Perancangan Data Warehouse Rumah Sakit Berbasis Online Analytical Processing (OLAP)

<p class="BodyCxSpFirst">Pemanfaatan teknologi informasi sangat penting bagi rumah sakit, karena berpengaruh pula terhadap kualitas pelayanan kesehatan yang secara manual diubah menjadi digital dengan menggunakan teknologi informasi.Dalam penelitian ini penulis menggunakan metodologi <em>Nine step</em> sebagai acuan dalam merancang suatu <em>data warehouse</em><em>,</em> untuk pemodelan menggunakan skema konstelasi fakta dengan 3 tabel fakta dan 11 tabel dimensi. Perbedaan penelitian ini dengan penelitian sebelumnya terletak pada sumber data yang diekstrak langsung dari <em>database</em> SIMRS yang digunakan rumah sakit, sehingga tidak ada ekstraksi data secara manual.Penelitian ini bertujuan untuk menghasilkan desain data warehouse berbasis Online Analytical Processing (OLAP) sebagai sarana penunjang kualitas pelayanan kesehatan rumah sakit. OLAP yang dihasilkan akan berupa desain data warehouse dengan berbagai dimensi yang akan menghasilkan tampilan informasi berupa Chart maupun Grafik sehingga informasinya mudah dibaca dan dipahami oleh berbagai pihak.</p><p class="BodyCxSpFirst"> </p><p class="BodyCxSpFirst"><em><strong>Abtract</strong></em></p><p class="BodyCxSpFirst"><em>The use of information technology is very important for hospitals, because it also affects the quality of health services, which manualy changed to digital using information technology. In this study, the authors used the Nine step methodology as a reference in designing a data warehouse for modeling using a fact constellation schema with 3 fact tables and 11 dimension tables. the different in this study from previous research is that the data source was taken directly from the SIMRS database used by the hospital, so there is no manual data extraction.</em><em>The aim of this research is to be able to produce a Data Warehouse design based on Online Analytical Processing (OLAP) as a means of supporting the quality of hospital health services. The resulting OLAP will be a data warehouse design with various dimensions will produce the displays information in the form of a graph or chart so that the information is easy to read and understand by various parties.</em></p><p class="BodyCxSpLast"><em> </em></p><p class="BodyCxSpFirst"><em><strong><br /></strong></em></p>

Schema Evolution in Multiversion Data Warehouses

Data warehouses (DWs) evolve in both their content and schema due to changes of user requirements, business processes, or external sources to name a few. Although multiple approaches using temporal and/or multiversion DWs have been proposed to handle these changes, an efficient solution for this problem is still lacking. The authors' approach is to separate concerns and use temporal DWs to deal with content changes, and multiversion DWs to deal with schema changes. To address the former, previously, they have proposed a temporal multidimensional (MD) model. In this paper, they propose a multiversion MD model for schema evolution to tackle the latter problem. The two models complement each other and allow managing both content and schema evolution. In this paper, the semantics of schema modification operators (SMOs) to derive various schema versions are given. It is also shown how online analytical processing (OLAP) operations like roll-up work on the model. Finally, the mapping from the multiversion MD model to a relational schema is given along with OLAP operations in standard SQL.

HTAP With Reactive Streaming ETL

In database management systems (DBMSs), query workloads can be classified as online transactional processing (OLTP) or online analytical processing (OLAP). These often run within separate DBMSs. In hybrid transactional and analytical processing (HTAP), both workloads may execute within the same DBMS. This article shows that it is possible to run separate OLTP and OLAP DBMSs, and still support timely business decisions from analytical queries running off fresh transactional data. Several setups to manage OLTP and OLAP workloads are analysed. Then, benchmarks on two industry standard DBMSs empirically show that, under an OLTP workload, a row-store DBMS sustains a 1000 times higher throughput than a columnar DBMS, whilst OLAP queries are more than 4 times faster on a columnar DBMS. Finally, a reactive streaming ETL pipeline is implemented which connects these two DBMSs. Separate benchmarks show that OLTP events can be streamed to an OLAP database within a few seconds.

HTAP With Reactive Streaming ETL

In database management systems (DBMSs), query workloads can be classified as online transactional processing (OLTP) or online analytical processing (OLAP). These often run within separate DBMSs. In hybrid transactional and analytical processing (HTAP), both workloads may execute within the same DBMS. This article shows that it is possible to run separate OLTP and OLAP DBMSs, and still support timely business decisions from analytical queries running off fresh transactional data. Several setups to manage OLTP and OLAP workloads are analysed. Then, benchmarks on two industry standard DBMSs empirically show that, under an OLTP workload, a row-store DBMS sustains a 1000 times higher throughput than a columnar DBMS, whilst OLAP queries are more than 4 times faster on a columnar DBMS. Finally, a reactive streaming ETL pipeline is implemented which connects these two DBMSs. Separate benchmarks show that OLTP events can be streamed to an OLAP database within a few seconds.

