Towards Scalable Blockchain Analysis

Abstract

Analysing the blockchain is becoming more and more relevant for detecting attacks and frauds on cryptocurrency exchanges and smart contract activations. However, this is a challenging task due to the continuous growth of the blockchain. For example, in early 2017 Ethereum was estimated to contain approximately 300GB of data [4], a number that keeps growing day after day. In order to analyse such ever-growing amount of data, this paper argues that blockchain analysis should be treated as a novel type of application for Big Data platforms. We also explore the application of parallelization techniques from the Big Data domain, in particular Map/Reduce, to extract and analyse information from the blockchain. We show that our approach significantly improves the index generation by 7.77 times, with a setup of 20 worker nodes, 1 Ethereum node and 1 Database node. We also share our findings of our massively parallel setup for querying Ethereum in terms of architecture and the bottlenecks. This should help researchers setup similar infrastructures for analysing the blockchain in the future.

Secure and Internet-Less Connectivity to a Blockchain Network for Limited Connectivity Bank Users

“…One of the main uses for blockchain is with Internet of Things (IoT) [15] [16], because the ledger can store not only identities of the devices but other features that are used to analyze the behaviour of the 'things'. In that case, blockchain is a strong ally of Big Data [17] [18] [19], because can store a lots of information, either raw or through smart contracts, making the analysis easier and more trustable. Most recent applications of blockchain are a core system to implement DNS and PKI systems [20] [21] [22] [23], where the data could be stored and accessed in a easy way.…”

Enhancing Key Management in LoRaWAN with Permissioned Blockchain

“…They basically differ from each other in terms of performance, consensus algorithm, and read permission. In public blockchains, the elevated amount of participating nodes and validations result in decreased throughput and increased latency [32]. Also, any node in the world can make part of the consensus process in public blockchains and all transactions are visible to anyone else.…”

Analysing Microsoft Access Projects: Building a Model in a Partially Observable Domain

“…Such a migration process must coexist with an original project that is under maintenance and development. These requirements imply the following constraints: (1) The CPU and memory usage must be scoped to selective migrations (the migration solution must be able to run cheaply in the working environment of the developers without performance penalties). (2) The model must be able to supply as much data as possible.…”

“…To reach this deeper knowledge, it analyses the application by parsing its files in the context of the eclipse java compiler. To the best of our knowledge, at least within the MDE based approaches: (1) there is no other model extraction technique but by parsing: either the source code of the source application or the exportation from a binary format to a text format (for example XML/HTML). (2) The approaches are based on batch processing instead of online access.…”

“…#RQ3: Is the obtained model suitable for migration? Using these questions as general guidelines and getting inspiration from previous work on a different domain [1,2], we propose a model fully built on binary sources by applying reverse engineering on the run-time of the Access development environment, and re-engineering to transform the available data into an unified model. Section 2 details the background of our research and in particular we describe Access as a partially observable domain and stress its opacity.…”