فقہ اسلامی میں قسامت کا تصور

Islam lays great emphasis on security and the sanctity of human life. The holy Quran terms killing of an innocent person as killing of the whole humanity. It prohibits unjust killing of human being in unequivocal terms. The holy Qur’an and Sunnah terms killing of an innocent person as one of the greatest sins. An eternal torment is the destiny of a killer who takes life of a person unjustly. However, it is also a bitter fact that hardly   a crime free society could be found   anywhere in the world. Peace prevails only in those societies where culprits are brought to justice. This is why Islamic penal code has prescribed punishments for all kinds of crimes. It has prescribed punishment of Qisâs in case of intentional murder and Diyat (blood money in case of killing of a person by mistake, it is also due in case if remission is made by the heirs in intentional murder case). To prove the crime of murder, testimony of two reliable witnesses or confession of the killer is required before the court. However, if a corpse is found in a place where killer is unknown and witnesses are unavailable,    then Islam enjoins the process of Qasâmah to safeguard rights of the heirs of the deceased. Qasâmah is a process of taking oath by fifty persons selected by the heirs of the slain. In this article the concept of Qasâmah has been elaborated. It  has three parts , in the  first part conditions for the validity of  Qasâmah has been elaborated, while in the second part its process has been discussed with elaborate opinions  of jurists  regarding taking of  oath, as some of them opine that  the  heirs of the slain  have to take oath, mentioning name of the killer,   while others say  oath will be taken by the defendants that they  didn’t kill him, Both these opinions  have been discussed by producing arguments of  the both sides. While in the third part the issue of Qisâs and Diyat has been discussed as according to some jurists the Qasâmah entails Qisâs while other say that it entails Diyat only; arguments of both sides have been discussed in detail.

Context-Aware Ubiquitous Data Mining Framework

Developments in information and communication technology have made it realistic to produce data at high rate, resulting to stress communication and computational infrastructure and making it difficult to store and transmit that data. To deal with these unbounded and continuous data streams is a sheer challenge for researchers from communication, data storage, computational and data mining domains because of its continuity, unbounded in nature, endless arrival and concept drifting with the passage of time. Extracting useful and hidden information from these ubiquitous data streams is one of the major goals during the last decade. Both supervised and unsupervised techniques of knowledge discovery were being researched e.g. clustering and frequent pattern mining. Frequent patterns reveal important and hidden information in the data and there are many application areas where these can be very helpful to improve overall performance of the system. Aims: Main objectives of this research are three-fold. Firstly our target is to review the existing scientific and analytical techniques addressing data mining in ubiquitous and continuous streams of data. Secondly, addressing data collected from ubiquitous devices and investigating device resources for local or centralized mining in streaming data environment. Finally, our focus is to devise comparatively efficient and accurate methodology for finding closed frequent itemsets in streaming data. Similarly, incorporation of contextual information in mining process is also addressed. Methods: As a first step of this research, we performed a regress review of existing scientific techniques and algorithms specifically designed to extract hidden information from the ubiquitous data streams and studied limitations and problem areas to develop a new and improved version of existing techniques. Then we performed analytical study on ubiquitous data mining resource restriction and limitations and comparison of centralized and distributed data mining in ubiquitous environment. Regarding the final aims of this research, we have developed a generic framework that is adaptable, scalable and incorporating contextual information to improve data mining results and outcomes.