Big data summarization using semantic feture for IoT on cloud

2014 ◽  
Vol 7 ◽  
pp. 1095-1103 ◽  
Author(s):  
Yoo-Kang Ji ◽  
Yong-Il Kim ◽  
Sun Park
Keyword(s):  
Big Data ◽  
Data Summarization

A soft computing approach to big data summarization

2018 ◽  
Vol 348 ◽  
pp. 4-20 ◽  
Author(s):  
Grégory Smits ◽  
Olivier Pivert ◽  
Ronald R. Yager ◽  
Pierre Nerzic
Keyword(s):  
Big Data ◽  
Soft Computing ◽  
Data Summarization ◽  
Computing Approach

scholarly journals Scalable data summarization on big data

2014 ◽  
Vol 32 (3) ◽  
pp. 313-314 ◽  
Author(s):  
Feifei Li ◽  
Suman Nath
Keyword(s):  
Big Data ◽  
Data Summarization

Data Summarization Techniques for Big Data—A Survey

2015 ◽  
pp. 1109-1152 ◽  
Author(s):  
Z. R. Hesabi ◽  
Z. Tari ◽  
A. Goscinski ◽  
A. Fahad ◽  
I. Khalil ◽  
...  
Keyword(s):  
Big Data ◽  
Data Summarization

Big Data Summarization Using Novel Clustering Algorithm and Semantic Feature Approach

2017 ◽  
Vol 4 (3) ◽  
pp. 108-117
Author(s):  
Shilpa G. Kolte ◽  
Jagdish W. Bakal
Keyword(s):  
Big Data ◽  
Clustering Algorithm ◽  
Federal Court ◽  
Semantic Feature ◽  
Experimental Results ◽  
Semantic Features ◽  
Legal Cases ◽  
Data Summarization ◽  
Summarization Method ◽  
Better Than

This paper proposes a big data (i.e., documents, texts) summarization method using proposed clustering and semantic features. This paper proposes a novel clustering algorithm which is used for big data summarization. The proposed system works in four phases and provides a modular implementation of multiple documents summarization. The experimental results using Iris dataset show that the proposed clustering algorithm performs better than K-means and K-medodis algorithm. The performance of big data (i.e., documents, texts) summarization is evaluated using Australian legal cases from the Federal Court of Australia (FCA) database. The experimental results demonstrate that the proposed method can summarize big data document superior as compared with existing systems.

Find Out About 'Big Data' to Track Outcomes

ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  
Big Data

Find Out About 'Big Data' to Track Outcomes

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

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