{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:34:14Z","timestamp":1765546454246},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>With the popularization of portable devices, numerous applications continuously produce huge streams of geo-tagged textual data, thus posing challenges to index geo-textual streaming data efficiently, which is an important task in both data management and AI applications, e.g., real-time data streams mining and targeted advertising. This, however, is not possible with the state-of-the-art indexing methods as they focus on search optimizations of static datasets, and have high index maintenance cost. In this paper, we present NQ-tree, which combines new structure designs and self-tuning methods to navigate between update and search efficiency. Our contributions include: (1) the design of multiple stores each with a different emphasis on write-friendness and read-friendness; (2) utilizing data compression techniques to reduce the I\/O cost; (3) exploiting both spatial and keyword information to improve the pruning efficiency; (4) proposing an analytical cost model, and using an online self-tuning method to achieve efficient accesses to different workloads. Experiments on two real-world datasets show that NQ-tree outperforms two well designed baselines by up to 10\u00d7.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/672","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"4838-4845","source":"Crossref","is-referenced-by-count":21,"title":["Toward Efficient Navigation of Massive-Scale Geo-Textual Streams"],"prefix":"10.24963","author":[{"given":"Chengcheng","family":"Yang","sequence":"first","affiliation":[{"name":"Inception Institute of Artificial Intelligence"}]},{"given":"Lisi","family":"Chen","sequence":"additional","affiliation":[{"name":"Inception Institute of Artificial Intelligence"}]},{"given":"Shuo","family":"Shang","sequence":"additional","affiliation":[{"name":"Inception Institute of Artificial Intelligence"}]},{"given":"Fan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Inception Institute of Artificial Intelligence"}]},{"given":"Li","family":"Liu","sequence":"additional","affiliation":[{"name":"Inception Institute of Artificial Intelligence"}]},{"given":"Ling","family":"Shao","sequence":"additional","affiliation":[{"name":"Inception Institute of Artificial Intelligence"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:50:57Z","timestamp":1564300257000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/672"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/672","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}