000			05609cam a2200517 i 4500
001			21910341
003			OSt
005			20220504220526.0
008			210219s2022 gw a b 001 0 eng d
010			_a 2021933301
015			_aGBC1K9396 _2bnb
016	7		_a020425716 _2Uk
020			_a9783110697803 _q(hbk.)
020			_a3110697807 _q(hbk.)
020			_z9783110697971 (ePub ebook)
035			_a(OCoLC)on1289269204
040			_aUKMGB _beng _cUKMGB _erda _dOCLCF _dOCLCO _dYDX _dFIE _dDLC
041			_aeng
042			_alccopycat
050	0	0	_aQA76.9.B45 _bD56 2022
082	0	4	_a005.7 DIN _223
092			_20
100	1		_aDinov, Ivo D., _eauthor.
245	1	0	_aData science : _btime complexity, inferential uncertainty, and spacekime analytics / _cIvo D. Dinov, Milen Velchev Velev.
264		1	_aBerlin ; _aBoston : _bDe Gruyter, _c[2022]
300			_axxvi, 463 pages : _billustrations (some color) ; _c24 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	1		_aDe Gruyter STEM
504			_aIncludes bibliographical references and index.
520			_aThe 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. -- _cProvided by publisher.
650		0	_aData mining.
650		0	_aBig data.
650		0	_aComputer science.
650		7	_aComputer science. _2fast _0(OCoLC)fst00872451
650		7	_aData mining. _2fast _0(OCoLC)fst00887946
700	1		_aVelev, Milen Velchev, _eauthor.
776	0	8	_iebook version : _z9783110697971
830		0	_aDe Gruyter STEM.
843			_aPhotocopy
887			_2CamTech Library
906			_a7 _bcbc _ccopycat _d2 _encip _f20 _gy-gencatlg
942			_2ddc _cBK _n0
999			_c135 _d135