Demo Data from EDBT 2026 / reviewers in the wild / expert
Junbei Zhang
dblp:161/9648
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 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.
| Computer networks
1 paper |
Internet of things and sensor networks · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Concurrent programming · 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 |
|---|---|---|---|---|
Internet of things and sensor networks › industrial iot
internet of things |
0.2 | 1 | 2015 | SIFT: building an internet of safe things · IPSN 2015 |
Concurrent programming › concurrency control
conflict detection |
0.2 | 1 | 2015 | SIFT: building an internet of safe things · IPSN 2015 |
Ubiquitous computing and smart environments › smart home
home automation |
0.1 | 1 | 2015 | SIFT: building an internet of safe things · IPSN 2015 |
Methods — techniques the papers use, named apart from their topics
policy verification · 0.7declarative programming · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | SIFT: building an internet of safe thingsabstractAs the number of connected devices explodes, the use scenarios of these devices and data have multiplied. Many of these scenarios, e.g., home automation, require tools beyond data visualizations, to express user intents and to ensure interactions do not cause undesired effects in the physical world. We present SIFT, a safety-centric programming platform for connected devices in IoT environments. First, to simplify programming, users express high-level intents in declarative IoT apps. The system then decides which sensor data and operations should be combined to satisfy the user requirements. Second, to ensure safety and compliance, the system verifies whether conflicts or policy violations can occur within or between apps. Through an office deployment, user studies, and trace analysis using a large-scale dataset from a commercial IoT app authoring platform, we demonstrate the power of SIFT and highlight how it leads to more robust and reliable IoT apps. Chieh-Jan Mike Liang, Börje Karlsson 0001, Nicholas D. Lane, Feng Zhao 0001, Junbei Zhang, Zheyi Pan, Yong Yu 0001 |
IPSN | 5 |