Data mapping process to handle semantic data problem on student grading system

Many applications are developed on education domain. Information and data for each application are stored in distributed locations with different data representations on each database. This situation leads to heterogeneity at the level of integration data. Heterogeneity data may cause many problems. One major issue is about the semantic relationships data among applications on education domain, in which the learning data may have the same name but with a different meaning, or learning data that has a different name with same meaning. This paper discusses on semantic data mapping process to handle semantic relationships problem on education domain. There are two main parts in the semantic data mapping process. The first part is the semantic data mapping engine to produce data mapping language with turtle (.ttl) file format as a standard XML file schema, that can be used for Local Java Application using Jena Library and Triple Store. The Turtle file contains detail information about data schema of every application inside the database system. The second part is to provide D2R Server that can be accessed from outside environment using HTTP Protocol. This can be done using SPARQL Clients, Linked Data Clients (RDF Formats) and HTML Browser. To implement the semantic data process, this paper focuses on the student grading system in the learning environment of education domain. By following the proposed semantic data mapping process, the turtle file format is produced as a result of the first part of the process. Finally, this file is used to be combined and integrated with other turtle files in order to map and link with other data representation of other applications

Semantic data mapping technology to solve semantic data problem on heterogeneity aspect

The diversity of applications developed with different programming languages, application/data architectures, database systems and representation of data/information leads to heterogeneity issues. One of the problem challenges in the problem of heterogeneity is about heterogeneity data in term of semantic aspect. The semantic aspect is about data that has the same name with different meaning or data that has a different name with the same meaning. The semantic data mapping process is the best solution in the current days to solve semantic data problem. There are many semantic data mapping technologies that have been used in recent years. This research aims to compare and analyze existing semantic data mapping technology using five criteria’s. After comparative and analytical process, this research provides recommendations of appropriate semantic data mapping technology based on several criteria’s. Furthermore, at the end of this research we apply the recommended semantic data mapping technology to be implemented with the real data in the specific application. The result of this research is the semantic data mapping file that contains all data structures in the application data source. This semantic data mapping file can be used to map, share and integrate with other semantic data mapping from other applications and can also be used to integrate with the ontology language

RDB to RDF 변환을 위한 의미 정보 보존 맵리듀스 처리

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 김형주.Today, most of the data on the web is stored in relational databases, which is called deep web. Semantic web is a movement to the next generation of the web, where all data are augmented with well-defined semantics and linked together in machine-readable format. RDB2RDF approaches have been proposed and standardized by W3C, which publishes relational data to semantic web by converting relational data into RDF formatted data. We propose a system that automatically transforms relational data into RDF data and creates OWL ontology based on the schema of database. Some approaches have been proposed, but most of them did not fully make use of schema information to extract rich semantics, nor did they experimented on large databases for performance. We utilize Hadoop framework in transformation process, which enables distributed system for scalability. We present mapping rules that implements augmented direct mapping to create local ontology with rich semantics. The results show that our system successfully transforms relational data into RDF data with OWL ontology, with satisfactory performance on large-sized databases.Abstract i Introduction 3 Related Work 7 2.1 Semantic ETL Systems 7 2.2 Hadoop MapReduce 8 2.3 Mapping Approaches 9 Mapping Rules 14 3.1 General Rule 1 19 3.2 General Rule 2 20 3.3 General Rule 3 20 3.4 General Rule 4 21 3.5 General Rule 5 21 3.6 Constraint Rule 1 22 3.7 Constraint Rule 2 22 3.8 Constraint Rule 3 23 3.9 Constraint Rule 4 24 3.10 Constraint Rule 5 24 3.11 Constraint Rule 6 25 3.12 Discussion 26 Our Approach 28 4.1 Preprocessing 28 4.1.1 Schema Caching Method 30 4.1.2 Relational Data 32 4.2 Hadoop Algorithm 33 Experiment 36 5.1 Ontology Extraction 37 5.2 Performance 38 5.3 Scalability 41 Conclusion 42 Reference 44 Appendix 46Maste

OntoIntegra: Método para la integración de datos almacenados en fuentes heterogéneas.

Data sources integration is about interconnecting and accessing heterogeneous data sources. With the evolution of the organizations, this topic has become an important research field both for academy and industry. The project “Information and Communication Technologies supporting the educational process and the knowledge management in higher education” (ELINF) needs to integrate heterogeneous data sources for supporting the authority control process in its software. This work aims to develop a method with semantic components to contribute to the integration of authority control related data, required by the ELINF project, which is stored in heterogeneous data sources into a software. The current research describes the developed method. At the same time, it describes the study case used for validating it, through the comparison of a software application developed without the proposed method and another one instantiating the method regarding to its capacity to integrate heterogeneous data sources

Contaminant Hydrogeology Knowledge Base (CHKb) of Georgia, USA

Hydrogeologists collect data through studies that originate from a diverse and growing set of instruments that measure, for example, geochemical constituents of surface and groundwater. Databases store and publish the collected data on the Web, and the volume of data is quickly increasing, which makes accessing data problematic and time consuming for individuals. One way to overcome this problem is to develop ontology to formally and explicitly represent the domain (e.g., contaminant hydrogeology) knowledge. Using OWL and RDF, contaminant hydrogeology ontology (CHO) is developed to manage hydrological spatial data for Georgia, USA. CHO is a conceptual computer model for the contaminant hydrogeology domain in which concepts (e.g. contaminant, aquifer) and their relationships (e.g. pollutes) are formerly and explicitly defined. Cyberinfrastructure for exposing CHO and datasets (i.e., CHKb) as Linked Data on the Web is developed. Cyberinfrastructure consists of storing, managing, querying, and visualizing CHKb that can be accessed from URL: cho.gsu.edu