Data Science: Time Complexity, Inferential Uncertainty, and Spacekime Analytics

The book "Spacekime Analytics: A Transdisciplinary Bridge between Artificial Intelligence, Data Science, and Quantum Mechanics" explores the relationship between AI, data science, and quantum mechanics by introducing the concept of spacekime. It proposes a new mathematical foundation for data science that represents longitudinal data as manifolds, enabling innovative methods for scientific inference, phenotyping, and forecasting. Spacekime analytics provides mechanisms to handle large, heterogeneous, and continuously-tracked digital information, transforming it into higher-dimensional manifolds. The book discusses applications in economic forecasting, brain activation identification, and cohort phenotyping, and addresses mathematical, inferential, computational, and philosophical topics. It highlights the urgent need for novel data analytical strategies to understand compound phenomena observed as multiplex longitudinal processes.

\n Format: Hardback
\n Length: 489 pages
\n Publication date: 20 December 2021
\n Publisher: De Gruyter
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The rate at which new information is expanding far outpaces our capacity to comprehend and effectively harness it to enhance human experiences. To address this imbalance, innovative and groundbreaking scientific approaches and seamless human-machine interfaces are imperative. By expanding the concept of time from a positive real number to a 2D complex time (kime), this book unveils a profound connection between artificial intelligence (AI), data science, and quantum mechanics. It presents a novel mathematical framework for data science, elevating the 4D spacetime to a higher dimension where longitudinal data, such as time-series observations, are represented as manifolds, akin to kime-surfaces. This groundbreaking framework empowers the development of cutting-edge analytical methods for model-based and model-free scientific inference, derived computed phenotyping, and statistical forecasting. The book serves as a transdisciplinary bridge, facilitating the translation of quantum mechanical principles, including particles and wavefunctions, into data science concepts, such as datum and inference-functions. It encompasses a multitude of open mathematical problems that require further exploration, technological challenges that demand attention, and computational statistics algorithms that require thorough development and validation.

Spacekime analytics offer powerful mechanisms to efficiently handle, process, and interpret vast, diverse, and continuously tracked digital information from various 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 enables the transformation of time-varying data, such as time-series observations, into higher-dimensional manifolds, offering a deeper understanding and analysis of complex systems.

\n Weight: 928g\n
Dimension: 240 x 170 (mm)\n
ISBN-13: 9783110697803\n \n