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

2016 ◽  
Vol 2 (3) ◽  
pp. 157 ◽  
Author(s):  
Arda Yunianta ◽  
Norazah Yusof ◽  
Arif Bramantoro ◽  
Haviluddin Haviluddin ◽  
Mohd Shahizan Othman ◽  
...  
Keyword(s):  
Data Representation ◽  
Grading System ◽  
Data Mapping ◽  
File Format ◽  
Semantic Relationships ◽  
Semantic Data ◽  
Data Schema ◽  
Triple Store ◽  
Mapping Process ◽  
Learning Data

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 on E-Learning Usage Index Tool to Handle Heterogeneity of Data Representation

2014 ◽  
Vol 69 (5) ◽  
Author(s):  
Arda Yunianta ◽  
Norazah Yusof ◽  
Mohd Shahizan Othman ◽  
Abdul Aziz ◽  
Nataniel Dengen ◽  
...  
Keyword(s):  
Data Representation ◽  
Current Data ◽  
Data Mapping ◽  
Semantic Technology ◽  
Semantic Data ◽  
Triple Store ◽  
Integration Problem ◽  
E Learning ◽  
Semantic Ontology ◽  
Future Work

Distribution and heterogeneity of data is the current issues in data level implementation. Different data representation between applications makes the integration problem increasingly complex. Stored data between applications sometimes have similar meaning, but because of the differences in data representation, the application cannot be integrated with the other applications. Many researchers found that the semantic technology is the best way to resolve the current data integration issues. Semantic technology can handle heterogeneity of data; data with different representations and sources. With semantic technology data mapping can also be done from different database and different data format that have the same meaning data. This paper focuses on the semantic data mapping using semantic ontology approach. In the first level of process, semantic data mapping engine will produce data mapping language with turtle (.ttl) file format that can be used for Local Java Application using Jena Library and Triple Store. In the second level process, D2R Server that can be access from outside environment is provided using HTTP Protocol to access using SPARQL Clients, Linked Data Clients (RDF Formats) and HTML Browser. Future work to will continue on this topic, focusing on E-Learning Usage Index Tool (IPEL) application that is able to integrate with others system applications like Moodle E-Learning Systems. 

scholarly journals Using social media data to map the areas most affected by ISIS in Syria

2020 ◽  
Vol 26 (1) ◽  
pp. 464-470
Author(s):  
Mohamad Hasan
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Geographical Information ◽  
Data Mapping ◽  
Islamic State ◽  
Social Media Data ◽  
The Social ◽  
Mapping Process ◽  
Data Source ◽  
Media Data

This paper presents a model to collect, save, geocode, and analyze social media data. The model is used to collect and process the social media data concerned with the ISIS terrorist group (the Islamic State in Iraq and Syria), and to map the areas in Syria most affected by ISIS accordingly to the social media data. Mapping process is assumed automated compilation of a density map for the geocoded tweets. Data mined from social media (e.g., Twitter and Facebook) is recognized as dynamic and easily accessible resources that can be used as a data source in spatial analysis and geographical information system. Social media data can be represented as a topic data and geocoding data basing on the text of the mined from social media and processed using Natural Language Processing (NLP) methods. NLP is a subdomain of artificial intelligence concerned with the programming computers to analyze natural human language and texts. NLP allows identifying words used as an initial data by developed geocoding algorithm. In this study, identifying the needed words using NLP was done using two corpora. First corpus contained the names of populated places in Syria. The second corpus was composed in result of statistical analysis of the number of tweets and picking the words that have a location meaning (i.e., schools, temples, etc.). After identifying the words, the algorithm used Google Maps geocoding API in order to obtain the coordinates for posts.

A Framework for Ontology-Based Heterogeneous Data Integration for Cost Management in Product Family Design

2008 ◽  
Author(s):  
Xiaomeng Chang ◽  
Janis Terpenny
Keyword(s):  
Product Family ◽  
Heterogeneous Data ◽  
Schema Integration ◽  
Product Family Design ◽  
Related Factors ◽  
Product Families ◽  
Heterogeneous Data Integration ◽  
Related Information ◽  
Semantic Data ◽  
Data Schema

High quality, high impact and economical products and systems are important goals for an enterprise. The usage of product families can be strategic to achieving these goals, yet defining these families can be challenging, requiring the consideration of numerous cost factors. This requires bringing together a great number of heterogeneous data sources of varying formats in a manner that allows the product development team to easily locate and reuse information in a collaborative manner across time and space. To date, our work has focused on the development and use of an Activity-Based Cost ontology (ABC ontology) to guide designers drill down to get at information for product family design. However, this ontology is built in such a way that it can only support information retrieval from the ontology and does not bring together and connect heterogeneous data resources. It does not address the problem of designers who struggle with obtaining relevant details from different departments in an enterprise. While there have been several semantic data schema integration tools for heterogeneous data resources integration, these tools cannot guide users to related information, that would lead to the root cause of the high cost. In this paper, in order to better manage cost in product family design, an ontology-based framework is put forward that builds on our prior work and combines the advantages of ABC ontology and data schema integration tools. The ontology-based framework can guide users to the proper information aspects through querying the central ontology, and give users detailed information about these aspects from heterogeneous data resources with the support of local ontologies. Ultimately, this framework will facilitate designers with better utilization of cost-related factors for product family design from a whole enterprise perspective.

