scholarly journals Analisis dan Desain Software Jejaring Kata Menggunakan Graph Database

2021 ◽  
Vol 3 (2) ◽  
pp. 176-182
Author(s):  
Muhammad Rizal Musthofa ◽  
Bintang Miftaqul Huda ◽  
Muhammad Zaim Maulana ◽  
Muhammad Ainul Yaqin
Keyword(s):  
Graph Database ◽  
Waterfall Model

Jejaring kata atau wordnet merupakan representasi hubungan semantik berupa sinonim antar kata. Saat ini penelitian terkait wordnet berbahasa Indonesia masih begitu minim. Adapun wordnet berbahasa Indonesia yang ada, belum menggunakan graph database sehingga belum bisa menampilkan kemiripan antar kata secara visual. Oleh karena itu, masalah yang dirumuskan pada penelitian ini yaitu bagaimana mengembangkan software jejaring kata untuk menghitung kemiripan kata berabahasa Indonesia. Paper ini bertujuan untuk membuat software wordnet berbahasa Indonesia untuk menentukan tingkat kemiripan antar kata berbahasa Indonesia. Dengan Wordnet ini akan memudahkan proses pencarian nilai similaritas pada suatu kata. Metode yang digunakan dalam pengembangan aplikasi ini menggunakan metode waterfall model yang terdiri dari tahap requirement, desain, implementasi, verifikasi, dan maintenance. Aplikasi ini terdiri atas struktur dataset kata berbahasa Indonesia berbentuk graph database Neo4J yang digunakan dalam pencarian kemiripan antar dua kata. Proses pencarian nilai similaritas dalam aplikasi ini menggunakan metode path similarity sehingga diperolah nilai kemiripan dari dua kata yang diinputkan ke dalam sistem. Adapun penelitian ini hanya menggunakan dataset kata berbahasa Indonesia dan memiliki jumlah dataset yang masih terbatas. Penelitian ini berkontribusi dalam pengembangan software jejaring kata berbahasa Indonesia menggunakan graph database. Hasil penelitian yang didapat berupa software jejaring kata untuk menghitung kamiripan kata menggunakan graph database. Dengan adanya pengembangan software tersebut, mampu menyelesaikan permasalahan seputar perhitungan kemiripan kata.

Perancangan dan Pembuatan Situs Reseller Management pada Cargo Fashion

2018 ◽  
Vol 9 (2) ◽  
pp. 82-87
Author(s):  
Enrico Siswanto
Keyword(s):  
Small Business ◽  
Web Based ◽  
Index Terms ◽  
Waterfall Model ◽  
Reasonable Price

Cargo Fashion is a small business that focuses on selling branded garments exports with the best quality and reasonable price. Cargo Fashion still has difficulty in making sales to reseller because still using manual way. Therefore, Cargo Fashion requires a website-based system that can accommodate reseller sales wherever and whenever. Website designed using Waterfall method and built using the PHP and MySQL language for the database. The results of this study is a website that can make sales for resellers and at the same time facilitate the owner of Cargo Fashion in checking the stock of goods and view sales reports. The system was tested to the owner and 100% accepted and meet all the requirements. Index Terms—reseller management, web-based application, waterfall model

scholarly journals Advantages of using graph databases to explore chromatin conformation capture experiments

2021 ◽  
Vol 22 (S2) ◽  
Author(s):  
Daniele D’Agostino ◽  
Pietro Liò ◽  
Marco Aldinucci ◽  
Ivan Merelli
Keyword(s):  
Web Application ◽  
High Throughput Sequencing ◽  
Cell Types ◽  
Graph Database ◽  
Graph Databases ◽  
Sources Of Information ◽  
Chromosome Conformation ◽  
Wide Scale ◽  
User Friendly ◽  
Different Cell Types

Abstract Background High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells. Methods Here we discuss the use of a graph database for storing and analysing data achieved by performing Hi-C experiments. The main issue is the size of the produced data and, working with a graph-based representation, the consequent necessity of adequately managing a large number of edges (contacts) connecting nodes (genes), which represents the sources of information. For this, currently available graph visualisation tools and libraries fall short with Hi-C data. The use of graph databases, instead, supports both the analysis and the visualisation of the spatial pattern present in Hi-C data, in particular for comparing different experiments or for re-mapping omics data in a space-aware context efficiently. In particular, the possibility of describing graphs through statistical indicators and, even more, the capability of correlating them through statistical distributions allows highlighting similarities and differences among different Hi-C experiments, in different cell conditions or different cell types. Results These concepts have been implemented in NeoHiC, an open-source and user-friendly web application for the progressive visualisation and analysis of Hi-C networks based on the use of the Neo4j graph database (version 3.5). Conclusion With the accumulation of more experiments, the tool will provide invaluable support to compare neighbours of genes across experiments and conditions, helping in highlighting changes in functional domains and identifying new co-organised genomic compartments.

scholarly journals Applying graph database technology for analyzing perturbed co-expression networks in cancer

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Claire M Simpson ◽  
Florian Gnad
Keyword(s):  
Relational Databases ◽  
Molecular Mechanisms ◽  
Biological Data ◽  
Database Management System ◽  
Graph Database ◽  
Graph Databases ◽  
Graph Representations ◽  
Rnaseq Data ◽  
Database Technology ◽  
Speed Accuracy

Abstract Graph representations provide an elegant solution to capture and analyze complex molecular mechanisms in the cell. Co-expression networks are undirected graph representations of transcriptional co-behavior indicating (co-)regulations, functional modules or even physical interactions between the corresponding gene products. The growing avalanche of available RNA sequencing (RNAseq) data fuels the construction of such networks, which are usually stored in relational databases like most other biological data. Inferring linkage by recursive multiple-join statements, however, is computationally expensive and complex to design in relational databases. In contrast, graph databases store and represent complex interconnected data as nodes, edges and properties, making it fast and intuitive to query and analyze relationships. While graph-based database technologies are on their way from a fringe domain to going mainstream, there are only a few studies reporting their application to biological data. We used the graph database management system Neo4j to store and analyze co-expression networks derived from RNAseq data from The Cancer Genome Atlas. Comparing co-expression in tumors versus healthy tissues in six cancer types revealed significant perturbation tracing back to erroneous or rewired gene regulation. Applying centrality, community detection and pathfinding graph algorithms uncovered the destruction or creation of central nodes, modules and relationships in co-expression networks of tumors. Given the speed, accuracy and straightforwardness of managing these densely connected networks, we conclude that graph databases are ready for entering the arena of biological data.

Sign in / Sign up

Export Citation Format

Share Document