Article Review of Architecture and Quality in Data

Question: Describe about the Facts for Article Review of Architecture and Quality in Data? Answer: Article Summary: Architecture and Quality in Data Warehouses The Data Warehousing reflects the process of providing widely applicable and the combination of data in a view that is present in various dimensions. The Data Warehousing includes the Online Analytical Processing (OLAP) utensils. OLAP assists in analyzing the multidimensional data in an effortful and interactive way. Because of transactions, previous data of an organization gets updated. In a situation where an executive, tries to access the historical data, actually asks for the historical information stored in the warehouse (Deb, Hose Pedersen, 2015). This feature of data warehouse indicates the non-volatile nature. In addition, other features of a data warehouse are subject orientation, integration from heterogeneous source and storing data in respect of time. The Data Mining is fundamentally related to discovering the connections between the internal and external factors. It makes the organizations able to determine the data related to transactions by providing a view of drillin g down' into summarized information. Architecture of a Data Warehouse: Databases, data shifting agents and respiratory are the primary three physical aspects of the data warehouse. The primary perspective of the article is to interpret Metadatabase schema. This schema collects and links all the applicable parts of the database architecture and quality. Figure 1: Typical Architecture of Data Warehouse (Source: Jarke et al. 2013, pp 164) In figure 1, the architecture of warehouse does not support crucial quality problems and management approaches. For this reason, the article proposed conceptual, logical and physical perspective separately in figure 2 (Jarke et al. 2013). Figure 2: Meta Data Framework (Source: Jarke et al. 2013, pp 165) The article argues over having a conceptual enterprise. The traditional data warehouse includes some weaknesses like the issue of wrong aggregation, lack of compatibility of the operational department with enterprise views. To eliminate the effect the DW may need to setup new sources or sources of OTLP in the organization perspective (Kmpgen, ORiain Harth, 2012). By defining various models on the company showed in figure 2 as views, the wrapping and aggregation processes will be capable of providing interpretability, stability or completeness as per enterprise model. The third approach of the article is the implementation of safe and efficient logical transformation (Deb, Hose Pedersen, 2015). The article included the process of collecting architectural framework of a persistent object data model in a comprehensive but comparatively transparent way. Figure 3: Architecture Notation (Source: Jarke et al. 2013, pp 166) Figure 3 assist in understanding the modules each perspective offers. In addition, it also provides the information of the individual module. The conceptual outlook explains the enterprise models included in the information systems of an organization. Data model of logical schema or the actual data models assist in formulating logical perspective of the data warehouse. According to the article agents and data, stores are the essential physical components of DW (Deb, Hose Pedersen, 2015).. ETL Process: ETL refers to the activity of transporting data from source system to the data warehouse. Extract means collecting data from ERP, SAP, etc. systems and converting them into warehouse compatible format. Applying, cleaning, filtering, etc. are the part of transforming. Loading refers to the process of storing the data into respiratory or DW perspective (Kmpgen, ORiain Harth, 2012). Operations of OLAP: Slice and dice apply to navigating pages interactively using the various aspects of the slice. ROLAP, MOLAP, HOLAP and Specialized SQL Servers are different types of OLAP servers. A subset of the multi-dimensional array that is related to single value reflects slice. More than two dimensions of data cube or consecutive slices refer to dice (Deb, Hose Pedersen, 2015).. Drill up and down defines viewing various levels of most summarized and most detailed data respectively. Roll-up is for the computation of all the data relationship. Pivot changes the inclination of a report. Students View: The architecture in figure 1 is only capable of doing the jobs in data warehousing. On the other hand, it is very crucial for a data warehouse to support various quality problems and management policies. Figure 2 covers these parts for a data warehouse. Online transaction processing (OTLP) reflects the process of making transaction oriented applications more smooth and manageable. The hosting of data warehouse often makes nonsense for an organization. The process can increase the cost of the organization as it requires new employees. It is imperative to evaluate the capability in respect to the storage available and increase in workload that can affect overall performance. OLAP is significantly dependent on IT professionals. The traditional architecture includes traditional OLAP tools that are not able to serve proper and required computational services. References: Deb Nath, R. P., Hose, K., Pedersen, T. B. (2015). Towards a Programmable Semantic Extract-Transform-Load Framework for Semantic Data Warehouses. InProceedings of the ACM Eighteenth International Workshop on Data Warehousing and OLAP(pp. 15-24). ACM. Han, J., Kamber, M., Pei, J. (2011).Data mining: concepts and techniques. Elsevier. Jarke, M., Jeusfeld, M. A., Quix, C., Vassiliadis, P. (2013). Architecture and Quality in Data Warehouses. InSeminal Contributions to Information Systems Engineering(pp. 161-181). Springer Berlin Heidelberg. Kampgen, B., ORiain, S., Harth, A. (2012). Interacting with statistical linked data via OLAP operations. InThe Semantic Web: ESWC 2012 Satellite Events(pp. 87-101). Springer Berlin Heidelberg.