ROAD: A New Spatial Object Search Framework for Road Networks

Lee, K. C. K.; Lee, Wang-Chien; Zheng, Baihua; Tian, Yuan

doi:10.1109/tkde.2010.243

Cited by 80 publications

(101 citation statements)

References 17 publications

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“…Distance Indices on Social Networks. Road network distance query has been well studied in [13], [14]. However, they cannot work on large social networks because the vertex degree in road networks is generally constant but dynamic in social networks due to the power law property.…”

Section: Related Workmentioning

confidence: 99%

“…The main workflow is as follows: In each iteration, we first access the 3D list for each keyword and get the cube cb with the best estimated scores among all unseen cubes in CQ (line 4). Next we evaluate all the records stored in cb (lines 8-12), then we keep expanding the search to the three neighbors of cb (lines [13][14][15][16], until the current top-k records are more relevant than the next best unseen cube in CQ. Following Equation 5 in computing the score of a record, Equation 6 illustrates how EstimateBestScore estimates the score of a cube cb: if EstimateBestScore(q, cb) > " then 16 Push nc to CQ…”

Section: Related Workmentioning

confidence: 99%

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Real time personalized search on social networks

Bao

et al. 2015

2015 IEEE 31st International Conference on Data Engineering

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Abstract-Internet users are shifting from searching on traditional media to social network platforms (SNPs) to retrieve up-to-date and valuable information. SNPs have two unique characteristics: frequent content update and small world phenomenon. However, existing works are not able to support these two features simultaneously. To address this problem, we develop a general framework to enable real time personalized top-k query. Our framework is based on a general ranking function that incorporates time freshness, social relevance and textual similarity. To ensure efficient update and query processing, there are two key challenges. The first is to design an index structure that is update-friendly while supporting instant query processing. The second is to efficiently compute the social relevance in a complex graph. To address these challenges, we first design a novel 3D cube inverted index to support efficient pruning on the three dimensions simultaneously. Then we devise a cube based threshold algorithm to retrieve the top-k results, and propose several pruning techniques to optimize the social distance computation, whose cost dominates the query processing. Furthermore, we optimize the 3D index via a hierarchical partition method to enhance our pruning on the social dimension. Extensive experimental results on two real world large datasets demonstrate the efficiency and the robustness of our proposed solution.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Real time personalized search on social networks

Bao

et al. 2015

2015 IEEE 31st International Conference on Data Engineering

View full text Add to dashboard Cite

show abstract

“…This raises an interesting question: "Which existing method(s) are applicable in the main memory scenario?" To answer this question, we plot the storage space and response time of existing methods (for a moderate-sized road network and a POI dataset) in Figure 1, where the measurements are obtained from our implementation or from previous experimental studies [5], [6]. Note that the storage space equates to the memory consumption in the main memory scenario.…”

Section: Introductionmentioning

confidence: 99%

“…Among these methods, SWH [4] outperforms LBC [8] and INE [7]. For network indexing methods, like G-tree [9], SWH-LM [4], SPIE [5], ROAD [6], SILC [10], the space is occupied by an index, indicated as slash bars in the figure. Observe that both the Distance Index [11] and the SILC Index [10] will soon exceed the main memory for larger moderate-sized road networks.…”

Section: Introductionmentioning

confidence: 99%

Beyond Millisecond Latency <inline-formula><tex-math>$k$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="u-ieq1-2426702.gif"/></alternatives></inline-formula>NN Search on Commodity Machine

Liao

Yiu

et al. 2015

IEEE Trans. Knowl. Data Eng.

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Abstract-The k nearest neighbor (kNN) search on road networks is an important function in web mapping services. These services are now dealing with rapidly arriving queries, that are issued by a massive amount of users. While overlay graph-based indices can answer shortest path queries efficiently, there have been no studies on utilizing such indices to answer kNN queries efficiently. In this paper, we fill this research gap and present two efficient kNN search solutions on overlay graph-based indices. Experimental results show that our solutions offer very low query latency (0.1ms) and require only small index sizes, even for 10-million-node networks.

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“…ROAD [59,60] is proposed for spatial object search on road networks. It is extensible to diverse object types and efficient for processing various location-dependent spatial queries, as it maintains objects separately from an underlying network and adopts an effective search space pruning technique.…”

Section: Queries On Road Networkmentioning

confidence: 99%

Efficient spatial keyword query processing on geo-textual data

Zheng¹

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The past decades have witnessed a transformation from a desktop-based web to a predominantly mobile web, where more often than not, users access the web from mobile devices. As a result, a huge volume of geo-textual web objects that have both geographical location and textual description have been generated. In literature, there have been lots of efforts in enabling efficient processing largescale geo-textual data under a variety of problem settings. In spite of the remarkable progress in this field, unsolved challenges remain. In this PhD thesis, I investigate a few interesting but challenging problems of this area. The contribution of this thesis can be summarized in three aspects. First, a set of new query predicates has been defined with a target of more diversified data types (e.g., activity trajectories), underlying spaces (e.g., road network) and query semantics, by which users can acquire their interested results more easily and effectively. Second, from a technical point of view, I have developed I/O efficient indexing structures and search algorithms to assure that a query can be answered within a reasonably small amount of time especially when dealing with massive geo-textual objects, which is not uncommon in real application scenarios. Last but not least, extensive empirical studies have been conducted based on real and large-scale datasets, which uncovered interesting patterns, rules and trends. These insights have shown directions for further improving my methodologies and also shed lights on future research. Below is a brief description of the contributions:First, I study the keyword-oriented queries on activity trajectories (KOAT). The activity trajectory is semantically enriched trajectory data, which embeds the information about behaviors of the moving objects. Therefore, searching activity trajectory is able to support a variety of applications for a better quality of location-based services. This work aims to return k trajectories that contain the most relevant keywords to the query and yield the least travel effort in the meantime. The main difference between this work and conventional spatial keyword query is that no query location exists in KOAT, which means the search area cannot be localized. To improve the query performance, a spatial-textual ranking function is first proposed between query keywords and activity trajectories. Then a best-first search algorithm based on a hybrid index structure is developed by applying effective pruning rules and efficient refinement strategies.Second, the problem of keyword-aware continuous k nearest neighbour search on road networks is studied, which computes the k nearest vertices that contain the query keywords issued by a moving object, and maintains the results continuously as it is moving on the road network. In particular, a iii framework, called labeling approach for continuous query (LARC), is proposed for processing the query with both low computation and communication overheads. First, a keyword-based pivot tree (KP-tree) is proposed ...

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ROAD: A New Spatial Object Search Framework for Road Networks

Cited by 80 publications

References 17 publications

Real time personalized search on social networks

Real time personalized search on social networks

Beyond Millisecond Latency <inline-formula><tex-math>$k$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="u-ieq1-2426702.gif"/></alternatives></inline-formula>NN Search on Commodity Machine

Efficient spatial keyword query processing on geo-textual data

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