Ethereum Network: from Blockchain to Google Trends

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Ethereum Network: from Blockchain to Google Trends ( ethereum-network-from-blockchain-google-trends )

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contain the terms and agreements between the parties involved. The feature that sets smart contracts apart is that they automatically verify whether the terms of the agreement have been fulfilled or not. Additionally, to ensure reliability, fault tolerance, and transparency, the codes (smart contracts) are replicated on many nodes in the blockchain. Interestingly, the Ethereum blockchain has hosted roughly 1.5 million smart contracts in the last few years [13]. 2.2. Big Data Processing Big data is a term used to describe data that is too large to be processed with conventional methods, and keeps growing exponentially with time. Big data processing tools and frameworks such as Apache Hadoop enable distributed processing of large-scale data, a task made possible by a network of heterogeneous computation units. In essence, a Hadoop cluster can be a cluster of commodity PCs (or virtual machines) that are connected and communicate with a master node. The philosophy of Hadoop is “moving the computation to the data”. This entails each node serving as both a storage unit and a processing unit. These two components, HDFS (Hadoop Distributed File System - storage) and YARN (Yet Another Resource Negotiator - computation), have been designed to work together in a single cluster [14,15]. By distributing the data and performing the computation in parallel, Hadoop is able to achieve scalability from a single node to thousands of nodes. Hadoop uses MapReduce to distribute the data among worker nodes. Similarly to the blockchain, the data is split into a number of blocks, each assigned to a node across the cluster. Apache Spark is another big data framework which is built on top of HDFS. By reducing disk read and write operations, Spark provides fast in-memory processing [16]. It is also suitable for both batch and streaming data, making it a versatile tool for a vast range of big data processing tasks. These tasks include machine learning, graph processing, and interactive queries on big data. Finally, Spark is written in Scala but has APIs in Java, R, and Python. 2.3. The Social Factor As it was previously mentioned, the interaction between complex networks such as the Ethereum blockchain and social networks can be quite fascinating. Analyzing social factors may offer an opportunity to gain insight into the events that induce changes in the market. With this in mind, Google Trends is chosen as a data source for assessing public interest in cryptocurrencies such as Ether and Bitcoin. The data shows the public interest measured by the aggregated sum of online searches. Google Trends data is publicly available with a few restrictions on data frequency. 3. Data and Tools The Ethereum dataset used for this analysis was originally collected from the dumps uploaded to a repository on Google’s BigQuery, now available as a public dataset. The dataset was uploaded to a Hadoop cluster and stored in HDFS. Due to its high speed and performance, Spark (in this study, specifically its python API, PySpark) was used to process the dataset (in order of TBs). The monthly total number of transactions, average gas, and gas price were aggregated and extracted from the dataset. 4. Analyses and Results An initial analysis of the data is visualized in fig 1, revealing a surge in the number of transactions, including smart contracts, in early 2018. Interestingly, this event coincides with the sudden surge in Ether and Bitcoin prices. The possible links between Ether and Bitcoin prices will be discussed in more depth. Another observation points to a rather steady decrease in gas price, with a slight increase in the beginning or at the end of each year. In addition, the average gas used for transactions seem to have reached a steady state as of late 2017. Finally, since with more complex contracts, more gas would be needed, the strong correlation (pearson correlation coefficient of 0.94) between smart contract’s difficulty and required gas can be explained. Fig. 1. Ethereum contracts and transactions over time Moving forward, Google Trends’ data is incorporated into the analysis as an exogenous feature. The first logical notion could be that with more people searching Ethereum online, more of them might invest and make transactions on the network. Hence, the number of transactions and the public interest in Ethereum are depicted in fig 2. It should be noted that the y axis is indicative of only the number of transactions and not the volume of online searches. Daily Google Trends data have been collected in 180-day intervals, then scaled and concatenated and finally normalized on the scale of 100. For a better visualization, they have been scaled once more to fit the y axis of the plot. The final results show that following the first abrupt increase in Google Trends’ data in mid 2017, the number of transactions didn’t

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