On k-skip shortest paths

2017

Data Sci. Eng.

Performing online selective queries against graphs is a challenging problem due to the unbounded nature of graph queries which leads to poor computation locality. It becomes even difficult when a graph is too large to be fit in the memory. Although there have been emerging efforts on managing large graphs in a distributed and parallel setting, e.g., Pregel, HaLoop and etc, these computing frameworks are designed from the perspective of scalability instead of the query efficiency. In this work, we present our solution methodology for online selective graph queries based on the shortest path distance semantic, which finds various applications in practice. The essential intuition is to build a distance-aware index for online distance-based query processing and to eliminate redundant graph traversal as much as possible. We discuss how the solution can be applied to two types of research problems, distance join and vertex set bonding, which are distancebased graph pattern discovery and finding the structurewise bonding of vertices, respectively.

Section: Related Workmentioning

confidence: 99%

Distance-Aware Selective Online Query Processing Over Large Distributed Graphs

Zhang

2017

Data Sci. Eng.

“…Regardless of the uncertainty, we use simple graphs just as other studies on graph database [7][8][9][10][11] . Without loss of generality, we mainly process undirected simple graphs.…”

Section: Correlated Uncertain Graphsmentioning

confidence: 99%

Threshold-Based Shortest Path Query over Large Correlated Uncertain Graphs

Cheng

Yuan

J. Comput. Sci. Technol.

et al. 2015

With the popularity of uncertain data, queries over uncertain graphs have become a hot topic in the database community. As one of the important queries, the shortest path query over an uncertain graph has attracted much attention of researchers due to its wide applications. Although there are some efficient solutions addressing this problem, all existing models ignore an important property existing in uncertain graphs: the correlation among the edges sharing the same vertex. In this paper, we apply Markov network to model the hidden correlation in uncertain graphs and compute the shortest path. Unfortunately, calculating the shortest path and corresponding probability over uncertain graphs modeled by Markov networks is a #P-hard problem. Thus, we propose a filtering-and-verification framework to accelerate the queries. In the filtering phase, we design a probabilistic shortest path index based on vertex cuts and blocks of a graph. We find a series of upper bounds and prune the vertices and edges whose upper bounds of the shortest path probability are lower than the threshold. By carefully picking up the blocks and vertex cuts, the index is optimized to have the maximum pruning capability, so that we can filter a large number of vertices which make no contribution to the final shortest path query results. In the verification phase, we develop an efficient sampling algorithm to determine the final query answers. Finally, we verify the efficiency and effectiveness of our solutions with extensive experiments.

“…To avoid calculating all-pairs shortest path, we adopt k-skip shortest paths [12] and vertex labeling hierarchy [3] techniques. For example, Figure 2 shows an original graph at the bottom, a 2-skip graph in the middle and two sub-graphs on the top to be stored in the cloud.…”

Section: Figure 1 Link Graph and Outsourced Graphs (By Gao)mentioning

confidence: 99%

“…For the first step, building k-skip sub graph, the k-skip graph was introduced in the context of spatial network databases [12]. It is able to answer k-skip queries significantly faster than finding the original shortest paths in their entirety.…”

Section: Construction Of K-skip Graphmentioning

confidence: 99%

Sensitive and Neighborhood Privacy on Shortest Paths in the Cloud

Wang

Proceedings of International Conference on Information Integration and Web-Based Applications &Amp; Services

Ting

et al. 2013

Efficient shortest path calculation has been studied extensively, in particular, in the distributed environment. However, preserving privacy in the cloud environment has just attracted latest attention. To preserve fixed-pattern one-neighborhood privacy in the cloud, current approach requires the calculation of all-pairs shortest paths in advance, which is time consuming for large graphs. In addition, specific paths that are sensitive and require hiding the source and destination vertices are not well addressed. In this work, we propose a new flexible k-neighborhood privacy-protection and efficient shortest distance computation scheme for sensitive shortest paths in the cloud environment. Combining the construction of kskip shortest path sub-graphs, sensitive vertex adjustment, vertex hierarchy labeling and bottom-up partitioning techniques, the proposed approach not only subsumes one-neighborhood privacy but also provides efficient partitioning and query processing for sensitive shortest paths. Numerical experiments demonstrating the characteristics of proposed approach are presented.