Unstructured Data Service Model Utilizing Context-Aware Big Data Analysis

Author(s):  
Yonghoon Kim ◽  
Mokdong Chung
Author(s):  
Rohit Rastogi ◽  
Devendra Kumar Chaturvedi ◽  
Parul Singhal

The Delhi and NCR healthcare systems are rapidly registering electronic health records and diagnostic information available electronically. Furthermore, clinical analysis is rapidly advancing, and large quantities of information are examined and new insights are part of the analysis of this technology experienced as big data. It provides tools for storing, managing, studying, and assimilating large amounts of robust, structured, and unstructured data generated by existing medical organizations. Recently, data analysis data have been used to help provide care. The present study aimed to analyse diabetes with the latest IoT and big data analysis techniques and its correlation with stress (TTH) on human health. The authors have tried to include age, gender, and insulin factor and its correlation with diabetes. Overall, in conclusion, TTH cases increasing with age in case of males and not following the pattern of diabetes variation with age, while in the case of females, TTH pattern variation is the same as diabetes (i.e., increasing trend up to age of 60 then decreasing).


Author(s):  
Arpit Kumar Sharma ◽  
Arvind Dhaka ◽  
Amita Nandal ◽  
Kumar Swastik ◽  
Sunita Kumari

The meaning of the term “big data” can be inferred by its name itself (i.e., the collection of large structured or unstructured data sets). In addition to their huge quantity, these data sets are so complex that they cannot be analyzed in any way using the conventional data handling software and hardware tools. If processed judiciously, big data can prove to be a huge advantage for the industries using it. Due to its usefulness, studies are being conducted to create methods to handle the big data. Knowledge extraction from big data is very important. Other than this, there is no purpose for accumulating such volumes of data. Cloud computing is a powerful tool which provides a platform for the storage and computation of massive amounts of data.


2014 ◽  
Vol 590 ◽  
pp. 698-701
Author(s):  
Hye Jin Pyo ◽  
Hoon Jeong ◽  
Nan Ju Kim ◽  
Eui In Choi

It's a major issue that how can find worthy information in big data. Because big datacan be used in company's success according how to take full advantage of big data analysis. Currently, search technologies aboutbeing stored distributed and duplicated data does not need to strong consistency. Therefore, nowadays we utilize variety of storage based on NoSQL for allowing loosens of strict consistency. MongoDB and Elastic Search have been focused of search and store unstructured data. But they have weak points. So, in this paper, we are going to propose new framework using term-based partitioning which can make up MongoDB and Elastic Search’s limitations.


2019 ◽  
Vol 9 (1) ◽  
pp. 01-12 ◽  
Author(s):  
Kristy F. Tiampo ◽  
Javad Kazemian ◽  
Hadi Ghofrani ◽  
Yelena Kropivnitskaya ◽  
Gero Michel

2020 ◽  
Vol 25 (2) ◽  
pp. 18-30
Author(s):  
Seung Wook Oh ◽  
Jin-Wook Han ◽  
Min Soo Kim

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