علماء اہلسنت (بریلوی) کی تفاسیر کا اسلوب بیان: ایک جائزہ

The Holy Quran is the first and very important source of Islamic Law. This book was revealed in Arabic language. When Islam spread over the Subcontinent with the passage of time then It was needed to translate this book in the regional languages for the better understanding of Allah's message. The people of this era could not understand the real teaching of Quran without its translation. The Ulema e Ahlesunat (Barelvi) school of thought took participation in the field of Tafseer to convey this holy message to the people of Subcontinent. The intellectual efforts of the scholars of the Ahlesunat (Barelvi) thoughts can be found in every field of Islamic teaching. These Ulema extended their contributions in Islamic teaching through illustration of the Quran. They wrote translations of the Quran in different ages and tried to solve the problems which were raised in this era about Islamic teaching. In this research article the authors analyzed the style of selected Mofasereen of the Ahlesunat( Barelvi's )school of thought

Urban Planning Using Big Data Analytics Based Internet of Things

The recent expansion in the field of Internet of Things (IoT) and Big Data is providing a large production potential in the course of the new era of smart urban. The IoT infrastructure for the design of smart urban consists of devices, objects, sensors and citizens producing huge gigantic data (i.e. Big Data). IoT-based smart urban environment provides the digital traces of human and objects that can be analyzed. The major aim of smart urban is to efficiently utilize the data to administer and resolve the issuesconfront by recent smart cities regarding data processing.This study presents Hadoop-based Big Data analytics architecture to address the challenges in data generated in IoT environment. The proposed architecture is based on customization of Hadoop architecture and external entities to efficiently process Big Data generated in IoT environment. The proposed scheme is comprised of Big Data loading into Hadoop and Big Data processing. The existing solutions provide manual and serial data injection into Hadoop. Moreover, the existing solutions do not tackle the communication overhead efficiently that affects the processing. Data loading and storing is performed by proposing parallel and utility-oriented solution based on multiple attributes. Unlike traditional MapReduce architecture, customized YARN-based cluster management solution is provided to manage the cluster resourcesefficiently and process the data using Map-Reduce algorithm separately. The proposed architecture is tested with a variety of reliable datasets using Hadoop framework. The comparison of proposed architecture with existing solutions and default architecture of Hadoop is provided to verify and reveal that the proposed architecture is more efficient then existing smart urban architecture using Big Data analytics for processing data produced in IoT-environment.