Supporting Continuous Skyline Queries in Dynamically Weighted Road Networks

Abstract

The paper focuses on the design of an optimum method for handling the continuous skyline query problem in road networks. Existing studies on processing the continuous skyline query focus exclusively on static road networks, which are limited because the state of roads in road networks is constantly changing. Therefore, to apply current methods for dynamically weighted road networks, a distributed skyline query method based on a grid partition method has been proposed in this paper. The method adopts the concepts of a distributed computing framework and road network preprocessing computations in which multiple parallel computing nodes are allocated and organized in grids. Using this approach, the road network map is simplified to a hub graph with much smaller scale such that the query load of the central node can be significantly reduced. The theoretical analysis and experimental results both indicate that, by using the proposed method, the system can achieve quick response time for users as well as a good balance between response times and accuracy. Therefore, it can be concluded that using the proposed method is beneficial for handling continuous skyline queries in a dynamically weighted road network.

Support Personalized Weighted Local Differential Privacy Skyline Query

“…Skyline query research is divided into two areas: optimization of skyline query algorithm and application of skyline query algorithm to related research areas [10]. Skyline query algorithm mainly includes block nested-loop algorithm, nearest-neighbor algorithm, branch-and-bound algorithm, etc.…”

“…(1) Construct the judgment matrix Q of the data set according to equation (4) (2) λ max ←Q (3) According to equation ( 5), we obtain the consistency index (4) According to equation ( 6), we calculate CR (5) if CR ≥ 0.1 then (6) Denotes a failure of the consistency checking ( 7) else (8) By passing the consistency check, the feature vector ω′ is calculated based on λ max (9) end if (10) 15) According to equation (10), we can get 􏽥 ω…”

“…pr′ np.full(shape k j , ll_value pr 1 ) (9) pr′[x ij − 1] pr 2 (10) x ij ′ np.random.choice(a range(1, k j + 1), p pr′ (11) end for (12) end for (13) end for (14) for each X i in List do (15) ag True ( 16) for each X j in List do (17) if X i ≠ X j then (18) if Compare (X i , X j ) then (19) return True or Flase (20) ag Flase (21) end if (22) end if (23) end for (24) return X i . Index…”