Open AccessCONNEKT: Co-Located Nearest Neighbor Search using KNN Querying with K-D Tree
Author(s) -
S. Sharmiladevi,
S. Siva Sathya,
Naveen Kumar
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1741.078219
Subject(s) - computer science , k nearest neighbors algorithm , tree (set theory) , data mining , context (archaeology) , set (abstract data type) , best bin first , nearest neighbor search , spatial analysis , location based service , process (computing) , artificial intelligence , geography , mathematics , remote sensing , mathematical analysis , telecommunications , archaeology , programming language , operating system
Data about entities or objects associated with geographical or location information could be called as spatial data. Spatial data helps in identifying and positioning anyone or anything globally anywhere across the world. Instances of various spatial features that are closely found together are called as spatial co-located patterns. So far, the spatial co-located patterns have been used only for knowledge discovery process but it would serve a wide variety of applications if analyzed intensively. One such application is to use co-location pattern mining for a context aware based search. Hence the main aim of this work is to extend the K-Nearest Neighbor (KNN) querying to co-located instances for context aware based querying or location-based services (LBS). For the above-said purpose, co-located nearest neighbor search algorithm namely “CONNEKT” is proposed. The co-located instances are mapped onto a K-dimensional tree (K-d tree) inorder to make the querying process efficient. The algorithm is analyzed using a hypothetical data set generated through QGIS