I2B+tree: Interval B+ tree variant towards fast indexing of time-dependent data

Carneiro, Edgar; Carvalho, Alexandre Valle de; Oliveira, Marco Amaro

doi:10.23919/cisti49556.2020.9140897

Cited by 5 publications

(8 citation statements)

References 17 publications

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“…As presented in the works of Carneiro et al (2020), the I2B+ tree differs from the original IB+ tree by having every tree node storing pointers to both of its siblings, independently of it being a leaf node or an intermediary node. Figure 1 shows the difference between both index structures by illustrating the distinct tree configurations when storing the same example dataset.…”

Section: Main Differencesmentioning

confidence: 99%

“…Given the respective characteristics of each scenario, we named them accordingly: small intervals scenario in the first case and big intervals scenario for the second one. Both the nomenclature and the dataset generation methodology are exactly the same as the ones employed by Carneiro et al (2020).…”

Section: Synthetic Datasetsmentioning

confidence: 99%

“…Considering the results obtained by Carneiro et al (2020), we extend on their evaluation by studying the index structures' performance using trees with the order parameter set to 4, 10 and 20, and the time-split alpha parameter set to 0 and 0.2. The order choices, namely 10 and 20, are based on Carneiro et al findings.…”

Section: Experimentationmentioning

confidence: 99%

“…From the performed literature review, where we examined several index structures capable of indexing valid-time information, the IB+ tree emerged as the most promising one. However, from the more recent work employing this index structure (Guo et al, 2014;Lock and Booss, 2010;Vutukuri, 2018), only Carneiro et al (2020) provided an analysis of the IB+ tree with a focus on performance on growing volumes of data. In their work, Carneiro et al introduce the Improved Interval B+ tree (I2B+ tree), a time-efficiency focused variant of the IB+ tree.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, in this work, we extend the work of Carneiro et al (2020) by conducting a comparative study on the performance of the IB+ tree and the I2B+ tree. We start by analysing the main differences between both index structures and examine how those differences can impact the performances of each tree.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Comparative Study on the Performance of the IB+ Tree and the I2B+ Tree

Carvalho¹,

Carneiro²,

Oliveira³

2021

J INFORM SYSTEMS ENG

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Index structures were often used to optimise fetch operations to external storage devices (secondary memory). Nowadays, this also holds for increasingly large amounts of data residing in main-memory (primary memory). Within this scope, this work focuses on index structures that efficiently insert, query and delete valid-time data from very large datasets. This work performs a comparative study on the performance of the Interval B+ tree (IB+ tree) and the Improved Interval B+ tree (I2B+ tree): a variant that improves the time-efficiency of the deletion operation by reducing the number of traversed nodes to access siblings. We performed an extensive analysis of the performance of two operations: insertions and deletions, on both index structures, using multiple datasets with growing volumes of data, distinct temporal distributions and tree parameters (time-split alpha and node order). Results confirm that the I2B+ tree globally outperforms the IB+ tree, since, on average, deletion operations are 7% faster, despite insertions requiring 2% more time. Furthermore, results also allowed to determine the key factors that augment the performance difference on deletions between both trees.

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Section: Main Differencesmentioning

confidence: 99%

Section: Synthetic Datasetsmentioning

confidence: 99%

Section: Experimentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Comparative Study on the Performance of the IB+ Tree and the I2B+ Tree

Carvalho¹,

Carneiro²,

Oliveira³

2021

J INFORM SYSTEMS ENG

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A Survey of Spatio-Temporal Big Data Indexing Methods in Distributed Environment

Tian

Zhai

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the widespread use of mobile and sensing devices, and the popularity of online map-based services, such as navigation services, the volume of spatio-temporal data is growing rapidly. Conventional big data technologies in existing distributed systems cannot effectively process spatio-temporal big data with temporal continuity and spatial proximity. How to construct an effective index for the application requirements of spatio-temporal data in a distributed environment has become one of the hotspots of spatio-temporal big data research. Many spatio-temporal indexing methods have been proposed to support efficient query processing of spatio-temporal data. In this article, the various spatio-temporal big data indexing methods proposed by domestic and foreign researchers from 2010 to 2020 are classified and summarized according to the distributed environment and application background, and the hot issues that need to be paid attention to in the future are proposed according to the changes in application requirements Index Terms-Big Data, distributed system, spatio-temporal index. I. INTRODUCTION W ITH the maturity and widespread use of perception technology, a large number of records containing temporal and spatial marker information are generated, which are called spatio-temporal data [1]. Spatio-temporal data present multisource, heterogeneous, and multidimensional scale characteristics. Space and time are the basic attributes of spatio-temporal data and the basic characteristics of spatio-temporal data processing. With the rapid development of mobile Internet, location services and other technologies and the popularization of mobile devices, e.g., traffic trajectories [2], social media [3], remote sensing image [4], [5], climate observation [6], and other data containing spatio-temporal information rapidly accumulate, are forming a special kind of spatio-temporal big data [7]. In a general sense, spatio-temporal big data refers to the massive spatio-temporal data collection that is difficult to carry out data management, scientific computing, and value discovery within Manuscript

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k-dStHash tree for indexing big spatio-temporal datasets

Hooda,

Gill

2024

IJECE

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Today’s era is witness of tremendous ever growing spatial, temporal and spatiotemporal data. The huge spatio-temporal data immensely pushes the need for design and development of novel methods tailored for indexing spatio-temporal data. In this research paper, we propose the design of a novel spatio-temporal data indexing method, named as k-dStHash. We have proposed the algorithm k-dStHashInsertion for inserting spatio-temporal objects and an algorithm k-dStHashSrchPlaceTime has been used to search for the objects at given location and time. It is able to handle datasets with duplicate keys which has been ignored in many research works. Though the algorithm k-dStHashInsertion takes 1.3-1.5 times longer time to insert data in k-dStHash data structure as it needs to find a specific location to organize data efficiently, but when it comes to search for required records it is even more than 90 times faster when analyzed in comparison to brute force method. It is generalized enough to organize any kind of k-dimensional data and time-based data also including object finding, fleet management, clustering, leader identification, nearest neighbor, human/animal tracking, path finding and many more.

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I2B+tree: Interval B+ tree variant towards fast indexing of time-dependent data

Cited by 5 publications

References 17 publications

A Comparative Study on the Performance of the IB+ <i>Tree</i> and the I2B+ <i>Tree</i>

A Comparative Study on the Performance of the IB+ <i>Tree</i> and the I2B+ <i>Tree</i>

A Survey of Spatio-Temporal Big Data Indexing Methods in Distributed Environment

k-dStHash tree for indexing big spatio-temporal datasets

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I2B+tree: Interval B+ tree variant towards fast indexing of time-dependent data

Cited by 5 publications

References 17 publications

A Comparative Study on the Performance of the IB+&nbsp;<i>Tree</i>&nbsp;and the I2B+&nbsp;<i>Tree</i>

A Comparative Study on the Performance of the IB+&nbsp;<i>Tree</i>&nbsp;and the I2B+&nbsp;<i>Tree</i>

A Survey of Spatio-Temporal Big Data Indexing Methods in Distributed Environment

k-dStHash tree for indexing big spatio-temporal datasets

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Product

Resources

About

A Comparative Study on the Performance of the IB+ <i>Tree</i> and the I2B+ <i>Tree</i>

A Comparative Study on the Performance of the IB+ <i>Tree</i> and the I2B+ <i>Tree</i>