According to the author, the data warehouse helps in providing the storage for the huge amount of the historical data to handle the resources. There is a need to ensure that the security approaches are for handling the issues related to the data warehouse management. The data needs to be analyzed by the organization which is not only for identifying the marketing trends but also for examining the factors that affect the bottom line (Ghani, Jaber & Suryana, 2015). The crucial factors are not only to work on storing and retrieving the data but then driving a proper information from it. The organizations work on containing a massive amount of data like the information of financial standards, credit card numbers to ensure that the sensitive data does not mainly fall in the wrong hands. The data security is important as it helps in focusing on confidentiality, integrity and the availability. The emphasis is on the information which is retrieving from the different unauthorized disclosures which are either through the indirect logical inference or through using the direct retrieval processes (Ghani et al., 2015). The involvement of data protection includes availability to ensure that there are authorization of data processes with data security solutions for databases. The Datawarehouse approaches helps in empowering and setting the requirements which are important for on-line transaction process (OLTP) systems which are not for the data warehouse. The extraction, transformation, cleaning and properly handling the data is important where the security concerns need to be addressed at a different layer of DWH system. The security approaches for integrity involves the data protection from accidental and malicious changes with contamination or destruction of the same (Ghani et al., 2015). The problem is of the access-controlled mechanisms as they do not tend to capture the data that leads on for the issue of integrity. The categorization is based on restriction-based inference control techniques that are for handling the safety of a query. It is important to handle the combined access and inference control approaches to remove the security threats and access controls with inference which provides a good solution (Ghani et al., 2015). The two-tier architecture has certain issues where inference check is about run-time query processing which might lead to the unaccepted delays. Hence, for overcoming the drawbacks the research has been important for a three-tier architecture in order to provide access control mainly in between the first and second tiers.
The journal will discuss about the telemedicine which is mainly supported through the architecture of data warehouse. Here, the emphasis is on the interaction and proper collaboration of the sharing of information mainly in between the healthcare providers and the patient. This aims for integrating the framework which is supported by the data warehouse techniques that provides important elements of information at the time of consultation. The health informatics is growing with applications to medical and health data (Aleem, Capretz & Ahmed, 2015). The data warehouse is about handling the architected environment which is considered important for the decision support system. The data warehouse generally contains the source of the valuable data mining, where the data is in the warehouse which is considered to be comprehensive and integrated according to the date of transaction and time. The architecture designing is depending upon different layers where the functionalities of system and test cases are expected to work with evaluation on dataset tables into Object Linking and Embedding (OLE) database (Aleem et al., 2015). The integration layer is for providing the mechanisms which holds on the map for the different attributes with a clean and a simple view of enterprise. The data warehouse layers are considered to be a major concern for the data storage where the medical data is set in terms of data mart relation. The metadata and the repository is for different components databases, fields and objects. The highlighted factors are related to telemedicine data which can be easily stored in smartphones and then sending the information to the doctor for further analysis (Aleem et al., 2015). The methods might be facing issues of the limited storage but there might be problems in the storage of the data into the server of data warehouse.
The paper deals about the business intelligence in healthcare with the other important perspectives. The effort is mainly to identify the differences which are related to the traditional and the new healthcare standards. The data warehouse involves the data handling with the methods that require a unique business industry (George, Kumar & Kumar, 2015). There is a need to focus on the BI requirements that incudes strategic information which is important for handling operational systems with providing inputs. The motivation factor of the research is to work on analysis of requirements where the healthcare industry needs to focus on bringing the unique tools that ae important for handling a better return on investments. The data warehouse is considered important for the clinical business intelligence where there are improvements of care delivery of information technologies (George et al., 2015). The healthcare standards are set to maintain the capability of handling huge investments where clinical and industrial DW and BI is defined for the hard matrices. The analysis is for the actors and the users who are able to provide the different variations in system. The business intelligence is important for the Datawarehouse where the matrices are based on choosing the goals to meet the project desire (George et al., 2015). Apart from this, there are selection of data sources where the methods could be used for deployment to clean the data that is considered to be important. The architecture is based on choosing the security and permissions where the data source, database and security helps in handling the analytical use cases. The scenarios are based on managing the Data Warehouse Context that is considered important with no-defined architecture and framework. The identification of the gaps helps in satisfying the demands of BI in healthcare with analytics that are not well-defined (George et al., 2015). The two major aspects and the classes are clinical with setting the aspects which act in a parallel line. The gaps will be addressed depending upon dimensional modeling and administrative setup. The value-based healthcare, e-health and m-health is for the current and future directions for healthcare where there is utmost accuracy to match with the response time and the needs as well.
The data warehousing solutions are important for the large organizations that works on supporting the decision-making tasks. Here, there is a need to work on the different challenges and how the organizations are able to work on implementation and evaluating the success rate (Asrani & Jain, 2016). The framework is depending upon procedural aspects where the development aims to standardize the processes with in depth analysis to come with the framework of data warehousing projects. The solutions have been effective with information assets for organization to facilitate the correct and timely decisions. The challenges are maintained mainly for setting the cost-effective aid decision making with catering issues that includes the suitable business intelligence. The maintenance is important where the findings are based on the framework where the decision-making needs have to be properly developed (Asrani & Jain, 2016). The higher failure rates of project implementation could be of big size and project complexities and so there is a need to work on establishing the different database management techniques for developers’ communities. The research is about the concern for a higher failure rate of data warehousing and then focusing on availability of solutions for the different issues (Asrani & Jain, 2016). The practices are determined with identifying and gathering requirements where the designing is followed by testing and maintenance. The operational systems aims to collect the information which relates to defining the use of UML for handling resource management. The data mapping profile and the deployment profile is set for defining the related documents which are helpful in identifying and classifying the dimensional patterns which are for specific domains. The primary collection methods are depending upon several conceptual models which are for focusing on constructing UML designs with data warehouse conceptual designing. The aim is mainly to set a framework which is based on the functional symbols with annotations through typing them. OLAP is depending upon defining constraints with relative importance (Asrani & Jain, 2016). The failures need to be evaluated with maintenance that is considered important for initiating a data warehousing project. The methodology is based on the software development life cycle which is applied with certain adjustments for the projects. The architecture is based on designing the solutions and then investigating the designs of operational systems effectively. The important factors of the schema design is to take hold of the requirement where the focus is on the analysis and identifying the resources of input for schema designing and then dealing with them. For the data warehouse, there is a need to handle different operational systems with collection and reporting methods that are based on decision making requirements.
Aleem, S., Capretz, L. F., & Ahmed, F. (2015). Security issues in data warehouse. arXiv preprint arXiv:1507.05644.
Asrani, D., & Jain, R. (2016). Designing a Framework to Standarize Data Warehouse Development Process for Effective Data Warehousing Practices. International Journal of Database Management Systems, 8(4).
George, J., Kumar, V., & Kumar, S. (2015, July). Data warehouse design considerations for a healthcare business intelligence system. In World congress on engineering. Available at:< https://www.iaeng.org/publication/WCE2015/WCE2015_pp308-311.pdf>
Ghani, M. K. A., Jaber, M. M., & Suryana, N. (2015). Telemedicine supported by data warehouse architecture. ARPN J. Eng. Appl. Sci, 10(2), 415-417.