Supporting Data mining of large databases by visual feedback queries

In this paper, we describe a query system that provides visual relevance feedback in querying large databases. Our goal is to support the process of data mining by representing as many data items as possible on the display. By arranging and coloring the data items as pixels according to their relevance for the query, the user gets a visual impression of the resulting data set. Using an interactive query interface, the user may change the query dynamically and receives immediate feedback by the visual representation of the resulting data set. Furthermore, by using multiple windows for different parts of a complex query, the user gets visual feedback for each part of the query and, therefore, may easier understand the overall result. Our system allows to represent the largest amount of data that can be visualized on current display technology, provides valuable feedback in querying the database, and allows the user to find results which, otherwise, would remain hidden in the database

Using Visualization to Support Data Mining of Large Existing Databases

In this paper. we present ideas how visualization technology can be used to improve the difficult process of querying very large databases. With our VisDB system, we try to provide visual support not only for the query specification process. but also for evaluating query results and. thereafter, refining the query accordingly. The main idea of our system is to represent as many data items as possible by the pixels of the display device. By arranging and coloring the pixels according to the relevance for the query, the user gets a visual impression of the resulting data set and of its relevance for the query. Using an interactive query interface, the user may change the query dynamically and receives immediate feedback by the visual representation of the resulting data set. By using multiple windows for different parts of the query, the user gets visual feedback for each part of the query and, therefore, may easier understand the overall result. To support complex queries, we introduce the notion of approximate joins which allow the user to find data items that only approximately fulfill join conditions. We also present ideas how our technique may be extended to support the interoperation of heterogeneous databases. Finally, we discuss the performance problems that are caused by interfacing to existing database systems and present ideas to solve these problems by using data structures supporting a multidimensional search of the database

Modeling batch annealing process using data mining techniques for cold rolled steel sheets

The annealing process is one of the important operations in production of cold rolled steel sheets, which significantly influences the final product quality of cold rolling mills. In this process, cold rolled coils are heated slowly to a desired temperature and then cooled. Modelling of annealing process (prediction of heating and cooling time and trend prediction of coil core temperature) is a very sophisticated and expensive work. Modelling of annealing process can be done by using of thermal models. In this paper, Modelling of steel annealing process is proposed by using data mining techniques. The main advantages of modelling with data mining techniques are: high speed in data processing, acceptable accuracy in obtained results and simplicity in using of this method. In this paper, after comparison of results of some data mining techniques, feed forward back propagation neural network is applied for annealing process modelling. A good correlation between results of this method and results of thermal models has been obtained

A Sensitive and Reliable Carbon Monoxide Monitor for Safety-Focused Applications in Coal Mine Using a 2.33-μ\mu m Laser Diode

In this paper, a stable and reliable carbon monoxide (CO) monitoring system with high sensitivity (at sub-ppm level) was designed and demonstrated with particular reference to use in the mining industry, tailoring the design specifically for forecasting spontaneous combustion, a major hazard to miners. An appropriate strong CO absorption line was used to minimize the interferences expected from gases present in ambient air, with several preferred CO absorption lines selected and investigated, therefore choosing a distributed feedback (DFB) laser operating at a wavelength of 2330.18 nm as the excitation source. Through a detailed investigation, a minimum detection limit of ~0.2 ppm and a measurement precision of <50 ppb were achieved with a 1 s averaging time. Further in tests, a long-term continuous monitoring evaluation was carried out, demonstrated the excellent stability and reliability of the developed CO monitor. The results obtained have validated the potential of this design of a CO monitoring system for practical monitoring applications underground to enhance safety in the mining industry

HEAT PUMP AND AIR CONDITIONING GRADING SYSTEMS AND METHODS

An expectation module determines an expected average power consumption of a heat pump for a predetermined period as a function of indoor and outdoor temperatures of the building during the predetermined period. A difference module determines a power difference between an average power consumption of the heat pump during the predeter mined period and the expected average power consumption of the heat pump for the predetermined period. A grade determination module determines a grade of the heat pump for the predetermined period based on the power difference of the predetermined period. A reporting module generates a displayable report including the grade of the heat pump for the predetermined period