Real-Time MARC Data Generation Using AI and Big Data
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Solution Overview
Problem
Conventional methods for generating MARC data require expert librarian knowledge and are inefficient, often resulting in errors and inconsistencies across libraries, as they rely solely on bibliographic data and fail to include essential non-bibliographic information like KDC/DDC, reference documents, and customized symbols.
Innovation Solution
A system utilizing big data analysis with AI to generate MARC data in real time by constructing a database with book information, forming mapping tables, and calculating similarity between new book data and existing data to include non-bibliographic information such as KDC/DDC, author type, and subject terms, and transmitting the data to libraries in a user-requested format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If MARC data is generated using only bibliographic data from books, then the generation process is simple, but the accuracy and completeness of MARC data is very low
Solution Approach 1:
The system pre-collects and stores various types of data (bibliographic data, big data from multiple sources, librarian expert opinions) in a database before MARC data generation is needed. When a book is processed, the system queries this pre-prepared database to obtain classification numbers, subject terms, and other metadata, eliminating the need for real-time expert intervention while maintaining high accuracy.
Solution Approach 2:
The patent introduces an intermediary database system that stores pre-collected bibliographic data, big data from multiple sources, and librarian expert opinions. This intermediary database acts as a mediator between the book input and MARC data generation, providing the necessary metadata without requiring direct expert librarian involvement in each generation process.
2Manufacturing precision
If expert librarians manually generate MARC data, then the quality and accuracy of MARC data is high, but a lot of time is taken and efficiency is low
Solution Approach 1:
The system enables self-service MARC data generation by automatically querying pre-stored bibliographic data, big data from multiple sources, and expert opinions from the database. The system serves itself without requiring expert librarian intervention for each book, maintaining high quality through pre-collected expert knowledge while achieving high efficiency through automation.
Solution Approach 2:
Expert librarian knowledge and bibliographic data are collected and stored in the database in advance. When MARC data needs to be generated, the system simply queries this pre-prepared information, eliminating the need for librarians to manually research and input data for each book, thus maintaining quality while dramatically improving efficiency.
3Adaptability or versatility
If MARC data is generated for each library individually by librarians, then the data can be customized for each library, but the process is repetitive and inefficient
Solution Approach 1:
The system creates a universal database that stores bibliographic data, big data from multiple sources, and expert opinions that can serve multiple libraries simultaneously. The database is designed to be multi-functional, allowing any library to query and obtain customized MARC data based on their specific needs without requiring separate manual processing for each library.
Solution Approach 2:
The system creates master copies of bibliographic data, big data, and expert opinions in a centralized database. Each library can then obtain customized MARC data by querying and copying the relevant information from this master database, eliminating repetitive data input while maintaining the ability to customize data according to each library's specific requirements.
4Productivity
If book vendors generate and provide MARC data, then the process is automated, but the data quality is insufficient and lacks expert librarian knowledge
Solution Approach 1:
The system introduces an intermediary database that incorporates not only automated bibliographic data but also pre-collected big data from multiple sources and expert librarian opinions. This intermediary database serves as a quality assurance layer, ensuring that automated generation processes access high-quality, expert-validated information while maintaining automation efficiency.
Solution Approach 2:
The patent creates a composite data structure that combines multiple types of information: bibliographic data from books, big data from multiple external sources, and expert librarian opinions. This composite approach ensures that the generated MARC data benefits from both automation efficiency and expert knowledge quality, as all three data types are integrated in the database.
Data Source
AI summary
The present invention relates to a method and a system for generating MARC data in real time, and the method for generating MARC data in real time, according to one embodiment of the present invention, can comprise the steps of: (a) constructing a database by using MARC data and book information about a book having the MARC data generated therein; (b) receiving book information about a new book, and generating MARC data of the new book on the basis of the database and the received book information about the new book; and (c) providing the generated MARC data about the new book to a user.