Demo Data from EDBT 2026 / reviewers in the wild / expert
Voon Hou Su
dblp:313/9352
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Blockchain and cryptocurrency security · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Graph data management · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Blockchain and cryptocurrency security
blockchain data management |
0.6 | 1 | 2022 | Automating ETL and Mining of Ethereum Blockchain Network · WSDM 2022 |
Methods — techniques the papers use, named apart from their topics
graph representation · 1.1ETL pipeline · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Automating ETL and Mining of Ethereum Blockchain NetworkabstractThe popularity of blockchain technology led to the development of many web platforms with different functionalities. Ethereum, a decentralized, open-source blockchain featuring smart contracts, introduces an interesting ecosystem of human users and autonomous agents (the contracts). It is the most actively used blockchain platform, hosting ether, the second largest cryptocurrency by market capitalization, as its native store of value. The Ethereum blockchain contains a vast amount of user-to-user, user-to-contract, contract-to -user, and contract-to-contract interactions that can be modeled as complex networks. To mine these interactions as graphs through a preferred analytics toolbox, an end-user has to extract, transform, and load (ETL) the required data into the desired network format. However, it is costly and time-consuming to manage the ETL pipeline for the massive and complex blockchain data. To support research in this domain, we develop an end-to-end, automated tool - EtherNet, which performs ETL tasks from a single source of truth (Google BigQuery), and provides graph equivalent representations for visualization and mining on the entire Ethereum blockchain network. Voon Hou Su, Sourav Sen Gupta 0001, Arijit Khan 0001 |
WSDM | 1 |