Sociological Studies,2015,Vol 30,No. 01
【Abstract】 Using the updated Google Books Corpus containing around 8-million books, this paper surveys trends in the usage of sociology-related keywords in English language books from the middle 19th century. It tracks the semantics of cultural influence of sociology over centuries, focusing on disciplines terrains, important figures, major theories, research fields, methodologies, as well as the rise of Chinese sociology. This study sheds more lights on how to explore big data in humanity and social science researches, and calls for the “societalimics” methodology in future studies.
Development of science and technology, times echo of big data—acquisition, innovation and reconstruction of new information of earth sciences
Progress in Geophysics,2016,Vol 31,No. 01
【Abstract】 Big data, a big revolution of the future development of human society, is an outcome of the development of information technology of human society, which is changing and deciding the styles of life, producing and thinking of human with the information storm as its prediction and revolution means. All kinds of functions of Big data are profoundly affecting the pattern of future human society. However, people did not thoroughly recognize its potential influences on technological innovation. At present, the broad discussion of the Big data-based was extensively published in the related journals of technology, information and management, etc. The understanding to the following aspects should be unified to a organized platform: 1) the definition, properties and development of Big data and the understanding to Big data; 2) the new connotation of the up-to-date Big data implied in science and technology; 3) the rapid development of information and network technology and their responses to Big data; 4) Big data prompting the regeneration of Earth sciences; 5) the conversion of Big data, the up-to-data agriculture and Big biology react to Big data; 6) some problems on Big data must be emphasized in the development of scientific technologies.
Sociological Studies,2015,Vol 30,No. 03
【Abstract】 The New Computational Sociology Conference held in Stanford University in August of 2014 posted a new concept of “new computational sociology,” showed us the infinite charm and bright prospects of new computational sociology. Based on a comprehensive interpretation and analysis of the existing research results of new computational sociology, the paper reviewed the history of new computational sociology, divided it into five areas by its major subjects: the acquisition and analysis of big data, integration of qualitative research and quantitative research, social experiment studies of the Internet, computer social simulation study and research and development of new social computing tools. The paper made an introduction and analysis of each of these five subjects via valid research. With the help of Kuhn’s theory of scientific revolution, the paper made an analysis and exploration of the question: will the new computational sociology be the “revolution” of sociology?
Geomatics and Information Science of Wuhan University,2017,Vol 42,No. 01
【Abstract】 China Centre for Resources Satellite Data and Application (CRESDA) is the core platform fostering, distributing, processing, and integrating earthobservation satellite resources. It provides highquality and effective services for the State Council and the relevant departments of government and local authorities. In the era of big data, the data center benefits from big data opportunities as well as suffering from big data challenges. Big data challenges facing the center are discussed and then a big data solution is presented. In particular, five major challenges include the 3V dimensions of big data (i.e. volume, variety, and velocity) and the specific challenges facing the CRESDA, i.e., extensibility and integration of multiple disparate management systems. To tackle these challenges, a distributed architecture is proposed to manage all resources inside the data center based on a Hadoop-like framework for storing and processing big remote sensing data. It is hoped that the proposed architecture can lend more support to national decision-making, improve the level of the national spatial information resource application and serve as a new economic growth source for earthobservation satellite data applications.
?Big data and innovation in China’s foreign policy making: from the perspective of decision-making theory
Foreign Affairs Review,2015,Vol 32,No. 04
【Abstract】 China’s foreign policy making has become more arduous a task due to the profound complex internal and external changes; therefore it is both necessary and urgent for China to innovate the foreign policy making mechanism so as to enhance its efficiency. From the perspective of decision-making theory, China’s foreign policy making mechanism has deficiency of varying degrees in the phases of information collection, program design, policy selection, policy implementation and feedback. Meanwhile, with the development of cyber society and information technology, “big data” with its three dimensions of material, technology and thinking has huge potential to exert multiple positive effects upon decision-making thinking and decision making methods, and thus provides important inspiration and possibility for the innovation of China’s foreign policy making mechanism. It is advisable that China’s foreign policy making mechanism makes full use of the advantage of big data in the future and carries out innovations of varying forms and content in each phase of decision-making so as to enhance its ability to cope with the complicated variable international situation and show the feature of more proactive big-power diplomacy.
