An approach preserve quality medical drug data (Semi-structure)‎ toward meaningful data lake by cluster

مقدم أطروحة جامعية

al-Husaynah, Arin Mutib Nasir

مشرف أطروحة جامعية

al-Fayyumi, Muhammad
al-Ziyadat, Wail

الجامعة

جامعة الإسراء

الكلية

كلية تكنولوجيا المعلومات

القسم الأكاديمي

قسم هندسة البرمجيات

دولة الجامعة

الأردن

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2019

الملخص الإنجليزي

Big data is facing many challenges in different aspects, which appear in characteristics such as: Velocity, Volume, Value and Veracity.

Processing and analysis of big data are challenging issues to acquire quality information in order to support accurate medical drug practice.

The quality of data taxonomy is indicated by three basic elements: are meaningful, predication and decision-making.

These elements have been encouraged in previous work that focused on the same challenges of big data.

Consequently, the proposed approach preserves the quality of medical drug data toward meaningful data lake by clustering.

It consists of four components.

Data collection and pre-processing represent the first component in the data lake.

Profile data is treated with semi-structured data to clean it up.

The second component is extracting data through enforcing rules on whole data to produce different groups and generate weight based on constraints within groups.

In component three, data is organized and clustering.

This component complies with schema profiling refering to component two in the data lake.

Weight outputs of component three are inputs for component four, where K-Mean clustering is applied to obtain different clusters.

Each cluster presents an alternative drug to achieve meaningful drug data that is consistent with component three in the data lake.

An experimental approach was followed through using Food and Drug Administration (FDA) data and symptoms in R framework.

ANOVA statistical test was carried out to calculate sum of square error, P-Value and F-Value.

The results showed the efficiency of the proposed approach.

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

الموضوعات

• العلوم الطبية
• الأدوية
• معالجة البيانات
• البيانات

عدد الصفحات

54

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-890027