A large population of users get affected by sudden slowdown orshutdown of an enterprise application. System administrators andanalysts spend considerable amount of time dealing with functionaland performance bugs. These problems are particularly hard todetect and diagnose in most computer systems, since there is ahuge amount of system generated supportability data (counters,logs etc.) that need to be analyzed.Hence, timely identification of significant change in applicationbehavior is very important to prevent negative impact on the ser-vice. In that light many a research efforts have been made to proposeautomated frameworks for anomaly detection and system failureprediction in distributed environments. While many a frameworksfor detecting anomalous behavior exist , very few models dedi-cated for system failure have been proposed. Again, most of thoseproposed works mostly predict the failure events without actuallypinpointing the problem instances/bugs responsible for failure re-port. Further , the design of most of these frameworks are too muchdependent on the logging instrumentation of the particular cloudplatform which makes the portability of these frameworks challeng-ing. In our work, we propose a system failure prediction frameworkfor large scale distributed systems by learning the bug wise signa-tures. The key novelty of our framework is its ability to raise failurealerts apriori alongside pinpointing the problem scenario. The de-sign of our framework is quite flexible in the sense it leverages onsome abstractions defined on the event/performance logs generatedby the wide scale cloud platforms.The high prediction FPR ascer-tains the power of this model to alert the corresponding supportengineers so that once notified they troubleshoot the system anduse their domain to triage the failure.

The SunPy project facilitates and promotes the use and development of several community-led, free, and open source data analysis software packages for solar physics based on the scientific Python environment. The project achieves this goal by developing and maintaining the sunpy core package and supporting an ecosystem of affiliated packages. The SunPy project is pleased to announce the 1.0 release of the sunpy package. This new release is the first stable release of the packages and includes several important new features such as improved data downloading capabilities, a large enhancement in coordinate and coordinate transformations capabilities, new map utility functions, and a new logging functionality amongst others. This talk will present how the sunpy package can be used for solar data analysis and discuss the roadmap for package.

Clustering is the task of assigning a set of objects into groups so that the objects within the same cluster are more similar to each other than to those in other clusters based on some similarity measures. Clustering of documents is an important task in text mining based on their research topics. In this field, cluster analysis is the task of grouping a set of documents in such a way that the documents in the same cluster have similar topic and documents of different clusters have different topics. The proposed method introduces a novel graph based clustering method which uses the importance factor of a document based on a better mathematical approach than well known classical methods. Document with the maximum importance factor in a cluster is considered as the centroid of the cluster. Publicly available synthetic dataset is used to evaluate the performance of the proposed algorithm and the method is compared with some traditional graph based methods to demonstrate its accuracy.