A Unified Indexing Strategy for the Mixed Data of a Future Marine GIS

2017 ◽  
Vol 70 (4) ◽  
pp. 735-747
Author(s):  
Tao Liu ◽  
Xie Han ◽  
Jie Yang ◽  
Liqun Kuang
Keyword(s):  
Big Data ◽  
Information Systems ◽  
Spatial Databases ◽  
Three Dimensional ◽  
Data Retrieval ◽  
Mixed Data ◽  
Spatial Indexing ◽  
Data Types ◽  
Indexing Structure ◽  
Data Organisation

Spatial indexing technology is widely used in Geographic Information Systems (GIS) and spatial databases. As a data retrieval technology, spatial indexing is becoming increasingly important in the big-data age. The purpose of this study is to propose a unified indexing strategy for the mixed data of a future marine GIS. First, data organisation of the system is described. Second, the display condition of each type of data is introduced. These conditions are the basis for the construction of a unified indexing structure. Third, a unified indexing structure for mixed data is presented. The construction process and the search method of the indexing structure are described. Finally, we implement the indexing strategy in our system “Automotive Intelligent Chart Three-dimensional Electronic Chart Display and Information Systems” (AIC 3D ECDIS). Our strategy can provide fast and integrated data retrieval. The spatial indexing strategy we propose breaks through the limitation of data types in our system. It can also be applied in other GIS systems. With the advent of the big-data age, mixed data indexing will become more and more important.

Query Processing in Spatial Databases

2009 ◽  
pp. 269-278
Author(s):  
Antonio Corral ◽  
Michael Vassilakopoulos
Keyword(s):  
Information Systems ◽  
Query Processing ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Database ◽  
Database System ◽  
Data Types ◽  
Spatial Data Models ◽  
Large Scale Integration ◽  
Efficient Query Processing

Spatial data management has been an active area of intensive research for more than two decades. In order to support spatial objects in a database system several important issues must be taken into account such as: spatial data models, indexing mechanisms and efficient query processing. A spatial database system (SDBS) is a database system that offers spatial data types in its data model and query language and supports spatial data types in its implementation, providing at least spatial indexing and efficient spatial query processing (Güting, 1994). The main reason that has caused the active study of spatial database management systems (SDBMS) comes from the needs of the existing applications such as geographical information systems (GIS), computer-aided design (CAD), very large scale integration design (VLSI), multimedia information systems (MIS), data warehousing, multi-criteria decision making, location-based services, etc.

The design of environmental geographic information systems

1988 ◽  
Vol 324 (1579) ◽  
pp. 373-380 ◽  
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Three Dimensional ◽  
Geographic Information ◽  
Spatial Index ◽  
Data Sets ◽  
Data Types ◽  
Remotely Sensed Data ◽  
Key Issues ◽  
Image Object

The development of geographic information systems (gis) is recognized as a prerequisite for the effective exploitation of remotely sensed data. Current commercial systems represent a solution with a strong bias from either the mapping or the remotesensing market. They thus lack full cross discipline functionality and a model-oriented approach. This paper examines some of the key issues in truly integrated gis design. The five data types of image, object (vector), terrain, tabular and knowledge are identified along with the operations required with them within a gis. The term ‘geoschema’ is introduced (analogous to schema within a database) to describe the organization of the geographical datasets. Three-dimensional data handling, the necessity of qualifying data and the user interface are given particular attention. An efficient method of implementing an integrated spatial index into the data sets is described. 1

Spatial Network Databases

2009 ◽  
pp. 307-315 ◽  
Author(s):  
Michael Vassilakopoulos
Keyword(s):  
Information Systems ◽  
Spatial Data ◽  
Computer Aided Design ◽  
Geographical Information Systems ◽  
Large Scale ◽  
Spatial Databases ◽  
Query Language ◽  
Geographical Information ◽  
Data Types ◽  
Large Scale Integration

A Spatial Database is a database that offers spatial data types, a query language with spatial predicates, spatial indexing techniques, and efficient processing of spatial queries. All these fields have attracted the focus of researchers over the past 25 years. The main reason for studying spatial databases has been applications that emerged during this period, such as Geographical Information Systems, Computer-Aided Design, Very Large Scale Integration design, Multimedia Information Systems, and so forth. In parallel, the field of temporal databases, databases that deal with the management of timevarying data, attracted the research community since numerous database applications (i.e., Banking, Personnel Management, Transportation Scheduling) involve the notion of time.

