Visualization of Ontology-Based Data Warehouse for Malaria Spread Incidences Using Protege

Abstract

Malaria is a communicable disease caused by a plasmodium parasite and transmitted among human by Anopheles mosquitoes. Late medication of this disease can cause a death of patients. Indonesia has many endemic areas with a high volume of patients diagnosed by Malaria. Currently, this incidences data is stored in Microsoft Excel files. We need to build a data warehouse to easily manage these data. Here, we create ontology of Malaria's incidence data to figure out the important information in Malaria data warehouse that we want to build. We identify entities, classes, subclasses, and relationships between these entities. We employed Protégé to build and visualize the ontology of Malaria incidence data. Keywords: ontology, data warehouse, malaria, visualization, Protégé AbstrakMalaria adalah penyakit menular yang disebabkan oleh parasite plasmodium dan dipindahkan ke tubuh manusian oleh Nyamuk Anopheles. Penanganan yang lambat terhadap penyakit malaria dapat menyebabkan kematian pasien. Indonesia adalah salah satu negara yang memiliki banyak wilayah endemik dengan volume yang cukup tinggi terhadap pasien didiagnosis penyakit malaria. Saat ini, data dari kasus-kasus malaria di berbagai wilayah endemic di Indonesia masih disimpan dalam banyak file excel. Akibatnya, terdapat kesulitan untuk memperoleh informasi yang cepat dalam pengambilan keputusan untuk penanganan kasus malaria. Oleh karena itu, perlu dibangun sebuah data warehouse untuk mengatur data tersebut secara terpusat. Pada artikel ini, dibuat ontology untuk mengidentifikasi informasi dan parameter penting dari elemen-elemen yang harus ada dalam data warehouse, seperti: entitas, kelas, sub-kelas, dan hubungan antar entitas. Protégé digunakan untuk membangun visualisasi dan memudahkan pemahaman terhadap hubungan antar entitas dalam data kasus malaria.

Decision Support System to Determine Promotional Methods and Targets with K-Means Clustering

“…Semantic-based data warehouses are developed because they see some weaknesses in traditional data warehouse such as stages to generate OLAP reports that must go through several processes such as landing zone, staging area, integrated store, and analytic layer, Whereas it's previously already through the ETL process (extract, transform, load) [1]. Semantic-based data warehouses can handle different data sources, both internal and external sources, where external data handling has many challenges such as characteristics, structural, and heterogeneous syntax [3][4] [5] [6].…”