Big Data have emerged and become the norm in the past decade with its well-known 4V challenges and now adds value (a 5th V) for scientific research and the development of new applications. Such Big...

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2019.1698743

Big Spatiotemporal Data Analytics: a research and innovation frontier

Kernel Density Estimation (KDE) is a classic algorithm for analyzing the spatial distribution of point data, and widely applied in spatial humanities analysis. A heat-map permits intuitive visualiz...

A quad-tree-based fast and adaptive Kernel Density Estimation algorithm for heat-map generation

Energy consumption prediction of a CNC machining process is important for energy efficiency optimization strategies. To improve the generalization abilities, more and more parameters are acquired for energy prediction modeling. While the data collected from workshops may be incomplete because of misoperation, unstable network connections, and frequent transfers, etc. This work proposes a framework for energy modeling based on incomplete data to address this issue. First, some necessary preliminary operations are used for incomplete data sets. Then, missing values are estimated to generate a new complete data set based on generative adversarial imputation nets (GAIN). Next, the gene expression programming (GEP) algorithm is utilized to train the energy model based on the generated data sets. Finally, we test the predictive accuracy of the obtained model. Computational experiments are designed to investigate the performance of the proposed framework with different rates of missing data. Experimental results demonstrate that even when the missing data rate increases to 30%, the proposed framework can still make efficient predictions, with the corresponding RMSE and MAE 0.903 kJ and 0.739 kJ, respectively.

Energy Consumption Prediction of a CNC Machining Process With Incomplete Data

The recent ten years witnessed the great achievements on rich applications of Geospatial Big Data across a variety of disciplines. For example, a huge number of Landsat images are utilized in mappi...

https://www.tandfonline.com/doi/pdf/10.1080/10095020.2020.1854981

Geospatial big data for urban planning and urban management

Optimizing and accelerating space–time Ripley ’s K function based on Apache Spark for distributed spatiotemporal point pattern analysis

Big enterprise registration data imputation: Supporting spatiotemporal analysis of industries in China

The boom in publicly accessible big data sources, such as social networks and the internet of things, is creating new opportunities as well as new technologies and applications to study and understand socioeconomic activities. Visual analytics is promising social sensing approach that helps users effectively identify interesting events and discover hidden patterns, anomalies, and relations from datasets. Big data however also represents a challenge to current data processing and analytical technologies, given the heterogeneity of multi-source data, extremely large data volume and intensive computation impede the efficiency and feasibility of visual analysis. In this chapter, we introduce cutting-edge data storage, computing, and visualization technologies that can tackle these challenges. Data storage and index mechanism optimize query and reduce response time for multi-dimensional big data access; high Performance Computing (HPC) technology accelerates big data preprocessing and mining; web-based interactive visualization technologies allow users to synthesize and manipulate data in a dynamic and user-friendly manner. Taking big enterprise registration data as an example, we demonstrate how to apply these new technologies to build a web-based visual analytics framework that supports studies in economic geography. The necessity and value of these technologies for big socioeconomic data processing and visualization is analyzed and discussed. We conclude, by analyzing and discussing the versatility and portability of these technologies in other application scenarios such as in the humanities and social sciences.

High Performance Spatiotemporal Visual Analytics Technologies and Its Applications in Big Socioeconomic Data Analysis

The invention discloses a space-time POI data point mode analysis method based on a Ripley's K function in a distributed environment. The method comprises the following steps: firstly, configuring a customized serializer oriented to a space-time object and a space-time index, then establishing a space-time KDB tree, carrying out data repartitioning on an observation point, then constructing a local space-time R tree index, and constructing a series of point pairs; then, for each point pair, searching a space-time weight corresponding to the point pair in a double-layer cache; then calculatinga space-time Ripley's K function calculation result under different space-time distance thresholds corresponding to the observation point; generating a simulation point based on the observation point,and obtaining a space-time Ripley's K function calculation result corresponding to the simulation point under different space-time distance thresholds; and finally, obtaining a final data distribution output result according to the upper bound and the lower bound of the space-time Ripley's K function simulation. By means of the method, the efficiency of space-time POI data point mode analysis canbe greatly improved.

Yuan Wang

Papers

Big enterprise registration data imputation: Supporting spatiotemporal analysis of industries in China

Optimizing and accelerating space–time Ripley ’s K function based on Apache Spark for distributed spatiotemporal point pattern analysis

High Performance Spatiotemporal Visual Analytics Technologies and Its Applications in Big Socioeconomic Data Analysis

Space-time POI data point mode analysis method based on Ripley's K function in distributed environment