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Journal articles on the topic 'Multidimensional indexing'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Vlachos, Michail, Marios Hadjieleftheriou, Dimitrios Gunopulos, and Eamonn Keogh. "Indexing Multidimensional Time-Series." VLDB Journal 15, no. 1 (2005): 1–20. http://dx.doi.org/10.1007/s00778-004-0144-2.

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2

Sandin, Fredrik, Blerim Emruli, and Magnus Sahlgren. "Random indexing of multidimensional data." Knowledge and Information Systems 52, no. 1 (2016): 267–90. http://dx.doi.org/10.1007/s10115-016-1012-2.

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3

Safaei, Ali Asghar, and Saeede Habibi-Asl. "Multidimensional indexing technique for medical images retrieval." Intelligent Data Analysis 25, no. 6 (2021): 1629–66. http://dx.doi.org/10.3233/ida-205495.

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Retrieving required medical images from a huge amount of images is one of the most widely used features in medical information systems, including medical imaging search engines. For example, diagnostic decision making has traditionally been accompanied by patient data (image or non-image) and previous medical experiences from similar cases. Indexing as part of search engines (or retrieval system), increases the speed of a search. The goal of this study, is to provide an effective and efficient indexing technique for medical images search engines. In this paper, in order to archive this goal, a

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4

Malhotra, Shweta, Mohammad Najmud Doja, Bashir Alam, and Mansaf Alam. "Skipnet-Octree Based Indexing Technique for Cloud Database Management System." International Journal of Information Technology and Web Engineering 13, no. 3 (2018): 1–13. http://dx.doi.org/10.4018/ijitwe.2018070101.

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This article describes how data indexing plays a very crucial role in query processing. Systems based on traditional indexes like B-tree, R-tree, Bitmap, inverted indexing techniques are not suitable for efficient query evaluation as these systems are based on simple key-value pair and used only for point queries. In cloud data repositories, point queries are not sufficient for query as a cloud consists of multidimensional data. For multidimensional query processing, many techniques have been developed. In this article, a dynamic double layer indexing structure with the help of a Skipnet overl

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5

Ben-Arie, J., Zhiqian Wang, P. Pandit, and S. Rajaram. "Human activity recognition using multidimensional indexing." IEEE Transactions on Pattern Analysis and Machine Intelligence 24, no. 8 (2002): 1091–104. http://dx.doi.org/10.1109/tpami.2002.1023805.

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6

Califano, A., and R. Mohan. "Multidimensional indexing for recognizing visual shapes." IEEE Transactions on Pattern Analysis and Machine Intelligence 16, no. 4 (1994): 373–92. http://dx.doi.org/10.1109/34.277591.

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7

Zhang, Ying, Wenjie Zhang, Qianlu Lin, Xuemin Lin, and Heng Tao Shen. "Effectively Indexing the Multidimensional Uncertain Objects." IEEE Transactions on Knowledge and Data Engineering 26, no. 3 (2014): 608–22. http://dx.doi.org/10.1109/tkde.2013.21.

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8

Terry, Justin, and Bela Stantic. "Indexing method for multidimensional vector data." Computer Science and Information Systems 10, no. 3 (2013): 1077–104. http://dx.doi.org/10.2298/csis120702022t.

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9

Urruty, Thierry. "KpyrRec: a recursive multidimensional indexing structure." International Journal of Parallel, Emergent and Distributed Systems 23, no. 3 (2008): 235–45. http://dx.doi.org/10.1080/17445760701536159.

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10

Zhang, Chong, Weidong Xiao, Daquan Tang, and Jiuyang Tang. "P2P-based multidimensional indexing methods: A survey." Journal of Systems and Software 84, no. 12 (2011): 2348–62. http://dx.doi.org/10.1016/j.jss.2011.07.027.

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11

Aggarwal, Charu, Joel Wolf, Philip Yu, and Marina Epelman. "Using Unbalanced Trees for Indexing Multidimensional Objects." Knowledge and Information Systems 1, no. 3 (1999): 309–36. http://dx.doi.org/10.1007/bf03325102.

