Top-k point of interest retrieval using standard indexes

Skovsgaard, Anders; Jensen, Christian S.

doi:10.1145/2666310.2666399

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…Instead of our own raster approach, we could have used another spatial indexing structure such as an R-Tree [45,46] or a B-Tree, which stores spatial regions encoded as bit strings [47]. In fact, we tried both approaches before coming up with our rasterbased design but faced performance issues.…”

Section: Subscription Indexing Structurementioning

confidence: 99%

GeoBroker: Leveraging geo-contexts for IoT data distribution

Hasenburg

Bermbach

2020

Computer Communications

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In the Internet of Things, the relevance of data often depends on the geographic context of data producers and consumers. Today's data distribution services, however, mostly focus on data content and not on geo-context, which could help to reduce the dissemination of excess data in many IoT scenarios. In this paper, we propose to use the geo-context information associated with devices to control data distribution. We define what geocontext dimensions exist and compare our definition with concepts from related work.Furthermore, we designed GeoBroker, a data distribution service that uses the location of things, as well as geofences for messages and subscriptions, to control data distribution. This way, we enable new IoT application scenarios while also increasing overall system efficiency for scenarios where geo-contexts matter by delivering only relevant messages. We evaluate our approach based on a proof-of-concept prototype and several experiments.

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Section: Subscription Indexing Structurementioning

confidence: 99%

GeoBroker: Leveraging geo-contexts for IoT data distribution

Hasenburg

Bermbach

2020

Computer Communications

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“…Given a database of POIs, a set of geotagged photos represented in the pFOV model, and a query photo, authors identify the most prominent POI captured in the query photo. Skovsgaard et al [25,26] focus on retrieving the top-k points of interest from spatial-keyword data (e.g., geo-tagged twitter data). Toyama et al [27] introduce an image database that supports image indexing and search based on the image camera locations and recording time.…”

Section: Retreival Of Top-k Points Of Interests and Objectsmentioning

confidence: 99%

Efficient Detection of Points of Interest from Georeferenced Visual Content

Lü

Colmenares

2017

Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data

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Many people take photos and videos with smartphones and more recently with 360 • cameras at popular places and events, and share them in social media. Such visual content is produced in large volumes in urban areas, and it is a source of information that online users could exploit to learn what has got the interest of the general public on the streets of the cities where they live or plan to visit. A key step to providing users with that information is to identify the most popular k spots in specified areas. In this paper, we propose a clustering and incremental sampling (C&IS) approach that trades off accuracy of top-k results for detection speed. It uses clustering to determine areas with high density of visual content, and incremental sampling, controlled by stopping criteria, to limit the amount of computational work. It leverages spatial metadata, which represent the scenes in the visual content, to rapidly detect the hotspots, and uses a recently proposed Gaussian probability model to describe the capture intention distribution in the query area. We evaluate the approach with metadata, derived from a non-synthetic, user-generated dataset, for regular mobile and 360 • visual content. Our results show that the C&IS approach offers 2.8×-19× reductions in processing time over an optimized baseline, while in most cases correctly identifying 4 out of 5 top locations.

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“…Using the text index employed, indexing techniques can also be classified as inverted file based (e.g., [67]) and signature-file based (e.g., [29]). In addition, some techniques (e.g., [67]) loosely combine a spatial and a text index, while other others integrate them tightly, resulting in hybrid index structures (e.g., [29]), and yet another approach captures an object's spatio-textual part as a compact bit string that can be indexed using a standard index [46] such as a B-tree.…”

Section: Indexing Techniquesmentioning

confidence: 99%

Querying Geo-Textual Data

Cong

Jensen

2016

Proceedings of the 2016 International Conference on Management of Data

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Over the past decade, we have moved from a predominantly desktop based web to a predominantly mobile web, where users most often access the web from mobile devices such as smartphones. In addition, we are witnessing a proliferation of geo-located, textual web content. Motivated in part by these developments, the research community has been hard at work enabling the efficient computation of a variety of query functionality on geo-textual data, yielding a sizable body of literature on the querying of geo-textual data.With a focus on different types of keyword-based queries on geo-textual data, the tutorial also explores topics such as continuous queries on streaming geo-textual data, queries that retrieve attractive regions of geo-textual objects, and queries that extract properties, e.g., topics and top-k frequent words, of the objects in regions. The tutorial is designed to offer an overview of the problems addressed in this body of literature and offers an overview of pertinent concepts and techniques. In addition, the tutorial suggests open problems and new research direction.

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Top-k point of interest retrieval using standard indexes

Cited by 8 publications

References 20 publications

GeoBroker: Leveraging geo-contexts for IoT data distribution

GeoBroker: Leveraging geo-contexts for IoT data distribution

Efficient Detection of Points of Interest from Georeferenced Visual Content

Querying Geo-Textual Data

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