#Arangodb vs mongodb how to#

MCAFEE, Andrew, Big data: the management revolution.

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#Arangodb vs mongodb how to#

From conventional group decision making to large-scale group decision making: What are the challenges and how to meet them in big data era? A state-of-the-art survey. In: 2015 20th International Conference on Control Systems and Computer Science. TRUICA, Ciprian-Octavian, Performance evaluation for CRUD operations in asynchronously replicated document oriented database.LIAN, Yanqi, Review on big data applications in safety research of intelligent transportation systems and connected/automated vehicles.In: Proceedings of the 2018 International Conference on Big Data Engineering and Technology. Big Data Platform Architecture under The Background of Financial Technology: In The Insurance Industry As An Example. In: IFIP International Conference on Artificial Intelligence Applications and Innovations. KYRIAZIS, Dimosthenis, Polic圜LOUD: Analytics as a Service Facilitating Efficient Data-Driven Public Policy Management.KYRIAZIS, Dimosthenis, The CrowdHEALTH project and the Hollistic Health Records: Collective Wisdom Driving Public Health Policies.Informatics Empowers Healthcare Transformation, 2017, 238: 19. KBIOASSIST, S., Crowdhealth: Holistic health records and big data analytics for health policy making and personalized health.IEEE journal of biomedical and health informatics, 2015, 19.4: 1209-1215. Big data, big knowledge: big data for personalized healthcare. VICECONTI, Marco HUNTER, Peter HOSE, Rod.In: 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS). KAPASSA, Evgenia, An innovative ehealth system powered by 5G network slicing.In: 2016 International Conference on Computational Science and Computational Intelligence (CSCI). GOKALP, Mert Onuralp, Big data for industry 4.0: A conceptual framework.Big data in healthcare: management, analysis and future prospects. International Institute for Analytics, 2013, 3: 1-31. SeedScientific - How Much Data Is Created Every Day?, Google Scholar.The performance of these databases was measured based on specific metrics and criteria, including the total execution time for the same CRUD operations and their corresponding demands for resources, concluding to the most suitable database for storing Big Data. More specifically, taking into account the nature of Big Data and the main categories of databases that currently exist, three (3) NoSQL document-based databases were considered for this comparative study, namely the ArangoDB, the MongoDB and the CouchDB. Towards this direction, in this paper we contribute into the selection of the most appropriate database for efficiently storing and retrieving Big Data.

Those approaches refer to several domains such as healthcare, transportation, governance, or insurance. Thus, many enterprises and research initiatives strive to find a way to effectively and efficiently collect, store and analyze Big Data in order to improve their services and make efficient decisions. There is no wonder that this epoch is generally characterized as the “Era of Big Data”. A distinctive aspect of the current era is the ferocious amount of data that is generated and processed in a daily basis.