EEG data analysis by using map-reduce technique

العناوين الأخرى

تحليل بيانات مخطط كهربائية الدماغ (EEG)‎ باستخدام تقنية Map-Reduce

مقدم أطروحة جامعية

Fulayyih, Ali Adil

مشرف أطروحة جامعية

al-Hamami, Ala Husayn

أعضاء اللجنة

Utair, Muhammad Abd Allah
al-Sarhan, Hasan Muayyadi

الجامعة

جامعة عمان العربية

الكلية

كلية العلوم الحاسوبية و المعلوماتية

القسم الأكاديمي

قسم علم الحاسوب

دولة الجامعة

الأردن

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2016

الملخص الإنجليزي

Database is defined as a set of data, which is organized and distributed in a manner that make it easy to user to access these data in more efficient way.

With rapid growth in data capturing and with concurrently in developing of power processing, data transmission, storage capability enable users to integrate various Databases in centralized data management for maximizing the vision to access, analysis, retrieval which called Data Warehouse and then it leads to emerge Data Mining which is used for revealing and extracting hidden precious value form large Databases (Data Warehouse).However, the arising and rapid growing in the size of data sets make it more complex that it is about 85% of these data is unstructured and semi structured.

To deal with the problem of big-size and more complex data, a new branch of IT has been emerged, which is called Big-Data.

In the era of Big-Data, the traditional methods of data analytics may not be suitable and efficient to manage and process the large amount and complex of data.

This thesis aimed to study the use of Map-Reduce processing technique to handle Big-Data distributed on the Cloud-Computing, and to develop an efficient way to handle these Big-Data.

The work was evaluated using Hadoop server, and applied on Electroencephalogram (EEG) Big-Data as a case study.

Using Hadoop in Cloud-Computing as an environment for this kind of applications is so efficient for-at least- four reasons: the highly fault tolerance it has, the automated data distributed it performs, the balancing of the computation load across different nodes it performs, parallel computation property it has and as close as possible the computation location from data position property it has that reflects in network overhead of transferring.

The proposed approach was to transform EEG text file to list then can deal whit it due to the fact that it has index, which formerly it was impossible to extract valuable information for EEG researchers.

The experiment result showed clear enhancement on managing and processing the EEG big data with average of 50% reduction on response time.

The obtained results provide EEG researchers and specialist with an easy and fast method of handling the EEG data.

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

عدد الصفحات

152

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-722669