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Data mining often requires data integration. Careful integration can help reduce and avoid redundancies and inconsistencies in the resulting data set. Data mining is a discovery process. By that we mean a process that looks at organizing and recognizing patterns in large amounts of information. Data mining is multidisciplinary. Data integration is the combination of technical and business processes used to combine data from disparate sources into meaningful and valuable information. A complete data integration solution delivers trusted data from a variety of sources. At first glance, the biggest challenge is the technical implementation of integrating data from disparate often incompatible sources. However, a much bigger challenge lies in the entirety of data integration. Integration can be physical or virtual. Physical is copying the data to warehouse and Virtual is keep the data only at the sources.
Data integration is also valid within a single organization Integrating data from different departments or sectors. The data integration initiative within a company must be an initiative of business, not IT. There should be a champion who understands the data assets of the enterprise and will be able to lead the discussion about the long term data integration initiative in order to make it consistent, successful and beneficial. Manual Integration or Common User Interface users operate with all the relevant information accessing all the source systems or web page interface. No unified view of the data exists. Application Based Integration requires the particular applications to implement all the integration efforts. This approach is manageable only in case of very limited number of applications.
Middleware Data Integration transfers the integration logic from particular applications to a new middleware layer. Although the integration logic is not implemented in the applications anymore, there is still a need for the applications to partially participate in the data integration. Uniform Data Access or Virtual Integration leaves data in the source systems and defines a set of views to provide and access the unified view to the customer across whole enterprise. For example, when a user accesses the customer information, the particular details of the customer are transparently acquired from the respective system.
The main benefits of the virtual integration are nearly zero latency of the data updates propagation from the source system to the consolidated view, no need for separate store for the consolidated data. However, the drawbacks include limited possibility of data’s history and version management, limitation to apply the method only to ‘similar’ data sources and the fact that the access to the user data generates extra load on the source systems which may not have been designed to accommodate. Common Data Storage or Physical Data Integration usually means creating a new system which keeps a copy of the data from the source systems to store and manage it independently of the original system. The most well know example of this approach is called Data Warehouse. The benefits comprise data version management, combining data from very different sources (mainframes, databases, flat files, etc.). The physical integration, however, requires a separate system to handle the vast volumes of data.
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