16354

1698 دكتري

دكتري

نرم افزار

اصفهان : دانشگاه صنعتي اصفهان

1399

سيزده، 144ص. : مصور، جدول، نمودار

ناصر قديري

احمد براآني دستجردي

مهران صفاياني، الهام محمودزاده

1399/12/17

كتابنامه

كامپيوتر

مهندسي برق و كامپيوتر

1399/12/18

2639076

Improving Data Veracity by Conflict Resolution in Spatial Data Fusion Mohammad Khodizadeh Nahari m khodizadeh@ec iut ac ir 2020 10 21 Department of Electrical and Computer Engineering Isfahan University of Technology 84156 83111 Isfahan IranSupervisor Dr Nasser Ghadiri nghadiri@iut ac irAdvisor Dr Ahmad Baraani Dastjerdi ahmadb@eng ui ac irAdvisor Dr J rg R diger Sack sack@scs carleton caDepartment Graduate Program Coordinator Dr Behzad Nazari Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Software Engineering Faculty of Computer Engineering University of Isfahan Isfahan Iran School of Computer Science Carleton University Ottawa CanadaAbstractData utilization plays a vital role in the development of human societies Today the data are stored inheterogeneous sources Providing an integrated view by fusion of the data is essential to enhance data utilization Data quality is a challenging issue in the data fusion One of the main challenges that affects the quality is datainconsistency We worked on apparent inconsistencies that are caused by the difference in data representation Intelligent entity recognition improves the quality of data fusion and data veracity One of the weaknesses of theexisting methods is the lack of attention to the level of abstraction and granularity of the data in different sources In this research a framework is introduced for managing inconsistencies In addition a new metric for assessmentof granularity level of data is introduced GLQM In the framework using data granulation and knowledge bases increases data veracity The proposed algorithms belong to online category This is achieved with the help ofblocking technique An indispensable type of data is spatial data with diverse application By applying datagranulation approach on several methods in the field of spatial data the quality of data fusion based on the F Score has been improved KeywordsData fusion Data granulation Entity recognition Spatial dataIntroductionData utilization plays a vital role in the development of human societies The amount ofgenerated data increases dramatically every day The use of data from a single source oftenprovides limited actionable results In contrast fusing data from multiple sources would oftencreate significantly more value resulted from filling the weakness of a source in some datarecords by another source Data fusion techniques combine the data obtained from differentsources to produce a uniform and more accurate result compared with cases where they areutilized individually Hall and Llinas 1997 During the data fusion process the veracity andreliability of data can be enhanced through data inconsistency management

ناصر قديري

احمد براآني دستجردي

مهران صفاياني، الهام محمودزاده