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Relevant bibliographies by topics / Data dimension

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Academic literature on the topic 'Data dimension'

Author: Grafiati

Published: 28 May 2022

Last updated: 30 July 2025

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Journal articles on the topic "Data dimension"

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Yasmine Aljarallah, Mutasim Alfadhel. "Data Governance Evaluation of the Data Management Office at King Saud University based on the National Data Management Office standards." Journal of Information Systems Engineering and Management 10, no. 10s (2025): 794–805. https://doi.org/10.52783/jisem.v10i10s.1530.

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This study aimed to investigate the implementation of data governance at the Data Management Office of King Saud University by examining the adoption of the strategic plan, implementation mechanisms, and compliance with data governance standards and controls established by the National Data Management Office (NDMO), the national regulatory and reference authority for data management and governance.Using a descriptive-analytical approach, a questionnaire was designed based on three dimensions: the strategic dimension, the executive dimension, and the challenges dimension. The study sample inclu

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Adi Wicaksana, I. Gusti Ngurah, Achmad Nizar Hidayanto, Handini Mekkawati, and Rizha Febriyanti. "DATA QUALITY ASSESSMENT: A CASE STUDY ON ASSET VALUATION COMPARISON DATA." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 9, no. 2 (2024): 263–72. http://dx.doi.org/10.33480/jitk.v9i2.5184.

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To realize a data-driven organization, good data quality is needed as a foundation for solving various problems related to data management. The case study used in this research is asset valuation comparison data. The purpose of this research is to define dimensions, measure and analyze data quality on asset valuation comparison data. There are three dimensions used in measuring data quality in this study which are adjusted based on existing regulations at Ministry X, namely accuracy, completeness, and validity. This research uses the stages in the Total Data Quality Management (TDQM) framework

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Zhihui, Ni, Wu Lichun, Wang Ming-hui, Yi Jing, and Zeng Qiang. "The Fractal Dimension of River Length Based on the Observed Data." Journal of Applied Mathematics 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/327297.

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Although the phenomenon that strictly meets the constant dimension fractal form in the nature does not exist, fractal theory provides a new way and means for the study of complex natural phenomena. Therefore, we use some variable dimension fractal analysis methods to study river flow discharge. On the basis of the flood flow corresponding to the waterline length, the river of the overall and partial dimensions are calculated and the relationships between the overall and partial dimensions are discussed. The law of the length in section of Chongqing city of Yangtze River is calibrated by using

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Le Dinh, Thang, Nguyen Anh Khoa Dam, Chan Nam Nguyen, Thi My Hang Vu, and Nguyen Cuong Pham. "From Customer Data to Smart Customer Data: The Smart Data Transformation Process." ITM Web of Conferences 41 (2022): 05002. http://dx.doi.org/10.1051/itmconf/20224105002.

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Nowadays, smart data has emerged as a new trend in creating more business value for enterprises that is defined as the data that is gathered and processed to create new insights to support business decisions. However, the transformation from data into actionable insights is still a real challenge for enterprises. For this reason, this paper presents a smart data transformation process, which aims at transforming customer data into smart customer data in order to offer actionable insights. The purpose of the study is to propose a transformation process that can be used to operate a knowledge st

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Kuffo, Leonardo, Elena Krippner, and Peter Boncz. "PDX: A Data Layout for Vector Similarity Search." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–26. https://doi.org/10.1145/3725333.

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We propose Partition Dimensions Across (PDX), a data layout for vectors (e.g., embeddings) that, similar to PAX [6], stores multiple vectors in one block, using a vertical layout for the dimensions (Figure 1). PDX accelerates exact and approximate similarity search thanks to its dimension-by-dimension search strategy that operates on multiple-vectors-at-a-time in tight loops. It beats SIMD-optimized distance kernels on standard horizontal vector storage (avg 40% faster), only relying on scalar code that gets auto-vectorized. We combined the PDX layout with recent dimension-pruning algorithms A

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Rossi, Rogério, and Kechi Hirama. "Characterizing Big Data Management." Issues in Informing Science and Information Technology 12 (2015): 165–80. http://dx.doi.org/10.28945/2204.