Science of Surveying and Mapping,2017,Vol 42,No. 02
【Abstract】 There is no quantification for descriptions of real estate conveniences, and it is difficult to compare the conveniences between different real estates. To solve these problems, a comprehensive index model measuring the real estate conveniences using big data was proposed in this article. In general, the descriptions of real estate conveniences were qualitative and simplex, lacking in comprehensive quantitative evaluation, which causes difficulties to conveniences comparison of different real estates. Walk score was introduced in this paper through classification of ancillary facilities and quantitative calculation of walking distance between real estate and ancillary facilities, and then a real type index measuring real estate conveniences synthetically was found. Results of experiment based on Baidu online map showed that the proposed model could measure the convenience index of any real estate automatically.
Geomatics and Information Science of Wuhan University,2017,Vol 42,No. 02
【Abstract】 Geospatial data in databases have shifted to conform to the characteristics of big-data in tandem with the development of the Internet, mobile Internet, cloud computing, and especially, spatial data acquisition technologies. Faced with spatial big data, traditional spatial database management techniques based on Relational Database Management Systems have encountered problems including the unstructured characteristics of the spatial object, the high scalability of storage capacity, and the high concurrency in big data application environment. This paper focuses on the mainstream of NoSQL databases that successfully deal with unstructured big data and are widely used in Internet applications, but lack spatial characteristics. The data operational and query modes cannot meet the requirements of GIS applications. To resolve this problem, this paper proposed a strategy that takes a NoSQL database as a warehouse for spatial big data and a traditional spatial database as the application server. The storage system architecture and the key technology and solutions were discussed. A prototype system was developed based on MongoDB, PostgreSQL and SQLite to verify the feasibility and effectiveness of the strategy.
Scenario Analysis and Application Research on Big Data in Smart Power Distribution and Consumption Systems
Proceedings of the CSEE,2015,Vol 35,No. 08
【Abstract】 The smart grid is one of the most important technical fields for big data technology applications. With the development of smart grids, the deployments of advanced metering infrastructure (AMI), equipment condition monitoring systems result in the production and accumulation of a lot of data. Thus, it is of great significance to fully mine the value of these data. Firstly, aiming at smart power distribution and consumption systems, the paper described the big data and its characteristics. Secondly, the overall business requirements and application scenarios based on big data were analyzed. Among them two typical application scenario analyses were carried out, which were customer electricity usage behavior analysis and load forecasting. Then the research methods of the business application in big data environment were put forward. Finally, necessary big data key technologies were proposed and the technological framework on big data in power distribution and consumption systems was presented.
Science of Surveying and Mapping,2017,Vol 42,No. 07
【Abstract】 The arrival of the big data age is changing the way people thinking, working and living. This paper analyzes the current situation of big data and its application, which puts forward the concept of spatio-temporal big data and thinks that the spatio-temporal big data is the fusion of spatio-temporal data and big data. This paper also analyzes the changes of science paradigms, spatio-temporal information transmission and cognitive model brought by spatio-temporal big data, which based on the background, characteristics, nature and type of big and spatio-temporal big data. Meanwhile, the theoretical, technical and product systems are discussed.
Proceedings of the CSEE,2018,Vol 38,No. 01
【Abstract】 Big data technology is currently a research hotspot in every field. Based on the big data technology, this paper discussed its applications in power distribution system. Firstly, the significance was illuminated that big data technology would bring into the development of power distribution system and this paper also has made a brief conclusion on the current application situation of big data technology in power distribution system. Then, based on big data technology, advanced operation management, maintenance arrangement, network planning and asset management in power distribution network was analyzed. Next, three core research areas were summarized to support the application of big data in power distribution system. Finally, some suggestions were made to stimulate the development of big data, and other big data-based applications in power distribution system have also been conjectured.
