IG-Tree: an efficient spatial keyword index for planning best path queries on road networks

Haryanto, Anasthasia Agnes; Islam, Md. Saiful; Taniar, David; Cheema, Muhammad Aamir

doi:10.1007/s11280-018-0643-5

Cited by 17 publications

(4 citation statements)

References 48 publications

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“…The index structure of spatial data mainly includes R-tree based indexes (such as Rtree [13], R*-tree [14], S2I [15], etc. ), Grid-based indexes (such as TS [16], IG-tree [17], etc.) and Quad-tree based indexes (such as I3 [18], ILQ [19], etc.).…”

Section: Index Structure Of Spatio-textual Datamentioning

confidence: 99%

“…Unbalance tree structure and high storage cost TS [16],IG-tree [17] Grid Inverted file Advantages: High efficiency for region searching Disadvantages: High storage and update costs However, the top-k result objects returned by existing methods are usually similar to each other and not representative, so it is more necessary to choose top-k typicality results. The concept of typicality analysis originated from the cognitive science [31], and has been gradually applied to numeric data analysis [32], image processing and recognition [33], recommendation system [34] and other fields in recent years.…”

Section: Disadvantagesmentioning

confidence: 99%

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Top-k approximate selection for typicality query results over spatio-textual data

Meng,

Zhang,

Huo

et al. 2023

Knowl Inf Syst

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Spatial keyword query is a classical query processing mode for spatio-textual data, which aims to provide users the spatio-textual objects with the highest spatial proximity and textual similarity to the given query. However, the top-k result objects obtained by using the spatial keyword query mode are often similar to each other while users hope that the system can pick top-k typicality results from the candidate query results in order to make users understand the representative features of the candidate result set. To deal with the problem of typicality analysis and typical object selection of spatio-textual data query results, a typicality evaluation and top-k approximate selection approach is proposed. First, the approach calculates the synthetic distances on dimensions of geographic location, textual semantics, and numeric attributes between all spatio-textual objects. And then, a hybrid index structure that can simultaneously support the location, text, and numeric multi-dimension matching is presented in order to expeditiously obtain the candidate query results. According to the synthetic distances between spatio-textual objects, a Gaussian kernel probability density estimation-based method for measuring the typicality of query results is proposed. To facilitate the query result analysis and top-k typical object selection, the Tournament strategybased and Local neighborhood-based top-k typical object approximate selection algorithms are presented, respectively. The experimental results demonstrated that the text semantic relevancy measuring method for spatio-textual objects is accurate and reasonable, and the Local neighborhood-based top-k typicality result approximate selection algorithm achieved both the low error rate and high execution efficiency.

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Section: Index Structure Of Spatio-textual Datamentioning

confidence: 99%

Section: Disadvantagesmentioning

confidence: 99%

Top-k approximate selection for typicality query results over spatio-textual data

Meng,

Zhang,

Huo

et al. 2023

Knowl Inf Syst

View full text Add to dashboard Cite

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“…To improve the performance of , the authors further propose an approximate algorithm, namely , which not only returns better approximate guarantees, but is more efficient than . In the literature, a string of other keyword-aware routing problems are studied [37,38,50,87,97,108]. All these works aim at finding optimal routes by considering keywords and other application-specified constraints at the same time.…”

Section: Problem 12 (mentioning

confidence: 99%

Keyword Search on Large Graphs: A Survey

Yang

Zhang

2021

Data Sci. Eng.

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With the prevalence of Internet access and online services, various big graphs are generated in many real applications (e.g., online social networks and knowledge graphs). An important task on analyzing and mining these graphs is keyword search. Essentially, given a graph G and query Q associated with a set of keywords, the keyword search aims to find a substructure (e.g., rooted tree or subgraph) S in G such that nodes in S collectively cover part of or all keywords in Q, and in the meanwhile, S is optimal on some user specified semantics. Keyword search on graphs can be applied in many real-life applications, such as point-of-interests recommendation and web search facility. In spite of the great importance of graph keyword search, we, however, notice that the latest survey on this topic is far out of date. Consequently, there is prompt need to conduct a comprehensive survey in this research direction. Motivated by this, in this survey, we systematically review graph keyword search studies by classifying the existing works into different categories according to the specific problem definition. This survey aims to provide the researchers a comprehensive understanding of existing graph keyword search solutions.

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“…The spatial road network concept has been the focus of many previous studies [6,[15][16][17][18]. This section reviews the works that have used the notion of a spatial road network to determine the shortest path between two given locations.…”

Section: Routing In a Spatial Road Networkmentioning

confidence: 99%

Independent Map Enhancement for a Spatial Road Network: Fundamental Applications and Opportunities

Alamri

2020

IJGI

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In many developing cities, the improvement of transport infrastructure is usually accompanied by major road construction and maintenance. This paper presents approaches and opportunities using peer-to-peer updating to improve spatial road networks undergoing construction and maintenance, which in turn will improve traffic flow and benefit cities overall. In many cities, the spatial road network requires maintenance, and these works often require traffic detours. With the current GPS (Global Positioning System) services, there is a noticeable delay in the updating of many spatial road networks. Thus, when a driver plans a trip to a certain location (such as Starbucks), his/her usual route may have changed, but the spatial road network in the GPS has not been updated. This can affect the user in many ways. For example, a trip that usually takes five minutes might now take half an hour, taking into account the additional time required to find alternative roads and possibly encountering more unexpected road closures, until the driver reaches his/her destination. This paper addresses this issue by proposing solutions that offer several advantages including a new peer-to-peer updating mechanism that helps to direct the driver to another route when road changes occur. Moreover, the peer-to-peer updating mechanism can enable the independent monitoring of road conditions and the updating of maps for newly-constructed roads, as well as the analysis of road congestions, traffic density, and people movements at certain times. Note that this work focuses on the conceptual ideas and approaches intended to improve independent maps, and the detailed algorithms have been left for future work.

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IG-Tree: an efficient spatial keyword index for planning best path queries on road networks

Cited by 17 publications

References 48 publications

Top-k approximate selection for typicality query results over spatio-textual data

Top-k approximate selection for typicality query results over spatio-textual data

Keyword Search on Large Graphs: A Survey

Independent Map Enhancement for a Spatial Road Network: Fundamental Applications and Opportunities

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