DLMetaChain: An IoT Data Lake Architecture Based on the Blockchain
Authors:
- 18 July 2022
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-8
Abstract
Nowadays, the IoT ecosystem is evolving rapidly, with multiple heterogeneous sources producing high volumes of data and processes transforming this data into meaningful or “smart” information. These volumes of data, including IoT data, need to be stored in repositories that can host raw, unprocessed, relational and non-relational types of data, such as Data Lakes. Due to the weakness of metadata management, security & access control is one of the main challenges of Big Data storage architectures as Data Lakes can be replaced without oversight of the contents. Recently, the Blockchain technology has been introduced as an effective solution to build trust between different entities, where trust is either nonexistent or unproven, and to address security and privacy concerns. In this paper we introduce DLMetaChain, an extended Data Lake metadata mechanism that consists of data from heterogeneous data sources which interact with IoT data. The extended mechanism mainly focuses on developing an architecture to ensure that the data in the Data Lake is not modified or altered by taking into advantage the capabilities of the Blockchain.Keywords
This publication has 14 references indexed in Scilit:
- ParkChain: An IoT Parking Service Based on Blockchain2019
- A systematic literature review of blockchain-based applications: Current status, classification and open issuesTelematics and Informatics, 2019
- CoreKGProceedings of the VLDB Endowment, 2018
- Blockchain with Internet of Things: Benefits, Challenges, and Future DirectionsInternational Journal of Intelligent Systems and Applications, 2018
- Data lake: a new ideology in big data eraITM Web of Conferences, 2018
- Internet of Things: Architectures, Protocols, and ApplicationsJournal of Electrical and Computer Engineering, 2017
- Challenges and Benefits of Deploying Big Data Analytics in the Cloud for Business IntelligenceProcedia Computer Science, 2017
- A hybrid machine-crowdsourcing system for matching web tables2014
- From Data to Actionable Knowledge: Big Data Challenges in the Web of Things [Guest Editors' Introduction]IEEE Intelligent Systems, 2013
- Data integration for the relational webProceedings of the VLDB Endowment, 2009