QB4MobOLAP: A Vocabulary Extension for Mobility OLAP on the Semantic Web

The accessibility of devices that track the positions of moving objects has attracted many researchers in Mobility Online Analytical Processing (Mobility OLAP). Mobility OLAP makes use of trajectory data warehousing techniques, which typically include a path of moving objects at a particular point in time. The Semantic Web (SW) users have published a large number of moving object datasets that include spatial and non-spatial data. These data are available as open data and require advanced analysis to aid in decision making. However, current SW technologies support advanced analysis only for multidimensional data warehouses and Online Analytical Processing (OLAP) over static spatial and non-spatial SW data. The existing technology does not support the modeling of moving object facts, the creation of basic mobility analytical queries, or the definition of fundamental operators and functions for moving object types. This article introduces the QB4MobOLAP vocabulary, which enables the analysis of mobility data stored in RDF cubes. This article defines Mobility OLAP operators and SPARQL user-defined functions. As a result, QB4MobOLAP vocabulary and the Mobility OLAP operators are evaluated by applying them to a practical use case of transportation analysis involving 8826 triples consisting of approximately 7000 fact triples. Each triple contains nearly 1000 temporal data points (equivalent to 7 million records in conventional databases). The execution of six pertinent spatiotemporal analytics query samples results in a practical, simple model with expressive performance for the enabling of executive decisions on transportation analysis.

Análise de Dados Pluviométricos Multi-fonte Baseada em Técnicas OLAP e de Visualização: uma Abordagem Prática

Estudos sobre o clima têm ganhado relevância devido ao aumento do número de eventos climáticos com impactos severos observados na última década, em especial em áreas urbanas. Por exemplo, ocorrências de grandes valores acumulados de chuva podem causar inundações e deslizamentos de terra, impactando o trânsito da cidade e até mesmo custando a vida de cidadãos. Para possibilitar o monitoramento de volumes de chuva, estações pluviométricas se encontram instaladas pelo país. Entretanto, tais estações são controladas por múltiplas organizações e oferecem dados em formatos distintos. Neste artigo propomos a ferramenta TEMPO (sisTema dE Monitoramento PluviométricO) que utiliza técnicas OLAP (Online Analytical Processing) para propor mecanismos de armazenamento, consulta e análise eficientes de dados pluviométricos. Para avaliar a ferramenta, apresentamos um estudo de caso de integração e análise dos dados do CEMADEN e do Alerta Rio.

Designing business intelligence (BI) for production, distribution and customer services: a case study of a UAE-based organization

PurposeBusiness intelligence (BI) is a strategic approach that can use analytical tools to collect and integrate information, apply business rules and ensure the appropriate visible output of organizational information. This study aims to present the design and implementation of BI in areas of business process improvement for production, distribution and customer services.Design/methodology/approachThis study highlights the process of BI in the production, distribution and customer services based on the National Food Products Company (NFPC) in the United Arab Emirates (UAE). This study discusses the step-by-step development process of BI and refers to graphical illustrations of the business needs and the organization's target key performance indicators (KPI).FindingsBased on the business needs and chosen KPIs to maximize production and improve distribution and customer services the BI tool shows that the “star scheme” is the most appropriate one. Relational Online Analytical Processing (ROLAP) based on Mondrian system is employed as Online Analytical Processing (OLAP) architecture since the NFPC's technological infrastructure was better adapted to this vision. The analysis starts with data retrieval from two databases' customer' and production and distribution databases. Finally, visualization and reporting processes that respect the end-users improve the NFPC's decisions.Practical implicationsThe study will help other organizations, BI developers, data warehouses (DW) developers and administrators, project managers as well as academic researchers understand how to develop a successful BI framework and implement BI based on business needs.Originality/valueThis is a unique and original study on the BI experience from a UAE-based organization and will encourage other organizations to apply BI in their business process.

Analisa In Depth Online Analytical Processing (OLAP) Business Intelligence (BI) dengan Filter Inheritance pada Distribusi Sosial di Kecamatan Setiabudi

A lot of census data in the sub-district is very useful and helps the social service to provide social assistance in a sub-district. With this Business Intelligence system, it can help analyze information on providing social assistance with the help of using the Tableau Tools so that the information is more detailed and displays a graphic / dashboard visualization. This research is to analyze how certain people who receive social assistance for residents of Setiabudi sub-district, and each provision of social assistance will be collected from the sub-district and submitted to each sub-district to be able to data with certainty, using the number of data on the head of the family registered in Setiabudi District.Keywords:Business Intelligence, Tableau Tools, OLAP, Government Agencies.