Spatiotemporal data mining: a survey on challenges and open problems

Zhe Jiang Arun Sharma S. Shekhar

26 Juni 2022

Spatiotemporal data mining aims to discover interesting, useful but non-trivial patterns in big spatial and spatiotemporal data. They are used in various application domains such as public safety, ecology, epidemiology, earth science, etc. This problem is challenging because of the high societal cost of spurious patterns and exorbitant computational cost. Recent surveys of spatiotemporal data mining need update due to rapid growth. In addition, they did not adequately survey parallel techniques for spatiotemporal data mining. This paper provides a more up-to-date survey of spatiotemporal data mining methods. Furthermore, it has a detailed survey of parallel formulations of spatiotemporal data mining.

Xu Yan Jianjun Wei Yaoting Jiang + 2 lainnya

22 September 2024

This paper introduces a novel spatiotemporal feature representation model designed to address the limitations of traditional methods in multidimensional time series (MTS) analysis. The proposed approach converts MTS into one-dimensional sequences of spatially evolving events, preserving the complex coupling relationships between dimensions. By employing a variable-length tuple mining method, key spatiotemporal features are extracted, enhancing the interpretability and accuracy of time series analysis. Unlike conventional models, this unsupervised method does not rely on large training datasets, making it adaptable across different domains. Experimental results from motion sequence classification validate the model's superior performance in capturing intricate patterns within the data. The proposed framework has significant potential for applications across various fields, including backend services for monitoring and optimizing IT infrastructure, medical diagnosis through continuous patient monitoring and health trend analysis, and internet businesses for tracking user behavior and forecasting sales. This work offers a new theoretical foundation and technical support for advancing time series data mining and its practical applications in human behavior recognition and other domains.

Eugenio Cesario

12 Mei 2023

Urban environments continuously generate larger and larger volumes of data, whose analysis can provide descriptive and predictive models as valuable support to inspire and develop data-driven Smart City applications. To this aim, Big data analysis and machine learning algorithms can play a fundamental role to bring improvements in city policies and urban issues. This paper introduces how Big Data analysis can be exploited to design and develop data-driven smart city services, and provides an overview on the most important Smart City applications, grouped in several categories. Then, it presents three real-case studies showing how data analysis methodologies can provide innovative solutions to deal with smart city issues. The first one is an approach for spatio-temporal crime forecasting (tested on Chicago crime data), the second one is methodology to discover mobility hotsposts and trajectory patterns from GPS data (tested on Beijing taxi traces), the third one is an approach to discover predictive epidemic patterns from mobility and infection data (tested on real COVID-19 data). The presented real-world cases prove that data analytics models can effectively support city managers in tackling smart city challenges and improving urban applications.

Suman Rajest S Mr Bhopendra Singh Mr Regin R Mr

28 Februari 2021

This article reviews the approaches used in data mining to perform a geographical study of regional datasets coupled with Geographic Information Systems (GIS). Firstly, we can look at the functions of data mining used by such data and then illustrate their precision compared to their classic data use. We will further explain the research conducted in this sector and point out that two separate methods exist: one is focused on space database learning while the other is based on space statistics. Finally, we will address the key distinctions between these two methods and their similar features.

Annie Y. H. Chou F. S. Tseng

2022

In this paper, we propose a multidimensional spatiotemporal modeling framework of data warehouse creation for tracing dynamic events in contemporary applications, like crowd contact tracing for Covid-19 prevention. Such a framework offers a natural and consistent solution for slowly changing dimension management. It provides a progressive evolution from traditional static data management to modern dynamic data analysis with spatiotemporal tracking capabilities for IoT applications. Based on such a framework, en-tity-centered resource integration and related business intelligence applications can be rig-orously developed, managed and properly tracked. © 2022 Institute of Information Science. All rights reserved.