom Vol. 68, No. 2

177--186

Increasing development in information and communication technology leads to the generation of large amount of data from various sources. These collected data from multiple sources grows exponentially and may not be structurally uniform. In general, these are heterogeneous and distributed in multiple databases. Because of large volume, high velocity and variety of data mining knowledge in this environment becomes a big data challenge. Distributed Association Rule Mining(DARM) in these circumstances becomes a tedious task for an effective global Decision Support System(DSS). The DARM algorithms generate a large number of association rules and frequent itemset in the big data environment. In this situation synthesizing highfrequency rules from the big database becomes more challenging. Many algorithms for synthesizing association rule have been proposed in multiple database mining environments. These are facing enormous challenges in terms of high availability, scalability, efficiency, high cost for the storage and processing of large intermediate results and multiple redundant rules. In this paper, we have proposed a model to collect data from multiple sources into a big data storage framework based on HDFS. Secondly, a weighted multi-partitioned method for synthesizing high-frequency rules using MapReduce programming paradigm has been proposed. Experiments have been conducted in a parallel and distributed environment by using commodity hardware. We ensure the efficiency, scalability, high availability and costeffectiveness of our proposed method.

Performance evaluation of MapReduce using full virtualisation on a departmental cloud

72%

González-Vélez H. , Kontagora M.

tom Vol. 21, no 2

275-284

This work analyses the performance of Hadoop, an implementation of the MapReduce programming model for distributed parallel computing, executing on a virtualisation environment comprised of 1+16 nodes running the VMWare workstation software. A set of experiments using the standard Hadoop benchmarks has been designed in order to determine whether or not significant reductions in the execution time of computations are experienced when using Hadoop on this virtualisation platform on a departmental cloud. Our findings indicate that a significant decrease in computing times is observed under these conditions. They also highlight how overheads and virtualisation in a distributed environment hinder the possibility of achieving the maximum (peak) performance.