arXiv subjects
Publications and source records attributed to Ciprian Amariei.
We believe that leveraging real-time blockchain operational data is of particular interest in the context of the current rapid expansion of rollup networks in the Ethereum ecosystem. Given the compatible but also competing ground that rollups offer for applications, stream-based monitoring can be of use both to developers and to EVM networks governance. In this paper, we discuss this perspective and propose a basic monitoring pipeline.
Decentralized applications (DApps) gained traction in the context of the blockchain technology. Ethereum is currently the public blockchain that backs the largest amount of the existing DApps. Onboarding new users to Ethereum DApps is a notoriously hard issue to solve. This is mainly caused by lack of cryptocurrency ownership, needed for transaction fees. Several meta-transaction patterns emerged for decoupling users from paying these fees. However, such solutions are mostly offered via off-chain, often paid relayer services and do not fully address the security issues present in the meta-transaction path. In this paper, we introduce a new meta-transaction architecture that makes use of the Intel Software Guard Extensions (SGX). Unlike other solutions, our approach would offer the possibility to deploy a fee-free Ethereum DApp on a web server that can directly relay meta-transactions to the Ethereum network while having essential security guarantees integrated by design.
The 2018 Grand Challenge targets the problem of accurate predictions on data streams produced by automatic identification system (AIS) equipment, describing naval traffic. This paper reports the technical details of a custom solution, which exposes multiple tuning parameters, making its configurability one of the main strengths. Our solution employs a cell grid architecture essentially based on a sequence of hash tables, specifically built for the targeted use case. This makes it particularly effective in prediction on AIS data, obtaining a high accuracy and scalable performance results. Moreover, the architecture proposed accommodates also an optionally semi-supervised learning process besides the basic supervised mode.
The DEBS Grand Challenge 2018 is set in the context of maritime route prediction. Vessel routes are modeled as streams of Automatic Identification System (AIS) data points selected from real-world tracking data. The challenge requires to correctly estimate the destination ports and arrival times of vessel trips, as early as possible. Our proposed solution partitions the training vessel routes by reported destination port and uses a nearest neighbor search to find the training routes that are closer to the query AIS point. Particular improvements have been included as well, such as a way to avoid changing the predicted ports frequently within one query route and automating the parameters tuning by the use of a genetic algorithm. This leads to significant improvements on the final score.
The 2017 Grand Challenge focused on the problem of automatic detection of anomalies for manufacturing equipment. This paper reports the technical details of a solution focused on particular optimizations of the processing stages. These included customized input parsing, fine tuning of a k-means clustering algorithm and probability analysis using a lazy flavor of a Markov chain. We have observed in our custom implementation that carefully tweaking these processing stages at single node level by leveraging various data stream characteristics can yield good performance results. We start the paper with several observations concerning the input data stream, following with our solution description with details on particular optimizations, and we conclude with evaluation and a discussion of obtained results.