國立高雄大學圖資館 |

Data sciencetime complexity, inferential uncertainty, and spacekime analytics /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data scienceIvo D. Dinov, Milen Velchev Velev.
其他題名:	time complexity, inferential uncertainty, and spacekime analytics /
作者:	Dinov, Ivo D.
其他作者:	Velev, Milen Velchev.
出版者:	Berlin ;De Gruyter,c2022.
面頁冊數:	1 online resource :ill.
標題:	Data mining.
電子資源:	https://www.degruyter.com/isbn/9783110697827
ISBN:	9783110697827$q(ePDF)

Data sciencetime complexity, inferential uncertainty, and spacekime analytics /
Dinov, Ivo D.

Data sciencetime complexity, inferential uncertainty, and spacekime analytics /[electronic resource] :Ivo D. Dinov, Milen Velchev Velev. - Berlin ;De Gruyter,c2022. - 1 online resource :ill. - De Gruyter STEM. - De Gruyter STEM..

Includes bibliographical references and index.

The amount of new information is constantly increasing, faster than our ability to fully interpret and utilize it to improve human experiences. Addressing this asymmetry requires novel and revolutionary scientific methods and effective human and artificial intelligence interfaces. By lifting the concept of time from a positive real number to a 2D complex time (kime), this book uncovers a connection between artificial intelligence (AI), data science, and quantum mechanics. It proposes a new mathematical foundation for data science based on raising the 4D spacetime to a higher dimension where longitudinal data (e.g., time-series) are represented as manifolds (e.g., kime-surfaces). This new framework enables the development of innovative data science analytical methods for model-based and model-free scientific inference, derived computed phenotyping, and statistical forecasting. The book provides a transdisciplinary bridge and a pragmatic mechanism to translate quantum mechanical principles, such as particles and wavefunctions, into data science concepts, such as datum and inference-functions. It includes many open mathematical problems that still need to be solved, technological challenges that need to be tackled, and computational statistics algorithms that have to be fully developed and validated. Spacekime analytics provide mechanisms to effectively handle, process, and interpret large, heterogeneous, and continuously-tracked digital information from multiple sources. The authors propose computational methods, probability model-based techniques, and analytical strategies to estimate, approximate, or simulate the complex time phases (kime directions). This allows transforming time-varying data, such as time-series observations, into higher-dimensional manifolds representing complex-valued and kime-indexed surfaces (kime-surfaces). The book includes many illustrations of model-based and model-free spacekime analytic techniques applied to economic forecasting, identification of functional brain activation, and high-dimensional cohort phenotyping. Specific case-study examples include unsupervised clustering using the Michigan Consumer Sentiment Index (MCSI), model-based inference using functional magnetic resonance imaging (fMRI) data, and model-free inference using the UK Biobank data archive. The material includes mathematical, inferential, computational, and philosophical topics such as Heisenberg uncertainty principle and alternative approaches to large sample theory, where a few spacetime observations can be amplified by a series of derived, estimated, or simulated kime-phases. The authors extend Newton-Leibniz calculus of integration and differentiation to the spacekime manifold and discuss possible solutions to some of the "problems of time". The coverage also includes 5D spacekime formulations of classical 4D spacetime mathematical equations describing natural laws of physics, as well as, statistical articulation of spacekime analytics in a Bayesian inference framework. The steady increase of the volume and complexity of observed and recorded digital information drives the urgent need to develop novel data analytical strategies. Spacekime analytics represents one new data-analytic approach, which provides a mechanism to understand compound phenomena that are observed as multiplex longitudinal processes and computationally tracked by proxy measures. This book may be of interest to academic scholars, graduate students, postdoctoral fellows, artificial intelligence and machine learning engineers, biostatisticians, econometricians, and data analysts. Some of the material may also resonate with philosophers, futurists, astrophysicists, space industry technicians, biomedical researchers, health practitioners, and the general public.