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12

Csabai, I., L. Dobos, M. Trencséni, et al. "Multidimensional indexing tools for the virtual observatory." Astronomische Nachrichten 328, no. 8 (2007): 852–57. http://dx.doi.org/10.1002/asna.200710817.

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13

Nam, Beomseok, and Alan Sussman. "Analyzing design choices for distributed multidimensional indexing." Journal of Supercomputing 59, no. 3 (2011): 1552–76. http://dx.doi.org/10.1007/s11227-011-0567-7.

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14

Levit-Gurevich, L. K., and D. M. Yaroshevskii. "A dynamic programming scheme with multidimensional step indexing." Automation and Remote Control 67, no. 9 (2006): 1373–88. http://dx.doi.org/10.1134/s0005117906090037.

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15

Vrhel, M. J. "Indexing of Multidimensional Lookup Tables in Embedded Systems." IEEE Transactions on Image Processing 13, no. 10 (2004): 1319–26. http://dx.doi.org/10.1109/tip.2004.834658.

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16

Seid, Mehammed, Getaneh Demeke, Nasre Alam Md, Worku Getachew, and bayih Tizazu. "Optimizing Multi-Dimensional Data-Index Algorithms for Mic Architectures." International Journal of Innovative Science and Research Technology 7, no. 9 (2022): 366–72. https://doi.org/10.5281/zenodo.7114859.

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A data structure for geographical partitioning called multi-dimensional data-indexing enables effective CPU-based nearest-neighbor searches. Despite not being a natural match for Many-Integrated Core Architecture (MIC) implementation, depth-first search MultiDimensional Data-Indexing can nevertheless be successful with the right engineering choices. We suggested a technique that minimizes data structure memory trace by limiting the maximum height of the DFS Multi-Dimensional Data-Indexing. With tens of thousands to tens of millions of points in the MIC kernel code, we optimize the multi-core M

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17

KITA, KENJI, and MASAMI SHISHIBORI. "Efficient Multidimensional Indexing Using One-dimensional Self-Organizing Maps." Journal of Natural Language Processing 10, no. 5 (2003): 41–54. http://dx.doi.org/10.5715/jnlp.10.5_41.

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18

., Aretty Narayana. "SCALABLE AND EFFICIENT CLUSTER-BASED FRAMEWORK FOR MULTIDIMENSIONAL INDEXING." International Journal of Research in Engineering and Technology 02, no. 08 (2013): 166–70. http://dx.doi.org/10.15623/ijret.2013.0208028.

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19

Sharma, Mayank, Navin Rajpal, B. V. Ramana Reddy, and Ravindra Kumar Purwar. "Normalised LCS-based method for indexing multidimensional data cube." International Journal of Intelligent Information and Database Systems 7, no. 2 (2013): 180. http://dx.doi.org/10.1504/ijiids.2013.053550.

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20

Cha, G. H. "The Segment-Page Indexing Method for Large Multidimensional Queries." Computer Journal 48, no. 1 (2005): 101–14. http://dx.doi.org/10.1093/comjnl/bxh057.

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21

Seon, Hwi-Joon, Ki-Jung Kim, and Yeong-Seok Lee. "Spatial Indexing Method Considering Spatial Relationships in Multidimensional Search Space." Korean Society of Technical Education and Training 25, no. 4 (2020): 107–15. http://dx.doi.org/10.29279/kostet.2020.25.4.107.

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22

Seon, Hwi-Joon, Ki-Jung Kim, and Yeong-Seok Lee. "Spatial Indexing Method Considering Spatial Relationships in Multidimensional Search Space." Korean Society of Technical Education and Training 25, no. 4 (2020): 107–15. http://dx.doi.org/10.29279/kostet.2020.25.4.107.

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23

Scott, G., and Chi-Ren Shyu. "Knowledge-Driven Multidimensional Indexing Structure for Biomedical Media Database Retrieval." IEEE Transactions on Information Technology in Biomedicine 11, no. 3 (2007): 320–31. http://dx.doi.org/10.1109/titb.2006.880551.

