Applying the ETL Process to Blockchain Data. Prospect and Findings

Galici, Roberta; Ordile, Laura; Marchesi, Michele; Pinna, Andrea; Tonelli, Roberto

doi:10.3390/info11040204

Cited by 18 publications

(15 citation statements)

References 22 publications

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“…When comes a large amount of data, the access tree has a large amount of child nodes, which increases complexity and storage consumption. In [36], authors proposed ETL based blockchain, which consists of address and transactions. The significant challenges in this paper as follows:…”

Section: Problem Statementmentioning

confidence: 99%

Centralization of Big Data Using Distributed Computing Approach in IoT

Sawiris¹,

El-Gaber²,

Abdel-Fattah³

2021

IJIES

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Centralized big data is an emerging field that manages information in one common repository. Currently, Internet of Things (IoT) devices are increasing massively for sensing and transmitting data to the big data environment. This information is sensitive and easily traced by attackers due to the centralized data management. To address the security and privacy issues and avoid the system in a single point of failure, in this paper the researchers proposed a new model, namely, BOBS CRABID i.e. Blockchain Based Secure Centralized Big Data model using distributed computing approach. In big data, Hadoop MapReduce is one of the distributed computing approaches which handles all information in a distributed manner. The BOBS CRABID model is designed by three ideas as Multi-Factor authentication, IoT Devices Data Collection and Processing, and Optics based MapReduce for Data Clustering. In multi-factor authentication, a lightweight camellia key generation algorithm (LCKGA) is used for their authenticating all devices based on ID, IP address, MAC and PUF. These credentials are saved in Blockchain Security Entity for authentication verification. All transactions are hashed and stored in the blockchain using Keccak hash algorithm which performs better than the traditional hashing (SHA) 2. Then the researchers conducted IoT devices data collection and pre-processing using Cross Correlation and Min-Max Normalization for redundant data pruning and normalization, respectively. Finally, clustering is performed in a distributed way for optimizing the storage and scalability performance. For that MapReduce is used in which OPTICS algorithm is applied for clustering based on the data size, type of context and nature of data (sensitive or non-sensitive). In this way, data is stored in the big data environment. Experiments were conducted for the proposed BOBS CRABID model and compared with the well-known methods using Hadoop 2.7.2. The proposed BOBS-CRABID model achieves better performance in terms of throughput (increase 1500mbps), response time (reduce 150ms), energy consumption (reduce 35%), attack detection rate (increase 30%), accuracy (increase 20%), computation overhead (300 kb), and time reduce 4s).

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Section: Problem Statementmentioning

confidence: 99%

Centralization of Big Data Using Distributed Computing Approach in IoT

Sawiris¹,

El-Gaber²,

Abdel-Fattah³

2021

IJIES

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“…This methodology considers the volume, velocity, and variety of data generated by different forms of cryptocurrencies. The schema adopts the ETL (Extract, Transform and Load) (Galici et al, 2020) process for performing econometric analysis in both Bitcoin and Etheruem-like cryptocurrencies (Section 3).…”

Section: Do Crypto Economies In Their Current State Provide Alternative Financial Systems That Attain Better Wealth Distribution Than Tramentioning

confidence: 99%

Characterizing Wealth Inequality in Cryptocurrencies

2021

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Cryptocurrencies often tend to maintain a publically accessible ledger of all transactions. This open nature of the transactional ledger allows us to gain macroeconomic insight into the USD 1 Trillion crypto economy. In this paper, we explore the free market-based economy of eight major cryptocurrencies: Bitcoin, Ethereum, Bitcoin Cash, Dash, Litecoin, ZCash, Dogecoin, and Ethereum Classic. We specifically focus on the aspect of wealth distribution within these cryptocurrencies as understanding wealth concentration allows us to highlight potential information security implications associated with wealth concentration. We also draw a parallel between the crypto economies and real-world economies. To adequately address these two points, we devise a generic econometric analysis schema for cryptocurrencies. Through this schema, we report on two primary econometric measures: Gini value and Nakamoto Index which report on wealth inequality and 51% wealth concentration respectively. Our analysis reports that, despite the heavy emphasis on decentralization in cryptocurrencies, the wealth distribution remains in-line with the real-world economies, with the exception of Dash. We also report that 3 of the observed cryptocurrencies (Dogecoin, ZCash, and Ethereum Classic) violate the honest majority assumption with less than 100 participants controlling over 51% wealth in the ecosystem, potentially indicating a security threat. This suggests that the free-market fundamentalism doctrine may be inadequate in countering wealth inequality within a crypto-economic context: Algorithmically driven free-market implementation of these cryptocurrencies may eventually lead to wealth inequality similar to those observed in real-world economies.

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“…Wallet services and identity-management policies are used to ensure that the unauthorized parties cannot use BAT for analysis. BAT acts as an Extraction Transfer Load (ETL) [34] tool for the blockchain. A system administrator provides a configuration file to BAT that specifies the data required as well as about the components of the BAT such as the type of Blockchain Data Cache (BDC) required.…”

Section: Block Analytics Tool (Bat)mentioning

confidence: 99%

BAT—Block Analytics Tool Integrated with Blockchain Based IoT Platform

et al. 2020

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The Internet of Things (IoT) is the novel paradigm of connectivity and the driving force behind state-of-the-art applications and services. However, the exponential growth of the number of IoT devices and services, their distributed nature, and scarcity of resources has increased the number of security and privacy concerns ranging from the risks of unauthorized data alterations to the potential discrimination enabled by data analytics over sensitive information. Thus, a blockchain based IoT-platform is introduced to address these issues. Built upon the tamper-proof architecture, the proposed access management mechanisms ensure the authenticity and integrity of data. Moreover, a novel approach called Block Analytics Tool (BAT), integrated with the platform is proposed to analyze and make predictions on data stored on the blockchain. BAT enables the data-analysis applications to be developed using the data stored in the platform in an optimized manner acting as an interface to off-chain processing. A pharmaceutical supply chain is used as the use case scenario to show the functionality of the proposed platform. Furthermore, a model to forecast the demand of the pharmaceutical drugs is investigated using a real-world data set to demonstrate the functionality of BAT. Finally, the performance of BAT integrated with the platform is evaluated.

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Applying the ETL Process to Blockchain Data. Prospect and Findings

Cited by 18 publications

References 22 publications

Centralization of Big Data Using Distributed Computing Approach in IoT

Centralization of Big Data Using Distributed Computing Approach in IoT

Characterizing Wealth Inequality in Cryptocurrencies

BAT—Block Analytics Tool Integrated with Blockchain Based IoT Platform

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