IR-Tree: An Efficient Index for Geographic Document Search

By Zhisheng Li, Ken C. K. Lee, Baihua Zheng, Wang-Chien Lee, Dik Lun Lee, Xufa Wang. IEEE Transactions on Knowledge and Data Engineering, 2011

303 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Geography, Planning and Development, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b330dcbe39551ad6275c454c7e1227b53ec025ef

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Gao Cong
2. J. Shane Culpepper
3. Christian S. Jensen
4. Dingming Wu
5. Zhifeng Bao

In Geography, Planning and Development

1. Gao Cong
2. Christian S. Jensen
3. Dingming Wu

In Signal Processing

1. Gao Cong
2. Christian S. Jensen
3. J. Shane Culpepper
4. Zhifeng Bao
5. Sheng Wang
6. Dingming Wu
7. Lisi Chen

Citations in the corpus

• A Survey on Trajectory Data Management, Analytics, and Learning (2020, background): “Spatial -textual KS [25, 52] keywords points TF-IDF Inverted list Top-: T:SK [105, 195] point, keywords points Aggregate IR-tree T:STT [140, 202] points, keywords trajectories Aggregate Grid-index, Inverted list”
• Spatial Keyword Query Processing: An Experimental Evaluation (2013, methodology): “Inverted file Most geo-textual indices [6,7,10,12,14,16,18,19,26] use the inverted file for text indexing.”