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24

Poveda, José, and Michael Gould. "Multidimensional binary indexing for neighbourhood calculations in spatial partition trees." Computers & Geosciences 31, no. 1 (2005): 87–97. http://dx.doi.org/10.1016/j.cageo.2004.09.012.

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25

Singh, Om Prakash, Somnath Dey, and Debasis Samanta. "Fingerprint indexing using minutiae-based invariable set of multidimensional features." International Journal of Biometrics 6, no. 3 (2014): 272. http://dx.doi.org/10.1504/ijbm.2014.064410.

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26

CHI, PIN-HAO, GRANT SCOTT, and CHI-REN SHYU. "A FAST PROTEIN STRUCTURE RETRIEVAL SYSTEM USING IMAGE-BASED DISTANCE MATRICES AND MULTIDIMENSIONAL INDEX." International Journal of Software Engineering and Knowledge Engineering 15, no. 03 (2005): 527–45. http://dx.doi.org/10.1142/s0218194005002439.

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Indexing protein tertiary structures has been shown to provide a scalable solution for structure-to-structure comparisons in large protein structure retrieval systems. To conduct similarity searches against 53,356 polypeptide chains in a database with real-time responses, two critical issues must be addressed, information extraction and suitable indexing. In this paper, we apply computer vision techniques to extract the predominant information encoded in each 2D distance matrix, generated from 3D coordinates of protein chains. Distance matrices are capable of representing specific protein stru

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27

Javed, Shaikh. "Hierarchical Quantum-Classical Multidimensional Tree (HQCMT): A Novel Data Structure for Enhanced Multidimensional Query Processing." International Scientific Journal of Engineering and Management 04, no. 07 (2025): 1–9. https://doi.org/10.55041/isjem04770.

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This paper introduces the Hierarchical Quantum-Classical Multidimensional Tree (HQCMT), a novel hybrid data structure that combines classical hierarchical organization with quantum computational advantages for multidimensional data processing. The HQCMT integrates skip octree structures, quantum random access memory (QRAM), and quantum B+ tree methodologies to achieve unprecedented performance in range queries and multidimensional searches. Our theoretical analysis demonstrates up to 251× speedup in memory access operations compared to classical approaches, with time complexity improvements fr

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28

Wu, Guangjun, Bingqing Zhu, Jun Li, Yong Wang, and Yungang Jia. "H2SA-ALSH: A Privacy-Preserved Indexing and Searching Schema for IoT Data Collection and Mining." Wireless Communications and Mobile Computing 2022 (April 18, 2022): 1–12. http://dx.doi.org/10.1155/2022/9990193.

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Currently, smart devices of Internet of Things generate massive amount of data for different applications. However, it will expose sensitive information to external users in the process of IoT data collection, transmission, and mining. In this paper, we propose a novel indexing and searching schema based on homocentric hypersphere and similarity-aware asymmetric LSH (H2SA-ALSH) for privacy-preserved data collection and mining over IoT environments. The H2SA-ALSH collects multidimensional data objects and indexes their features according to the Euclidean norm and cosine similarity. Additionally

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29

Zolotov, V. A., and V. A. Semenov. "Advanced indexing methods for large multidimensional data in complex dynamic scenes." Proceedings of the Institute for System Programming of RAS 24 (2013): 381–416. http://dx.doi.org/10.15514/ispras-2013-24-17.

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30

Zhang, Rong, Weining Qian, Aoying Zhou, and Minqi Zhou. "An efficient peer-to-peer indexing tree structure for multidimensional data." Future Generation Computer Systems 25, no. 1 (2009): 77–88. http://dx.doi.org/10.1016/j.future.2008.02.010.

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31

Katayama, Norio, and Shin'ichi Satoh. "Application of multidimensional indexing methods to massive processing of multimedia information." Systems and Computers in Japan 31, no. 13 (2000): 31–41. http://dx.doi.org/10.1002/1520-684x(20001130)31:13<31::aid-scj4>3.0.co;2-7.

