Recent Developments in Data Warehouses and Its Application in E-Commerce – Article Example

Download full paperFile format: .doc, available for editing

The paper "Recent Developments in Data Warehouses and Its Application in E-Commerce" is a great example of an article on information technology. The data warehouse provides a platform for effectual data extraction/mining. In essence, the data warehouse comprises exhaustive and abridged information, integrated data, metadata, and historical data. Arguably, all of these components improve the data extraction procedure and the views for achievement. Data marts, such as online analytical processing (OLAP) are an architectural annex of data warehouse for the reason that the mart has tailored data and runs on data at an extreme level of summarization.

Data warehousing is a tactical enterprise and IT scheme in various organizations at the moment. In this regard, the review paper seeks to provide an insight into the recent developments of data warehouse and its technical application in the field of e-commerce. : . Keywords: Data Warehouse, Business Intelligence, OLAP, E-commerce Introduction A data warehouse is the notion of data mined from functional systems and made accessible as historical snapshots for impromptu queries and listed reporting. In essence, features that differentiate data in the data warehouse from that established in the functional setting are that it is: structured in a way that pertinent data is grouped mutually for unproblematic access (Su et al. , 2009).

Furthermore, numerous copies of the data from different points over time are reserved jointly, and once the data is stored in the data warehouse it is not restructured. Instead, the historical snapshots stockpiled in the Data Warehouse are occasionally invigorated with data from the functional databases. According to Vela et al. (2012), the key issues addressed by a data warehouse are that clients endure a hard time generating ad-hoc or other focused reports and queries and.

This is brought about by numerous aspects such as the majority of the data is stockpiled in the Adaptable Data Base System (ADABAS), which is prickly for clients to access. In addition, the data stores were intended for business procedures and not ad-hoc reporting. Another aspect is that achieving the data often needs waiting for a programmer to either offer a tailored downloaded program or build up the report and at other times every data may not be reliable as of the same point in due course (Hwang et al. , 2004). According to Wu et al.

(2001), data warehousing is a cluster of decision support tools, intended to enhance the knowledge worker, such as director, manager, and market analyst to make enhanced and quicker decisions. Essentially, the previous five years have observed volatile progress in Data warehousing, both in terms of products and services provided, and in the acceptance of these technologies by the manufacturing industry. Data warehousing technologies have been set out productively in numerous industries, such as producing industries for order consignment and client support, retail for inventory administration and customer profiling, monetary services for analysis in risk, credit card, claims, and scam detection.

Other industries that have adopted Data Warehousing include utilities for power application evaluation, transportation for fleet management, healthcare for results analysis, telecommunications for both fraud detection, and call analysis. Schneider (2008) study presents a guideline of data warehousing technologies, concentrating on the unique prerequisites that data warehouses direct on database management systems (DBMSs). Schneider (2008) further notes that a data warehouse is a subject-oriented, incorporated, time changeable, non-impulsive set of data that is employed mainly in managerial decision making.

Characteristically, the data warehouse is kept independently from the business’ s functional databases. In essence, there are numerous reasons for doing this, for instance, the data warehouse brace on-line analytical processing (OLAP), the operation and functionality specifications are rather distinct from those of the on-line transaction processing (OLTP) applications conventionally braced by the functional databases.

Download full paperFile format: .doc, available for editing
Contact Us