Data analytics and visualization to support library decisions | |
| Author | Karna, Neha |
| Subject(s) | Data mining-Data processing Information visualization Library administration-Decision making |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Computer Science |
| Publisher | Asian Institute of Technology |
| Abstract | Data analytics in libraries have been increased in the fields like medicine, politics, tourism etc. Data analytics has been relatively new approach in many domains for business under standing and making decisions. But comparing to other fields, data analytics in libraries have been falling behind. Data analytics is new approach in terms of libraries compared to many other fields. Within time, the libraries has accumulated large amounts of data related to patrons, books, digital libraries. Despite the availability of data and benefits of using data analytics in libraries, library analytics has still been falling behind and has a huge scope. Thus, this study focuses on detailed analytics using different techniques and approach. This study discusses the previous research done using data mining techniques in academic libraries. With the previous research, the study focuses on many library decisions which are made by strategic and tactical level of management for acquisitions, serials and public service sections. The study explores the use of descriptive analytics as library dashboard and predictive analytics to analyze the acquisition purchase in the academic libraries. The dashboard provides the scope to explore the digital libraries management as well as the visiting patterns of the patrons of the library. These analysis supports some of the decisions of the library. In the similar way, the predictive analytics will help the library to make decision to purchase books. We have used the third party software Google Data Studio for the descriptive analytics. These all analysis helps the library to support their decisions efficiently and less time consuming than the traditional approach. |
| Year | 2020 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Computer Science (CS) |
| Chairperson(s) | Chutiporn Anutariya; |
| Examination Committee(s) | Vatcharapon Esichaikul;Dailey, Matthew N.; |
| Scholarship Donor(s) | AIT Fellowship; |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2020 |