22 papers · ranked by Valyu relevance
Rejwana Tasnim Rimi, K. M. Azharul Hasan, Tatsuo Tsuji
Multidimensional query processing is an important access pattern for multidimensional scientific data. We propose an in-memory multidimensional query processing algorithm for dense data using a higher-dimensional array. We developed a new array system namely a Converted two-dimensional Array (C2A) of a multidimensional…
Xin Chi, Jie Hua, Xiao Ren, Azizur Rahman + 1 more
Visualisation techniques have been one of the best data processing and analysis methods in recent decades, and they have assisted in data understanding efforts in various fields. Visualisation techniques for low-dimensional data are well developed and applied in multiple sectors; however, multidimensional data…
Bryar A. Hassan, Shko M. Qader
As information becomes increasingly sizable for organizations to maintain the challenge of organizing data still remains. More importantly, the on-going process of analysing incoming data occurs on a continual basis and organizations should employ existing procedures that may not be adequate or efficient when…
Mahbubur Rahman
Learning from the multidimensional data has been an interesting concept in the field of machine learning. However, such learning can be difficult, complex, expensive because of expensive data processing, manipulations as the number of dimension increases. As a result, we have introduced an ordered index-based data…
Jennifer Hefner, Mary Caroline Skelton, MengWei Pang, Ting Xu + 7 more
Background Oral health surveys largely facilitate the prevention and treatment of oral diseases as well as the awareness of population health status. As oral health is always surveyed from a variety of perspectives, it is a difficult and complicated task to gain insights from multidimensional oral health surveys.…
Senthilkumar Devaraj, S. Paulraj
Multidimensional medical data classification has recently received increased attention by researchers working on machine learning and data mining. In multidimensional dataset (MDD) each instance is associated with multiple class values. Due to its complex nature, feature selection and classifier built from the MDD are…
Boyi Guo, Stephanie C. Hicks
The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve…
Saida Aissi, Mohamed Salah Gouider
— Data warehouse store and provide access to large volume of historical data supporting the strategic decisions of organisations. Data warehouse is based on a multidimensional model which allow to express user's needs for supporting the decision making process. Since it is estimated that 80% of data used for decision…
Paul Mireault
Dimensions are an integral part of many models we use every day. Without thinking about it, we frequently use the time dimension: many financial and accounting spreadsheets have columns representing months or years. Representing a second dimension is often done by repeating blocs of formulas in a worksheet of creating…
Denis Polunin, Irina Shtaiger, Vadim Efimov
Biologists more and more have to deal with objects with non-numeric descriptions: texts (e.g. genetic sequences or even whole genomes), graphs, images, etc. There even could be no variables or descriptions at all when variability of objects is defined by similarity matrix. It is also possible to have too many variables…
Fadila Bentayeb, Nora Maïz, Hadj Mahboubi, Cécile Favre + 4 more
'Sabine Loudcher' 'Nouria Harbi' 'Omar Boussaïd' 'Jérôme Darmont'] Research in data warehousing and OLAP has produced important technologies for the design, management and use of information systems for decision support. With the development of Internet, the availability of various types of data has increased. Thus…
Victor Cavaller
This article consists of a conceptual analysis-from the perspective of communication sciences-of the relevant aspects that should be considered during operational steps in data visualization. The analysis is performed taking as a reference the components that integrate the communication framework theory-the message…
Vinicius Hansel, Pothuvilage Karunarathne, Tiago Cabral Borelli, Robert Quinn + 1 more
Clustering analysis is a foundational step in exploratory data analysis workflows, with dimensionality reduction methods commonly used to visualize multidimensional data in lower-dimensional spaces and infer sample clustering. Principal Component Analysis (PCA) is widely applied in metabolomics but is often suboptimal…
Christos Chatzis, David Horner, Rasmus Bro, Ann-Marie Malby Schoos + 2 more
Temporal multivariate data is ubiquitous in many domains, for instance, being collected over time at planned visits (every few months/years) in longitudinal cohorts, or every few minutes/hours in challenge tests. The analysis of such data often focuses on revealing the underlying temporal patterns common across…
Yimin Zheng, Zhihang Zheng, André F. Rendeiro, Edwin Cheung
Contemporary data visualization is challenged by the growing complexity and size of datasets, often comprising numerous interrelated features. Traditional visualization methods struggle to capture these complex relationships fully or are specialized to a domain requiring familiarity with multiple visualization tools.…
José L. Medina-Franco, Ana L. Chávez-Hernández, Edgar López-López, Fernanda I. Saldívar-González
Technological advances and practical applications of the chemical space concept in drug discovery, natural product research, and other research areas have attracted the scientific community´s attention. The large- and ultra-large chemical spaces are associated not only with the significant increase in the number of…
Ethan R. Deyle, Gerald Pao, George Sugihara
The foundation of Empirical dynamic modeling (EDM) is in representing time-series data as the trajectory of a dynamic system in a multidimensional state space rather than as a collection of traces of individual variables changing through time. Takens’s theorem provides a rigorous basis for adopting this state-space…
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
Edgar López-López, José L. Medina-Franco
Drug-induced liver injury (DILI) is the principal reason for failure in developing drug candidates. It is the most common reason to withdraw from the market after a drug has been approved for clinical use. Therefore, a current challenge is enhancing the accuracy of DILI events' predictive models. In this context, data…
Christina Humer, Rachel Nicholls, Henry Heberle, Moritz Heckmann + 7 more
Chemical reaction optimization (RO) is an iterative process that results in large and high-dimensional datasets. Current tools only allow for limited analysis and understanding of parameter spaces, making it hard for scientists to review or follow changes throughout the process. With the recent emergence of using…
Fernanda I. Saldívar-González, José L. Medina-Franco
Chemical space is a powerful, general, and practical conceptual framework in drug discovery and other areas in chemistry that addresses the diversity of molecules and it has various applications. Moreover, chemical space is a cornerstone of chemoinformatics as a scientific discipline. In response to the increase in the…
Authors not listed
In recent years, the development of large language models (LLMs) has revolutionized various fields of natural science, yet their application in molecular data processing remains constrained due to the reliance on single-modality inputs and outputs. To bridge the gap between experimenters and computational tools, we…