Demo Data from VLDB 2026 / reviewers in the wild / expert
Adrian Helfenstein
dblp:40/11360
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2012
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Energy systems and smart grids · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Ubiquitous computing and smart environments · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Energy systems and smart grids
energy disaggregation |
0.1 | 1 | 2012 | Leveraging smart meter data to recognize home appliances · PerCom 2012 |
Energy systems and smart grids › smart metering
smart meter data analysis |
0.1 | 1 | 2012 | Leveraging smart meter data to recognize home appliances · PerCom 2012 |
Ubiquitous computing and smart environments
smart home |
0.0 | 1 | 2012 | Leveraging smart meter data to recognize home appliances · PerCom 2012 |
Methods — techniques the papers use, named apart from their topics
signature recognition · 0.3signal filtering · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | Leveraging smart meter data to recognize home appliancesabstractThe worldwide adoption of smart meters that measure and communicate residential electricity consumption gives rise to the development of new energy efficiency services. Several particularly promising applications involve the disaggregation of individual appliances within a particular household in terms of their energy demand. In this paper we present an infrastructure and a set of algorithms that make use of smart meters together with smartphones to realize new energy efficiency services (such as itemized electricity bills or targeted energy saving advice). The smartphones, together with a novel filtering approach, much simplify the training process for appliances signature recognition. We also report on the performance of our system that was tested with 8 simultaneous devices, achieving recognition rates of 87%. Markus Weiss, Adrian Helfenstein, Friedemann Mattern, Thorsten Staake |
PerCom | 2 |