Quad-kd trees: A general framework for kd trees and quad trees

Bereczky, Nikolett; Duch, Amalia; Németh, Krisztián; Roura, Salvador

doi:10.1016/j.tcs.2015.12.030

Cited by 15 publications

(8 citation statements)

References 34 publications

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“…The experiments carried out on public data set shows that the search method is 2 to 3 times faster than the propagation-assisted K-d tree search method at nearly the same accuracy. A simple and flexible multidimensional data structure has been introduced in [24].The quad K-d tree is a general purpose and hierarchical data structure for the storage of multidimensional points. Quad K-d trees include both the point quad trees and K-d trees as particular cases and therefore it constitutes a general framework for the study of fundamental properties of trees similar to them.…”

Section: B K-d Treementioning

confidence: 99%

CONNEKT: Co-Located Nearest Neighbor Search using KNN Querying with K-D Tree

Sharmiladevi¹,

Sathya²,

Kumar³

2019

IJRTE

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Data about entities or objects associated with geographical or location information could be called as spatial data. Spatial data helps in identifying and positioning anyone or anything globally anywhere across the world. K-d tree) inorder to make the querying process efficient. The algorithm is analyzed using a hypothetical data set generated through QGIS. Instances of various spatial features that are closely found together are called as spatial co-located patterns. So far, the spatial co-located patterns have been used only for knowledge discovery process but it would serve a wide variety of applications if analyzed intensively. One such application is to use co-location pattern mining for a context aware based search. Hence the main aim of this work is to extend the K-Nearest Neighbor (KNN) querying to co-located instances for context aware based querying or location-based services (LBS). For the above-said purpose, co-located nearest neighbor search algorithm namely "CONNEKT" is proposed. The co-located instances are mapped onto a K-dimensional tree (

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Section: B K-d Treementioning

confidence: 99%

CONNEKT: Co-Located Nearest Neighbor Search using KNN Querying with K-D Tree

Sharmiladevi¹,

Sathya²,

Kumar³

2019

IJRTE

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“…We created a novel transport mechanism called Digital Fountain Based Communication Protocol and performed a detailed performance evaluation of it [186]. In [187] we have had a curious contribution of establishing a general framework for kd trees and quad trees, which could be a useful construction in theoretical computer science. In 2016, we also took part in a great editorial created a special issue of the prestigious journal of Photonic Network Communications [188].…”

Section: Internet Cloud Communications Iot Analytics and Software Dmentioning

confidence: 99%

BME VIK Annual Research Report on Electrical Engineering and Computer Science 2016

Charaf

Harsányi

Poppe

et al. 2017

Period. Polytech. Elec. Eng. Comp. Sci.

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Preface Since being established in 1949, the Faculty of Electrical Engineering and Informatics (VIK) BME has played a flagship role in the development of electronics, IT and computer science in Hungary.We László Jakab (dean, BME VIK) János Levendovszky (vice-dean in charge of scientific affairs, BME VIK) are proud of combining engineering applications with sound scientific results, which is the assurance of high-level industrial collaboration leading to novel results and innovation. The various collaborations with our industrial partners has made it clear that the industry expects methods and results which make their industrial processes more effective and increase productivity and quality. Thus, participation in these collaborations give a competitive edge and ensure the continuous development of VIK. These factors have positioned our Department of Automation and Applied InformaticsDepartment of Automation and Applied Informatics (AUT) is one of the largest and dynamically developing departments at BME with diverse scope competences such as control theory, embedded systems, software modelling, applied software development, Internet of Things, power electronics, mechatronics, and many others. The department's main activities are education, research and development. Training versatile electrical and software engineers with solid practical knowledge is our top priority. Our profile also includes developing high quality software and hardware solutions for industry partners. All of our activities are backed by strong research background.In 2016, the department organized and hosted a number of domestic and international events. In cooperation with the industry, AUT has organized the IoT4U conference. It was dedicated to Internet of Things, cloud and data management as well as the integration of IoT into various formats. RobonAUT, the yearly domestic amateur robotics contest was organized by the department in February 2016. The yearly contest is open to teams of students, organized either in student projects within the department or independent research teams.The next sections summarize the key research results of the department in year 2016. Software Development Methods and Mobile PlatformsThe diversity of mobile platforms necessitates the development of the same mobile application for all major mobile platforms, what requires considerable development effort. Mobile application developers are multiplatform developers, but they still prioritize the platforms, therefore, not all platforms are equally important for them. Appropriate methods, processes and tools are required to support the development in order to achieve better productivity.The research on educational mobile applications that adapt to the mental state of the user required a method for estimating the difficulty of tasks found in the serious games. There are generated or random assignments where the effort required from the user is not known or cannot be estimated in advance.61 (2) IoT and Smart CityThe Internet of Things (IoT) is transforming the surroun...

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“…Future work includes extensions of the analysis in this work to fixed partial match queries in standard Kdt trees trees [6], quad trees of dimension higher than 2 [13] and Quad-K-d trees [2].…”

Section: Random Partial Matchmentioning

confidence: 99%

Partial Match Queries in Relaxed K-dt trees

Duch

Lau

2017

2017 Proceedings of the Fourteenth Workshop on Analytic Algorithmics and Combinatorics (ANALCO)

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The study of partial match queries on random hierarchical multidimensional data structures dates back to Ph. Flajolet and C. Puech's 1986 seminal paper on partial match retrieval. It was not until recently that fixed (as opposed to random) partial match queries were studied for random relaxed K-d trees, random standard K-d trees, and random 2-dimensional quad trees. Based on those results it seemed natural to classify the general form of the cost of fixed partial match queries into two families: that of either random hierarchical structures or perfectly balanced structures, as conjectured by Duch, Lau and Martínez (On the Cost of Fixed Partial Queries in K-d trees Algorithmica, 75(4): 2016). Here we show that the conjecture just mentioned does not hold by introducing relaxed K-dt trees and providing the average-case analysis for random partial match queries as well as some advances on the average-case analysis for fixed partial match queries on them. In fact this cost -for fixed partial match queries-does not follow the conjectured forms.

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Quad-kd trees: A general framework for kd trees and quad trees

Cited by 15 publications

References 34 publications

CONNEKT: Co-Located Nearest Neighbor Search using KNN Querying with K-D Tree

CONNEKT: Co-Located Nearest Neighbor Search using KNN Querying with K-D Tree

BME VIK Annual Research Report on Electrical Engineering and Computer Science 2016

Partial Match Queries in Relaxed K-dt trees

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