Construction of a multi-source heterogeneous hybrid platform for big data

2021 ◽  
pp. 1-10
Author(s):  
Ying Wang ◽  
Yiding Liu ◽  
Minna Xia
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Forest Fire ◽  
Original Data ◽  
Big Data Analysis ◽  
Multiple Sources ◽  
Data Types ◽  
Fire Monitoring ◽  
Data Platform

Big data is featured by multiple sources and heterogeneity. Based on the big data platform of Hadoop and spark, a hybrid analysis on forest fire is built in this study. This platform combines the big data analysis and processing technology, and learns from the research results of different technical fields, such as forest fire monitoring. In this system, HDFS of Hadoop is used to store all kinds of data, spark module is used to provide various big data analysis methods, and visualization tools are used to realize the visualization of analysis results, such as Echarts, ArcGIS and unity3d. Finally, an experiment for forest fire point detection is designed so as to corroborate the feasibility and effectiveness, and provide some meaningful guidance for the follow-up research and the establishment of forest fire monitoring and visualized early warning big data platform. However, there are two shortcomings in this experiment: more data types should be selected. At the same time, if the original data can be converted to XML format, the compatibility is better. It is expected that the above problems can be solved in the follow-up research.

scholarly journals Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 884
Author(s):  
Chia-Ming Tsai ◽  
Yi-Horng Lai ◽  
Yung-Da Sun ◽  
Yu-Jen Chung ◽  
Jau-Woei Perng
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Training Data ◽  
Network Architectures ◽  
Point Cloud Data ◽  
Data Types ◽  
Raw Data ◽  
Cloud Data

Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

Research on the university intelligent learning analysis system based on AI

2021 ◽  
pp. 1-10
Author(s):  
Meng Huang ◽  
Shuai Liu ◽  
Yahao Zhang ◽  
Kewei Cui ◽  
Yana Wen
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Academic Performance ◽  
Clustering Algorithm ◽  
Back Propagation ◽  
Three Dimensional ◽  
Training Model ◽  
Future Trend ◽  
Artificial Intelligence Technology ◽  
Visualization Technology

The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators.

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

scholarly journals Gynecology Meets Big Data in the Disruptive Innovation Medical Era: State-of-Art and Future Prospects

2021 ◽  
Vol 18 (10) ◽  
pp. 5058
Author(s):  
Rola Khamisy-Farah ◽  
Leonardo B. Furstenau ◽  
Jude Dzevela Kong ◽  
Jianhong Wu ◽  
Nicola Luigi Bragazzi
Keyword(s):  
Big Data ◽  
Information And Communication Technologies ◽  
Imaging Techniques ◽  
Treatment Options ◽  
Wearable Sensors ◽  
Three Dimensional ◽  
Disruptive Innovation ◽  
Healthcare Management ◽  
The Internet ◽  
Big Data Visualization

Tremendous scientific and technological achievements have been revolutionizing the current medical era, changing the way in which physicians practice their profession and deliver healthcare provisions. This is due to the convergence of various advancements related to digitalization and the use of information and communication technologies (ICTs)—ranging from the internet of things (IoT) and the internet of medical things (IoMT) to the fields of robotics, virtual and augmented reality, and massively parallel and cloud computing. Further progress has been made in the fields of addictive manufacturing and three-dimensional (3D) printing, sophisticated statistical tools such as big data visualization and analytics (BDVA) and artificial intelligence (AI), the use of mobile and smartphone applications (apps), remote monitoring and wearable sensors, and e-learning, among others. Within this new conceptual framework, big data represents a massive set of data characterized by different properties and features. These can be categorized both from a quantitative and qualitative standpoint, and include data generated from wet-lab and microarrays (molecular big data), databases and registries (clinical/computational big data), imaging techniques (such as radiomics, imaging big data) and web searches (the so-called infodemiology, digital big data). The present review aims to show how big and smart data can revolutionize gynecology by shedding light on female reproductive health, both in terms of physiology and pathophysiology. More specifically, they appear to have potential uses in the field of gynecology to increase its accuracy and precision, stratify patients, provide opportunities for personalized treatment options rather than delivering a package of “one-size-fits-it-all” healthcare management provisions, and enhance its effectiveness at each stage (health promotion, prevention, diagnosis, prognosis, and therapeutics).

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