تحریم خمر - طب جدید كے تناظر میں

It has been held since ages that wine is a panacea for mankind in the realm of medicine. Moreover, it is touted with emphasis that it is a great source of mental and physical solace and satisfaction and for this very reason masses in the days of ignorance, in general, would have it with relish for solace and satisfaction. Even in the contemporary advanced times, it is believed in general, that wine is the source of various medical benefits coupled with the belief that wine releases tension and stress. Under the same impression, wine is drunk, with relish in Europe and America down to this day. Modern medical science has proved all these nations as categorically wrong and baseless and has set forth that all these notions about wine are based on assumptions and abstractions with nothing concrete and solid. In the assay under consideration, the harmful and baneful influence and effects of wine have been brought under the lime light and proved, in the light of the experiments of the medical scientists, that no medical benefits, whatsoever, be derived from drinking wine rather it has detrimental impacts and that it creates mental unrest instability rather than providing the drinker with solace, ease and satisfaction

Self-Prediction of Performance Metrics for Dbms Workload

Database Management System (DBMS) workload involves homogenous as well as heterogeneous data and concurrent users. Humans are incapable to manage the versatile data and dynamic behavior of DBMS workload. There is a need of fast computations of current server’s loads and requirements, AI algorithms and machine learning techniques. Autonomic computing technology using types of workload Decision Support System (DSS) or Online Transaction Processing (OLTP) and its performance requirement can help servers, adaptation of the workloads. If we know the type of workload, we can design such systems that predict the identified workload performance and adapt the changes in the behavior of the workload. For managing the workload, we have to face number of problems for the DBMS to better perform. Before executing, we can predict and control the workload to tune the DBMS. Predicting performance of the workload is important for tuning a DBMS and makes the DBMS aware of itself making it autonomic. The optimizer and DBMS can tune itself accordingly. Evolving behavior of workload can be handled by making the system adaptive. We have developed a framework called Autonomic Workload Performance Predictor (AWPP) for predicting the performance of the workload making it adaptive to the changing behavior of the workload. Case-based reasoning approach is applied and results are compared with other well-known machine learning techniques to observe the accuracy and effectiveness and significance of AWPP framework. MySQL database management system is being used to execute different benchmark workload to validate the proposed workload performance prediction framework. For training and testing TPC- H and TPC-C like queries are used as our representative workload. We have taken the various benchmark workloads of DSS and OLTP for experimentation. CBR approach produced effective, accurate and significant results while predicting the performance of workload using the information available before executing a workload and adapting the workload on evolution. These predictions will be helpful for optimizer and DBMSs algorithms as well as for workload management, capacity planning, system sizing.