Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Cluster Computing
Erschienen in: Cluster Computing 3/2021

30.03.2021

A microservices persistence technique for cloud-based online social data analysis

verfasst von: Feras Al-Obeidat, Anoud Bani-Hani, Oluwasegun Adedugbe, Munir Majdalawieh, Elhadj Benkhelifa

Erschienen in: Cluster Computing | Ausgabe 3/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

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.
Vorheriger Artikel A hybrid block-based ensemble framework for the multi-class problem to react to different types of drifts
Nächster Artikel Virtual network function placement with bounded migrations

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Sebei, H., Taieb, M.A.H., Aouicha, M.B.: Review of social media analytics process and big data pipeline. Soc. Netw. Anal. Mining 8(1), 30–31 (2018)CrossRef
2.
Horrocks, I., Giese, M., Kharlamov, E., Waaler, A.: Using semantic technology to tame the data variety challenge. IEEE Internet Comput. 20(6), 62–66 (2016)CrossRef
3.
Newman, S.: Building microservices: designing fine-grained systems. O’Reilly Media Inc, Newton (2015)
4.
Florio, L., Di Nitto, E.: GRU-an approach to introduce decentralized autonomic behavior in microservices architectures. In: 2016 IEEE International Conference on Autonomic Computing (ICAC) pp. 357–362 IEEE. (2016)
5.
Akbulut, A., Perros, H.G.: Software versioning with microservices through the API gateway design pattern. In: 2019 9th International Conference on Advanced Computer Information Technologies (ACIT) pp. 289–292 IEEE. (2019)
6.
Taibi, D., Lenarduzzi, V., Pahl, C., Janes, A.: Microservices in agile software development: a workshop-based study into issues, advantages, and disadvantages. In: Proceedings of the XP2017 Scientific Workshops pp. 1–5 (2017)
7.
Dragoni, N., Lanese, I., Larsen, S.T., Mazzara, M., Mustafin, R., Safina, L.: Microservices: How to make your application scale. In: International Andrei Ershov Memorial Conference on Perspectives of System Informatics, pp. 95–104. Springer, Cham (2017)
8.
Adedugbe, O.: Development and Evaluation of a Holistic, Cloud-driven and Microservices-based Architecture for Automated Semantic Annotation of Web Documents, Doctoral dissertation, Staffordshire University (2019)
9.
Jonas, E., Schleier-Smith, J., Sreekanti, V., Tsai, C.C., Khandelwal, A., Pu, Q., Shankar, V., Carreira, J., Krauth, K., Yadwadkar, N., Gonzalez, J.E.: Cloud programming simplified: A berkeley view on serverless computing. arXiv preprint arXiv:​1902.​03383. (2019)
10.
Talia, D.: A view of programming scalable data analysis: from clouds to exascale. J. Cloud Comput. 8(1), 4 (2019)CrossRef
11.
Khan, S., Shakil, K.A., Alam, M.: Big Data Computing Using Cloud-Based Technologies, Challenges and Future Perspectives. arXiv preprint arXiv:​1712.​05233. (2017)
12.
Khan, S., Shakil, K.A., Alam, M.: Cloud-based big data analytics—a survey of current research and future directions. In: Big data analytics, pp. 595–604. Springer, Singapore (2018)CrossRef
13.
Marozzo, F., Talia, D., Trunfio, P.: A workflow management system for scalable data mining on clouds. IEEE Trans. Serv. Comput. 11(3), 480–492 (2016)CrossRef
14.
Zaharia, M., Xin, R.S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M.J., Ghodsi, A.: Apache spark: a unified engine for big data processing. Commun. ACM 59(11), 56–65 (2016)CrossRef
15.
Mathur, N., Purohit, R.: Issues and challenges in convergence of big data, cloud and data science. Int. J. Comput. Appl. 160, 9 (2017)
16.
Podhoranyi, M., Vojacek, L.: Social Media Data Processing Infrastructure by Using Apache Spark Big Data Platform: Twitter Data Analysis. In: Proceedings of the 2019 4th International Conference on Cloud Computing and Internet of Things pp. 1–6 (2019)
17.
Kumar, A., Bawa, S.: DAIS-Dynamic Access and Integration Services framework for cloud-oriented storage systems. Clust. Comput. 23, 3289–3308 (2020)CrossRef
18.
Sun, S., Gong, J., Zomaya, A.Y., Wu, A.: A distributed incremental information acquisition model for large-scale text data. Clust. Comput. 22(1), 2383–2394 (2019)CrossRef
19.
Khaleq, A.A., Ra, I.: Cloud-Based Disaster Management as a Service: A Microservice Approach for Hurricane Twitter Data Analysis. In: 2018 IEEE Global Humanitarian Technology Conference (GHTC) pp. 1–8 IEEE. (2018)
20.
Fernando, S., Birch, D., Molina-Solana, M., Mcilwraith, D., Guo, Y.: Compositional Microservices for Immersive Social Visual Analytics. In: 2019 23rd International Conference Information Visualisation (IV) pp. 216–223 IEEE (2019)
21.
