Enterprises need data quality management (DQM) to respond to strategic and operational challenges demanding high-quality corporate data. Hitherto, companies have assigned accountabilities for DQM mostly to IT departments. They have thereby ignored the organizational issues that are critical to the success of DQM. With data governance, however, companies implement corporate-wide accountabilities...

v-shaped and triangular cantilevers are widely employed in atomic force microscope (afm) imaging techniques due to their stability. for the design of vibration control systems of afm cantilevers which utilize patched piezo actuators, obtaining an accurate system model is indispensable prior to acquiring the information related to natural modes. a general differential quadrature element method (...

A general framework for proving an order of convergence for set-valued Runge Kutta methods is given in the case of linear differential inclusions, if the attainable set at a given time should be approximated. The set-valued method is interpreted as a (set-valued) quadrature method with disturbed values for the fundamental solution at the nodes of the quadrature method. If the precision of the q...

plates and shells are the significant structural components for many engineering and industrial applications. in this study, free vibration analysis of annular plates is investigated. for this, two different numerical methods such as differential quadrature and discrete singular convolution methods have been performed for numerical simulations. frequency values have been obtained via these two ...

Several data quality management (DQM) tasks like duplicate detection or consistency checking depend on domain specific knowledge. Many DQM approaches have potential for bringing together domain knowledge and DQM metadata. We provide an approach which uses this knowledge modeled in ontologies instead of aquiring that knowledge by cost-intensive interviews with domain-experts. These ontologies ca...

In this paper we introduce data quality mining (DQM) as a new and promising data mining approach from the academic and the business point of view. The goal of DQM is to employ data mining methods in order to detect, quantify, explain and correct data quality deficiencies in very large databases. Data quality is crucial for many applications of knowledge discovery in databases (KDD). So a typica...