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32

Sharma, Deepak. "A Study on Feature Extraction and Image Retrieval for Image Mining." International Journal of Advance Research and Innovation 1, no. 2 (2013): 16–19. http://dx.doi.org/10.51976/ijari.121304.

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This paper provides a comprehensive survey of the technical achievements in the research area of image retrieval, especially content-based image retrieval, an area that has been so active and prosperous in the past few years. The survey includes the research aspects of image feature representation and extraction, multidimensional indexing, and system design.

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33

Rao, Sirisala Nageswara. "Optimized Cost Model for k-NN Queries in R*-Trees over Random Distribution." Advanced Materials Research 403-408 (November 2011): 3315–21. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3315.

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Efficient storage and retrieval of multidimensional data in large volumes has become one of the key issues in the design and implementation of commercial and application software. The kind of queries posted on such data is also multifarious. Nearest neighbor queries are one such category and have more significance in GIS type of application. R-tree and its sequel are data partitioned hierarchical multidimensional indexing structures that help in this purpose. Today’s research has turned towards the development of powerful analytical method to predict the performance of such indexing structures

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34

Khalid, Muhammad Waqas, Junaid Zahid, Muhammad Ahad, Aadil Hameed Shah, and Fakhra Ashfaq. "Dynamics of unidimensional and multidimensional inequality in Pakistan." International Journal of Social Economics 46, no. 2 (2019): 170–81. http://dx.doi.org/10.1108/ijse-07-2017-0278.

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Purpose The purpose of this paper is to measure the unidimensional and multidimensional inequality in the case of Pakistan and compare their results at the provincial as well as regional (urban and rural areas) level. The authors collected data from Pakistan Social and Living Standard Measurement and Household Integrated Economic Survey for fiscal years of 1998–1999 and 2013–2014. Design/methodology/approach The authors used Gini coefficient for unidimensional inequality and multidimensional indexing approach of Araar (2009) for multidimensional inequality. Findings The findings predicted that

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35

Uher, Vojtěch, Petr Gajdoš, Václav Snášel, Yu-Chi Lai, and Michal Radecký. "Hierarchical Hexagonal Clustering and Indexing." Symmetry 11, no. 6 (2019): 731. http://dx.doi.org/10.3390/sym11060731.

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Space-filling curves (SFCs) represent an efficient and straightforward method for sparse-space indexing to transform an n-dimensional space into a one-dimensional representation. This is often applied for multidimensional point indexing which brings a better perspective for data analysis, visualization and queries. SFCs are involved in many areas such as big data analysis and visualization, image decomposition, computer graphics and geographic information systems (GISs). The indexing methods subdivide the space into logic clusters of close points and they differ in various parameters including

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36

Junge, Laura, Christian Frey, Graham Ashcroft, and Edmund Kügeler. "Simulation of Indexing and Clocking with a New Multidimensional Time Harmonic Balance Approach." International Journal of Turbomachinery, Propulsion and Power 9, no. 2 (2024): 17. http://dx.doi.org/10.3390/ijtpp9020017.

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Alongside the capability to simulate rotor–stator interactions, a central aspect within the development of frequency-domain methods for turbomachinery flows is the ability of the method to accurately predict rotor–rotor and stator–stator interactions on a single-passage domain. To simulate such interactions, state-of-the-art frequency-domain approaches require one fundamental interblade phase angle, and therefore it can be necessary to resort to multi-passage configurations. Other approaches neglect the cross-coupling of different harmonics. As a consequence, the influence of indexing on the p

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37

Qian, Gang, Qiang Zhu, Qiang Xue, and Sakti Pramanik. "A space-partitioning-based indexing method for multidimensional non-ordered discrete data spaces." ACM Transactions on Information Systems 24, no. 1 (2006): 79–110. http://dx.doi.org/10.1145/1125857.1125860.

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38

Shan, Man-Kwan, and Suh-Yin Lee. "Multidimensional interval filter: A new indexing method for subpicture query of image retrieval." Pattern Recognition Letters 19, no. 13 (1998): 1241–55. http://dx.doi.org/10.1016/s0167-8655(98)00103-2.

