Multi-source Skyline Query Processing in Road Networks

Abstract

Skyline query processing has been investigated extensively in recent years, mostly for only one query reference point. An example of a single-source skyline query is to find hotels which are cheap and close to the beach (an absolute query), or close to a user-given location (a relatively query). A multi-source skyline query considers several query points at the same time (e.g., to find hotels which are cheap and close to the University, the Botanic Garden and the China Town). In this paper, we consider the problem of efficient multi-source skyline query processing in road networks. It is not only the first effort to consider multi-source skyline query in road networks but also the first effort to process the relative skyline queries where the network distance between two locations needs to be computed on-the-fly. Three different query processing algorithms are proposed and evaluated in this paper. The Lower Bound Constraint algorithm (LBC) is proven to be an instance optimal algorithm. Extensive experiments using large real road network datasets demonstrate that LBC is four times more efficient than a straightforward algorithm.

Dynamic Skyline Queries in Large Graphs

“…Although several efficient dynamic algorithms have been proposed, such as B 2 S 2 and VS 2 [16] in spatial data and EDC and LBC algorithm [6] in road network, they cannot be applied to general graph data as discussed in Section 1. The two algorithms (EDC and LBC algorithms) proposed in [6] are not applicable to a DSG-query, since they employ Euclidean distances as lower bounds of shortest path distances in a road network. There is no coordinate associated with each vertex, thus, it is impossible to employ Euclidean distances as lower bounds of shortest path distances in general graphs.…”

“…Specifically, given a set of query points Q = {q i } (i = 1...n), for each record r ∈ D, we compute a new vector r d of dimension n, where r d 's i-th dimension is computed as Euclidean Distance between r and q i . The spatial skylines refer to all vectors r d whose values are not dominated by other r d in the record set D. A similar query, called multi-source skyline query in road networks, is studied by Deng et al [6], where the values of records are defined as the shortest path lengths on road networks from data points to query points. Compared to static skylines, dynamic skylines offer users more flexibility in specifying their search criteria.…”

“…The most related work is multi-source skyline query processing in road networks [6], which also uses shortest path distance as the measure. Three different algorithms have been proposed to find dynamic skylines in road networks.…”

“…Three different algorithms have been proposed to find dynamic skylines in road networks. Two of the algorithms (EDC and LBC) [6] utilize Euclidean distance as the lower bound of shortest path distance in a road network to perform pruning. However, for a general graph G, we cannot define Euclidean distances to bound the shortest path distances between any two vertices in G, since there is no coordinate associated with each vertex.…”

“…In this paper, we study dynamic skyline queries in a large graph (DSG-query for short). Although dynamic skylines have been studied in Euclidean space [16], road network [6], and metric space [4,7], there is no previous work on dynamic skylines over large graphs. We employ a filter-and-refine framework to speed up the query processing that can answer DSG-query efficiently.…”