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Big data management is a reality for an increasing number of organizations in many areas and represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial to facilitate the management of big data in any kind of organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management can be supported by these three dimensions: technology, people and processes. Hence, this article discusses these dimensions: the technological dime

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Ummu Hani’ Hair Zaki, Izyan Izzati Kamsani, Roliana Ibrahim, Norzehan Sakamat, and Eser Kandogan. "Random Dimension Manipulation for Efficient High-Dimensional Data Clustering." Journal of Advanced Research in Applied Sciences and Engineering Technology 51, no. 1 (2024): 129–40. http://dx.doi.org/10.37934/araset.51.1.129140.

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High-dimensional data is collected from various sources, fields, and applications such as medicine, science, business and more to provide helpful information to others. Unfortunately, the complexity of high-dimensional data has made it difficult to interpret and understand. As a result, sophisticated data analysis is required to extract knowledge and information from it. This can be illustrated through a visualization presentation. However, overlap between data can occur during visualization as data increases. Indirectly, it can cause a cluttered visual presentation. As a result, it affects th

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Hu, Yong De, Jing Chang Pan, and Xin Tan. "High-Dimensional Data Dimension Reduction Based on KECA." Applied Mechanics and Materials 303-306 (February 2013): 1101–4. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1101.

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Kernel entropy component analysis (KECA) reveals the original data’s structure by kernel matrix. This structure is related to the Renyi entropy of the data. KECA maintains the invariance of the original data’s structure by keeping the data’s Renyi entropy unchanged. This paper described the original data by several components on the purpose of dimension reduction. Then the KECA was applied in celestial spectra reduction and was compared with Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) by experiments. Experimental results show that the KECA is a good method

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Vlassis, Nikos, Yoichi Motomura, and Ben Kröse. "Supervised Dimension Reduction of Intrinsically Low-Dimensional Data." Neural Computation 14, no. 1 (2002): 191–215. http://dx.doi.org/10.1162/089976602753284491.

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High-dimensional data generated by a system with limited degrees of freedom are often constrained in low-dimensional manifolds in the original space. In this article, we investigate dimension-reduction methods for such intrinsically low-dimensional data through linear projections that preserve the manifold structure of the data. For intrinsically one-dimensional data, this implies projecting to a curve on the plane with as few intersections as possible. We are proposing a supervised projection pursuit method that can be regarded as an extension of the single-index model for nonparametric regre

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Badakhshan Farahabadi, Fazel, Kianoush Fathi Vajargah, and Rahman Farnoosh. "Dimension Reduction Big Data Using Recognition of Data Features Based on Copula Function and Principal Component Analysis." Advances in Mathematical Physics 2021 (July 11, 2021): 1–8. http://dx.doi.org/10.1155/2021/9967368.

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Nowadays, data are generated in the world with high speed; therefore, recognizing features and dimensions reduction of data without losing useful information is of high importance. There are many ways to dimension reduction, including principal component analysis (PCA) method, which is by identifying effective dimensions in an acceptable level, reducing dimension of data. In the usual method of principal component analysis, data are usually normal, or we normalize data; then, the principal component analysis method is used. Many studies have been done on the principal component analysis method

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Dissertations / Theses on the topic "Data dimension"

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Peng, Wei. "Clutter-based dimension reordering in multi-dimensional data visualization." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-01115-222940.

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Boulesteix, Anne-Laure. "Dimension reduction and Classification with High-Dimensional Microarray Data." Diss., lmu, 2005. http://nbn-resolving.de/urn:nbn:de:bvb:19-28017.

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Samko, Oksana. "Low dimension hierarchical subspace modelling of high dimensional data." Thesis, Cardiff University, 2009. http://orca.cf.ac.uk/54883/.

