Data Science: Time Complexity and Inferential Uncertainty (TCIU) Spacekime »

Ivo D. Dinov | Milen V. Velev | Contact us |
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Review the basics of ODEs/PDEs, the Kaluza-Klein Theory, and the DSPA materials.

Outline:
The SOCR Data Science Fundamentals project explores
new theoretical representation and analytical strategies to
understand large and complex data. It utilizes information
measures, entropy KL divergence, PDEs, Dirac’s bra-ket operators.
This fundamentals of data science research project will explore
time-complexity and inferential uncertainty in modeling, analysis
and interpretation of large, heterogeneous, multi-source,
multi-scale, incomplete, incongruent, and longitudinal data.

See The Enigmatic Kime: Time Complexity in Data Science Video, a recording at the University of Michigan Institute for Data Science (MIDAS) Seminar Series, a PDF Slidedeck is available here.

Mission and Objectives

Defining Characteristics of Big Datasets

Data science

Internet of Things (IoT)

Examples of Driving Motivational Challenges

Neuroimaging-genetics

Census-like Population Studies

4D Nucleome

Climate Change

High-dimensional Data

Scientific Inference and Forecasting

Problems of Time

Wavefunctions

Dirac bra-ket notation

Operators

Commutator

Non-Trivial commutator (position/momentum)

Trivial commutator (energy/momentum)

Anthropic principle

Minkowski spacetime

Events

Kime, Kevents and the Spacekime Metric

Problems of Time

Kime-solutions to Time-problems

Common use of Time

Rate of change

Velocity

Newton’s equations of motion

Position (x) and Momentum (p)

Wavefunctions

Schrödinger equation

Wave equation

Lorentz transformation

Euler–Lagrange equation

Wheeler-DeWitt equation

Analogous Kime Extensions

Rate of change

Kime motion equations

Lorentz transformation in space-kime

General properties of the transformations

Backwards motion in the two-dimensional kime

Rotations in kime and space hyperplanes

Velocity-addition law

Generalization of the principle of invariance of the speed of light

Generalization of Heisenberg's Uncertainty Principle

5D spacekime manifold Waves and the Doppler effect

Wirtinger derivatives

The Copenhagen vs. Spacekime Interpretations

Kime Philosophy

Space-Kime Formalism

Antiparticle in space-kime

The causal structure of space-kime

Observables (Datasets)

Inference Function

Inner Product

Eigenspectra (Eigenvalues and Eigenfunctions)

Fundamental Law of Data Science Inference

Superposition Principle

Terminology

Spacetime IID vs. Spacekime Sampling

Uncertainty in Data Science

Quantum Mechanics Formulation

Statistics Formulation

Decision Science Formulation

Information Theoretic Formulation

Data Science Formulation

Kime-Phases Circular distribution plots

Exogenous Feature Time-series analysis

Structured Big Data Analytics Case-Study (UKBB)

Spacekime Analytics of Financial Market and Economics Data

Multisource Brain Data

Financial Market and Economics Analytics (see the 3D Scene of the longitudinal EU Econometrics Indicators)

The authors are profoundly indebted to all of their mentors,
advisors, and collaborators for inspiring the study, guiding the
courses of their careers, nurturing their curiosity, and providing
constructive and critical feedback. Among these scholars are
Gencho Skordev (Sofia University), colleagues ar Burgas Technical University), Kenneth Kuttler (Michigan Tech
University, De Witt L. Sumners and Fred Huffer (Florida State
University), Jan de Leeuw, Nicolas Christou, and Michael Mega
(UCLA), Arthur Toga (USC), Brian Athey, Kathleen Potempa, Janet
Larson, Patricia Hurn, Gilbert Omenn, and Eric Michielssen (University of Michigan).
Many other colleagues, students, researchers, and fellows have
shared their expertise, creativity, valuable time, and critical
assessment for generating, validating, and enhancing these
open-science resources. Among these are Yufei Yang, Yuming Sun,
Lingcong Xu, Simeone Marino, Yi Zhao, Nina Zhou, Alexandr Kalinin,
Syed Husain, and many others. In addition, colleagues from the
Statistics Online Computational Resource (SOCR) and the Michigan
Institute for Data Science (MIDAS) provided encouragement and
valuable suggestions.
Special thanks to Yongkai Qiu,
Yufei Yang, and Zhe Yin
for their substantial efforts in developing, packaging, documenting, and validating the
TCIU R source code.

The research and development reported in this book was partially supported by the US National Science Foundation (grants 1916425, 1734853, 1636840, 1416953, 0716055 and 1023115), US National Institutes of Health (grants P20 NR015331, U54 EB020406, P50 NS091856, P30 DK089503, R01CA233487, R01MH121079), the Burgas University “Prof. Dr. A. Zlatarov”, and the University of Michigan.

The research and development reported in this book was partially supported by the US National Science Foundation (grants 1916425, 1734853, 1636840, 1416953, 0716055 and 1023115), US National Institutes of Health (grants P20 NR015331, U54 EB020406, P50 NS091856, P30 DK089503, R01CA233487, R01MH121079), the Burgas University “Prof. Dr. A. Zlatarov”, and the University of Michigan.