An Approach Preserve Quality Medical Drug Data (Semi-structure) Toward Meaningful Data Lake by Cluster
| المؤلف الرئيسي: | أبو صحينة، عرين متعب ناصر (مؤلف) |
|---|---|
| مؤلفين آخرين: | Al-Zyadat, Wael (Advisor), Al Fayoumi, Mohammad A. (Advisor) |
| التاريخ الميلادي: |
2019
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| موقع: | عمان |
| الصفحات: | 1 - 54 |
| رقم MD: | 990819 |
| نوع المحتوى: | رسائل جامعية |
| اللغة: | الإنجليزية |
| الدرجة العلمية: | رسالة ماجستير |
| الجامعة: | جامعة الاسراء الخاصة |
| الكلية: | كلية الدراسات العليا |
| الدولة: | الاردن |
| قواعد المعلومات: | Dissertations |
| مواضيع: | |
| رابط المحتوى: |
عدد مرات التحميل
14
| المستخلص: |
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 referring 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. |
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