Demo Data from EDBT 2026 / reviewers in the wild / expert
Konstantin Sozinov
dblp:238/0608
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0005-4593-0994ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 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.
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Performance modeling and evaluation · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Performance modeling and evaluation › online controlled experiments
a/b testing |
0.3 | 1 | 2025 | ForTune: Running Offline Scenarios to Estimate Impact on Business Metrics · KDD (1) 2025 |
Performance modeling and evaluation
online controlled experiments |
0.3 | 1 | 2025 | ForTune: Running Offline Scenarios to Estimate Impact on Business Metrics · KDD (1) 2025 |
Methods — techniques the papers use, named apart from their topics
offline simulation · 1.7counterfactual estimation · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | ForTune: Running Offline Scenarios to Estimate Impact on Business MetricsabstractMaking ideal decisions as a product leader in a web-facing company is incredibly challenging. Beyond navigating the ambiguity of customer satisfaction and achieving business goals, leaders must also ensure their products and services remain relevant, desirable, and profitable. Data and experimentation are crucial for testing product hypotheses and informing decisions. Online controlled experiments, such as A/B testing, can provide highly reliable data to support decisions. However, these experiments can be time-consuming and costly, particularly when assessing impacts on key business metrics like retention or long-term value. Georges Dupret, Konstantin Sozinov, Carmen Barcena Gonzalez, Ziggy Zacks, Amber Yuan, Ben Carterette, Manuel Mai, Andrey Gatash, Gwo Liang Lien, Shubham Bansal, Roberto Sanchis-Ojeda, Mounia Lalmas-Roelleke |
KDD (1) | 2 |