A spatiotemporal algebra in Hadoop for moving objects

Abstract

Spatiotemporal data represent the real-world objects that move in geographic space over time. The enormous numbers of mobile sensors and location tracking devices continuously produce massive amounts of such data. This leads to the need for scalable spatiotemporal data management systems. Such systems shall be capable of representing spatiotemporal data in persistent storage and in memory. They shall also provide a range of query processing operators that may scale out in a cloud setting. Currently, very few researches have been conducted to meet this requirement. This paper proposes a Hadoop extension with a spatiotemporal algebra. The algebra consists of moving object types added as Hadoop native types, and operators on top of them. The Hadoop file system has been extended to support parameter passing for files that contain spatiotemporal data, and for operators that can be unary or binary. Both the types and operators are accessible for the MapReduce jobs. Such an extension allows users to write Hadoop programs that can perform spatiotemporal analysis. Certain queries may call more than one operator for different jobs and keep these operators running in parallel. This paper describes the design and implementation of this algebra, and evaluates it using a benchmark that is specific to moving object databases.;

Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018

“…The release of the New Generation Artificial Intelligence Development Plan in 2017 marked the beginning of the new era of AI in China. At present, China has systematically arranged AI from the national level, and the Report of 19th National Congress of China put forward "promoting the deep integration of the Internet, big data, AI and the real economy" and strengthened the comprehensive support of AI for science and technology, economy, social development, and national security [4,36]. During this period, the hotspots of international research on AI in China focused on specific frontier technologies and practical commercial application technologies such as "big data", "convolution neural network", "electronic skin", "image classification", "sensors", "movies", "electronic games", and "applications", which is in line with the current policy advocating integration of artificial intelligence and the real economy at the national level.…”

“…Some scholars believe that AI is the intelligent simulation of human behavior through the use of advanced technology, and that the process involves extremely complex human-machine relationships; from the perspective of information perception, data management, deep learning, bionic behavior, and language interaction, AI includes five core elements: cross media perceptual computing, autonomous deep learning, big data intelligent management, virtualized bionics, and simulated language interaction [3].The research of artificial intelligence has attracted the attention of many scholars, and many fields involves the application of artificial intelligence, machine learning, and pattern recognition, etc. There are published studies and applications involving data transmission, pattern recognition, behavioral research, robotics, and computer engineering [4][5][6][7][8], as well as research and applications in physical sciences, health-related issues, natural sciences, and industrial academic areas [9][10][11]. Furthermore, there have also been some studies related to applications of different sensors, such as binary, digital cameras, depth data, and wearable sensors, that use AI and data classification fields [12][13][14][15][16][17][18].The latest China AI Development Report 2018 proposed that the two main lines of development of AI core technologies are brain science and brain-like intelligence technology, and machine learning represented by deep neural networks; currently, brain science and brain-like intelligence technology research progress is limited.…”

“…The research of artificial intelligence has attracted the attention of many scholars, and many fields involves the application of artificial intelligence, machine learning, and pattern recognition, etc. There are published studies and applications involving data transmission, pattern recognition, behavioral research, robotics, and computer engineering [4][5][6][7][8], as well as research and applications in physical sciences, health-related issues, natural sciences, and industrial academic areas [9][10][11]. Furthermore, there have also been some studies related to applications of different sensors, such as binary, digital cameras, depth data, and wearable sensors, that use AI and data classification fields [12][13][14][15][16][17][18].…”

DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier

“…Research [17] works on feature extraction on the unmanned aerial vehicles (UAV). Algebraic representation of Spatio-temporal real-world objects is presented in [18]. Local descriptors are used to track a person in two different cameras with support vector machines (SVM) [19].…”

Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields

“…In real-life environments, there are some fascinating applications in which the analysis of human activities plays a significant role. Some applications include human/object detection and recognition based on vision object analysis and processing areas such as tracking and detection [1,2], computer engineering [3], physical sciences [4], health-related issues, natural sciences, and industrial academic areas [5]. Most of the authors [6][7][8][9][10][11] recognized the human activities in indoor environments based on different methodologies.…”