A Distributed Hybrid Indexing for Continuous KNN Query Processing over Moving Objects

Abstract

The magnitude of highly dynamic spatial data is expanding rapidly due to the instantaneous evolution of mobile technology, resulting in challenges for continuous queries. We propose a novel indexing approach model, namely, the Velocity SpatioTemporal indexing approach (VeST), for continuous queries, mainly Continuous K-Nearest Neighbor (CkNN) and continuous range queries using Apache Spark. The proposed structure is based on a selective velocity partitioning method, i.e., since different objects have varying speeds, we divide the objects into two sets according to the actual mean speed we calculate before building the index and accessing data. Then the adopted indexing structure base unit comprises a nonoverlapping R-tree and a two dimension grid. The tree divides the space into nonoverlapping minimum bounding regions that point to the grids. Then, the uniform grid stores the object data of leaf nodes. This access method reduces the update cost and improves response time and query precision. In order to enhance performances for large-scale processing, we design a compact multilayer index structure on a distributed setting and propose a CKNN search algorithm for accurate results using a candidate cell identification process. We provide a comprehensive vision of our indexing model and the adopted query technique. The simulation results show that for query intervals of 100, the proposed approach is 13.59 times faster than the traditional approach, and the average time of the VeST approach is less than 0.005 for all query intervals. This proposed method improves response time and query precision. The precision of the VeST algorithm is almost equal to 100 % regardless of the length of the query interval.

Advancing Urban Life: A Systematic Review of Emerging Technologies and Artificial Intelligence in Urban Design and Planning

“…Burini et al [35] proposed the J-CO framework based on JSON format. At the same time, Bareche and Xia [36] developed the VeST indexing technique, and Wang et al [37] implemented the STR method, all contributing to more precise and dynamic analysis of urban spatial data. These techniques allow for more accurate and efficient processing of larger-scale and complex urban spatial data in geo-design.…”

“…[33] Geospatial Data Management Introduces VeST, a novel indexing model for efficient CKNN queries on moving objects in a distributed environment. [36] Introduces STR, a multivariate hierarchical regionalization method for uncovering spatiotemporal patterns, focusing on spatial, temporal contiguity, and attribute similarity.…”

Graph-Indexed kNN Query Optimization on Road Network

“…In response to the natural extension of kNN queries for multiple moving objects, i.e., Aggregate k Nearest Neighbor (AkNN) queries, Abeywickrama T [17] proposed the Compressed Object Landmark Tree (COLT) data structure, which implements efficient hierarchical graph traversal and performs various aggregation functions effectively. Bareche I et al [18] proposed a Velocity Spatiotemporal (VeST) indexing approach for continuous queries, mainly Continuous K-Nearest Neighbor (CKNN) and continuous range queries using Apache Spark. The proposed structure is based on a selective velocity-partition method, which reduces the update cost and improves the response time and query accuracy.…”

An integrated approach for fuzzy rule generation in dataset classification using hybrid grid partitioning and rough set theory

“…17, No. 2, May (2023)  An integrated approach for fuzzy rule generation in dataset classification using hybrid grid partitioning and rough set theory (Tokpa Braxton Ferguson) and fuzzy partitioning techniques, enabling the representation of gradual transitions and uncertainty within grid cells (Bareche & Xia, 2022). This approach allows for a more precise modeling of the data distribution and better captures the complexity of the dataset (Y. .…”