Distributed Ranking Methods for Geographic Information Retrieval

Kreveld, Marc van; Reinbacher, Iris; Arampatzis, Avi; Zwol, Roelof van

doi:10.1007/3-540-26772-7_18

Cited by 9 publications

(8 citation statements)

References 13 publications

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“…However, with very few exceptions [24,25], reports on experiments combining these indexing schemes are very scarce. The whole subject of Geo-IR is still at an early stage of development, and very few studies have so far been performed on such systems.…”

Section: Evaluation Methodologymentioning

confidence: 98%

Indexing and ranking in Geo-IR systems

Martins

Silva

Andrade

2005

Proceedings of the 2005 Workshop on Geographic Information Retrieval

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Section: Evaluation Methodologymentioning

confidence: 98%

Indexing and ranking in Geo-IR systems

Martins

Silva

Andrade

2005

Proceedings of the 2005 Workshop on Geographic Information Retrieval

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“…In the case of GIR some research into spatial diversity has been carried out in which the IR system attempts to provide results that are both relevant and also spatially diversified, i.e., from many different (but spatially relevant) locations (Kreveld et al, 2005;Tang and Sanderson, 2010). Tang and Sanderson, 2010 describe spatial diversity as the "more locations that are covered and more intense the coverage is, the better spatial diversity a list of documents achieves, which may satisfy users better."…”

Section: Diversity In Girmentioning

confidence: 99%

“…Documents are ranked in descending order of their document similarity scores. Various methods for combining evidence from spatial and textual similarity are used to rank the retrieved documents (Kreveld et al, 2005).…”

Section: Relevance Ranking In the Exemplar Systemsmentioning

confidence: 99%

Geographic Information Retrieval: Progress and Challenges in Spatial Search of Text

Purves¹,

Clough²,

Jones³

et al. 2018

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Significant amounts of information available today contain references to places on earth. Traditionally such information has been held as structured data and was the concern of Geographic Information Systems (GIS). However, increasing amounts of data in the form of unstructured text are available for indexing and retrieval that also contain spatial references. This monograph describes the field of Geographic Information Retrieval (GIR) that seeks to develop spatially-aware search systems and support user's geographical information needs. Important concepts with respect to storing, querying and analysing geographical information in computers are introduced, before user needs and interaction in the context of GIR are explored. The task of associating documents with coordinates, prior to their indexing and ranking forms the core of any GIR system, and different approaches and their implications are discussed. Evaluating the resulting systems and their components, and different paradigms for doing so continue to be an important area of research in GIR and are illustrated through a number of examples. The article concludes by setting out a range of future challenges for research in this field.

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“…Moreover, all these methods were centralized and not suitable for WSNs. [24] [25] [26] provided the scattered ranking algorithms for the spatial database. The problems addressed by them are P-problems while our LAP-( , ) problem is NP-hard.…”

Section: B the Performance Of Computing Lap-( )mentioning

confidence: 99%

Location Aware Peak Value Queries in sensor networks

Cheng

2012

2012 Proceedings IEEE INFOCOM

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In the applications of wireless sensor networks, the peak values, such as largest sensed values and their locations, are very useful for detecting abnormal events happened in the monitored region. Although the results returned by the traditional top-queries provide largest sensed values, they ignore the spatial-correlation of the sensed data so that the locations of the returned values are very close to each other and only tell a small area being abnormal or few number of abnormal events happening. Due to this reason, the Location Aware Peak Value Query, denoted by LAP-( , ) query, is proposed in this paper. For any given and , the LAP-( , ) query returns largest sensed values and their locations, and the distance between the any two locations is larger than . The problem of processing LAP-( , ) query is proved to be NP-hard, and two distributed approximation algorithms are proposed to solve this problem. One is a distributed greedy algorithm with ratio bound 5.8. The other one is a region partition based algorithm with ratio bound 3. The theoretical analysis and experimental results show that the proposed algorithms have high performance in terms of accuracy and energy consumption.

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Distributed Ranking Methods for Geographic Information Retrieval

Cited by 9 publications

References 13 publications

Indexing and ranking in Geo-IR systems

Indexing and ranking in Geo-IR systems

Geographic Information Retrieval: Progress and Challenges in Spatial Search of Text

Location Aware Peak Value Queries in sensor networks

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