Houmani, Z., Balouek-Thomert, D., Caron, E., Parashar, M.: Enhancing microservices architectures using data-driven service discovery and QoS guarantees. In: The 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing CCGrid p. 10 (2020)
22.
Ali, S., Jarwar, M.A., Chong, I.: Design methodology of microservices to support predictive analytics for IoT applications. Sensors 18(12), 4226 (2018)CrossRef
23.
Hsu, M.C., Lin, C.Y.: A Microservices-Based Social Data Analytics Platform Over DC/OS. In: International Conference on Network-Based Information Systems, pp. 673–683. Springer, Cham (2018)
24.
EmamiKhoonsari, P., Moreno, P., Bergmann, S., Burman, J., Capuccini, M., Carone, M., Cascante, M., de Atauri, P., Foguet, C., Gonzalez-Beltran, A.N., Hankemeier, T.: Interoperable and scalable data analysis with microservices: applications in metabolomics. Bioinformatics 35(19), 3752–3760 (2019)CrossRef
25.
Fernández-García, A.J., Iribarne, L., Corral, A., Criado, J., Wang, J.Z.: A microservice-based architecture for enhancing the user experience in cross-device distributed mashup UIs with multiple forms of interaction. Univ. Access Inf. Soc. 18(4), 747–770 (2019)CrossRef
26.
Dinh-Tuan, H., Beierle, F., Garzon, S.R.: MAIA: A Microservices-based Architecture for Industrial Data Analytics. In: 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS) pp. 23–30 IEEE. (2019)
27.
Hoque, S., Miranskyy, A.: Architecture for analysis of streaming data. In: 2018 IEEE International Conference on Cloud Engineering (IC2E) pp. 263–269 IEEE. (2018)
28.
Innerbichler, J., Gonul, S., Damjanovic-Behrendt, V., Mandler, B., Strohmeier, F.: Nimble collaborative platform: Microservice architectural approach to federated IOT. In: 2017 Global Internet of Things Summit (GIoTS) pp. 1–6 IEEE. (2017)
29.
Ciavotta, M., Alge, M., Menato, S., Rovere, D., Pedrazzoli, P.: A microservice-based middleware for the digital factory. Proc. Manuf. 11, 931–938 (2017)
30.
AL-Smadi, M, Qwasmeh, O, Talafha, B, Al-Ayyoub, M, Jararweh, Y, Benkhelifa, E : An enhanced framework for aspect-based sentiment analysis of Hotels' reviews: Arabic reviews case study. 2016 11th IEEE International Conference for Internet Technology and Secured Transactions (ICITST). 98–103.
31.
Rodriguez, M.A., Buyya, R.: Container-based cluster orchestration systems: a taxonomy and future directions. Software 49(5), 698–719 (2019)
32.
Stahl, D., Bosch, J.: Industry application of continuous integration modeling: a multiple-case study. In: 2016 IEEE/ACM 38th International Conference on Software Engineering Companion (ICSE-C) pp. 270–279 IEEE. (2016)
33.
Sachdeva, R.: Automated testing in DevOps. In: Pacific Northwest Software Quality Conference (2016)
34.
Zhao, J.T., Jing, S.Y., Jiang, L.Z.: Management of API Gateway Based on Micro-service Architecture. In: Journal of Physics: Conference Series Vol. 1087, No. 3 (2018)
35.
Eder, M.: Hypervisor-vs. container-based virtualization. Future Internet (FI) and Innovative Internet Technologies and Mobile Communications (IITM), 1. (2016)
36.
Taherizadeh, S., Stankovski, V.: Dynamic multi-level auto-scaling rules for containerized applications. Comput. J. 62(2), 174–197 (2019)CrossRef
37.
Jain, N., Choudhary, S.: Overview of virtualization in cloud computing. In: 2016 Symposium on Colossal Data Analysis and Networking (CDAN) pp. 1–4 IEEE. (2016)
38.
Shaheen, J.: Apache Kafka: real time implementation with Kafka architecture review. Int. J. Adv. Sci. Technol. 109, 35–42 (2017)CrossRef
Metadaten
Titel
A microservices persistence technique for cloud-based online social data analysis
verfasst von
Feras Al-Obeidat
Anoud Bani-Hani
Oluwasegun Adedugbe
Munir Majdalawieh
Elhadj Benkhelifa
Publikationsdatum
30.03.2021
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe 3/2021
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-021-03244-0

Weitere Artikel der Ausgabe 3/2021

Cluster Computing 3/2021 Zur Ausgabe

OriginalPaper

Performance evaluation of grouping and regrouping scheme for mitigating hidden station problem in IEEE 802.11ah network

OriginalPaper

A novel feature selection method for data mining tasks using hybrid Sine Cosine Algorithm and Genetic Algorithm

OriginalPaper

A robust blind medical image watermarking approach for telemedicine applications

OriginalPaper

Energy and cost trade-off for computational tasks offloading in mobile multi-tenant clouds

OriginalPaper

Cloud-based intrusion detection using kernel fuzzy clustering and optimal type-2 fuzzy neural network

OriginalPaper

OPSA: an optimized prediction based scheduling approach for scientific applications in cloud environment

Premium Partner

    Bildnachweise