Software even though intangible should undergo evolution to fit into the ever changing real world scenarios. Each issue faced by the development and service team directly reflects in the quality of the software product. According to the related work, very few research is going on in the field of ticket and its related incident; a part of corrective maintenance. In depth research on incident tickets should be viewed as critical since, it provides information related to the kind of maintenance activities that is performed in any timestamp. Therefore classifying and analyzing tickets becomes a critical task in managing the operations of the service since each incident will be having a service level agreement associated with it. Further, incident analysis is essential to identify the patterns associated. Due to the existence of huge population of software in each organization and millions of incidents get reported per software product every year, it is practically impossible to manually analyze all the tickets. This paper focuses on projecting the importance of ticket to maintain the quality of software products and also distinguish it from the defect associated with a software system. This paper projects the importance of identifying defects in software as well as handling the incident related tickets and resolving it when viewed from the perspective of quality. It also gives an overview of the scope defect analysis and ticket analytics provide to the researchers.

In this world of internet and social network, privacy is the one word that concerns everyone. All the data concerned to a person will get updated in the web and is available at ease. Hospitals and health centers when computerizing their center will knowingly or unknowingly be a part of this. Health related data is a very sensitive data that people are reluctant to disclose. Hospitals should go an extra mile to preserve the privacy of their clients. The techniques that are available in preserving privacy don’t serve its purpose. Thus a novel method completely new in the field of big data is introduced in privacy preservation namely personalized anonymity. The central idea of this technique can be distributed into two main components. The component one of the workdeal with attributes in the patient data which is used as a flag and can be used to differentiate sensitive attribute. The attributes include sensitive disclosure flag (SDF) as well as sensitive weigh (SW). The second component deals with a new demonstration called Frequency Distribution Block (FDB) and quasi-identifier Distribution Block (QIDB), which uses the SW and SDF for anonymity. The paper provides an overview of personalized anonymity technique in medical big data which in turn enhances the privacy of users.