The integration of remote sensing (RS) with geographical information system (GIS) is a hotspot in geographical information science.A good database structure is important to the integration of RS with GIS,which should ...The integration of remote sensing (RS) with geographical information system (GIS) is a hotspot in geographical information science.A good database structure is important to the integration of RS with GIS,which should be beneficial to the complete integration of RS with GIS,able to deal with the disagreement between the resolution of remote sensing images and the precision of GIS data,and also helpful to the knowledge discovery and exploitation.In this paper,the database structure storing the spatial data based on semantic network is presented.This database structure has several advantages.Firstly,the spatial data is stored as raster data with space index,so the image processing can be done directly on the GIS data that is stored hierarchically according to the distinguishing precision.Secondly,the simple objects are aggregated into complex ones.Thirdly,because we use the indexing tree to depict the relationship of aggregation and the indexing pictures expressed by 2_D strings to describe the topology structure of the objects,the concepts of surrounding and region are expressed clearly and the semantic content of the landscape can be illustrated well.All the factors that affect the recognition of the objects are depicted in the factor space,which provides a uniform mathematical frame for the fusion of the semantic and non_semantic information.Lastly,the object node,knowledge node and the indexing node are integrated into one node.This feature enhances the ability of system in knowledge expressing,intelligent inference and association.The application shows that this database structure can benefit the interpretation of remote sensing image with the information of GIS.展开更多
Data protection in databases is critical for any organization,as unauthorized access or manipulation can have severe negative consequences.Intrusion detection systems are essential for keeping databases secure.Advance...Data protection in databases is critical for any organization,as unauthorized access or manipulation can have severe negative consequences.Intrusion detection systems are essential for keeping databases secure.Advancements in technology will lead to significant changes in the medical field,improving healthcare services through real-time information sharing.However,reliability and consistency still need to be solved.Safeguards against cyber-attacks are necessary due to the risk of unauthorized access to sensitive information and potential data corruption.Dis-ruptions to data items can propagate throughout the database,making it crucial to reverse fraudulent transactions without delay,especially in the healthcare industry,where real-time data access is vital.This research presents a role-based access control architecture for an anomaly detection technique.Additionally,the Structured Query Language(SQL)queries are stored in a new data structure called Pentaplet.These pentaplets allow us to maintain the correlation between SQL statements within the same transaction by employing the transaction-log entry information,thereby increasing detection accuracy,particularly for individuals within the company exhibiting unusual behavior.To identify anomalous queries,this system employs a supervised machine learning technique called Support Vector Machine(SVM).According to experimental findings,the proposed model performed well in terms of detection accuracy,achieving 99.92%through SVM with One Hot Encoding and Principal Component Analysis(PCA).展开更多
This paper presents the basic concepts and principles,data structure and high efficient spatial index for multi_resolution image database.The database is characterized by arrangement of multi_resource image data and s...This paper presents the basic concepts and principles,data structure and high efficient spatial index for multi_resolution image database.The database is characterized by arrangement of multi_resource image data and seamless mosaic,distribution_based storage and management,integration with other spatial database software such as GeoStar and GeoGrid developed by Wuhan Technical University of Surveying and Mapping.展开更多
Until recently, many computational materials scientists have shown little interest in materials databases. This is now changing be-cause the amount of computational data is rapidly increasing and the potential for dat...Until recently, many computational materials scientists have shown little interest in materials databases. This is now changing be-cause the amount of computational data is rapidly increasing and the potential for data mining provides unique opportunities for discovery and optimization. Here, a few examples of such opportunities are discussed relating to structural analysis and classification, discovery of correlations between materials properties, and discovery of unsuspected compounds.展开更多
Scholarly communication of knowledge is predominantly document-based in digital repositories,and researchers find it tedious to automatically capture and process the semantics among related articles.Despite the presen...Scholarly communication of knowledge is predominantly document-based in digital repositories,and researchers find it tedious to automatically capture and process the semantics among related articles.Despite the present digital era of big data,there is a lack of visual representations of the knowledge present in scholarly articles,and a time-saving approach for a literature search and visual navigation is warranted.The majority of knowledge display tools cannot cope with current big data trends and pose limitations in meeting the requirements of automatic knowledge representation,storage,and dynamic visualization.To address this limitation,the main aim of this paper is to model the visualization of unstructured data and explore the feasibility of achieving visual navigation for researchers to gain insight into the