Storage-class memory technologies such as phase-change memory and memristors present a radically different interface to storage than existing block devices. As a result, they provide a unique opportunity to re-examine storage architectures. We find that the existing kernel-based stack of components, well suited for disks, unnecessarily limits the design and implementation of file systems for this new technology.We present Aerie, a flexible file-system architecture that exposes storage-class memory to user-mode programs so they can access files without kernel interaction. Aerie can implement a generic POSIX-like file system with performance similar to or better than a kernel implementation. The main benefit of Aerie, though, comes from enabling applications to optimize the file system interface. We demonstrate a specialized file system that reduces a hierarchical file system abstraction to a key/value store with fewer consistency guarantees but 20-109% higher performance than a kernel file system.

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In order to increase the effectiveness of delivery of quality education, it is important to evaluate the performance of two major stackholders namely students and faculty. Presently, Data Mining has emerged an important area of research in Higher and Technical Education. Data mining techniques are applied in higher education to address and give an insight to educational and administrative problems in HEIs. However, a large portion of the instructive mining research concentrates on modelling and predicting student's performance and a very few research models are available on faculty performance. While evaluating faculty performance, majority of the research used questionnaire as an important tool for collecting feedback from the students. The same method is being used in this research also. In this study, we have applied five Classification Techniques namely Logistic Regression, Decision Tree, Linear SVM, Neural Network and Naive Bayes and used student's results along with filled questionnaires to predict the performance of faculty. The accuracy, sensitivity and specificity of classification rules were estimated. The findings of the study indicate the effectiveness of classification in evaluation of faculty.

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