Applying spatial database techniques to other domains: A case study on top-k and computational geometric operators

• Main Author: MOURATIDIS, Kyriakos
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Database systems; Information management; Asymptotic complexity; Computational geometric; Large-scale datasets; Practical problems; Practical solutions; Spatial data management; Spatial database; Spatial problems; Data handling; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/4156; https://ink.library.smu.edu.sg/context/sis_research/article/5160/viewcontent/p25_Mouratidis.pdf
• Institution: Singapore Management University

In this seminar, we will explore how processing rich spatial data is not the only practical (and research-wise promising) application domain for traditional spatial database techniques. An equally promising direction, possibly with low-hanging fruits for research innovation, may be to apply the spatial data management expertise of our community to non-spatial types of queries, and to extend standard, more theoretical operators to large scale datasets with the objective of practical solutions (as opposed to favorable asymptotic complexity alone). As a case study, we will review spatial database work on top-k-related operators (i.e., non-spatial problems) and how it integrates fundamental computational geometric operators with spatial indexing/pruning to produce efficient solutions to practical problems.