A proposed authentication and group-key distribution model for data warehouse signature, DWS framework

AlMeghari, Mayada J.; Taha, Sanaa; Elmahdy, Hesham N.; Shen, Xuemin

doi:10.1016/j.eij.2020.09.002

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…Inadequate Authentication [12,41,42] Weak or missing authentication measures can lead to unauthorized access to medical data.…”

Section: Insufficientmentioning

confidence: 99%

Secure Emergency Data Sharing in Healthcare - A Data Warehouse View

Thantilage,

Yasarathna,

Le-Khac

et al. 2023

Preprint

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Clinical data's vast volume and diverse formats, compounded by the intricacies of healthcare and the heightened importance of security and privacy, necessitate sophisticated data management and governance systems. Existing studies often examine components like data distribution, privacy, and security in isolation. This research bridges the gaps by developing an efficient, integrated clinical data warehousing solution emphasizing both privacy and security at all stages. The study presents a distributed data warehouse architecture designed after meticulous research and analysis. Testing was conducted using clinical datasets, leading to the creation of privacy and security components such as the Digital Emergency Medical Card and Emergency Authenticator. The prototype's evaluations highlight a viable balance between system performance and stringent security standards, showcasing the efficiency of encryption and other security measures. Ultimately, this study offers a holistic solution, combining theory and practice, for secure clinical data management in healthcare.

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“…Inadequate Authentication [12,41,42] Weak or missing authentication measures can lead to unauthorized access to medical data.…”

Section: Insufficientmentioning

confidence: 99%

Secure Emergency Data Sharing in Healthcare - A Data Warehouse View

Thantilage,

Yasarathna,

Le-Khac

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Datamart is part of the data warehouse, which has the contents of a small series of data warehouses that support the needs at the company level, such as departments. Datamart is different from data warehouses because datamart focuses on the needs of company users in a business, does not contain detailed data, and contains less information than data warehouses [11].…”

Section: D) Datamartsmentioning

confidence: 99%

Improving the Data Management: ETL Implementation on Data Warehouse at Indonesian Vehicle Insurance Industry

Wiratama,

Abhinaya Bagioyuwono

2023

International Journal of Science, Technology & Management

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Unknowingly, risk is an essential part of an individual's life. Every day individuals have the potential to be threatened by possibilities that can produce something that has fatal social, human, and financial consequences. Insurance can help individuals to relieve the financial burden caused by unwanted things by transferring individual losses to insurance companies. This transfer of losses will distinguish individuals from possible bankruptcy and financial security. Automotive insurance is a liability for loss or damage to motorized vehicles. This time we will take the example of an insurance company specializing in automotive insurance, namely Top Gear Insurance (TGI). This company uses customer data storage using an ordinary manual database that does not yet use a data warehouse system. This ordinary manual database makes it difficult for TGI to retrieve data for reprocessing and makes data inaccessible from anywhere, asynchronous, concise, and inefficient. There is a solution to the TGI problem of creating a data warehouse with a star schema approach for storing and processing data. The data warehouse is likely to make the data within the company more accessible, efficient, simple, and understandable so that TGI can develop its business through data analysis from the data it already has. Datawarehouse has many business advantages, such as increasing Business Intelligence, data quality and consistency, saving time, and supporting historical data analysis and queries. The data warehouse consists of datamart, OLTP, OLAP, and Star Schema. Using Mondrian as a visualization showed that TGI can get information about customer data, policies, and claims easily, quickly, and concisely. That can also help TGI create customer profiles and targeted marketing and company evaluation based on the visualization provided.

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“…On the other hand, decision-making in the academic field by management requires integrated, non-departmental, relevant data for an extended period [2]. Data from each operational/transactional information system Denny Jean Cross Sihombing| 1127 must be integrated and grouped based on a specific subject and with relevant time dimensions so that reports can be generated quickly and easily and do not interfere with operational systems that can ultimately be used to help college management to make strategic decisions related to the academy [3], [4].…”

Section: Introductionmentioning

confidence: 99%

“…On each record, a form of the timestamp indicates when the time of a record is accurate. For example, timestamping giving a transaction date for each record [4].…”

Section: Introductionmentioning

confidence: 99%

Academic Data Warehouse Modeling in Higher Education Using Nine-Step Design Methodology

Sihombing

2022

Journal-ISI

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Data and information are essential in various fields today, as well as in the field of education, especially in universities. Some universities already have information systems that support data and information needs. However, the system has not been integrated, so it cannot provide data and information needs quickly and in an integrated manner. Information systems in universities are still primarily departmental because each was built at a different time and uses another platform. The departmental nature of this information system causes inaccuracies and inconsistencies of data that drive the information produced in reports and data reused in transactions to be invalid. Invalid data, in the end, also impacts decision-making taken by management. This study aims to develop a data warehouse at a university to integrate academic data using a star schema. The method used is the Nine Step Methodology. The result of this research is data warehouse architecture used in the academic field; fact tables and ERDs have been designed at the current stage of designing a Prototype of the Study Program Performance Sheet (LKPS).

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A proposed authentication and group-key distribution model for data warehouse signature, DWS framework

Cited by 4 publications

References 5 publications

Secure Emergency Data Sharing in Healthcare - A Data Warehouse View

Secure Emergency Data Sharing in Healthcare - A Data Warehouse View

Improving the Data Management: ETL Implementation on Data Warehouse at Indonesian Vehicle Insurance Industry

Academic Data Warehouse Modeling in Higher Education Using Nine-Step Design Methodology

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