Advanced solutions for medical information storing: Clinical data warehouse

Poenaru, Cristina Elena; Merezeanu, Daniel; Dobrescu, Radu; Posdarascu, Eugenie

doi:10.1109/ehb.2017.7995355

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Poenaru1 et al [39] mention some of the research issues associated with the storage of medical data in Health Information Systems (HIS), including sophisticated data modeling functions, advanced classification processes, and the integration of highly complex data. It demonstrates how data warehousing functionality can support this area.…”

Section: Literature Reviewmentioning

confidence: 99%

Analyses the Performance of Data Warehouse Architecture Types

Merceedi,

Yazdeen,

Ibrahim

et al. 2022

JSCDM

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The concept of storing historical data for retrieving them when needed has been conceived, and the idea was primitive to build repositories for historical data to store these data, despite the use of a specific technique for recovering these data from various storage modes. The data warehouse is the most reliable and widely used technology for scheduling, forecasting, and managing corporations. Also, it is concerned with the data storage facility that extensive collection of data. Data warehouses are called ancient modern techniques; since the early days of relational databases, the critical component of decision support, increasing focus for the database industry. Many commercial products and services are now available, and almost all of the primary database management system vendors provide them. When opposed to conventional online transaction processing applications, decision support puts slightly different demands on database technology. This paper analyzes the performance of the data warehouse architectures by studying and comparing many research works in this field. The study involves extracting, transforming, and loading the data from different recourses and the important characteristic of the architecture types. Furthermore, the tools and application service techniques used to build data warehouse architecture.

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Section: Literature Reviewmentioning

confidence: 99%

Analyses the Performance of Data Warehouse Architecture Types

Merceedi,

Yazdeen,

Ibrahim

et al. 2022

JSCDM

View full text Add to dashboard Cite

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“…The authors highlighted some issues of the system to support mobile application development, including compatibility with very large platforms and devices. Poenaru et al [ 33 ] proposed advanced solutions in the form of data warehousing for medical information storage to tackle some issues, such as complex-data modelling features, classification structures, and data integration. All of the aforementioned data warehousing solutions do not take care of any privacy and security issues for data storage, access, and analysis.…”

Section: Healthcare Data Privacymentioning

confidence: 99%

Big Data Warehouse for Healthcare-Sensitive Data Applications

Shahid

Nguyen

Kechadi

2021

Sensors

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Obesity is a major public health problem worldwide, and the prevalence of childhood obesity is of particular concern. Effective interventions for preventing and treating childhood obesity aim to change behaviour and exposure at the individual, community, and societal levels. However, monitoring and evaluating such changes is very challenging. The EU Horizon 2020 project “Big Data against Childhood Obesity (BigO)” aims at gathering large-scale data from a large number of children using different sensor technologies to create comprehensive obesity prevalence models for data-driven predictions about specific policies on a community. It further provides real-time monitoring of the population responses, supported by meaningful real-time data analysis and visualisations. Since BigO involves monitoring and storing of personal data related to the behaviours of a potentially vulnerable population, the data representation, security, and access control are crucial. In this paper, we briefly present the BigO system architecture and focus on the necessary components of the system that deals with data access control, storage, anonymisation, and the corresponding interfaces with the rest of the system. We propose a three-layered data warehouse architecture: The back-end layer consists of a database management system for data collection, de-identification, and anonymisation of the original datasets. The role-based permissions and secured views are implemented in the access control layer. Lastly, the controller layer regulates the data access protocols for any data access and data analysis. We further present the data representation methods and the storage models considering the privacy and security mechanisms. The data privacy and security plans are devised based on the types of collected personal, the types of users, data storage, data transmission, and data analysis. We discuss in detail the challenges of privacy protection in this large distributed data-driven application and implement novel privacy-aware data analysis protocols to ensure that the proposed models guarantee the privacy and security of datasets. Finally, we present the BigO system architecture and its implementation that integrates privacy-aware protocols.

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