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Building models of high-dimensional data in a low dimensional space has become extremely popular in recent years. Motion tracking, facial animation, stock market tracking, digital libraries and many other different models have been built and tuned to specific application domains. However, when the underlying structure of the original data is unknown, the modelling of such data is still an open question. The problem is of interest as capturing and storing large amounts of high dimensional data has become trivial, yet the capability to process, interpret, and use this data is limited. In this th

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Hassan, Tahir Mohammed. "Data-independent vs. data-dependent dimension reduction for pattern recognition in high dimensional spaces." Thesis, University of Buckingham, 2017. http://bear.buckingham.ac.uk/199/.

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There has been a rapid emergence of new pattern recognition/classification techniques in a variety of real world applications over the last few decades. In most of the pattern recognition/classification applications, the pattern of interest is modelled by a data vector/array of very high dimension. The main challenges in such applications are related to the efficiency of retrieval, analysis, and verifying/classifying the pattern/object of interest. The “Curse of Dimension” is a reference to these challenges and is commonly addressed by Dimension Reduction (DR) techniques. Several DR techniques

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Yahya, Waheed Babatunde. "Sequential Dimension Reduction and Prediction Methods with High-dimensional Microarray Data." Diss., lmu, 2009. http://nbn-resolving.de/urn:nbn:de:bvb:19-102544.

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XIA, QI. "Sufficient Dimension Reduction with Missing Data." Diss., Temple University Libraries, 2017. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/469880.

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Statistics<br>Ph.D.<br>Existing sufficient dimension reduction (SDR) methods typically consider cases with no missing data. The dissertation aims to propose methods to facilitate the SDR methods when the response can be missing. The first part of the dissertation focuses on the seminal sliced inverse regression (SIR) approach proposed by Li (1991). We show that missing responses generally affect the validity of the inverse regressions under the mechanism of missing at random. We then propose a simple and effective adjustment with inverse probability weighting that guarantees the validity of SI

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Wauters, John, and John Wauters. "Independence Screening in High-Dimensional Data." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/623083.

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High-dimensional data, data in which the number of dimensions exceeds the number of observations, is increasingly common in statistics. The term "ultra-high dimensional" is defined by Fan and Lv (2008) as describing the situation where log(p) is of order O(na) for some a in the interval (0, ½). It arises in many contexts such as gene expression data, proteomic data, imaging data, tomography, and finance, as well as others. High-dimensional data present a challenge to traditional statistical techniques. In traditional statistical settings, models have a small number of features, chosen base

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Battey, Heather Suzanne. "Dimension reduction and automatic smoothing in high dimensional and functional data analysis." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609849.

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Weng, Jiaying. "TRANSFORMS IN SUFFICIENT DIMENSION REDUCTION AND THEIR APPLICATIONS IN HIGH DIMENSIONAL DATA." UKnowledge, 2019. https://uknowledge.uky.edu/statistics_etds/40.

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The big data era poses great challenges as well as opportunities for researchers to develop efficient statistical approaches to analyze massive data. Sufficient dimension reduction is such an important tool in modern data analysis and has received extensive attention in both academia and industry. In this dissertation, we introduce inverse regression estimators using Fourier transforms, which is superior to the existing SDR methods in two folds, (1) it avoids the slicing of the response variable, (2) it can be readily extended to solve the high dimensional data problem. For the ultra-high dime

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Ahn, Jeongyoun Marron James Stephen. "High dimension, low sample size data analysis." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2006. http://dc.lib.unc.edu/u?/etd,375.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2006.<br>Title from electronic title page (viewed Oct. 10, 2007). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Statistics and Operations Research." Discipline: Statistics and Operations Research; Department/School: Statistics and Operations Research.

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Books on the topic "Data dimension"

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Gorban, Alexander N., Balázs Kégl, Donald C. Wunsch, and Andrei Y. Zinovyev, eds. Principal Manifolds for Data Visualization and Dimension Reduction. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-73750-6.

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N, Gorbanʹ A., ed. Principal manifolds for data visualization and dimension reduction. Springer, 2007.

