An Approach To Improve Data Quality From Veracity Of Data Accuracy For Sensitive Cost And Time Indicators
| المؤلف الرئيسي: | Mohammad, Banan Aref (Author) |
|---|---|
| مؤلفين آخرين: | Alzyadat, Wael (Advisor), Alfayomi, Mohammed (Advisor) |
| التاريخ الميلادي: |
2018
|
| موقع: | عمان |
| الصفحات: | 1 - 58 |
| رقم MD: | 991855 |
| نوع المحتوى: | رسائل جامعية |
| اللغة: | الإنجليزية |
| الدرجة العلمية: | رسالة ماجستير |
| الجامعة: | جامعة الاسراء الخاصة |
| الكلية: | كلية تكنولوجيا المعلومات |
| الدولة: | الاردن |
| قواعد المعلومات: | Dissertations |
| مواضيع: | |
| رابط المحتوى: |
عدد مرات التحميل
24
| المستخلص: |
Big data is a term which describe the characteristics of a dataset, such as volume, value and veracity. There are many challenges which prevent proceeding and working with big data by using traditional techniques to extract value. Project management is a dynamic process that utilizes the appropriate resources of an organization in many phases by measuring in four factor: scope, time, cost and quality. Improving data quality depends on relations between data and value, which are associated with veracity and accuracy of data and how we can get quality from value. In this research, we attempt to improve data quality from big data characteristics depending on trust of data by working with it in general and especially by using value, volume and veracity of data by finding out correlation statistical analysis results and distance equation. This approach was implemented by using IBM human resource scope with R framework through selecting "Deducer" package from R library. Implementation and conducting experiment have been carried out by using three main factors: time, cost and scope in two types: product and project, where the strongest relation linking them starts with project scope as the strongest factor followed by cost, product and finally time which is the weakest factor among them. In the final form, we select the best quality using two sides generally: quality degree and middle-quality interval. Especially, relative distance is the strongest factor in the experiment between time and cost, where both sides lead to more trust of data and high accuracy in the chosen process. |
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