Exploiting a page-level upper bound for multi-type nearest neighbor queries

Ma, Xiaobin; Shekhar, Shashi; Xiong, Hui; Zhang, Pusheng

doi:10.1145/1183471.1183501

Cited by 24 publications

(19 citation statements)

References 27 publications

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“…Furthermore, to minimize I/O costs, R-LORD computes the minimum bounding rectangle (MBR) of all ellipses generated from length-i optimal sub-routes, as shown in Figure 2(b), and uses this MBR as a range query to retrieve points from the R-tree [9] that indexes the category to be examined during the (i + 1) th step. PLUB [11] decomposes a general optimal route query to multiple total-order queries and processes them individually, e.g., using R-LORD. For instance, the query in Figure 1 is decomposed into three total-order queries: museum → restaurant → pub, restaurant → museum → pub, and restaurant → pub → museum.…”

Section: Optimal Route Query Processingmentioning

confidence: 99%

“…Since the properties of G Q are rather difficult to quantify, we consider three specific types of visit order graphs: one without any order constraints between data categories, one with a complete order of all categories, and a bipartite graph that requires half (i.e., m/2) of the categories be visited before the remaining ones. Zero-order and total-order graphs are common definitions used in previous studies on the optimal route query, e.g., [11], [13], and the bipartite graph has properties between the two extremes. In addition, we classify queries based on the effectiveness of the greedy algorithm.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…We first compare SBS with PLUB [11], which decomposes an optimal route query into multiple total orders (see Section 2.1). Table 8 shows the speedup of SBS compared to PLUB with varying values of m, e.g., the value 5.0 indicates that SBS is 5 times faster than PLUB.…”

Section: Experiments Using Real Datasetsmentioning

confidence: 99%

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Optimal Route Queries with Arbitrary Order Constraints

Yang

Mamoulis

2013

IEEE Trans. Knowl. Data Eng.

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Abstract-Given a set of spatial points DS, each of which is associated with categorical information, e.g., restaurant, pub, etc., the optimal route query finds the shortest path that starts from the query point (e.g., a home or hotel), and covers a user-specified set of categories (e.g., {pub, restaurant, museum}). The user may also specify partial order constraints between different categories, e.g., a restaurant must be visited before a pub. Previous work has focused on a special case where the query contains the total order of all categories to be visited (e.g., museum → restaurant → pub). For the general scenario without such a total order, the only known solution reduces the problem to multiple, total-order optimal route queries. As we show in this paper, this naïve approach incurs a significant amount of repeated computations, and, thus, is not scalable to large datasets. Motivated by this, we propose novel solutions to the general optimal route query, based on two different methodologies, namely backward search and forward search. In addition, we discuss how the proposed methods can be adapted to answer a variant of the optimal route queries, in which the route only needs to cover a subset of the given categories. Extensive experiments, using both real and synthetic datasets, confirm that the proposed solutions are efficient and practical, and outperform existing methods by large margins.

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Section: Optimal Route Query Processingmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

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Optimal Route Queries with Arbitrary Order Constraints

Yang

Mamoulis

2013

IEEE Trans. Knowl. Data Eng.

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“…Several papers [3,10,11,14] study route-search queries over datasets in which objects have neither scores nor probabilities. The work of [15] investigates two variants of the shortest-route problem.…”

Section: Related Workmentioning

confidence: 99%

Route Search over Probabilistic Geospatial Data

Kanza

Safra

Sagiv

2009

Advances in Spatial and Temporal Databases

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Abstract. In a route search over geospatial data, a user provides terms for specifying types of geographical entities that she wishes to visit. The goal is to find a route that (1) starts at a given location, (2) ends at a given location, and (3) travels via geospatial entities that are relevant to the provided search terms. Earlier work studied the problem of finding a route that is effective in the sense that its length does not exceed a given limit, the relevancy of the objects is as high as possible, and the route visits a single object from each specified type. This paper investigates route search over probabilistic geospatial data. It is shown that the notion of an effective route requires a new definition and, specifically, two alternative semantics are proposed. Computing an effective route is more complicated, compared to the non-probabilistic case, and hence necessitates new algorithms. Heuristic methods for computing an effective route, under either one of the two semantics, are developed. (Note that the problem is NP-hard.) These methods are compared analytically and experimentally. In particular, experiments on both synthetic and realworld data illustrate the efficiency and effectiveness of these methods in computing a route under the two semantics.

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“…An OSR query retrieves a route of minimum length starting from a given source location and passing through a number of locations (with different types) in a particular order imposed on the types of the locations. A multi-type nearest neighbor (MTNN) query solution was proposed in [13] by Ma et al Given a query point and a collection of locations (with difference types), a MTNN query finds the shortest path for the query point such that only one instance of each type is visited during the trip. From a spatial query perspective, MTNN is an extended solution of OSR by exploiting a page-level upper bound.…”

Section: Route Planning Querymentioning

confidence: 99%

The multi-rule partial sequenced route query

Chen

Sun

et al. 2008

Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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Trip planning search (TPS) represents an important class of queries in Geographic Information Systems (GIS). In many real-world applications, TPS requests are issued with a number of constraints. Unfortunately, most of these constrained TPS cannot be directly answered by any of the existing algorithms. By formulating each restriction into rules, we propose a novel form of route query, namely the multi-rule partial sequenced route (MRPSR) query. Our work provides a unified framework that also subsumes the well-known trip planning query (TPQ) and the optimal sequenced route (OSR) query. In this paper, we first prove that MRPSR is NP-hard and then present three heuristic algorithms to search for near-optimal solutions for the MRPSR query. Our extensive simulations show that all of the proposed algorithms can answer the MRPSR query effectively and efficiently. Using both real and synthetic datasets, we investigate the performance of our algorithms with the metrics of the route distance and the response time in terms of the percentage of the constrained points of interest (POI) categories. Compared to the LORD-based brute-force solution, the response times of our algorithms are remarkably reduced while the resulting route length is only slightly longer than the shortest route.

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Exploiting a page-level upper bound for multi-type nearest neighbor queries

Cited by 24 publications

References 27 publications

Optimal Route Queries with Arbitrary Order Constraints

Optimal Route Queries with Arbitrary Order Constraints

Route Search over Probabilistic Geospatial Data

The multi-rule partial sequenced route query

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