On Detecting and Enforcing the Non-Relational Constraints Associated to Dyadic Relations in MatBase

MatBase is a prototype data and knowledge base management expert intelligent system based on the Relational,Entity-Relationship,and(Elementary)Mathematical Data Models.Dyadic relationships are quite common in data modeling.Besides their relational-type constraints,they often exhibit mathematical properties that are not covered by the Relational Data Model.This paper presents and discusses the MatBase algorithm that assists database designers in discovering all non-relational constraints associated to them,as well as its algorithm for enforcing them,thus providing a significantly higher degree of data quality.

A Web-Based Approach for the Efficient Management of Massive Multi-source 3D Models

Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.

Visualizing risk factors of dementia from scholarly literature using knowledge maps and next-generation data models

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.

非字面义表达研究论纲

语言交际表义方式有两种：直义表达与蕴含表达。蕴含表达是将想要表达的意思＂包含＂于另一个字面义完全不同的句子之中体现出来,这就是非字面义表达。非字面义表达研究的核心问题是要从理论上说明这种表达如何实现曲折地表达说话主体的意向：如果表达的不是以自己实际要表达的对象本身作为所表达出来的,而是用同对象及其行为有关时空、历史、因果、条件、发展等因素做出描述,与对象建立＂曲折＂的关系,以进行推论、联想、想象等思维活动因而获得意义,这种表达就是这一意义的非字面义表达。非字面义表达式具有系统性,包含三个子系统：含义表达、词义变异的修辞表达和词义变义的惯用语的表达;它有四个特性：异质性、生成性、关系性以及历史性。非字面义表达这样的运用需要利用含意思维,并要求使用者要对对象表达的本体论事实做出本体论承诺。

Research on the dynamic management of cloud simulation derived data

Cloud simulation derived data is defined as the data related to service version,characteristics,relationships,runtime environments and cross-domain communication during service execution in cloud simulation environment,collectively.It is of great value and significance in cloud simulation for service description,service composition and resource management.The types of derived data are abundant and the amount of it is huge.Existing studies on cloud simulation usually assume all of the derived data required for a specific task is well organized and available anytime,which is of course impossible.Derived data needs to be expressed and managed in terms of knowledge to make sure the smooth execution of cloud simulation platform.Therefore,this paper presents a derived data management method in cloud simulation platform to enable derived data collection and dynamic knowledge storage.The prototype system of the proposed method are established and a virtual prototype of double girder crane is taken as an example to verify the effectiveness of the method.