Cloud customer relationship management (CRM)‎ ontology

العناوين الأخرى

مجموعة المفاهيم (انتولجي)‎ في سحابة إدارة علاقة الزبائن

مقدم أطروحة جامعية

al-Kubaysi, Samar Khalid

مشرف أطروحة جامعية

Kayid, Ahmad K. A.

أعضاء اللجنة

al-Hunayti, Muhammad Fawzi
Nasir al-Din, Hibah H. O.

الجامعة

جامعة الشرق الأوسط

الكلية

كلية تكنولوجيا المعلومات

القسم الأكاديمي

قسم نظم المعلومات الحاسوبية

دولة الجامعة

الأردن

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2014

الملخص الإنجليزي

Cloud Computing has opened wide doors on many applications and services that are provided to the customers in order to reach their requirements.

These applications (such as Customer Relationship Management) and services are provided by different providers.

Those providers use different architectures for their Cloud systems which are considered not compatible.

On the other hand, the providers have many ways to describe their services which considered as a big challenge that may face the customers.

This thesis explores the possibility to automate an environment where the customers can find the appropriate services provider that meets their requirements.

Moreover, this thesis presents a well-detailed Ontology construction and refinement process in the domain of Cloud CRM.

The Cloud CRM ontology has two major aspects: characteristics of the business functions and characteristics for the cloud provider (e.g., networks, servers, applications, services, and location).

This thesis also presents a description and performance evaluation of a new model which uses semantic matching to suggest appropriate provider of Cloud CRM to customers based on their requirements.

The model consists of three main components: Online User Interface, Query Analyzer, and Cloud CRM ontology.

The model consults the Cloud CRM ontology to suggest the appropriate Cloud CRM providers.

The performance of the model is evaluated by comparing the results of our approach using Semantic Matching, and the results of traditional Keyword Matching with the Human Experts results to a sample of query scenarios.

The experiments were conducted in order to check the efficiency of the proposed system.

However, the results demonstrated that the model with Semantic Matching usage has a (88 %) success rate, and it enhanced the results by (30 %) than the traditional Keyword Matching.

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

عدد الصفحات

118

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-694415