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Perlman, Geoff. Inside 4th Dimension 3.1. 2nd ed. SYBEX, 1994.

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Sindler, Hal. MRDS - 4D: Mineral Resources Data System using 4th DIMENSION. U.S. Dept. of the Interior, U.S. Geological Survey, 1995.

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Sindler, Hal. MRDS - 4D: Mineral Resources Data System using 4th DIMENSION. U.S. Dept. of the Interior, U.S. Geological Survey, 1995.

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Sindler, Hal. MRDS - 4D: Mineral Resources Data System using 4th DIMENSION. U.S. Dept. of the Interior, U.S. Geological Survey, 1995.

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Zhu, Lei, Jingjing Li, and Zheng Zhang. Dynamic Graph Learning for Dimension Reduction and Data Clustering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-42313-0.

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F, Nardocchio Elaine, ed. Reader response to literature: The empirical dimension. Mouton de Gruyter, 1992.

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Schimek, Heimo, and Charles Jencks. Cybertecture: Die 4. Dimension in der Architektur. Löcker Verlag, 2001.

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Mitchell, Jeremy. Electronic banking and the consumer: The European dimension. Policy Studies Institute, 1988.

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Book chapters on the topic "Data dimension"

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Gilbert, Nigel. "High dimension tables." In Analyzing Tabular Data. Routledge, 2022. http://dx.doi.org/10.4324/9781003259701-8.

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Larsen, Kasper Green. "Dimension Reduction." In Encyclopedia of Big Data Technologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_60-1.

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Taniar, David, and Wenny Rahayu. "Dimension Keys." In Data-Centric Systems and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81979-8_9.

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Larsen, Kasper Green. "Dimension Reduction." In Encyclopedia of Big Data Technologies. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_60.

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Li, Lexin. "Dimension Reduction for High-Dimensional Data." In Methods in Molecular Biology. Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60761-580-4_14.

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Alberti, Gianmarco. "The Third Dimension." In From Data to Insights. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781032726328-5.

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Zeugmann, Thomas. "VC Dimension." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_881-1.

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Zeugmann, Thomas. "VC Dimension." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_881.

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Appriou, Alain. "Spatial Dimension: Data Association." In Uncertainty Theories and Multisensor Data Fusion. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118578636.ch8.

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Sarlin, Peter. "Data and Dimension Reduction." In Computational Risk Management. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54956-4_4.

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Conference papers on the topic "Data dimension"

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Yang, Tian-Le. "Kernel-based Infinite-dimensional Dimension Reduction for Functional Data." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10649979.

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Wei, Peiyi. "A NLP-based method for extracting temporal dimension features from unstructured high-dimensional big data." In 2024 Sixth International Conference on Next Generation Data-driven Networks (NGDN). IEEE, 2024. http://dx.doi.org/10.1109/ngdn61651.2024.10744132.

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Huang, Aokang, Bin Liu, and Weihua Huang. "Detection of False Data Injection Attacks: An Extending Dimension Data Modification Scheme." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865444.

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Song, Jie, and Yubin Bao. "Partitioned Dimension: Modeling the Numerical Dimension in Data Warehouse." In 2010 12th Asia Pacific Web Conference (APWEB). IEEE, 2010. http://dx.doi.org/10.1109/apweb.2010.61.

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Ganguly, Arnab, Wing-Kai Hon, and Rahul Shah. "Stabbing Colors in One Dimension." In 2017 Data Compression Conference (DCC). IEEE, 2017. http://dx.doi.org/10.1109/dcc.2017.44.

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Houle, Michael E., Hisashi Kashima, and Michael Nett. "Generalized Expansion Dimension." In 2012 IEEE 12th International Conference on Data Mining Workshops. IEEE, 2012. http://dx.doi.org/10.1109/icdmw.2012.94.

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Zhuo, Xiaoyan, Aekyeung Moon, Jialing Zhang, and Seung Woo Son. "Cascaded Dimension Reduction for Effective Anomaly Detection." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671364.

