Big Data Analytics IEEE 2015 Projects
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The paper reviews methods for inferring patterns from big data using aggregation, filtering, and tagging, highlighting the importance of these steps in collecting relevant data. It provides an introduction to big data and discusses the functionality of various tools like Flume, Sqoop, Hive, and Mahout, along with relevant algorithms. Comparisons of aggregation and processing tools, as well as algorithms, are included based on their preprocessing, matching time, results, and reviews.
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Big Data Analytics IEEE 2015 Projects
- 1. Big Data Analytics IEEE 2015 Projects Web : www.kasanpro.com Email : sales@kasanpro.com List Link : http://kasanpro.com/projects-list/big-data-analytics-ieee-2015-projects Title :Inference Patterns from Big Data using Aggregation, Filtering and Tagging- A Survey Language : C# Project Link : http://kasanpro.com/p/c-sharp/big-data-omferemce-patterns-aggregation-filtering-tagging Abstract : This paper reviews various approaches to infer the patterns from Big Data using aggregation, filtering and tagging. Earlier research shows that data aggregation concerns about gathered data and how efficiently it can be utilized. It is understandable that at the time of data gathering one does not care much about whether the gathered data will be useful or not. Hence, filtering and tagging of the data are the crucial steps in collecting the relevant data to fulfill the need. Therefore the main goal of this paper is to present a detailed and comprehensive survey on different approaches. To make the concept clearer, we have provided a brief introduction of Big Data, how it works, working of two data aggregation tools (namely, flume and sqoop), data processing tools (hive and mahout) and various algorithms that can be useful to understand the topic. At last we have included comparisons between aggregation tools, processing tools as well as various algorithms through its pre-process, matching time, results and reviews.