Geomatics and Information Science of Wuhan University,2018,Vol 43,No. 03
【Abstract】 Multi-source big geo-data provide us an unprecedented opportunity to investigate geographic phenomena from perspective of their spatial distribution patterns, spatial interactions and dynamic evolution. Cities are the most concentrated areas of human activities and thus massive amount of geographic big data have been produced to improve our understanding of urban spaces. The spatial heterogeneity pattern in cities is an essential topic in geographic research and urban planning. Social sensing offers an analytical framework to characterize urban spatial heterogeneity from four dimensions: human, environment, statics and dynamics. This paper summarizes the contributions of different types of big geo-data in characterizing urban features. Borrowing the concept of “niche model” from ecological studies, a case study is introduced to demonstrate the quantification of spatial heterogeneity patterns in urban space incorporating multi-source big geo-data. The theoretical issues such as unit selection are also discussed to address some related problems.
China Environmental Science,2018,Vol 38,No. 07
【Abstract】 The potential influence information of tributyltin (TBT) was excavated to predict the relationship between TBT exposure and human-related diseases by using the “big data” of toxicology database. In comparative toxicogenomics database (CTD), 488 genes interacting with TBT were collected. Among the 488 genes, TP53 was the most associated genes, followed by ESR1 and FN1. Using CTD analysis, it was also found that cancer, nervous system diseases, cardiovascular diseases, urogenital disease (female), digestive system diseases, metabolic disease, urogenital disease (male), endocrine system diseases, immune system diseases and respiratory tract disease were the top 10 diseases related to TBT exposure. Based KEGG pathway and DAVID gene function annotation analysis, metabolic diseases were more susceptible to be induced by TBT exposure; moreover, intensive genes interacting with TBT were annotated in the pathways of glucose metabolism. PASS prediction found that biological activities of many sugar-related enzymes could be affected by TBT exposure, which suggested that the impact of TBT on glucose metabolism should be of concern.
Progress in Geophysics,2018,Vol 33,No. 02
【Abstract】 GeoCloud 1.0 is the first comprehensive integrated application platform in geological survey field in China. It was released publicly and begun to run online since November 2017, and it can offer the one-stop service for geosciences data integration, geological business integration and product application integration. In order to ensure GeoCloud run smoothly and provide high quality services for end user continuously, it is necessary to monitor about 400 servers with more than ten thousand indexes real-timely. This paper analyzes the interrelation of hardware and software resources of GeoCloud deeply, determines the quantitative monitoring indexes of all types of resources in this national GeoCloud, and proposes a new innovative all-in-cloud work pattern for monitoring the GeoCloud computing for the first time. The new idea opens a new kind of method to monitor resources in the cloud. A transparent GeoCloud monitoring platform (GMP) is designed based on big data technology, and our team developed the corresponding monitor big data algorithms and applications using Hadoop, HDFS, HBase. The platform and application are born in the cloud, exist in the cloud and they aim to supervise various servers. The one-stop software can complete the whole lifecycle of monitoring data includes acquisition, recognition, storage, display, query, analysis, mining and services. The online operation results show that GMP can reduce the workload of administrative staff of GeoCloud vastly. And even one-fourth people can perform original workload, such as analyzing the effect of geological services, evaluating the stability of GeoCloud computing system rapidly, diagnosing fault or error of GeoCloud intelligently, and locating the positions of problems accurately. In short, GMP plays a very significant role on operating smoothly for GeoCloud.
Brain Cognition and Spatial Cognition: On Integration of Geospatial Big Data and Artificial Intelligence
Geomatics and Information Science of Wuhan University,2018,Vol 43,No. 12
【Abstract】 The 21st century is an age of data explosion growth. In the era of big data, it is urgent to enhance the timeliness and intelligence level of the geospatial information science. Artificial intelligence is applied to geospatial information science, enhancing the perception and cognition ability of geospatial information processing, and realizing the three processes of perception, cognition and action of geospatial information science. Through the integration of geospatial big data and artificial intelligence (AI), the macroscopic, mesoscopic and microscopic scale of the earth space, earth observation brain (EOB), smart city brain (SCB) and smartphone brain (SPB) are proposed. EOB, SCB and SPB are highly intelligent systems in the geospatial information science. The concept model and the key technologies of EOB, SCB and SPB needed to be solved are introduced in detail, and a case is given to illustrate the process of perception, cognition and action in the primary stage of the EOB, SCB and SPB. In the future, EOB, SCB and SPB can observe when, where, what object, what change to push these right information to right person at the right time and right place.
