PENERAPAN GRAPH DATABASE UNTUK SISTEM REKOMENDASI KOLEKSI PERPUSTAKAAN DI ERA BIG DATA
DOI:
https://doi.org/10.33505/jodis.v3i2.149Keywords:
Graph database, Library, Big Data, Recommendation SystemAbstract
The library in its development utilizes information systems to provide easy access to collections by providing digital libraries. Digital libraries really need database support that is suitable to handle the needs of fast and accurate data access in digital storage space. Currently in the application of digital libraries in Higher Education still using traditional databases namely relational databases. The disadvantages of this database system are that the data is stored in a structured manner so that the difficulty when dealing with changes in data structure is due to the development of volume and data variations. Another disadvantage is that it is difficult to handle varied data relationships that arise between collections and users so that requiring complicated join operations results in slow database operations. In this paper, we will explain the application of graph database as a solution offered for problems that occur in digital libraries. In this study interviews and observations were used to collect data and determine input, process and output needs. Implementation is done by designing a database graph that fits the needs of the library, then executes the query in accordance with the design. In library information systems, the relationship that exists between collection data and user data is very necessary. Graph data base is the right alternative to describe relationships (networks) that occur between collections and users. By utilizing the relationships that are intertwined between the data, the library collection recommendation system can be explained based on these relationships.References
Cielen, D., Meysman, A. D. B., & Ali, M. (2016). Introducing Data Science: Big data, machine learning, and more, using Python tools. Manning Publications.
Feret, B., & Marcinek, M. (2005). The Future of The Academic Library and The Academic Librarian: A Delphi Study Reloaded. New Review of Information Networking, 11(1), 37–63. https://doi.org/10.1080/13614570500268381
Huang, Z., Chung, W., Ong, T.-H., & Chen, H. (2002). A graph-based recommender system for digital library. Proceedings of the Second ACM/IEEE-CS Joint Conference on Digital Libraries.
Jo, T. (2019). Text Mining: Concepts, Implementation, and Big Data Challenge. Springer International Publishing. https://doi.org/10.1007/978-3-319-91815-0
Needham, M., & Hodler, A. E. (2019). Graph Algorithms: Practical Examples in Apache Spark and Neo4j. O’Reilly Media.
Sarlina, M., & Setiadi, T. (2017). Pengukuran Kepuasan Pemustaka Terhadap Kualitas Pelayanan Perpustakaan Universitas Ahmad Dahlan Yogyakarta Dengan Menggunakan Metode Customer Satisfaction Index (CSI). Jurnal Sarjana Teknik Informatika, 5(2), 1–10. http://journal.uad.ac.id/index.php/JSTIF/article/view/10843
Setialana, P., Adji, T., & Ardiyanto, I. (2017). Perbandingan Performa Relational, Document-Oriented dan Graph Database Pada Struktur Data Directed Acyclic Graph. Jurnal Buana Informatika, 8(2), 77–86. https://doi.org/10.24002/jbi.v8i2.1079
Downloads
Published
Issue
Section
License
Copyright (c) 2020 Journal of Documentation and Information Science
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
- Hak publikasi dan pemanfaatan karya intelektual pada jurnal ini menjadi milik penuh penerbit, sedangkan hak moral menjadi milik penulis.
- Aspek legal formal akses dan pemanfaatan setiap artikel JoDIS tunduk di bawah lisensi Creative Commons Atribusi-Berbagi Serupa (CC BY-SA), yang berarti bahwa konten jurnal dapat dimanfaatkan secara bebas dan wajar (fair use) dalam bentuk serupa bahkan untuk kepentingan komersial.
- Untuk menghindari tindakan malpraktik publikasi dan plagiarisme penerbitan artikel, penulis diminta mengisi dan menandatangani pernyataan hak cipta pada Surat Pernyataan Keaslian Naskah dan Copyright Transfer.
