Abstract
In this digital world, many applications like mobile devices, organization’s ERPs, sensors, social networks, generate enormous amount of data. The data is so huge that conversional systems are not sufficient to process them efficiently. Hence, so many faced challenges in processing and extracting useful information from it. Thus, great interest is shown in the development of big data analytic models to cater for both real-time and off-line data processing. Therefore, in this paper an Efficient Analytic Model for Processing Real-time data has been proposed. The model comprises of three main units, i.e. Data acquisition unit; data processing unit; and data analysis and decision unit. For proposed model elaboration, a detailed analysis on real-time Social Sensor data for emotions detection is performed, using Apache Spark. Additionally, algorithms are proposed for detecting Negative, Positive and Neutral emotions in tweet-texts and locating on the map where people are tweeting from.
Keywords
Big Data analytic
Data processing
Real-time
Data acquisition
visualization
Decision Making
Authors
How to Cite this Article
Lamanguluka N.Henock, P. Jyotheeswari (2017).
"TOWARDS AN EFFICIENT DATA ANALYTIC MODEL FOR REAL-TIME SOCIAL-SENSOR EMOTIONS DETECTION".
International Journal of Contemporary Research in Computer Science and Technology,
3(4), pp. 30-34.
