A Context-Aware Method for Indexing Large-Scale SpatioTemporal Data

Tian, Ruijie; Zhang, Weishi; Wang, Fei; Xiong, Junting

doi:10.1109/bigdata55660.2022.10020916

Cited by 1 publication

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another method uses context dimension awareness for creating multi-level index. It selects a proper partitioning technique and splits the dataset into multiple balanced divisions [13]. STAQR algorithm takes altitude of a spatial location point and time into consideration to index the data.…”

Section: Introductionmentioning

confidence: 99%

k-dStHash tree for indexing big spatio-temporal datasets

Hooda,

Gill

2024

IJECE

View full text Add to dashboard Cite

Today’s era is witness of tremendous ever growing spatial, temporal and spatiotemporal data. The huge spatio-temporal data immensely pushes the need for design and development of novel methods tailored for indexing spatio-temporal data. In this research paper, we propose the design of a novel spatio-temporal data indexing method, named as k-dStHash. We have proposed the algorithm k-dStHashInsertion for inserting spatio-temporal objects and an algorithm k-dStHashSrchPlaceTime has been used to search for the objects at given location and time. It is able to handle datasets with duplicate keys which has been ignored in many research works. Though the algorithm k-dStHashInsertion takes 1.3-1.5 times longer time to insert data in k-dStHash data structure as it needs to find a specific location to organize data efficiently, but when it comes to search for required records it is even more than 90 times faster when analyzed in comparison to brute force method. It is generalized enough to organize any kind of k-dimensional data and time-based data also including object finding, fleet management, clustering, leader identification, nearest neighbor, human/animal tracking, path finding and many more.

show abstract

Section: Introductionmentioning

confidence: 99%