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39

Lampropoulos, Konstantinos, Fatemeh Zardbani, Nikos Mamoulis, and Panagiotis Karras. "Adaptive Indexing in High-Dimensional Metric Spaces." Proceedings of the VLDB Endowment 16, no. 10 (2023): 2525–37. http://dx.doi.org/10.14778/3603581.3603592.

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Similarity search in high-dimensional metric spaces is routinely used in many applications including content-based image retrieval, bioinformatics, data mining, and recommender systems. Search can be accelerated by the use of an index. However, constructing a high-dimensional index can be quite expensive and may not pay off if the number of queries against the data is not large. In these circumstances, it is beneficial to construct an index adaptively , while responding to a query workload. Existing work on multidimensional adaptive indexing partitions space into orthotopes (i.e., hyperrectang

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40

Alamri, Sultan, David Taniar, and Kinh Nguyen. "Vertical Indexing for Moving Objects in Multifloor Environments." Mobile Information Systems 2018 (2018): 1–15. http://dx.doi.org/10.1155/2018/4175298.

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The indexing and tracking of objects moving in indoor spaces has increasingly become an important area of research, which presents a fundamentally different challenge. There are two main reasons for why indoor should be treated as cellular space. Firstly, an indoor space has entities, such as rooms and walls, that constrain the movement of the moving objects. Secondly, the relevant notion of locations of an object is cell based rather than an exact Euclidean coordinate. As a solution, in our earlier works, we proposed a cell-based indexing structure, called the C-tree, for indexing objects mov

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41

Kumar, Shashank, Sanjay Madria, and Mark Linderman. "M-Grid: a distributed framework for multidimensional indexing and querying of location based data." Distributed and Parallel Databases 35, no. 1 (2017): 55–81. http://dx.doi.org/10.1007/s10619-017-7194-0.

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42

Qian, Gang, Qiang Zhu, Qiang Xue, and Sakti Pramanik. "Dynamic indexing for multidimensional non-ordered discrete data spaces using a data-partitioning approach." ACM Transactions on Database Systems 31, no. 2 (2006): 439–84. http://dx.doi.org/10.1145/1138394.1138395.

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43

Mateen, Ahmed. "LEGION BASED WEED EXTRACTION FROM UAV IMAGERY." Pakistan Journal of Agricultural Sciences 56, no. 04 (2019): 1045–52. http://dx.doi.org/10.21162/pakjas/19.8053.

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Multispectral images are the types of images which consist of many different bands of data for further processing. Vegetation Indexing has Different bands which are combined in the multispectral images and are used to accentuate the vegetated areas. In this one combination is most commonly used LIKE Ratio Vegetation Indexing (RVI). But using UAV imagery this problem has been solved because in UAV the area covered or captured by the cameras would be very wide so that the whole plot or crop can be viewed or captured from the UAV imagery. Legion is a Model used to do Scene Analysis Task, Image Se

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44

Tarullo, Amanda R., Charu T. Tuladhar, Katie Kao, Eleanor B. Drury, and Jerrold Meyer. "Cortisol and socioeconomic status in early childhood: A multidimensional assessment." Development and Psychopathology 32, no. 5 (2020): 1876–87. http://dx.doi.org/10.1017/s0954579420001315.

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AbstractThe hypothalamic–pituitary–adrenal (HPA) axis is sensitive to early life stress, with enduring consequences for biological stress vulnerability and health (Gunnar & Talge, 2008). Low socioeconomic status (SES) is associated with dysregulation of the stress hormone cortisol in early childhood. However, a mechanistic understanding of this association is lacking. Multidimensional assessment of both SES and cortisol is needed to characterize the intricate relations between SES and cortisol function in early childhood. We assessed parent-reported family income, parent education, occupat

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45

Ahmed, Tauheed, and Monalisa Sarma. "Locality sensitive hashing based space partitioning approach for indexing multidimensional feature vectors of fingerprint image data." IET Image Processing 12, no. 6 (2018): 1056–64. http://dx.doi.org/10.1049/iet-ipr.2017.0550.

