Demo Data from EDBT 2026 / reviewers in the wild / expert
Yi-Hao Jhang
dblp:177/0755
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 2
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
2 papers |
Spatial and temporal data management · 65% Graph data management · 35% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Graph data management
path query |
0.3 | 1 | 2017 | Efficient Cache-Supported Path Planning on Roads (Extended Abstract) · ICDE 2017 |
Methods — techniques the papers use, named apart from their topics
query caching · 0.3partial query matching · 0.2caching · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Efficient Cache-Supported Path Planning on Roads (Extended Abstract)abstractOwing to the wide availability of the global positioning system (GPS) and digital mapping of roads, road network navigation services have become a basic application on many mobile devices. Path planning, a fundamental function of road network navigation services, finds a route between the specified start location and destination. The efficiency of this path planning function is critical for mobile users on roads due to various dynamic scenarios, such as a sudden change in driving direction, unexpected traffic conditions, lost or unstable GPS signals, and so on. In these scenarios, the path planning service needs to be delivered in a timely fashion. In this paper, we propose a system, namely, Path Planning by Caching (PPC), to answer a new path planning query in real time by efficiently caching and reusing historical queried-paths. Unlike the conventional cachebased path planning systems, where a queried-path in cache is used only when it matches perfectly with the new query, PPC leverages the partially matched queries to answer part(s) of the new query. Comprehensive experimentation on a real road network database shows that our system outperforms the state of-the-art path planning techniques by reducing 32% of the computation latency on average. Ying Zhang 0047, Yu-Ling Hsueh, Wang-Chien Lee, Yi-Hao Jhang |
ICDE | 4 |
| 2016 | Efficient Cache-Supported Path Planning on RoadsabstractOwing to the wide availability of the global positioning system (GPS) and digital mapping of roads, road network navigation services have become a basic application on many mobile devices. Path planning, a fundamental function of road network navigation services, finds a route between the specified start location and destination. The efficiency of this path planning function is critical for mobile users on roads due to various dynamic scenarios, such as a sudden change in driving direction, unexpected traffic conditions, lost or unstable GPS signals, and so on. In these scenarios, the path planning service needs to be delivered in atimelyfashion. In this paper, we propose a system, namely,Path Planning by Caching (PPC), to answer a new path planning query in real time by efficiently caching and reusing historical queried-paths. Unlike the conventional cache-based path planning systems, where a queried-path in cache is used only when it matches perfectly with the new query, PPC leverages the partially matched queries to answer part(s) of the new query. As a result, the server only needs to compute the unmatched path segments, thus significantly reducing the overall system workload. Comprehensive experimentation on a real road network database shows that our system outperforms the state-of-the-art path planning techniques by reducing 32 percent of the computation latency on average. Ying Zhang 0047, Yu-Ling Hsueh, Wang-Chien Lee, Yi-Hao Jhang |
IEEE Trans. Knowl. Data Eng. | 4 |