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Data quality refers to the state of qualitative or quantitative pieces of information. There are many definitions of data quality, but data is generally considered high quality if it is "fit for [its] intended uses in operations , decision making and planning ". Furthermore, apart from these definitions, as the number of data sources increases, the question of internal data consistency becomes significant, regardless of fitness for use for any particular external purpose. People's views on data quality can often be in disagreement, even when discussing the same set of data used for the same purpose. When this is the case, data governance is used to form agreed upon definitions and standards for data quality. In such cases, data cleansing , including standardization, may be required in order to ensure data quality. Defining data quality in a sentence is difficult due to the many contexts data are used in, as well as the varying perspectives among end users, producers, and custodians of data. From a consumer perspective, data quality is: [4]. Arguably, in all these cases, "data quality" is a comparison of the actual state of a particular set of data to a desired state, with the desired state being typically referred to as "fit for use," "to specification," "meeting consumer expectations," "free of defect," or "meeting requirements. Before the rise of the inexpensive computer data storage , massive mainframe computers were used to maintain name and address data for delivery services.

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Cloud computing, with its immense potentials in low cost and on-demand services, is a promising computing platform for both commercial and non-commercial computation applications. It focuses on the sharing of information and computation in a large network that are quite likely to be owned by geographically disbursed different venders. Power efficiency in cloud data centers DCs has become an important topic in recent years as more and larger DCs have been established and the electricity cost has become a major expense for operating them. Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of DCs. Virtual machine VM assignment is the key in server consolidation. In the past few years, many methods to VM assignment have been proposed, but existing VM assignment approaches to the VM assignment problem consider the energy consumption by physical machines PM. In current paper a new approach is proposed that using a combination of the sine cosine algorithm SCA and ant lion optimizer ALO as discrete multi-objective and chaotic functions for optimal VM assignment. First objective of our proposed model is minimizing the power consumption in cloud DCs by balancing the number of active PMs. Optimum Utilisation of Resources.

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DOI: Lukaszweski and G. Sockalingum and M. Baker Published Chemistry Analytical Methods. Biofluids, such as serum and plasma, represent an ideal Optimum Utilisation of Resources for the diagnosis of disease due to their ease of collection, that can be performed worldwide, and their fundamental involvement in human function. The ability to diagnose disease rapidly with high sensitivity and specificity is essential to exploit advances in new treatments, in addition the ability to rapidly profile disease without the need for large scale medical equipment e. View PDF. Save to Library. Create Alert. Launch Research Feed. Share This Paper. Methods Citations.

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Fourier transform infrared applications to investigate induced biochemical changes in liver. Saliva as a first-line diagnostic tool: A spectral challenge for identification of cancer biomarkers.]