Approximate Shortest Distance Computing: A Query-Dependent Local Landmark Scheme

Abstract Shortest distance query is a fundamental operation in large-scale networks. Many existing methods in the literature take a landmark embedding approach, which selects a set of graph nodes as landmarks and computes the shortest distances from each landmark to all nodes as an embedding. To answer a shortest distance query, the precomputed distances from the landmarks to the two query nodes are used to compute an approximate shortest distance based on the triangle inequality. In this paper, we analyze the factors that affect the accuracy of distance estimation in landmark embedding. In particular, we find that a globally selected, query- independent landmark set may introduce a large relative error, especially for nearby query nodes. To address this issue, we propose a query-dependent local landmark scheme, which identifies a local landmark close to both query nodes and provides more accurate distance estimation than the traditional global landmark approach. We propose efficient local landmark indexing and retrieval techniques, which achieve low offline indexing complexity and online query complexity. Two optimization techniques on graph compression and graph online search are also proposed, with the goal of further reducing index size and improving query accuracy. Furthermore, the challenge of immense graphs whose index may not fit in the memory leads us to store the embedding in relational database, so that a query of the local landmark scheme can be expressed with relational operators. Effective indexing and query optimization mechanisms are designed in this context.

Existing System As the size of graphs that emerge nowadays from various application domains is dramatically increas¬ing, the number of nodes may reach the scale of hundreds of millions or even more. Due to the massive size, even simple graph queries become challenging tasks. One of them, the shortest distance query, has been extensively studied during the last four decades. Querying shortest paths or shortest distances between nodes in a large graph has important applications in many domains including road networks, social networks, communication networks, the Internet, and so on. For example, in road networks, the goal is to find shortest routes between locations; in social networks, the goal is to find the closest social relationships such as friendship or collaboration between users; while in the Internet, the goal is to find the nearest server to reduce access latency for clients. Although classical algorithms like breadth-first search (BFS), Dijkstra's algorithm [1], and A* search [2], [3], [4] can compute the exact shortest paths in a network, the massive size of modern information networks and the online nature of such queries make it infeasible to apply the classical algorithms online. On the other hand, it is space inefficient to precompute and store the shortest.

Architecture Diagram

System Specification HARDWARE REQUIREMENTS Processor : Intel Pentium IV   Ram : 512 MB   Hard Disk : 80 GB HDD   SOFTWARE REQUIREMENTS Operating System : Windows XP / Windows 7   FrontEnd : Java   BackEnd : MySQL 5

Conclusion In this paper, we propose a novel shortest path tree-based local landmark scheme, which finds a node close to the query nodes as a query-specific local landmark for a triangulation-based shortest distance estimation. Specifi¬cally, a local landmark is defined as the LCA of the query nodes in a shortest path tree rooted at a global landmark. Efficient algorithms for indexing and retrieving LCAs are introduced, which achieve low offline indexing complexity and online query complexity. This strategy significantly reduces the distance estimation error, compared with global landmark embedding. We also study the local landmark scheme on relational database for better scalability. Extensive experimental results on large-scale social net¬works and road networks demonstrate the effectiveness and efficiency of the proposed local landmark scheme.

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