Yun-Wu Huang

Hierarchical encoded path views for path query processing: an optimal model and its performance evaluation

Efficient path computation is essential for applications such as intelligent transportation systems (ITS) and network routing. In ITS navigation systems, many path requests can be submitted over the same, typically huge, transportation network within a small time window. While path precomputation (path view) would provide an efficient path query response, it raises three problems which must be addressed: 1) precomputed paths exceed the current computer main memory capacity for large networks; 2) disk-based solutions are too inefficient to meet the stringent requirements of these target applications; and 3) path views become too costly to update for large graphs (resulting in out-of-date query results). We propose a hierarchical encoded path view (HEPV) model that addresses all three problems. By hierarchically encoding partial paths, HEPV reduces the view encoding time, updating time and storage requirements beyond previously known path precomputation techniques, while significantly minimizing path retrieval time. We prove that paths retrieved over HEPV are optimal. We present complete solutions for all phases of the HEPV approach, including graph partitioning, hierarchy generation, path view encoding and updating, and path retrieval. In this paper, we also present an in-depth experimental evaluation of HEPV based on both synthetic and real GIS networks. Our results confirm that HEPV offers advantages over alternative path finding approaches in terms of performance and space efficiency.

Hierarchical optimization of optimal path finding for transportation applications

In this paper, the authors study the problem of efficient path query processing in the context of automobile navigation systems. To guarantee efficient response for path queries, a path view materialization strategy is used for pre-computing the best paths. A hierarchical encoded path view (HEPV) approach, which addresses issues of capacity, efficiency, and cost, is proposed. Experimental results reveal that HEPV is more efficient than previously known path-finding approaches.

Effective graph clustering for path queries in digital map databases

Clustering for Path Queries in Digital Map Databases * Ning Jingt Elke A. Rundensteiner Changsha Institute of Technology University of Michigan jning@eecs.umiclt.edu rtmdenst@eecs.umich.edu In this paper, we present an experimental evaluation of graph clustering strategies in terms of their effectiveness in optimizing I/O for path query processing in digital map databases. Clustering optimization is attractive because it does not incurs any run-time cost, and is complimentary to many of the existing techniques in path query optimization. We first propose a novel graph clustering technique, called Spatial Partition Clustering (SPC), that creates balanced partitions of links based on the spatial proximity of their origin nodes. We then select three alternative clustering techniques from the literature, namely two-way partitioning, approximately topological clustering, and random clustering, to compare their performance in path query processing with SPC. Experimental evahration indicates that our SPC performs the best for the high-locality graphs (such as GIS maps), whereas the two-way partitioning approach performs the best for no-locality random graphs.

A cost model for estimating the performance of spatial joins using R-trees

The development of a cost model for predicting the performance of spatial joins has been identified in the literature as an important and difficult problem. The authors present the first cost model that can predict the performance of spatial joins using R-trees. Based on two existing R-trees (join targets), the model first estimates the number of expected I/Os for the join process by assuming a zero buffer size. The method for this estimation extends the cost model for R-tree window queries (developed by Kamel and Faloutsos (1993) and by Pagel et al. (1993)) to also handle spatial joins (which are more complex). In the context of spatial join processing, this number of zero-buffer expected I/Os is not practical for performance prediction in a buffered environment. To model the buffer impact, they use an (exponential) distribution function to measure the probability that a bufferless I/O would cause a page fault in a buffered environment. Based on this probability and the zero-buffer expected I/O cost, the estimated number of I/Os for an R-tree join can then be computed. The comparisons between the predictions from the cost model and the actual results from the experiments based on real GIS maps show that the average relative error ratio is about 10% with a maximum of about 20% for a wide range of buffer sizes. Therefore, our model is a useful tool for the query optimization of spatial join queries.

Integrated query processing strategies for spatial path queries

Investigates optimization strategies for processing path queries with embedded spatial constraints, such as avoiding areas with certain characteristics. To resolve complex spatial constraints during path finding, we consider two decisions: (1) the spatial relation operations (e.g. intersection) between areas and links can be pre-processed or intermixed with path-finding, and (2) areas satisfying the query constraint can be pre-filtered or dynamically selected during path-finding. Based on these two decisions, we propose and implement the resulting four integrated query processing strategies, utilizing state-of-the-art technologies such as spatial joins for intersect computation, R-tree access structure for spatial overlap searching, and spatial clustering for efficient path searching. In this paper, we also report an experimental evaluation to show which strategies perform best in different scenarios.

Optimizing path query performance: graph clustering strategies ☆

Path queries over transportation networks are operations required by many Geographic Information Systems applications. Such networks, typically modeled as graphs composed of nodes and links and represented as link relations, can be very large and hence often need to be stored on secondary storage devices. Path query computation over such large persistent networks amounts to high I/O costs due to having to repeatedly bring in links from the link relation from secondary storage into the main memory buffer for processing. This paper is the first to present a comparative experimental evaluation of alternative graph clustering solutions in order to show their effectiveness in path query processing over transportation networks. Clustering optimization is attractive because it does not incur any run-time cost, requires no auxiliary data structures, and is complimentary to many of the existing solutions on path query processing. In this paper, we develop a novel clustering technique, called spatial partition clustering (SPC), that exploits unique properties of transportation networks such as spatial coordinates and high locality. We identify other promising candidates for clustering optimizations from the literature, such as two-way partitioning and approximate topological clustering. We fine-tune them to optimize their I/O behavior for path query processing. Our experimental evaluation of the performance of these graph clustering techniques using an actual city road network as well as randomly generated graphs considers variations in parameters such as memory buffer size, length of the paths, locality, and out-degree. Our experimental results are the foundation for establishing guidelines to select the best clustering technique based on the type of networks. We find that our SPC performs the best for the highly interconnected city map; the hybrid approach for random graphs with high locality; and the two-way partitioning based on link weights for random graphs with no locality.