knowledge hidden in scientific articles of digital repositories.Contemporary topics of research and practice,including modifiable risk factors leading to a dramatic increase in Alzheimer’s disease and other forms of dementia,warrant deeper insight into the evidence-based knowledge available in the literature.The goal is to provide researchers with a visual-based easy traversal through a digital repository of research articles.This paper takes the first step in proposing a novel integrated model using knowledge maps and next-generation graph datastores to achieve a semantic visualization with domain-specific knowledge,such as dementia risk factors.The model facilitates a deep conceptual understanding of the literature by automatically establishing visual relationships among the extracted knowledge from the big data resources of research articles.It also serves as an automated tool for a visual navigation through the knowledge repository for faster identification of dementia risk factors reported in scholarly articles.Further,it facilitates a semantic visualization and domain-specific knowledge discovery from a large digital repository and their associations.In this study,the implementation of the proposed model in the Neo4j graph data repository,along with the results achieved,is presented as a proof of concept.Using scholarly research articles on dementia risk factors as a case study,automatic knowledge extraction,storage,intelligent search,and visual navigation are illustrated.The implementation of contextual knowledge and its relationship for a visual exploration by researchers show promising results in the knowledge discovery of dementia risk factors.Overall,this study demonstrates the significance of a semantic visualization with the effective use of knowledge maps and paves the way for extending visual modeling capabilities in the future.展开更多
21世纪是大数据的时代,数据、模型驱动下的科学研究新范式与知识发现成为当今科学领域的新态势。本文介绍了在“深时数字地球”(Deep-time Digital Earth,DDE)国际大科学计划框架下构建的DDE-岩浆岩数据库。该数据库以“数据+编图+研究...21世纪是大数据的时代,数据、模型驱动下的科学研究新范式与知识发现成为当今科学领域的新态势。本文介绍了在“深时数字地球”(Deep-time Digital Earth,DDE)国际大科学计划框架下构建的DDE-岩浆岩数据库。该数据库以“数据+编图+研究”三位一体的建库思路,基于岩浆岩知识体系,构建了岩浆岩数据库,包括后台服务(云端)、网站(Web端)和科研工作平台(桌面端)。数据主要来源于公开发表的文献、研究团队测试、实验室测试数据等,涉及全球重要造山带、克拉通及部分海洋(大洋钻探数据)等。此外,还设立了22个地域性、学科性专题数据库。数据类型为岩浆岩岩石类型、产状、空间位置等基本信息,以及年代学、地球化学、(Sr-Nd-Hf-Pb-O)同位素及非传统(或新兴)同位素数据。与国际最常用的有关数据库相比,本数据库具有以下优势:(1)“数-图-文”三位一体的设计思路,拥有编图和研究平台;(2)以研究为导向,组织构建了22个专题数据库,更多一线专家参与数据库建设,在某些地域和领域(如中亚、非传统同位素等)形成优势;(3)对数据都尽可能挖掘和补充了年龄、经纬度等时空信息;(4)创建了学科专家可以依据新的发展和需求及时调整数据库结构的技术与功能,而不需要重新编程;(5)有强大的DDE平台支持,为与国际有关数据库互联互通提供了有利条件。最后,利用DDE-岩浆岩数据库的核心数据(年代学、同位素等),对复杂大陆拼合过程、地壳生长、地球深部物质组成架构与演化等重大地球科学问题进行了探索并取得了一些进展,说明该数据库将对推动数据驱动的岩浆岩研究具有重要意义。展开更多
文摘The integration of remote sensing (RS) with geographical information system (GIS) is a hotspot in geographical information science.A good database structure is important to the integration of RS with GIS,which should be beneficial to the complete integration of RS with GIS,able to deal with the disagreement between the resolution of remote sensing images and the precision of GIS data,and also helpful to the knowledge discovery and exploitation.In this paper,the database structure storing the spatial data based on semantic network is presented.This database structure has several advantages.Firstly,the spatial data is stored as raster data with space index,so the image processing can be done directly on the GIS data that is stored hierarchically according to the distinguishing precision.Secondly,the simple objects are aggregated into complex ones.Thirdly,because we use the indexing tree to depict the relationship of aggregation and the indexing pictures expressed by 2_D strings to describe the topology structure of the objects,the concepts of surrounding and region are expressed clearly and the semantic content of the landscape can be illustrated well.All the factors that affect the recognition of the objects are depicted in the factor space,which provides a uniform mathematical frame for the fusion of the semantic and non_semantic information.Lastly,the object node,knowledge node and the indexing node are integrated into one node.This feature enhances the ability of system in knowledge expressing,intelligent inference and association.The application shows that this database structure can benefit the interpretation of remote sensing image with the information of GIS.
基金thankful to the Dean of Scientific Research at Najran University for funding this work under the Research Groups Funding Program,Grant Code(NU/RG/SERC/12/6).
文摘Data protection in databases is critical for any organization,as unauthorized access or manipulation can have severe negative consequences.Intrusion detection systems are essential for keeping databases secure.Advancements in technology will lead to significant changes in the medical field,improving healthcare services through real-time information sharing.However,reliability and consistency still need to be solved.Safeguards against cyber-attacks are necessary due to the risk of unauthorized access to sensitive information and potential data corruption.Dis-ruptions to data items can propagate throughout the database,making it crucial to reverse fraudulent transactions without delay,especially in the healthcare industry,where real-time data access is vital.This research presents a role-based access control architecture for an anomaly detection technique.Additionally,the Structured Query Language(SQL)queries are stored in a new data structure called Pentaplet.These pentaplets allow us to maintain the correlation between SQL statements within the same transaction by employing the transaction-log entry information,thereby increasing detection accuracy,particularly for individuals within the company exhibiting unusual behavior.To identify anomalous queries,this system employs a supervised machine learning technique called Support Vector Machine(SVM).According to experimental findings,the proposed model performed well in terms of detection accuracy,achieving 99.92%through SVM with One Hot Encoding and Principal Component Analysis(PCA).
