زین الدین ابن نجىم اور ہربرٹ بروم کى فقہی و قانونی تعبیرات کے حوالے سے قاعدہ ازالہ ضرر و مشقت کا عمومی جائزہ

Both Zainuddin Ibn Nujaim (d. 970HJ) and Herbert Broom (d. 1882CE) are famous for the arrangement, interpretations and for sound applications of juristico-legal maxims and rules respectively in Muslim and western world of law and jurisprudence. The al-Ashbah wa-Al-Nazair  of Ibn Nujaim and Broom’s Legal Maxims of Herbert Broom speak of their deep approach to the concerned discipline. This article provides a general analysis of the juristico-legal interpretations regarding the elementary maxim of hardship and injuria remedium (hardship and harm remission) as made by Ibn Nujaim (d.970HJ) and Herbert Broom(d.1882CE) in their aforesaid books.

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.