A microservices persistence technique for cloud-based online social data analysis
| Autor: | Feras N. Al-Obeidat, Munir Majdalawieh, Oluwasegun Adedugbe, Elhadj Benkhelifa, Anoud Bani-Hani |
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| Rok vydání: | 2021 |
| Předmět: |
Computer Networks and Communications
Computer science business.industry Sentiment analysis 020206 networking & telecommunications Social data analysis Cloud computing 02 engineering and technology Microservices Predictive analytics Data science Software deployment 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Social media Orchestration (computing) business Software |
| Zdroj: | Cluster Computing. 24:2341-2353 |
| ISSN: | 1573-7543 1386-7857 |
| DOI: | 10.1007/s10586-021-03244-0 |
| Popis: | Social data analysis has become a vital tool for businesses and organisations for mining data from social media and analysing for diverse purposes such as customer opinion mining, pattern recognition and predictive analytics. However, the high level of volatility for social data means application updates due to analytical results requires spontaneous integration. In addition, while cloud computing has been hugely utilised to address computational overhead issues due to the volume of social data, results obtained still fall short of expected levels. Hence, a persistence mechanism for rapid deployment and integration of software updates for the analytical process is proposed. The persistence mechanism constitutes a significant component within a novel methodology which also leverages cloud computing, microservices and orchestration for online social data analysis, one which fully maximises cloud capabilities and fosters optimisation of cloud computing resources. The proposed methodology provides means of delivering real-time, persistent social data analytics as a cloud service, thereby facilitating spontaneous integration of solutions to maximise expectations from targeted social media audience. |
| Databáze: | OpenAIRE |
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