Streaming Social Media Data Analysis for Events Extraction and Warehousing using Hadoop and Storm: Drug Abuse Case Study

Jenhani, Ferdaous; Gouider, Mohamed Salah; Saïd, Lamjed Ben

doi:10.1016/j.procs.2019.09.316

Cited by 12 publications

(3 citation statements)

References 7 publications

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“…They used NLP, machine learning and unigram Naïve techniques for classification, and extracted tweets are loaded into Hadoop platform. The work in [42] developed a large scale architecture by combining Storm and Hadoop to process social media data and facilitate their integration into the traditional data warehouse. Recent researches started working on semantic driven approaches for TSA.…”

Section: Other Approachesmentioning

confidence: 99%

Twitter Sentiment Analysis Approaches: A Survey

Adwan¹,

Al-Tawil²,

Huneiti³

et al. 2020

Int. J. Emerg. Technol. Learn.

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Twitter is one of the most popular microblogging and social networking platforms where massive instant messages (i.e. tweets) are posted every day. Twitter sentiment analysis tackles the problem of analyzing users’ tweets in terms of thoughts, interests and opinions in a variety of contexts and domains. Such analysis can be valuable for several researchers and applications that require understanding people views about a particular topic or event. The study carried out in this paper provides an overview of the algorithms and approaches that have been used for sentiment analysis in twitter. The reviewed articles are categories into four categories based on the approach they use. Furthermore, we discuss directions for future research on how twitter sentiment analysis approaches can utilize theories and technologies from other fields such cognitive science, semantic Web, big data and visualization.

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Section: Other Approachesmentioning

confidence: 99%

Twitter Sentiment Analysis Approaches: A Survey

Adwan¹,

Al-Tawil²,

Huneiti³

et al. 2020

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

show abstract

“…GraphLab, Giraph, and GraphX use the Pregel model [6]. Storm can deal with flowing data [7]. Each system uses threads and basic ways to establish a parallel and distributed computing environment.…”

Section: Introductionmentioning

confidence: 99%

An innovativefractal architecture model for implementing MapReduce in an open multiprocessing parallel environment

Khudhair

AL-Rammahi

Rabee

2023

IJEECS

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One of the infrastructure applications that cloud computing offers as a service is parallel data processing. MapReduce is a type of parallel processing used more and more by data-intensive applications in cloud computing environments. MapReduce is based on a strategy called "divide and conquer," which uses regular computers, also called "nodes," to do processing in parallel. This paper looks at how open multiprocessing (OpenMP), the best shared-memory parallel programming model for high-performance computing, can be used with the proposed fractal network model in the MapReduce application. A well-known model, the cube, is used to compare the fractal network model and its work. Where experiments demonstrated that the fractal model is preferable to the cube model. The fractal model achieved an average speedup of 2.7 and an efficiency rate of 67.7%. In contrast, the cube model could only reach an average speedup of 2.5 and an efficiency rate of 60.4%.

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“…In GraphLab, Giraph, and GraphX, the Pregel model is implemented [5]. Storm [6] handles data streaming. Each system uses threads and simple methods to create a parallel and distributed computing environment.…”

Section: Introductionmentioning

confidence: 99%

New efficient fractal models for MapReduce in OpenMP parallel environment

Khudhair¹,

Rabee²,

AL-Rammahi³

2023

Bulletin EEI

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Parallel data processing is one of the specific infrastructure applications categorized as a service provided by cloud computing. In cloud computing environments, data-intensive applications increasingly use the parallel processing paradigm known as MapReduce. MapReduce is based on a strategy called "divide and conquer," which uses ordinary computers, also called "nodes," to do processing in parallel. This paper looks at how open multiprocessing (OpenMP), the best shared-memory parallel programming model for high-performance computing, can be used in the MapReduce application using proposed fractal network models. Two fractal network models are offered, and their work is compared with a well-known network model, the hypercube. The first fractal network model achieved an average speedup of 3.239 times while an efficiency ranged from 73-95%. In the second model of the network, the speedup got to 3.236 times while keeping an efficiency of 70-92%. Furthermore, the path-finding algorithm employed in the recommended fractal network models remarkably identified all paths and calculated the shortest and longest routes.

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Streaming Social Media Data Analysis for Events Extraction and Warehousing using Hadoop and Storm: Drug Abuse Case Study

Cited by 12 publications

References 7 publications

Twitter Sentiment Analysis Approaches: A Survey

Twitter Sentiment Analysis Approaches: A Survey

An innovativefractal architecture model for implementing MapReduce in an open multiprocessing parallel environment

New efficient fractal models for MapReduce in OpenMP parallel environment

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