Examining Heterogeneity Structured on a Large Data Volume with Minimal Incompleteness

Nahla Aljojo

doi:10.14500/aro.10857

Nahla Aljojo Department of Information system and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia https://orcid.org/0000-0003-2501-8533

Keywords: Heterogenouis dataset, Bitcoin transactions, Bitcoin tweets

Abstract

While Big Data analytics can provide a variety of benefits, processing heterogeneous data comes with its own set of limitations. A transaction pattern must be studied independently while working with Bitcoin data, this study examines twitter data related to Bitcoin and investigate communications pattern on bitcoin transactional tweet. Using the hashtags #Bitcoin or #BTC on Twitter, a vast amount of data was gathered, which was mined to uncover a pattern that everyone either (speculators, teaches, or the stakeholders) uses on Twitter to discuss Bitcoin transactions. This aim is to determine the direction of Bitcoin transaction tweets based on historical data. As a result, this research proposes using Big Data analytics to track Bitcoin transaction communications in tweets in order to discover a pattern. Hadoop platform MapReduce was used. The finding indicate that In the map step of the procedure, Hadoop's tokenize the dataset and parse them to the mapper where thirteen patterns were established and reduced to three patterns using the attributes previously stored data in the Hadoop context, one of which is the Emoji data that was left out in previous research discussions, but the text is only one piece of the puzzle on bitcoin transaction interaction, and the key part of it is “No certainty, only possibilities” in Bitcoin transactions

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Author Biography

Nahla Aljojo, Department of Information system and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia

Nahla ALJOJO obtained her PhD in Computing at Portsmouth University. She is currently working as Associate Professor at College of Computer Science and Engineering, Information system and information Technology Department, University of Jeddah, Jeddah, Saudi Arabia. Her research interests include: adaptivity in web-based educational systems, eBusiness, leadership’s studies, information security and data integrity, eLearning, education, machine learning, Deep Learning, Networking health informatics, environment and ecology, and logistics and supply chain management. Her contributions have been published in prestigious peer-reviewed journals.

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