A Business Process (BP) is a set of logically related activities performed to achieve a well-defined business outcome. The activities in a BP have control-flow and data-flow dependencies which are defined in the workflow model of the BP. In the service-oriented environment, these activities are performed by invoking Web services. Typically, BP development involves working with high-level process specifications, and manually elaborating these specifications into an implementation level workflow design. After this, each activity in the BP workflow is binded to the appropriate Web service operations for executable BP code generation. This is a very elaborate process requiring substantial manual effort and investment in the IT infrastructure, personnel and resources. This is specifically challenging for small and medium enterprises which often do not have adequate resources for coding, administration, and hosting of their BPs. Cloud infrastructure and serviceoriented middleware can be leveraged for rapid and cost effective development of BPs for such organizations. Our objective in this dissertation is to develop an integrated framework for collaborative BP development in a services cloud environment. The proposed framework enables an organization to compose, deploy, and execute their BPs using the Web services and resources available in the cloud. For collaborative BP development, the proposed framework makes use of the knowledge of existing BPs in a given domain to learn common execution patterns of different types of BPs and uses the learned patterns for composing a given type of BP for an organization based on its requirements. The proposed framework aims to assist organizations by enabling automated composition of BPs. Given the requirements and constraints of the user organization and the available Web services, the proposed framework helps the user organization to develop an implementation level BP workflow, which includes the required set of activities, the control-flow and data-flow between these activities, as well as mapping of the activities to appropriate Web services. This implementation level workflow can be translated directly into an executable BP for deployment and execution. The underlying approach for collaborative BP development is designed for structured BPs as well as knowledge-driven BPs. Structured BPs have a well-defined workflow which is predictable and stable over time. In contrast, knowledge-driven BPs are knowledge and data centric and their workflow structure and execution may evolve dynamically based on the changes in the environmental context, user requirements and composition goals. To support evolution of a knowledgedriven BP, the proposed framework also supports on-the-fly process composition to extend the BP workflow in a situation and context aware manner.
جناب محمد طفیل ادھر گذشہ تین مہینے میں اس برصغیر میں چار موتیں ہوئیں جن سے علمی حلقے کو بڑا صدمہ پہنچا، ان کی یادیں برابر آتی رہیں گی۔جناب محمد طفیل اڈیٹر نقوش لاہور اپنے کسی کام سے اسلام آباد آئے، رات کو خوش خوش سوئے تو اپنی میٹھی نیند ہی میں ۵؍ جولائی ۱۹۸۶ء کو اﷲ کو پیارے ہوئے، اور اپنے بے شمار قدردانوں کو سوگوار چھوڑ گئے، انھوں نے نقوش کو اپنی ادارت میں ایک علمی فیکٹری بنا رکھا تھا، جس طرح کسی فیکٹری سے مشینیں ڈھل کر نکلتی ہیں، اسی طرح نقوش سے طرح طرح کے علمی و ادبی نمبر نکلتے رہے، غزل نمبر، افسانہ نمبر، مکاتیب نمبر، خطوط نمبر، شخصیات نمبر، طنز و مزاح نمبر، منٹو نمبر، بہ طرس نمبر، لاہور نمبر، آپ بیتی نمبر، ادب آلعالیہ نمبر، ادبی معرکہ نمبر، غالب نمبر، میرتقی میر نمبر، شوکت تھانوی نمبر، میر انیس نمبر اور اقبال نمبر کے علاوہ آخر میں تیرہ جلدوں میں رسول نمبر نکالا، اور قرآن نمبر نکالنے کی فکر میں تھے کہ خود وہاں پہنچ گئے جہاں سے یہ مقدس صحیفہ نازل ہوا تھا۔ یہ سارے نمبر علمی، ادبی اور تاریخی، انسائیکلوپیڈیا بن گئے ہیں، اس لحاظ سے وہ خوش نصیب تھے کہ ان کا خاتمہ بالخیر رسول نمبر پر ہوا، اس کی تیرہ (۱۳) جلدیں دینی فیوض اور ملی برکات کا سرچشمہ بنی رہیں گی، یہ بیسویں صدی میں اردو زبان کا ایسا شاندار کارنامہ ہے جو مدت مدید تک یاد رکھا جائے گا، وہ اپنی دنیاوی زندگی میں لوگوں کو علمی کوثر، ادبی تسنیم اور دینی سلسبیل کے جام پر جام پلاتے رہے، دعا ہے کہ اب جہاں وہ پہنچ گئے ہیں وہاں برکت اخروی کی کوثر، مغفرت الٰہی کی تسنیم اور رحمت ایزدی کے سلسبیل سے سیراب ہوتے رہیں، آمین، وہ اپنے پیچھے یہ درس چھوڑ گئے ہیں...
One of the core issues in contemporary economic trends is the fair and just distribution of wealth into the society. In this research, a comparative study of ‘distribution of wealth’ in Capitalism, Communism and Islam has been conducted. Qualitative research method is adopted for the analysis of data. The review of literature reveals that central ideas of Capitalism, Communism and Islam are ‘liberty’, ‘equality’ and ‘justice’ respectively. After evaluating the basic infrastructure, methodological framework and practical consequences of these three systems, it has been established that Capitalism and Communism have badly failed in maintaining fair distribution of wealth. Both (Capitalism and Communism) enhanced global inequality which could be assessed from the present economic condition of world as half the world’s wealth is now in the hands of just 1% of the population. On the other hand, Islam has ensured the just distribution of wealth by taking two revolutionary steps: by giving 18 compulsory and 6 optional commandments about circulation of wealth and by overruling all means of hoarding. Moreover, unlike Capitalism and Communism, Islam has given special instructions to distribute wealth among poor, disable, and helpless persons of society who are incapable of participating in the process of production. These measures promote peace and prosperity in society and reduce the ratio of poverty. In the light of above findings, it is concluded that only Islamic economic system can guarantee the just and fair distribution of wealth. It is, therefore, recommended that Islamic countries should implement the Islamic economic system in order to get rid of economic instability.
Nowadays the excessive use of internet produces a huge amount of data due to the social
networks such as Twitter, Facebook, Orkut and Tumbler. These are microblogging sites
and are used to share the people opinions and suggestions on daily basis relevant to the
certain topic. These are beneficial for decision making or extracting conclusions. Analysis
of these feeds aims to assess the thinking and comments of people about some personality
or topic. Sentiment analysis is a type of text classification and is performed by various
techniques such as Machine Learning Techniques and shows that the text is negative,
positive or neutral. In this work, we provide a comparison of most recent sentiment
analysis techniques such as Na?ve Bayes, Bagging, Random Forest, Decision Tree,
Support Vector Machine and Maximum entropy. The purpose of the study is to provide an
empirical analysis of existing classification techniques for social media for analyzing the
good performance and better information retrieval. A comprehensive comparative
framework is designed to compare these techniques. Various benchmark datasets (UCI,
KAGGLE) available in different repositories are used for comparison purpose. We
presented an empirical analysis of six classifiers. The analysis results that Random Forest
performs much better as compared to other. Efforts are made to provide a conclusion
about different algorithms based on numerical and graphical metrics to conclude that
which algorithm is optimal.