A Hierarchical Path View Model for Path Finding in Intelligent Transportation Systems

Effective path finding has been identified as an important requirement for dynamic route guidance in Intelligent Transportation Systems (ITS). Path finding is most efficient if the all-pair (shortest) paths are precomputed because path search requires only simple lookups of the precomputed path views. Such an approach however incurs path view maintenance (computation and update) and storage costs which can be unrealistically high for large ITS networks. To lower these costs, we propose a Hierarchical Path View Model (HPVM) that partitions an ITS road map, and then creates a hierarchical structure based on the road type classification. HPVM includes a map partition algorithm for creating the hierarchy, path view maintenance algorithms, and a heuristic hierarchical path finding algorithm that searches paths by traversing the hierarchy. HPVM captures the dynamicity of traffic change patterns better than the ITS path finding systems that use the hierarchicalA * approach because: (1) during path search, HPVM traverses the hierarchy by dynamically selecting the connection points between two levels based on up-to-date traffic, and (2) HPVM can reroute the high-speed road traffic through local streets if needed. In this paper, we also present experimental results used to benchmark HPVM and to compare HPVM with alternative ITS path finding approaches, using both synthetic and real ITS maps that include a large Detroit map (> 28,000 nodes). The results show that the HPVM incurs much lower costs in path view maintenance and storage than the non-hierarchical path precomputation approach, and is more efficient in path search than the traditional ITS path finding using A* or hierarchical A* algorithms.

Improving Spatial Intersect Joins Using Symbolic Intersect Detection

We introduce a novel technique to drastically reduce the computation required by the refinement step during spatial intersect join processing. This technique, called Symbolic Intersect Detection (SID), detects most of the true hits during a spatial intersect join by scrutinizing symbolic topological relationships between candidate polygon pairs. SID boosts performance by detecting true hits early during the refinement step, thus avoiding expensive polygon intersect computations that would otherwise be required to detect the true hits. Our experimental evaluation with real GIS map data demonstrates that SID can identify more than 80% of the true hits with only minimal overhead. Consequently, SID outperforms known techniques for resolving polygon intersection during the refinement step by more than 50%. Most state-of-the-art methods in spatial join processing can benefit from SIDs performance gains because the SID approach integrates easily into the established two-phase spatial join process.

Path queries for transportation networks: dynamic reordering and sliding window paging techniques

It is well-known that standard relational database engines arc not equipped to efficiently process path finding queries required by diverse applications, such as Geographic Information Systems (GIS), Intelligent Transportation Systems (ITS), etc. In this paper, we explore the development of special-purpose disk-based techniques for supporting the processing of path queries on transportation networks. To process high-throughput path queries, our solution materializes parh views and employs several innovative p&h view rcfreshing strategies. One key technique is to perform dynamic reordering of link tuples in breadth-first order based on recent I/O activities. This reclustering approach trades extra I/O overhead required by reordering for the performance gains achievable by path starch on approximately topologically ordered link tuplcs. We also introduce a sliding window paging policy that further reduces page misses. We present experiments conducted in evaluating our approach and in comparing it with previously published disk-based methods using both real GIS data (city road maps) and synthetic grid graphs. Our results show that our new approach outperforms the alternative approaches significantly for highly cyclic graphs such as GIS maps.

Symbolic Intersect Detection: A Method for Improving Spatial Intersect Joins

Due to the increasing popularity of spatial databases, researchers have focused their efforts on improving the query processing performance of the most expensive spatial database operation: the spatial join. While most previous work focused on optimizing the filter step, it has been discovered recently that, for typical GIS data sets, the refinement step of spatial join processing actually requires a longer processing time than the filter step. Furthermore, two-thirds of the time in processing the refinement step is devoted to the computation of polygon intersections. To address this issue, we therefore introduce a novel approach to spatial join optimization that drastically reduces the time of the refinement step. We propose a new approach called Symbolic Intersect Detection (SID) for early detection of true hits. Our SID optimization eliminates most of the expensive polygon intersect computations required by a spatial join by exploiting the symbolic topological relationships between the two candidate polygons and their overlapping minimum bounding rectangle. One important feature of our SID optimization is that it is complementary to the state-of-the-art methods in spatial join processing and therefore can be utilized by these techniques to further optimize their performance. In this paper, we also develop an analytical cost model that characterizes SID’s effectiveness under various conditions. Based on real map data, we furthermore conduct an experimental evaluation comparing the performance of the spatial joins with SID against the state-of-the-art approach. Our experimental results show that SID can effectively identify more than 80% of the true hits with negligible overhead. Consequently, with SID, the time needed for resolving polygon intersect in the refinement step is improved by over 50% over known techniques, as predicted by our analytical model.