A unified ontology-based data integration approach for the internet of things

<span lang="EN-US">Data integration enables combining data from various data sources in a standard format. Internet of things (IoT) applications use ontology approaches to provide a machine-understandable conceptualization of a domain. We propose a unified ontology schema approach to solve all IoT integration problems at once. The data unification layer maps data from different formats to data patterns based on the unified ontology model. This paper proposes a middleware consisting of an ontology-based approach that collects data from different devices. IoT middleware requires an additional semantic layer for cloud-based IoT platforms to build a schema for data generated from diverse sources. We tested the proposed model on real data consisting of approximately 160,000 readings from various sources in different formats like CSV, JSON, raw data, and XML. The data were collected through the file transfer protocol (FTP) and generated 960,000 resource description framework (RDF) triples. We evaluated the proposed approach by running different queries on different machines on SPARQL protocol and RDF query language (SPARQL) endpoints to check query processing time, validation of integration, and performance of the unified ontology model. The average response time for query execution on generated RDF triples on the three servers were approximately 0.144 seconds, 0.070 seconds, 0.062 seconds, respectively.</span>

Using Data Integration to Improve Health and Welfare Insights

The Australian Institute of Health and Welfare (AIHW) is a leader in the provision of high-quality health and welfare information. Its work program has built a strong evidence base for better decisions that deliver improved health and welfare outcomes. The evolution of the AIHW’s data integration program has exemplified innovation in identifying and addressing key information gaps, as well as responsiveness to opportunities to develop and capture the data required to inform national priorities. The AIHW conducts data integration in partnership with data custodians and specialists in integration and analysis. A linkage project requiring the integration of Australian government data must be undertaken by an accredited integrating authority. The AIHW has met stringent criteria covering project governance, capability, and data management to gain this accreditation. In this capacity, the AIHW is trusted to integrate Australian government data for high-risk research projects. To date, the AIHW’s integration projects have generated improved research outcomes that have identified vulnerable population groups, improved the understanding of health risk factors, and contributed to the development of targeted interventions. These projects have fostered new insights into dementia, disability, health service use, patient experiences of healthcare, and suicide. Upcoming projects aim to further the understanding of interrelationships between determinants of wellbeing.

Analisa Kategori Barang dengan Penjualan Terbanyak dalam Jangka Waktu 3 Bulan Menggunakan Data Warehouse

UD. Makmur Sejahtera sebagai salah satu distributor terbesar untuk barang kebutuhan sehari-hari di Manokwari Papua, memiliki data-data transaksi penjualan untuk setiap kategori barang dan jenis barang. Data-data ini masih tersimpan secara fisik dalam bentuk nota serta belum didigitalkan untuk dapat dimanfaatkan secara maksimal untuk membantu UD. Makmur Sejahtera meningkatkan penjualan. Penelitian ini memiliki ide dasar pemanfaatan data digital transaksi penjualan untuk mengetahui kategori barang mana yang memiliki penjualan terbanyak dalam kurun waktu tiga bulan (Juli 2020 hingga September 2020) melalui proses Extraction, Transformation, Loading (ETL) berbasis Pentaho Data Integration, untuk kemudian disimpan dalam bentuk data multi dimensi, dikategorikan, dan divisualisasikan menggunakan Tableau. Hasil pengujian menunjukkan bahwa komoditas beras merupakan kategori barang dengan penjualan terbanyak pada kurun waktu tiga bulan serta implementasi Data Warehouse sangat membantu UD. Makmur Sejahtera di dalam mencapai tujuan bisnis usahanya.

NMR metabolomic and microarray-based transcriptomic data integration identifies unique molecular signatures of hypersensitivity pneumonitis

This original article focuses on integrated metabolomics and transcriptomics analysis to understand the pathogenesis of hypersensitivity pneumonitis (HP).