文摘This paper presents the basic concepts and principles,data structure and high efficient spatial index for multi_resolution image database.The database is characterized by arrangement of multi_resource image data and seamless mosaic,distribution_based storage and management,integration with other spatial database software such as GeoStar and GeoGrid developed by Wuhan Technical University of Surveying and Mapping.
文摘Until recently, many computational materials scientists have shown little interest in materials databases. This is now changing be-cause the amount of computational data is rapidly increasing and the potential for data mining provides unique opportunities for discovery and optimization. Here, a few examples of such opportunities are discussed relating to structural analysis and classification, discovery of correlations between materials properties, and discovery of unsuspected compounds.
文摘Scholarly communication of knowledge is predominantly document-based in digital repositories,and researchers find it tedious to automatically capture and process the semantics among related articles.Despite the present digital era of big data,there is a lack of visual representations of the knowledge present in scholarly articles,and a time-saving approach for a literature search and visual navigation is warranted.The majority of knowledge display tools cannot cope with current big data trends and pose limitations in meeting the requirements of automatic knowledge representation,storage,and dynamic visualization.To address this limitation,the main aim of this paper is to model the visualization of unstructured data and explore the feasibility of achieving visual navigation for researchers to gain insight into the knowledge hidden in scientific articles of digital repositories.Contemporary topics of research and practice,including modifiable risk factors leading to a dramatic increase in Alzheimer’s disease and other forms of dementia,warrant deeper insight into the evidence-based knowledge available in the literature.The goal is to provide researchers with a visual-based easy traversal through a digital repository of research articles.This paper takes the first step in proposing a novel integrated model using knowledge maps and next-generation graph datastores to achieve a semantic visualization with domain-specific knowledge,such as dementia risk factors.The model facilitates a deep conceptual understanding of the literature by automatically establishing visual relationships among the extracted knowledge from the big data resources of research articles.It also serves as an automated tool for a visual navigation through the knowledge repository for faster identification of dementia risk factors reported in scholarly articles.Further,it facilitates a semantic visualization and domain-specific knowledge discovery from a large digital repository and their associations.In this study,the implementation of the proposed model in the Neo4j graph data repository,along with the results achieved,is presented as a proof of concept.Using scholarly research articles on dementia risk factors as a case study,automatic knowledge extraction,storage,intelligent search,and visual navigation are illustrated.The implementation of contextual knowledge and its relationship for a visual exploration by researchers show promising results in the knowledge discovery of dementia risk factors.Overall,this study demonstrates the significance of a semantic visualization with the effective use of knowledge maps and paves the way for extending visual modeling capabilities in the future.
文摘21世纪是大数据的时代,数据、模型驱动下的科学研究新范式与知识发现成为当今科学领域的新态势。本文介绍了在“深时数字地球”(Deep-time Digital Earth,DDE)国际大科学计划框架下构建的DDE-岩浆岩数据库。该数据库以“数据+编图+研究”三位一体的建库思路,基于岩浆岩知识体系,构建了岩浆岩数据库,包括后台服务(云端)、网站(Web端)和科研工作平台(桌面端)。数据主要来源于公开发表的文献、研究团队测试、实验室测试数据等,涉及全球重要造山带、克拉通及部分海洋(大洋钻探数据)等。此外,还设立了22个地域性、学科性专题数据库。数据类型为岩浆岩岩石类型、产状、空间位置等基本信息,以及年代学、地球化学、(Sr-Nd-Hf-Pb-O)同位素及非传统(或新兴)同位素数据。与国际最常用的有关数据库相比,本数据库具有以下优势:(1)“数-图-文”三位一体的设计思路,拥有编图和研究平台;(2)以研究为导向,组织构建了22个专题数据库,更多一线专家参与数据库建设,在某些地域和领域(如中亚、非传统同位素等)形成优势;(3)对数据都尽可能挖掘和补充了年龄、经纬度等时空信息;(4)创建了学科专家可以依据新的发展和需求及时调整数据库结构的技术与功能,而不需要重新编程;(5)有强大的DDE平台支持,为与国际有关数据库互联互通提供了有利条件。最后,利用DDE-岩浆岩数据库的核心数据(年代学、同位素等),对复杂大陆拼合过程、地壳生长、地球深部物质组成架构与演化等重大地球科学问题进行了探索并取得了一些进展,说明该数据库将对推动数据驱动的岩浆岩研究具有重要意义。