Demo Data from VLDB 2026 / reviewers in the wild / expert
Sadia Ahmed
dblp:17/822
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · unresolved
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 |
Query processing and optimization · 50% Data mining · 50% | |
| Theoretical computer science
1 paper |
Mathematical optimization · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining
clustering |
0.4 | 1 | 2019 | A Unified Optimization Algorithm For Solving "Regret-Minimizing Representative" Problems · Proc. VLDB Endow. 2019 |
Data mining › clustering › exemplar-based clustering
k-medoids clustering |
0.4 | 1 | 2019 | A Unified Optimization Algorithm For Solving "Regret-Minimizing Representative" Problems · Proc. VLDB Endow. 2019 |
Query processing and optimization › regret minimization
rank-regret representative |
0.4 | 1 | 2019 | A Unified Optimization Algorithm For Solving "Regret-Minimizing Representative" Problems · Proc. VLDB Endow. 2019 |
Query processing and optimization
regret minimization |
0.4 | 1 | 2019 | A Unified Optimization Algorithm For Solving "Regret-Minimizing Representative" Problems · Proc. VLDB Endow. 2019 |
Mathematical optimization
linear programming |
0.1 | 1 | 2019 | A Unified Optimization Algorithm For Solving "Regret-Minimizing Representative" Problems · Proc. VLDB Endow. 2019 |
Methods — techniques the papers use, named apart from their topics
volume estimation · 0.8linear programming · 0.8edge sampling · 0.8k-medoids · 0.4k-medoid · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | A Unified Optimization Algorithm For Solving "Regret-Minimizing Representative" ProblemsabstractGiven a database with numeric attributes, it is often of interest to rank the tuples according to linear scoring functions. For a scoring function and a subset of tuples, the regret of the subset is defined as the (relative) difference in scores between the top-1 tuple of the subset and the top-1 tuple of the entire database. Finding the regret-ratio minimizing set (RRMS), i.e., the subset of a required size k that minimizes the maximum regret-ratio across all possible ranking functions, has been a well-studied problem in recent years. This problem is known to be NP-complete and there are several approximation algorithms for it. Other NP-complete variants have also been investigated, e.g., finding the set of size k that minimizes the average regret ratio over all linear functions. Prior work have designed customized algorithms for different variants of the problem, and are unlikely to easily generalize to other variants. In this paper we take a different path towards tackling these problems. In contrast to the prior, we propose a unified algorithm for solving different problem variants. Unification is done by localizing the customization to the design of variant-specific subroutines or "oracles" that are called by our algorithm. Our unified algorithm takes inspiration from the seemingly unrelated problem of clustering from data mining, and the corresponding k-medoid algorithm. We make several innovative contributions in designing our algorithm, including various techniques such as linear programming, edge sampling in graphs, volume estimation of multi-dimensional convex polytopes, and several others. We provide rigorous theoretical analysis, as well as substantial experimental evaluations over real and synthetic data sets to demonstrate the practical feasibility of our approach. Suraj Shetiya, Abolfazl Asudeh, Sadia Ahmed, Gautam Das 0001 |
Proc. VLDB Endow. | 3 |
| 2006 | Inter-Frame Interference in Time Hopping Impulse Radio Based UWB Systems for Coherent ReceiversabstractUltra Wideband (UWB) Systems take advantage of collecting energy from a large number of multipaths. In high data rate systems for time hopping (TH) UWB transmission, this large number of multipaths may leak into neighboring frames causing inter-frame interference (IFI). Most studies so far avoid this IFI by placing one or more constraints on chip length, frame length, minimum pulse to pulse duration, etc. In this paper, IFI will be investigated in detail for BPSK and PPM modulations in coherent transceivers. It will be shown that IFI effect is insignificant for these modulation options and for these transceiver architectures. Therefore, the constraints to avoid IFI can be removed for those systems. To the best knowledge of the authors, this type of analysis has not been conducted before and the simulation and semi-analytical analysis both result in a novel conclusion. Sadia Ahmed, Hüseyin Arslan |
VTC Fall | 1 |