Top-k Spatial Preference Queries in Directed Road Networks

Attique, Muhammad; Cho, Hyung‐Ju; Jin, Rize; Chung, Tae-Sun

doi:10.3390/ijgi5100170

Cited by 8 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this method, different parameters that affect a decision need to be identified first and then can be categorized by their priority by comparing weights and scales. The geographical data is also an important part of some problems and could be added as a layer in the AHP method [49][50][51][52]. The weighting system can use simple numbers, fuzzy numbers, or triangular fuzzy numbers [53][54][55][56].…”

Section: Analytic Hierarchy Process (Ahp) Ranking Methodsmentioning

confidence: 99%

Machine Learning and Geo-Based Multi-Criteria Decision Support Systems in Analysis of Complex Problems

Ferrante

Pirouz

Piro

2021

IJGI

View full text Add to dashboard Cite

Many complex problems require a multi-criteria decision, such as the COVID-19 pandemic that affected nearly all activities in the world. In this regard, this study aims to develop a multi-criteria decision support system considering the sustainability, feasibility, and success rate of possible approaches. Therefore, two models have been developed: Geo-AHP (applying geo-based data) and BN-Geo-AHP using probabilistic techniques (Bayesian network). The ranking method of Geo-APH is generalized, and the equations are provided in a way that adding new elements and variables would be possible by experts. Then, to improve the ranking, the application of the probabilistic technique of a Bayesian network and the role of machine learning for database and weight of each parameter are explained, and the model of BN-Geo-APH has been developed. In the next step, to show the application of the developed Geo-AHP and BN-Geo-AHP models, we selected the new pandemic of COVID-19 that affected nearly all activities, and we used both models for analysis. For this purpose, we first analyzed the available data about COVID-19 and previous studies about similar virus infections, and then we ranked the main approaches and alternatives in confronting the pandemic of COVID-19. The analysis of approaches with the selected alternatives shows the first ranked approach is massive vaccination and the second ranked is massive swabs or other tests. The third is the use of medical masks and gloves, and the last ranked is the lockdown, mostly due to its major negative impact on the economy and individuals.

show abstract

Section: Analytic Hierarchy Process (Ahp) Ranking Methodsmentioning

confidence: 99%

Machine Learning and Geo-Based Multi-Criteria Decision Support Systems in Analysis of Complex Problems

Ferrante

Pirouz

Piro

2021

IJGI

View full text Add to dashboard Cite

show abstract

“…Recent studies have investigated several spatial queries, such as nearest neighbor, reverse nearest neighbor, range, and various top-k queries for road networks [15][16][17][18][19]. Rocha et al [7] considered top-k spatial keyword queries for road networks, and proposed an efficient indexing technique and an overlay network to group objects in regions with similar textual description, thereby enabling the computation of upper-bound scores for all objects in the region.…”

Section: Top-k Keyword Queriesmentioning

confidence: 99%

Geo-Social Top-k and Skyline Keyword Queries on Road Networks

Attique

Afzal

Ali

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

The rapid growth of GPS-enabled mobile devices has popularized many location-based applications. Spatial keyword search which finds objects of interest by considering both spatial locations and textual descriptions has become very useful in these applications. The recent integration of social data with spatial keyword search opens a new service horizon for users. Few previous studies have proposed methods to combine spatial keyword queries with social data in Euclidean space. However, most real-world applications constrain the distance between query location and data objects by a road network, where distance between two points is defined by the shortest connecting path. This paper proposes geo-social top-k keyword queries and geo-social skyline keyword queries on road networks. Both queries enrich traditional spatial keyword query semantics by incorporating social relevance component. We formalize the proposed query types and appropriate indexing frameworks and algorithms to efficiently process them. The effectiveness and efficiency of the proposed approaches are evaluated using real datasets.

show abstract

“…On the other hand, GPS-enabled devices, such as smartphones, wireless sensor networks (WSN), and navigation systems, are responsible for generating a massive number of spatial query requests. The most common instances of spatial queries [1] associated with popular LBSs include shortest path queries, range queries [2,3], k-nearest neighbor (k-NN) queries [4,5], reverse k-NN queries [6], and preference queries [7][8][9]. To address the growing demand for such services, a significant amount of research has been conducted over the past few years to monitor and improve the processing of spatial queries.…”

Section: Introductionmentioning

confidence: 99%

Efficient Processing of All Nearest Neighbor Queries in Dynamic Road Networks

et al. 2021

Self Cite

View full text Add to dashboard Cite

The increasing trend of GPS-enabled smartphones has led to the tremendous usage of Location-Based Service applications. In the past few years, a significant amount of studies have been conducted to process All nearest neighbor (ANN) queries. An ANN query on a road network extracts and returns all the closest data objects for all query objects. Most of the existing studies on ANN queries are performed either in Euclidean space or static road networks. Moreover, combining the nearest neighbor query and join operation is an expensive procedure because it requires computing the distance between each pair of query objects and data objects. This study considers the problem of processing the ANN queries on a dynamic road network where the weight, i.e., the traveling distance and time varies due to various traffic conditions. To address this problem, a shared execution-based approach called standard clustered loop (SCL) is proposed that allows efficient processing of ANN queries on a dynamic road network. The key concept behind the shared execution technique is to exploit the coherence property of road networks by clustering objects that share common paths and processing the cluster as a single path. In an empirical study, the SCL method achieves significantly better performance than competitive methods and efficiently reduces the computational cost to process ANN queries in various problem settings.

show abstract

Top-k Spatial Preference Queries in Directed Road Networks

Cited by 8 publications

References 29 publications

Machine Learning and Geo-Based Multi-Criteria Decision Support Systems in Analysis of Complex Problems

Machine Learning and Geo-Based Multi-Criteria Decision Support Systems in Analysis of Complex Problems

Geo-Social Top-k and Skyline Keyword Queries on Road Networks

Efficient Processing of All Nearest Neighbor Queries in Dynamic Road Networks

Contact Info

Product

Resources

About