Demo Data from EDBT 2026 / reviewers in the wild / expert
Edison L. Ting
dblp:57/8166
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1
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.
| Databases, data mining, and information retrieval
1 paper |
Data integration and cleaning · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational finance and economics · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational finance and economics
financial data analysis |
0.0 | 1 | 2010 | Midas: integrating public financial data · SIGMOD Conference 2010 |
Methods — techniques the papers use, named apart from their topics
information extraction · 0.2data aggregation · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Midas: integrating public financial dataabstractThe primary goal of the Midas project is to build a system that enables easy and scalable integration of unstructured and semi-structured information present across multiple data sources. As a first step in this direction, we have built a system that extracts and integrates information from regulatory filings submitted to the U.S. Securities and Exchange Commission (SEC) and the Federal Deposit Insurance Corporation (FDIC). Midas creates a repository of entities, events, and relationships by extracting, conceptualizing, integrating, and aggregating data from unstructured and semi-structured documents. This repository enables applications to use the extracted and integrated data in a variety of ways including mashups with other public data and complex risk analysis. Sreeram Balakrishnan, Vivian Chu, Mauricio A. Hernández, C. T. Howard Ho, Rajasekar Krishnamurthy, Shixia Liu, Jan Pieper, Jeffrey S. Pierce, Lucian Popa 0001, Christine Robson, Lei Shi 0002, Ioana Stanoi, Edison L. Ting, Shivakumar Vaithyanathan, Huahai Yang |
SIGMOD Conference | 13 |