scholarly journals Towards a Documents Processing Tool using Traceability Information Retrieval and Content Recognition Through Machine Learning in a Big Data Context

2020 ◽  
Vol 5 (6) ◽  
pp. 1267-1277
Author(s):  
Othmane Rahmaoui ◽  
Kamal Souali ◽  
Mohammed Ouzzif
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Information Retrieval ◽  
Content Recognition ◽  
Data Context

scholarly journals gMSR: A Multi-GPU Algorithm to Accelerate a Massive Validation of Biclusters

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1782
Author(s):  
Aurelio López-Fernández ◽  
Domingo S. Rodríguez-Baena ◽  
Francisco Gómez-Vela
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Machine Learning Techniques ◽  
Graphic Processing Units ◽  
Continuous Increase ◽  
Specific Subset ◽  
Learning Techniques ◽  
Validation Measure ◽  
Cuda Architecture ◽  
Data Context

Nowadays, Biclustering is one of the most widely used machine learning techniques to discover local patterns in datasets from different areas such as energy consumption, marketing, social networks or bioinformatics, among them. Particularly in bioinformatics, Biclustering techniques have become extremely time-consuming, also being huge the number of results generated, due to the continuous increase in the size of the databases over the last few years. For this reason, validation techniques must be adapted to this new environment in order to help researchers focus their efforts on a specific subset of results in an efficient, fast and reliable way. The aforementioned situation may well be considered as Big Data context. In this sense, multiple machine learning techniques have been implemented by the application of Graphic Processing Units (GPU) technology and CUDA architecture to accelerate the processing of large databases. However, as far as we know, this technology has not yet been applied to any bicluster validation technique. In this work, a multi-GPU version of one of the most used bicluster validation measure, Mean Squared Residue (MSR), is presented. It takes advantage of all the hardware and memory resources offered by GPU devices. Because of to this, gMSR is able to validate a massive number of biclusters in any Biclustering-based study within a Big Data context.

scholarly journals A Survey of Parallel Clustering Algorithms Based on Spark

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Wen Xiao ◽  
Juan Hu
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Big Data ◽  
Information Retrieval ◽  
Social Network ◽  
Clustering Algorithms ◽  
Future Research ◽  
Parallel Design ◽  
Learning Tasks ◽  
Parallel Clustering

Clustering is one of the most important unsupervised machine learning tasks, which is widely used in information retrieval, social network analysis, image processing, and other fields. With the explosive growth of data, the classical clustering algorithms cannot meet the requirements of clustering for big data. Spark is one of the most popular parallel processing platforms for big data, and many researchers have proposed many parallel clustering algorithms based on Spark. In this paper, the existing parallel clustering algorithms based on Spark are classified and summarized, the parallel design framework of each kind of algorithms is discussed, and after comparing different kinds of algorithms, the direction of the future research is discussed.

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