{"type":"ScholarlyArticle","title":"Toward Efficient Navigation of Massive-Scale Geo-Textual Streams","authors":[{"name":"Chengcheng Yang","slug":"chengcheng-yang","given":"Chengcheng","family":"Yang","affiliation":"Inception Institute of Artificial Intelligence"},{"name":"Lisi Chen","slug":"lisi-chen","given":"Lisi","family":"Chen","affiliation":"Inception Institute of Artificial Intelligence"},{"name":"Shuo Shang","slug":"shuo-shang","given":"Shuo","family":"Shang","affiliation":"Inception Institute of Artificial Intelligence"},{"name":"Fan Zhu","slug":"fan-zhu","given":"Fan","family":"Zhu","affiliation":"Inception Institute of Artificial Intelligence"},{"name":"Li Liu","slug":"li-liu","given":"Li","family":"Liu","affiliation":"Inception Institute of Artificial Intelligence"},{"name":"Ling Shao","slug":"ling-shao","given":"Ling","family":"Shao","affiliation":"Inception Institute of Artificial Intelligence"}],"abstract":"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×.","doi":"10.24963/ijcai.2019/672","url":"https://ijcai.org/proceedings/2019/672","pdf":"https://ijcai.org/proceedings/2019/0672.pdf","markdown":"https://ijcai.org/proceedings/2019/672.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/672","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":672,"pages":{"first":4838,"last":4845},"keywords":["Multidisciplinary Topics and Applications: Databases","Multidisciplinary Topics and Applications: Information Retrieval"],"videos":[],"references":[{"position":1,"raw":"[Anh and Moffat, 2005] Vo Ngoc Anh and Alistair Moffat. 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