Science of Surveying and Mapping,2018,Vol 43,No. 12
【Abstract】 Aiming at the problem that the amount of data has increased dramatically in the era of big data, which makes it difficult to analyze the geo-political environment quantitatively, this paper proposed an effective way to use the big data technology to conduct geo-setting research. Through visual analysis and multi-view collaborative interaction, the purpose of quantitative analysis of the geo-setting was achieved, so that the final decision was more objective and scientific. The article focused on the application of visual analysis technology in geo-setting analysis, and based on the data environment along Belt and Road Initiative, established the geo-setting analysis model and visual analysis model for geo-relationship, visually analyzed geo-organization and the geophysical body, and finally gave the framework design and preliminary realization of the prototype system.
Geomatics and Information Science of Wuhan University,2018,Vol 43,No. 12
【Abstract】 In recent years, the rapid development of the earth observation capability and the intelligent computing technology has provided opportunities for the advancement and even revolution of remote sensing information technology. Remote sensing data processing technology has experienced the Digital Signal Processing Era from 60 s to 80 s of the last century, which utilizes the Statistical Model as the core, and the Quantitative Remote Sensing Era from 90 s marked by the Physical Model. Recently, it is developing towards Remotely Sensed Big Data Era which relies on Data Model by data-driven intelligent analysis. This paper summarizes the history of remote sensing information technology and presents the concept of remotely sensed big data and the characteristics of intelligent information extraction era. Firstly, from the view of remotely sensed big data, this paper discusses the construction of object-based remote sensing knowledge dataset and analyzes the data-driven intelligent information extraction strategy combined the knowledge of remote sensing and deep learning algorithm. Then the current status and development of intelligent algorithms represented by deep learning are introduced by typical applications on object detection, fine classification and parameter inversion based on remote sensing data. Consequently, the application potential of deep learning on intelligent information extraction in Remotely Sensed Big Data Era is discussed.
Geomatics and Information Science of Wuhan University,2019,Vol 44,No. 01
【Abstract】 Statistical analysis is an important way of extracting information from the national geographical conditions data. It can reflect the internal spatial characteristics of resources, environment, ecology and economy, and their interactions from different dimensions. In view of the high-efficiency management, high-intensity computation and deep-level service for statistical analysis based on the big data, this paper puts forward a technical framework of national geographical conditions statistical analysis, and discusses the core process of statistical analysis from three dimensions: big data storage and integration, key technologies for statistical computation, service modeling and application. This paper will help to improve the application level of national geographical conditions monitoring and statistical analysis service in natural resources supervision, ecological protection and restoration, etc., and can promote the transformation and upgrading of geographical information industry in China.
Big Data Platform Architecture and Key Techniques of Power Generation Dispatching for Hydro-thermal-wind-solar Hybrid System
Proceedings of the CSEE,2019,Vol 39,No. 01
【Abstract】 The rapid development of renewable energy sources such as hydropower, wind power and solar power leads to an explosive growth of the power dispatching data and presents the typical big data features such as multiple sources, heterogeneity and high dimension. How to deal with the integrated management and efficient application of electric power big data is one of major technology challenges facing power grid operation in China. Focusing on the complex hydro-thermal-wind-solar power generation dispatching system, this paper analyzed the big data characteristics of dispatching and the mutual relations of the ultra-large-scale power station groups. Based on the data analysis, the dispatching management functional system and the big data platform architecture of power generation dispatching were constructed. Multi-source data check techniques and multi-platform cooperative storage technology were developed to meet the common needs for different operation scenarios. Meanwhile, big-data fusion processing technology and analysis decision technology were proposed to serve for analyzing operation plans and making generation schedule, respectively. These techniques can help to realize the integrated function of collection, storage, analysis and knowledge extraction of electric power big data. Taking more than 400 large- and medium-sized power stations in Yunnan Power Grid as the background, an ultra-large-scale multi-source software system of power generation dispatching was constructed based on the big data platform architecture and related key technologies. Typical applications in practical engineering such as tracking monthly generation schedule, determining the forecast error of new energy power, and dispatching cascaded hydropower stations were presented. The results show that electric power big data technology can indeed provide new solutions for the efficient and practical operation of complex power generation dispatching system.