Continuous within query in road networks

Huang, Yuan-Ko; Lin, Lien-Fa

doi:10.1109/iwcmc.2011.5982707

Cited by 8 publications

(3 citation statements)

References 14 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The range query [9,10] is a well-known query, used to find a set of objects that are inside a spatial region specified by the user. If the spatial region is constructed according to the location of the query object q, another variation of range query, the within query [11,12], is presented to find the objects whose distances to q are less than or equal to a user-given distance d (i.e., finding the objects within the region centered at q with radius d). Recently, many efforts have been made on processing the range and within queries in different research domains, such as mobile information systems [3,13] and uncertain database systems [2,14].…”

Section: Centralized Processing Techniques For Location-based Queriesmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Processing of Location-Based Aggregate Queries Using MapReduce

Huang

2019

IJGI

Self Cite

View full text Add to dashboard Cite

The location-based aggregate queries, consisting of the shortest average distance query (SAvgDQ), the shortest minimal distance query (SMinDQ), the shortest maximal distance query (SMaxDQ), and the shortest sum distance query (SSumDQ) are new types of location-based queries. Such queries can be used to provide the user with useful object information by considering both the spatial closeness of objects to the query object and the neighboring relationship between objects. Due to a large amount of location-based aggregate queries that need to be evaluated concurrently, the centralized processing system would suffer a heavy query load, leading eventually to poor performance. As a result, in this paper, we focus on developing the distributed processing technique to answer multiple location-based aggregate queries, based on the MapReduce platform. We first design a grid structure to manage information of objects by taking into account the storage balance, and then develop a distributed processing algorithm, namely the MapReduce-based aggregate query algorithm (MRAggQ algorithm), to efficiently process the location-based aggregate queries in a distributed manner. Extensive experiments using synthetic and real datasets are conducted to demonstrate the scalability and the efficiency of the proposed processing algorithm.

show abstract

Section: Centralized Processing Techniques For Location-based Queriesmentioning

confidence: 99%

“…Shuffle key value cell(0,1,3,4) { , (17,10)} r 3 { , (17,9)} s 3 { , (9,14)} s 1 { , (8,15)} t 2 { , (16,11)} t 3…”

Section: Reduce Mapmentioning

confidence: 99%

Distributed Processing of Location-Based Aggregate Queries Using MapReduce

Huang

2019

IJGI

Self Cite

View full text Add to dashboard Cite

show abstract

“…window query) [8,21] is a wellknown query in the location-based services, which can be used to find a set of objects that are inside a spatial region specified by the user. If the spatial region is constructed according to the location of the query object q, another variation of range query, the within query [11,12], is presented to find the objects whose distances to q are less than or equal to a user-given distance d (i.e., finding the objects within the region centered at q with radius d). Recently, many efforts have been made on processing the range and within queries in different research domains, such as mobile information systems [13,18] and uncertain database systems [5,7].…”

Section: Conventional Location-based Queriesmentioning

confidence: 99%

Shortest Average-Distance Query on Heterogeneous Neighboring Objects

Huang

Kuo

Lee

et al. 2014

Proceedings of the 19th International Database Engineering &Amp; Applications Symposium on - IDEAS '15

Self Cite

View full text Add to dashboard Cite

Currently, most of the processing techniques for the conventional location-based queries focus only on a single type of objects. However, in real-life applications the user may be interested in obtaining information about different types of objects, in terms of their neighboring relationship. We term the different types of objects closer to each other the heterogeneous neighboring objects (HNOs for short). Efficient processing of the location-based queries on the HNOs is more complicated than that on a single data source, because the neighboring relationship between the HNOs inevitably affects the query result. In this paper, we present a novel and important query on the HNOs, namely the shortest averagedistance query (SAvgDQ for short), which can provide useful object information by considering both the spatial closeness of objects to the query object and the neighboring relationship between objects. Given a query object q and a distance d, the SAvgDQ retrieves a set of HNOs, such that the distances between any two objects in this set are less than or equal to d and its average distance to q is the smallest among all HNOs sets. To efficiently process the SAvgDQ, we develop an algorithm, the SAvgDQ processing algorithm, which operates based on three devised heuristics, the HNOsqualifying heuristic, the HNOs-pruning heuristic, and the SAvgD-pruning heuristic, to reduce the number of distance computations required for query processing. Comprehensive experiments are conducted to demonstrate the effectiveness of the heuristics and the efficiency of the proposed algorithm.

show abstract