scholarly journals High-Efficient Fuzzy Querying with HiveQL for Big Data Warehousing

Author(s):  
Bozena Malysiak-Mrozek ◽  
Jadwiga Wieszok ◽  
Witold Pedrycz ◽  
Weiping Ding ◽  
Dariusz Mrozek
Author(s):  
Sheik Abdullah A. ◽  
Priyadharshini P.

The term Big Data corresponds to a large dataset which is available in different forms of occurrence. In recent years, most of the organizations generate vast amounts of data in different forms which makes the context of volume, variety, velocity, and veracity. Big Data on the volume aspect is based on data set maintenance. The data volume goes to processing usual a database but cannot be handled by a traditional database. Big Data is stored among structured, unstructured, and semi-structured data. Big Data is used for programming, data warehousing, computational frameworks, quantitative aptitude and statistics, and business knowledge. Upon considering the analytics in the Big Data sector, predictive analytics and social media analytics are widely used for determining the pattern or trend which is about to happen. This chapter mainly deals with the tools and techniques that corresponds to big data analytics of various applications.


Digital technology is fast changing in the recent years and with this change, the number of data systems, sources, and formats has also increased exponentially. So the process of extracting data from these multiple source systems and transforming it to suit for various analytics processes is gaining importance at an alarming rate. In order to handle Big Data, the process of transformation is quite challenging, as data generation is a continuous process. In this paper, we extract data from various heterogeneous sources from the web and try to transform it into a form which is vastly used in data warehousing so that it caters to the analytical needs of the machine learning community.


Author(s):  
Abderrazak Sebaa ◽  
Fatima Chikh ◽  
Amina Nouicer ◽  
Abdelkamel Tari
Keyword(s):  
Big Data ◽  

2018 ◽  
pp. 375-406 ◽  
Author(s):  
Butch Quinto
Keyword(s):  
Big Data ◽  

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