Data Warehouse System Instance

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A Data Warehouse System Instance is a large subject-oriented, integrated, time-varying, non-volatile analytical database system that supports data warehouse tasks.



References

2020


2020

2020

  • (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/data_warehouse#Evolution_in_organization_use Retrieved:2020-2-28.
    • Offline operational data warehouse: Data warehouses in this stage of evolution are updated on a regular time cycle (usually daily, weekly or monthly) from the operational systems and the data is stored in an integrated reporting-oriented database.
    • Offline data warehouse: Data warehouses at this stage are updated from data in the operational systems on a regular basis and the data warehouse data are stored in a data structure designed to facilitate reporting.
    • On time data warehouse: Online Integrated Data Warehousing represent the real time Data warehouses stage data in the warehouse is updated for every transaction performed on the source data
    • Integrated data warehouse: These data warehouses assemble data from different areas of business, so users can look up the information they need across other systems.[1]

2009

  • (Mazón & Trujillo, 2009) ⇒ Jose-Norberto Mazón, and Juan Trujillo, (2009). “A Hybrid Model Driven Development Framework for the Multidimensional Modeling of Data Warehouses.” In: SIGMOD Record, 38(2).
    • Data warehouse (DW) systems provide a multidimensional (MD) view of huge amounts of historical data from operational sources, thus supplying useful information for decision makers to improve a business process in an organization. The MD paradigm structures information into facts and dimensions. A fact contains the interesting measures (fact attributes) of a business process (sales, deliveries, etc.), whereas a dimension represents the context for analyzing a fact (product, customer, time, etc.) by means of hierarchically organized dimension attributes. MD modeling requires specialized design techniques that resemble the traditional database design methods [16]. First, a conceptual design phase is performed whose output is an implementation-independent and expressive MD model for the DW. A logical design phase then aims to obtain a technology-dependent model from the previously defined conceptual MD model. This logical model is the basis for the implementation of the DW. Therefore, there are two cornerstones in MD modeling: the development of a conceptual MD model and the derivation of its corresponding logical representation.

2008

1999

  • (Zaiane, 1999) ⇒ Osmar Zaiane. (1999). “Glossary of Data Mining Terms." University of Alberta, Computing Science CMPUT-690: Principles of Knowledge Discovery in Databases.
    • QUOTE: Data mart: A small, single-subject warehouse used by individual departments or groups of users.
    • QUOTE: Data Warehouse: A system for storing and delivering massive quantities of data.

1997