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Yang, Xin, Sebastien Michea, and Hongyuan Zha. "Conical dimension as an intrisic dimension estimator and its applications." In Proceedings of the 2007 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2007. http://dx.doi.org/10.1137/1.9781611972771.16.

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Alonso, J., M. Arias, P. Villares, and M. Catalán. "Fractal dimension and altimeter data." In Remote Sensing, edited by Charles R. BostaterJr. and Rosalia Santoleri. SPIE, 2005. http://dx.doi.org/10.1117/12.624142.

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Bahadur, Nitish, and Randy Paffenroth. "Dimension Estimation using Second Order data in Finance." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671911.

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Reports on the topic "Data dimension"

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Ding, Chris, Xiaofeng He, Hongyuan Zha, and Horst Simon. Adaptive dimension reduction for clustering high dimensional data. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/807420.

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Francom, Devin Craig, Scott Alan Vander Wiel, and Brian Phillip Weaver. Nuclear Data Dimension Reduction. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1561059.

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Clauser, Charles E., John T. McConville, Claire C. Gordon, and Ilse O. Tebbetts. Selection of Dimensions for an Anthropometric Data Base. Volume 2. Dimension Evaluation Sheets. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada179472.

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Geraci, Gianluca, and Tian Yu Yen. Data-Driven Supervised Dimension Reduction for Scientific Discovery. Office of Scientific and Technical Information (OSTI), 2024. https://doi.org/10.2172/2480160.

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Sun, Ding. Dimension Data of Coax-Waveguide Couplers and Waveguide Terminators. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/1985110.

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Dungwatanawanich, N. Extending and Smoothing Two-dimension Equation of State Simulation Data. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1890073.

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Oskolkov, Nikolay. Dimension Reduction Methods for Life Sciences. Instats Inc., 2024. http://dx.doi.org/10.61700/gyxh9ued08xio1347.

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This seminar provides a comprehensive overview of dimension reduction techniques in R and Python for high-dimensional biological data, focusing on their practical applications in life sciences. Participants will gain both theoretical knowledge and practical experience in linear and nonlinear dimensionality reduction methods such as tSNE and UMAP, enhancing their ability to analyze complex datasets effectively. By the conclusion of the seminar, participants will understand the theoretical and practical foundations of these methods, with a wealth of examples that can be rapidly applied for their

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Van Veen, Barry D. Reducing Data Dimension to Lower Signal Processing Computational Requirements and Maximize Performance. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada266606.

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Bourgaux, Camille, and Anni-Yasmin Turhan. Temporal Query Answering in DL-Lite over Inconsistent Data. Technische Universität Dresden, 2017. http://dx.doi.org/10.25368/2022.236.

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In ontology-based systems that process data stemming from different sources and that is received over time, as in context-aware systems, reasoning needs to cope with the temporal dimension and should be resilient against inconsistencies in the data. Motivated by such settings, this paper addresses the problem of handling inconsistent data in a temporal version of ontology-based query answering. We consider a recently proposed temporal query language that combines conjunctive queries with operators of propositional linear temporal logic and extend to this setting three inconsistency-tolerant se

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Buathong, Thananon, Anna Dimitrova, Paolo Miguel M. Vicerra, and Montakarn Chimmamee. Years of Good Life: An illustration of a new well-being indicator using data for Thailand. Verlag der Österreichischen Akademie der Wissenschaften, 2021. http://dx.doi.org/10.1553/populationyearbook2021.dat.1.

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While Thailand has achieved high levels of economic growth in recent decades, poverty at the local level has been increasing. Indicators of human development at the national level often mask the differences in well-being across communities. When responding to the need for sustainable development research, the heterogeneity of a population should be emphasised to ensure that no one is left behind. The Years of Good Life (YoGL) is a well-being indicator that demonstrates the similarities and differences between subpopulations in a given sociocultural context over time. The data used in this anal

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