Ajit Singh
crossref research data count citation DLM RDA scholix researcher datasite DOI working group. data migration relational database MongoDB XAMPP NoSQL ACID transactions. Academician, Researcher, Author, Computer Science computing computer science predictive analytics Cloud Computing and Resource Management Distributed and Parallel Computing Systems Computer Networks and Communications Information Systems

Bio

Ajit Singh UGC NET Qualified, Research Scholar, Patliputra University, Bihar, IND Membership/Certification/Award: 1. IEEE Brand Ambassador Expert 2. Diploma of Membership for Ph.D Council from INSTITUT de DIPLOMATIE PUBLIQUE, United Kingdom, London. 3. Ambassador - World Literacy Foundation, Australia 4. Member - International Peace Bureau, Berlin, Germany 5. EURAXESS Research Mentor, Sofia University, Bulgaria 6. Academy Mentor - Web of Science Academy, US 7. Certified Reviewer - American Chemical Society (ACS) Reviewer Lab, Washington, US 8. Mendeley Advisor Certification, London, UK 9. WORLD RECORD CERTIFICATION AGENCY, UK, London M.Phil. Degree in Computer Science, and is a Microsoft's MCSE / MCDBA / MCSD. 25+ Years of rich teaching experience for UG and PG courses of Computer Science across several colleges of Patna University, INDIA. Being an academician, researcher and author, I have authored several Computer Science Academic Books for UG/PG courses. https://www.amazon.com/author/ajitsingh Contacts URL: http://www.ajitvoice.wordpress.com/ Email: [email protected]

Educational Journey

Patliputra University

Pursuing Ph.D in Computer Science in Department of Physics • Department of Physics

Patna University

Ph.D Computer Science, M. Phil. Computer Science • Predictive Analytics

Global Open University

M. Phil. Computer Science in Computer • Computer

2009
Show all 4 education

Experience

0 - 0

J D Women's College

Faculty Member

2009 - Present • MCA

Affiliations

World Wide Peace Organisation

Permanent

Member since 2021

International Peace Bureau

Individual

Member since 2021

Iot Council

IoT Council Member

Member since 2019
Show all 9 affiliations

Research

Data Migration from Relational Database to MongoDB

Article May 21, 2019

MongoDB is a document-oriented database which helps us group data more logically. This paper demonstrates the conversion of data from a native tabular form to unstructured documents. The document and collections within it needs not to be well defined prior to the creation of unstructured data in MongoDB. The MongoDB has lots of extensive built-in-features and is highly compatible with other software systems, with extensive and flexible ways of accessing data beyond JSON query, its highly compatible Business Intelligence Connector is highly compatible which makes it compatible with existing databases. High scalability is making it remarkable and popular in the World and hence made me think about writing a paper demonstrating the data conversion. This conversion has helped me in making the most of modern data to be compatible with MongoDB. Data is stored on the cloud as cloud-based storage is an excellent and most cost-effective solution. My solution is highly scalable as the built-in shading solution for data handling makes it one of the best big data handling tool. The data that i have used, is location based in MongoDB that can directly yeild document ACID transactions to maintain data integrity.

Enabling Resesrchers to Make their Data Count

Article April 16, 2019

Over the last years, many organizations have been working on infrastructure to facilitate sharing and reuse of research data. This means that researchers now have ways of making their data available, but not necessarily incentives to do so. Several Research Data Alliance (RDA) working groups have been working on ways to start measuring activities around research data to provide input for new Data Level Metrics (DLMs). These DLMs are a critical step towards providing researchers with credit for their work. In this paper, I describe the outcomes of the work of the Scholarly Link Exchange (Scholix) working group and the Data Usage Metrics working group. The Scholix working group developed a framework that allows organizations to expose and discover links between articles and datasets, thereby providing an indication of data citations. The Data Usage Metrics group works on a standard for the measurement and display of Data Usage Metrics. Here I explain how publishers and data repositories can contribute to and benefit from these initiatives. Together, these contributions feed into several hubs that enable data repositories to start displaying DLMs. Once these DLMs are available, researchers are in a better position to make their data count and be rewarded for their work.