scholarly journals Igniting Harmonized Digital Clinical Quality Measurement through Terminology, CQL, and FHIR

2020 ◽  
Vol 11 (01) ◽  
pp. 023-033
Author(s):  
Robert C. McClure ◽  
Caroline L. Macumber ◽  
Julia L. Skapik ◽  
Anne Marie Smith
Keyword(s):  
Care Delivery ◽  
Quality Measurement ◽  
Quality Measures ◽  
Data Representation ◽  
Evidence Based ◽  
Data Mapping ◽  
Local Data ◽  
Clinical Quality ◽  
Care Gaps ◽  
Data Elements

Abstract Background Electronic clinical quality measures (eCQMs) seek to quantify the adherence of health care to evidence-based standards. This requires a high level of consistency to reduce the effort of data collection and ensure comparisons are valid. Yet, there is considerable variability in local data capture, in the use of data standards and in implemented documentation processes, so organizations struggle to implement quality measures and extract data reliably for comparison across patients, providers, and systems. Objective In this paper, we discuss opportunities for harmonization within and across eCQMs; specifically, at the level of the measure concept, the logical clauses or phrases, the data elements, and the codes and value sets. Methods The authors, experts in measure development, quality assurance, standards and implementation, reviewed measure structure and content to describe the state of the art for measure analysis and harmonization. Our review resulted in the identification of four measure component levels for harmonization. We provide examples for harmonization of each of the four measure components based on experience with current quality measurement programs including the Centers for Medicare and Medicaid Services eCQM programs. Results In general, there are significant issues with lack of harmonization across measure concepts, logical phrases, and data elements. This magnifies implementation problems, confuses users, and requires more elaborate data mapping and maintenance. Conclusion Comparisons using semantically equivalent data are needed to accurately measure performance and reduce workflow interruptions with the aim of reducing evidence-based care gaps. It comes as no surprise that electronic health record designed for purposes other than quality improvement and used within a fragmented care delivery system would benefit greatly from common data representation, measure harmony, and consistency. We suggest that by enabling measure authors and implementers to deliver consistent electronic quality measure content in four key areas; the industry can improve quality measurement.

FRAMEWORK FOR DATA QUALITY ASSURANCE BETWEEN COMPOSITE SERVICES

2009 ◽  
Vol 19 (03) ◽  
pp. 307-337 ◽  
Author(s):  
JUNG-WON LEE ◽  
BYOUNGJU CHOI
Keyword(s):  
Quality Assurance ◽  
Data Quality ◽  
Error Detection ◽  
Service Oriented Architecture ◽  
Data Transformation ◽  
Data Errors ◽  
Semantic Data ◽  
Composite Services ◽  
Data Constraints ◽  
Learning Data

Today, businesses have to respond with flexibility and speed to ever-changing customer demand and market opportunities. Service-Oriented Architecture (SOA) is the best methodology for developing new services and integrating them with adaptability — the ability to respond to changing and new requirements. In this paper, we propose a framework for ensuring data quality between composite services, which solves semantic data transformation problems during service composition and detects data errors during service execution at the same time. We also minimize the human intervention by learning data constraints as a basis of data transformation and error detection. We developed a data quality assurance service based on SOA, which makes it possible to improve the quality of services and to manage data effectively for a variety of SOA-based applications. As an empirical study, we applied the service to detect data errors between CRM and ERP services and showed that the data error rate could be reduced by more than 30%. We also showed automation rate for setting detection rule is over 41% by learning data constraints from multiple registered services in the field of business.

scholarly journals VSDR: Visualization of Semantic Data Representation for Information Search over Semantic Web

2017 ◽  
Vol 7 (5) ◽  
pp. 105-109 ◽  
Author(s):  
N. Kanjanakuha ◽  
◽  
C. Techawut ◽  
R. Sukhahuta ◽  
P. Janecek
Keyword(s):  
Semantic Web ◽  
Information Search ◽  
Data Representation ◽  
Semantic Data

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

2017 ◽  
Vol 3 (3) ◽  
pp. 161 ◽  
Author(s):  
Arda Yunianta ◽  
Omar Mohammed Barukab ◽  
Norazah Yusof ◽  
Nataniel Dengen ◽  
Haviluddin Haviluddin ◽  
...  
Keyword(s):  
Real Data ◽  
Database Systems ◽  
Data Mapping ◽  
Semantic Data ◽  
Ontology Language ◽  
Data Problem ◽  
Mapping Technology ◽  
Application Data ◽  
Semantic Aspect ◽  
Mapping File

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.

Sign in / Sign up

Export Citation Format

Share Document