Data science : (Record no. 135)

MARC details
000 -LEADER
fixed length control field 05609cam a2200517 i 4500
001 - CONTROL NUMBER
control field 21910341
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220504220526.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 210219s2022 gw a b 001 0 eng d
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER
LC control number 2021933301
015 ## - NATIONAL BIBLIOGRAPHY NUMBER
National bibliography number GBC1K9396
Source bnb
016 7# - NATIONAL BIBLIOGRAPHIC AGENCY CONTROL NUMBER
Record control number 020425716
Source Uk
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783110697803
Qualifying information (hbk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 3110697807
Qualifying information (hbk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 9783110697971 (ePub ebook)
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)on1289269204
040 ## - CATALOGING SOURCE
Original cataloging agency UKMGB
Language of cataloging eng
Transcribing agency UKMGB
Description conventions rda
Modifying agency OCLCF
-- OCLCO
-- YDX
-- FIE
-- DLC
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
042 ## - AUTHENTICATION CODE
Authentication code lccopycat
050 00 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA76.9.B45
Item number D56 2022
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.7 DIN
Edition number 23
092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC)
Edition number 0
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Dinov, Ivo D.,
Relator term author.
245 10 - TITLE STATEMENT
Title Data science :
Remainder of title time complexity, inferential uncertainty, and spacekime analytics /
Statement of responsibility, etc. Ivo D. Dinov, Milen Velchev Velev.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Berlin ;
-- Boston :
Name of producer, publisher, distributor, manufacturer De Gruyter,
Date of production, publication, distribution, manufacture, or copyright notice [2022]
300 ## - PHYSICAL DESCRIPTION
Extent xxvi, 463 pages :
Other physical details illustrations (some color) ;
Dimensions 24 cm
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term unmediated
Media type code n
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term volume
Carrier type code nc
Source rdacarrier
490 1# - SERIES STATEMENT
Series statement De Gruyter STEM
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographical references and index.
520 ## - SUMMARY, ETC.
Summary, etc. 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. --
Assigning source Provided by publisher.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Data mining.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Big data.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Computer science.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Computer science.
Source of heading or term fast
Authority record control number or standard number (OCoLC)fst00872451
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Data mining.
Source of heading or term fast
Authority record control number or standard number (OCoLC)fst00887946
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Velev, Milen Velchev,
Relator term author.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information ebook version :
International Standard Book Number 9783110697971
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title De Gruyter STEM.
843 ## - REPRODUCTION NOTE
Type of reproduction Photocopy
887 ## - NON-MARC INFORMATION FIELD
Source of data CamTech Library
906 ## - LOCAL DATA ELEMENT F, LDF (RLIN)
a 7
b cbc
c copycat
d 2
e ncip
f 20
g y-gencatlg
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Books
Suppress in OPAC No
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
    Dewey Decimal Classification     CamTech Library CamTech Library STEM & Engineering 05/04/2022   005.7 DIN CamTech 000718 05/04/2022 1 05/04/2022 Books