ISBN: 9783110697827$q(ePDF)

Standard No.: 10.1515/9783110697827doiSubjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.B45 / D56 2022

Dewey Class. No.: 005.7

Data sciencetime complexity, inferential uncertainty, and spacekime analytics /
LDR:04775cmm a2200289 a 4500 001 650200
003 DE-B1597
005 20230502090707.0
006 m o d
007 cr cnu---unuuu
008 240227s2022 gw a ob 001 0 eng d
020 $a 9783110697827$q(ePDF)
020 $a 9783110697971$q(epub)
020 $z 9783110697803$q(print)
024 7 $a 10.1515/9783110697827 $2 doi
035 $a 9783110697827
040 $a DE-B1597 $b eng $c DE-B1597
041 0 $a eng
050 4 $a QA76.9.B45 $b D56 2022
082 0 4 $a 005.7 $2 23
100 1 $a Dinov, Ivo D. $3 821965
245 1 0 $a Data science $h [electronic resource] : $b time complexity, inferential uncertainty, and spacekime analytics / $c Ivo D. Dinov, Milen Velchev Velev.
260 $a Berlin ; $a Boston : $b De Gruyter, $c c2022.
300 $a 1 online resource : $b ill.
490 1 $a De Gruyter STEM
504 $a Includes bibliographical references and index.
520 $a The amount of new information is constantly increasing, faster than our ability to fully interpret and utilize it to improve human experiences. Addressing this asymmetry requires novel and revolutionary scientific methods and effective human and artificial intelligence interfaces. By lifting the concept of time from a positive real number to a 2D complex time (kime), this book uncovers a connection between artificial intelligence (AI), data science, and quantum mechanics. It proposes a new mathematical foundation for data science based on raising the 4D spacetime to a higher dimension where longitudinal data (e.g., time-series) are represented as manifolds (e.g., kime-surfaces). This new framework enables the development of innovative data science analytical methods for model-based and model-free scientific inference, derived computed phenotyping, and statistical forecasting. The book provides a transdisciplinary bridge and a pragmatic mechanism to translate quantum mechanical principles, such as particles and wavefunctions, into data science concepts, such as datum and inference-functions. It includes many open mathematical problems that still need to be solved, technological challenges that need to be tackled, and computational statistics algorithms that have to be fully developed and validated. Spacekime analytics provide mechanisms to effectively handle, process, and interpret large, heterogeneous, and continuously-tracked digital information from multiple sources. The authors propose computational methods, probability model-based techniques, and analytical strategies to estimate, approximate, or simulate the complex time phases (kime directions). This allows transforming time-varying data, such as time-series observations, into higher-dimensional manifolds representing complex-valued and kime-indexed surfaces (kime-surfaces). The book includes many illustrations of model-based and model-free spacekime analytic techniques applied to economic forecasting, identification of functional brain activation, and high-dimensional cohort phenotyping. Specific case-study examples include unsupervised clustering using the Michigan Consumer Sentiment Index (MCSI), model-based inference using functional magnetic resonance imaging (fMRI) data, and model-free inference using the UK Biobank data archive. The material includes mathematical, inferential, computational, and philosophical topics such as Heisenberg uncertainty principle and alternative approaches to large sample theory, where a few spacetime observations can be amplified by a series of derived, estimated, or simulated kime-phases. The authors extend Newton-Leibniz calculus of integration and differentiation to the spacekime manifold and discuss possible solutions to some of the "problems of time". The coverage also includes 5D spacekime formulations of classical 4D spacetime mathematical equations describing natural laws of physics, as well as, statistical articulation of spacekime analytics in a Bayesian inference framework. The steady increase of the volume and complexity of observed and recorded digital information drives the urgent need to develop novel data analytical strategies. Spacekime analytics represents one new data-analytic approach, which provides a mechanism to understand compound phenomena that are observed as multiplex longitudinal processes and computationally tracked by proxy measures. This book may be of interest to academic scholars, graduate students, postdoctoral fellows, artificial intelligence and machine learning engineers, biostatisticians, econometricians, and data analysts. Some of the material may also resonate with philosophers, futurists, astrophysicists, space industry technicians, biomedical researchers, health practitioners, and the general public.
588 $a Description based on print version record.
650 0 $a Data mining. $3 184440
650 0 $a Big data. $3 609582
650 0 $a Computer science. $3 199325
700 1 $a Velev, Milen Velchev. $3 960150
830 0 $a De Gruyter STEM. $3 930882
856 4 0 $u https://www.degruyter.com/isbn/9783110697827