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46

Romanuke, Vadim. "Uniform Sampling of the Infinite Noncooperative Game on Unit Hypercube and Reshaping Ultimately Multidimensional Matrices of Player’s Payoff Values." Electrical, Control and Communication Engineering 8, no. 1 (2015): 13–19. http://dx.doi.org/10.1515/ecce-2015-0002.

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Abstract The paper suggests a method of obtaining an approximate solution of the infinite noncooperative game on the unit hypercube. The method is based on sampling uniformly the players’ payoff functions with the constant step along each of the hypercube dimensions. The author states the conditions for a sufficiently accurate sampling and suggests the method of reshaping the multidimensional matrix of the player’s payoff values, being the former player’s payoff function before its sampling, into a matrix with minimally possible number of dimensions, where also maintenance of one-to-one indexi

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47

Gao, Jian, Xin Cao, Xin Yao, Gong Zhang, and Wei Wang. "LMSFC: A Novel Multidimensional Index Based on Learned Monotonic Space Filling Curves." Proceedings of the VLDB Endowment 16, no. 10 (2023): 2605–17. http://dx.doi.org/10.14778/3603581.3603598.

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The recently proposed learned indexes have attracted much attention as they can adapt to the actual data and query distributions to attain better search efficiency. Based on this technique, several existing works build up indexes for multi-dimensional data and achieve improved query performance. A common paradigm of these works is to (i) map multi-dimensional data points to a one-dimensional space using a fixed space-filling curve (SFC) or its variant and (ii) then apply the learned indexing techniques. We notice that the first step typically uses a fixed SFC method, such as row-major order an

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48

Rodrigues, Rosangela, Cristóbal Urbano, Patrícia Neubert, José Miguel Rodríguez-Gairín, and Marta Somoza-Fernández. "New academic journals: an international overview of indexing and access models." Information Research an international electronic journal 28, no. 4 (2024): 116–31. http://dx.doi.org/10.47989/ir284466.

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Introduction. An international analysis of academic journals newly created in the period from 2011 to 2020 according to type of publisher, place of publication, their relationship with open access, and their indexing in databases. Studies of the issues of concentration of journal publisher ownership, uses of metrics, and access to titles reveal a changing landscape that is nevertheless still dominated by large commercial oligopolies. One notable trend is the creation of new titles in various configurations. Method. To assess the global scenario, we analyse titles created from 2011 to 2020, foc

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49

Li, Hang, and Shuting Zhang. "Exploratory and Confirmatory Factor Analyses of L2 Linguistic Complexity Measures." International Journal of English Linguistics 11, no. 1 (2020): 192. http://dx.doi.org/10.5539/ijel.v11n1p192.

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This study investigated the latent structure of L2 linguistic complexity as a multidimensional construct and analyzed the relationship between the sub-constructs of L2 linguistic complexity by employing exploratory and confirmatory analyses of a set of linguistic complexity measures indexing different sources of L2 linguistic complexity. Based on relevant theories and empirical studies, 11 automated measures indexing distinct sources of syntactic and lexical complexity were selected and used to assess the linguistics complexity of 930 EFL argumentative essays, which were then equally divided i

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50

Liu, Haicheng, Peter van Oosterom, Theo Tijssen, Tom Commandeur, and Wen Wang. "Managing large multidimensional hydrologic datasets: A case study comparing NetCDF and SciDB." Journal of Hydroinformatics 20, no. 5 (2018): 1058–70. http://dx.doi.org/10.2166/hydro.2018.136.

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Abstract Management of large hydrologic datasets including storage, structuring, clustering, indexing, and query is one of the crucial challenges in the era of big data. This research originates from a specific problem: time series extraction at specific locations takes a long time when a large multidimensional (MD) dataset is stored in the NetCDF classic or the 64-bit offset format. The essence of this issue lies in the contiguous storage structure adopted by NetCDF. In this research, NetCDF file-based solutions and a MD array database management system applying a chunked storage structure ar

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