Data migration: relational RDBMS to non-relational NoSQL

As a part of achieving specific targets, business decision making involves processing and analyzing large volumes of data that leads to growing enterprise databases day by day. Considering the size and complexity of the databases used in today’s enterprises, it is a major challenge for enterprises to re-engineering their applications that can handle large amounts of data. Compared to traditional relational databases, non-relational NoSQL databases are better suited for dynamic provisioning, horizontal scaling, significant performance, distributed architecture and developer agility benefits. Based on the concept of Object Relational Mapping (ORM) and traditional ETL data migration technique this thesis proposes a methodology for migrating data from RDBMS to NoSQL. The performance of the proposed solution is evaluated through a comparative analysis of RDBMS and NoSQL implementations based on query performance evaluation, query structure and developmental agility.

Data migration: relational RDBMS to non-relational NoSQL

As a part of achieving specific targets, business decision making involves processing and analyzing large volumes of data that leads to growing enterprise databases day by day. Considering the size and complexity of the databases used in today’s enterprises, it is a major challenge for enterprises to re-engineering their applications that can handle large amounts of data. Compared to traditional relational databases, non-relational NoSQL databases are better suited for dynamic provisioning, horizontal scaling, significant performance, distributed architecture and developer agility benefits. Based on the concept of Object Relational Mapping (ORM) and traditional ETL data migration technique this thesis proposes a methodology for migrating data from RDBMS to NoSQL. The performance of the proposed solution is evaluated through a comparative analysis of RDBMS and NoSQL implementations based on query performance evaluation, query structure and developmental agility.

Determinants of Non-Performing Assets of Commercial Banks in India

Banks’ credit growth continues to decelerate in India due to huge non-performing assets (NPAs) overhangs in banks. Using the panel data methodology, this study empirically analyzed the determinants of NPAs of scheduled commercial banks in India during 2009-2020. Results indicated that the excessive credit growth in the past increased the surge in the current NPAS. The economic slowdown also aggravated loan delinquencies in Indian commercial banks. While higher priority sector lending created higher loan delinquencies, higher banks size and higher profitability reduced it. This study suggested that counter capital buffer, dynamic provisioning and a sound credit appraisal NPA improved the financial stability and monetary policy effectiveness. These findings are useful for policymakers, bankers and other stakeholders to make appropriate strategies to resolve the NPA issue in India.

Construction and Empirical Research on the Dynamic Provisioning Model of China’s Banking Sector under the Macro-Prudential Framework

Since the international financial crisis in 2008, to achieve the political goal of financial stability, academic circles, financial industry, and regulatory authorities worldwide have deeply reflected on the current economic regulatory theories and policy adjustment tools through introducing the macroprudential policy. The dynamic provisioning system is a counter-cyclical policy tool in the macro-prudential adjustment framework widely used in the world. This paper uses the binary Gaussian Copula function to combine the measurement method of the default distance in the contingent claims analysis method with the risk warning idea based on the Probit model and proposes the contingent claims analysis (CCA)–Probit–Copula dynamic provisioning model based on nine forward-looking indicators. Based on China’s actual conditions, this model solves present problems faced by the current dynamic provisioning system in China, such as insufficient historical credit data reserves of commercial banks, excessive reliance on subjective judgments, and conflicts with the current accounting system. Moreover, this model can put forward corresponding counter-cyclical provisioning requirements according to the influence degree of macro-cyclical factors to different commercial banks’ own default risk, which not only takes into account the security and liquidity of commercial banks, but also ensures their profitability and competitiveness. Based on the empirical test of historical data from listed commercial banks in China, it proves that the dynamic provisioning requirements proposed in this model can effectively adjust the overall credit scale of the banking industry in counter-cyclical ways, thereby achieving the policy goals of counter-cyclical adjustment under the macro-prudential framework and maintaining the security of China’s financial system and the sustainable development of the macroeconomy.

Software defined network emulation with OpenFlow protocol

In software defined network the network infrastructure layer where the entire network devices, like switches and routers reside is connected with the separate controller layer with the help of standard called OpenFlow. The open flow standard enables different vendor devices like juniper, cisco and Huawei switch to connect to the controller or a software program. The software program controls and manages the network devices. Therefore, software defined network architecture makes the network flexible, cost effective and manageable, enables dynamic provisioning of bandwidth, dynamic scale out and dynamic scale in compared to the traditional network. In this study, the architectures and principles of software defined network is explored by emulating the software defined network employing a mininet.