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Handling Uncertainty in Spatiotemporal Data

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Spatiotemporal Data Analytics and Modeling

Part of the book series: Big Data Management ((BIGDM))

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Abstract

Spatial technologies forge massive datasets fast and constantly. This gigantic dataset consists of the time series forecasting or spatial interpolation issue to time and space dimensions. Spatiotemporal data can be further modeled with different statistical, physical, and artificial intelligence (AI) methods, but due to handling uncertainty in spatiotemporal data is the major challenge in front of these models. The chapter’s fundamental motivation is to analyze the challenges and strategies for virtually managing uncertainty in spatiotemporal data. The primary difficulties behind the data are high-level feature extractions and long-term memory modeling. These data are technically intensive and result in inadequate model configuration and parameterization. Most AI models oriented with these data need more interpretability and essentially require elaborate training but can model complex nonlinear and Non-Gaussian problems. Predictive uncertainty comes from data and models, which a probability distribution and Bayesian inference could estimate. Therefore, this chapter addresses the detailed strategies for handling uncertainty, including algorithms and approaches for data management. The structure of uncertain data management requires exploring the components of uncertainty management, including data structures and relevant algorithms. This chapter also concentrates on the distinct challenges of handling uncertainty in moving object data and provides strategies for addressing these challenges. Another motivation behind this chapter is to study different domains where spatiotemporal data is encountered on an enormous scale and provides a close look at the computational and I/O requirements of several analysis algorithms for such data. Handling uncertainty in spatiotemporal data is a hot topic in the research area. This chapter will provide the researchers extensive and revised literature review and future research direction, which will undoubtedly be valuable for addressing the challenges in addressing uncertainty in spatiotemporal data in diverse applications. The view inside the chapter provides state-of-the-art advances in spatiotemporal data handling and highlights new generation necessities to solve uncertainty in spatiotemporal data.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Anjuman-I-Islam Kalsekar Technical Campus, New Panvel Mumbai, Maharashtra, India

    Shivaji D. Pawar

  2. Department of Humanities and Applied Science, Anjuman-I- Islam Kalsekar, Technical Campus, New Panvel, Mumbai, India

    Varsha S. Pawar

  3. Amity School of Engineering and Technology, Amity University Maharashtra, Mumbai, Maharashtra, India

    Satheesh Abimannan

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  1. Shivaji D. Pawar
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  2. Varsha S. Pawar
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  3. Satheesh Abimannan
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Editor information

Editors and Affiliations

  1. Computing Science and Information Engineering, National Chung Cheng University, Chiayi County, Taiwan

    John A

  2. Computer Science and Engineering, Amity University, Mumbai, Maharashtra, India

    Satheesh Abimannan

  3. College of Computing and Mathematics, King Fahd University of Petroleum and Mi, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

  4. Computing Science and Information Engineering, National Taipei University, New Taipei City, Taiwan

    Yue-Shan Chang

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Pawar, S.D., Pawar, V.S., Abimannan, S. (2024). Handling Uncertainty in Spatiotemporal Data. In: A, J., Abimannan, S., El-Alfy, ES.M., Chang, YS. (eds) Spatiotemporal Data Analytics and Modeling. Big Data Management. Springer, Singapore. https://doi.org/10.1007/978-981-99-9651-3_4

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  • DOI: https://doi.org/10.1007/978-981-99-9651-3_4

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