Data Mining in Scientific Data

2001 ◽  
pp. 61-85 ◽  
Author(s):  
Stephan Rudolph ◽  
Peter Hertkorn
Keyword(s):  
Data Mining ◽  
Scientific Data

Cognitive Aspects on the Representation of Dynamic Environmental Phenomena Using Animations

2001 ◽  
pp. 13-24 ◽  
Author(s):  
Patrick J. Ogao ◽  
Connie A. Blok
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Problem Solving ◽  
Spatial Data ◽  
Scientific Data ◽  
Data Availability ◽  
Data Exploration ◽  
Spatial Data Analysis ◽  
Wide Range ◽  
Interdisciplinary Nature

Measurements from dynamic environmental phenomena have resulted in the acquisition and generation of an enormous amount of data. This upsurge in data availability can be attributed to the interdisciplinary nature of environmental problem solving and the wide range of acquisition technology involved. In essence, users are dealing with data that is complex in nature, multidimensional and probably of a temporal nature. Also, the frequency by which this data is acquired far exceeds the rate at which it is being explored, a factor that has accelerated the search for innovative approaches and tools in spatial data analysis. These attempts have seen both analytical and visual techniques being used as aids in presentation and scientific data exploration. Examples are seen in techniques as in: data mining, data exploration and visualization.

NASA workshop on issues in the application of data mining to scientific data

2000 ◽  
Vol 2 (1) ◽  
pp. 70-79 ◽  
Author(s):  
Jeanne Behnke ◽  
Elaine Dobinson
Keyword(s):  
Data Mining ◽  
Scientific Data

Finding patterns in three-dimensional graphs: algorithms and applications to scientific data mining

2002 ◽  
Vol 14 (4) ◽  
pp. 731-749 ◽  
Author(s):  
Xiong Wang ◽  
J.T.L. Wang ◽  
D. Shasha ◽  
B.A. Shapiro ◽  
I. Rigoutsos ◽  
...  
Keyword(s):  
Data Mining ◽  
Three Dimensional ◽  
Scientific Data ◽  
Scientific Data Mining

Photovoltaics Informatics: Harnessing Energy Science via Data-driven Approaches

2012 ◽  
Vol 1425 ◽  
Author(s):  
Changwon Suh ◽  
Kristin Munch ◽  
David Biagioni ◽  
Stephen Glynn ◽  
John Scharf ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Management ◽  
Scientific Data ◽  
Current Data ◽  
High Rate ◽  
Rapid Screening ◽  
Data Management System ◽  
Process Conditions ◽  
Structure Property ◽  
Test Bed

ABSTRACTWe discuss our current research focus on photovoltaic (PV) informatics, which is dedicated to functionality enhancement of solar materials through data management and data mining-aided, integrated computational materials engineering (ICME) for rapid screening and identification of multi-scale processing/structure/property/performance relationships. Our current PV informatics research ranges from transparent conducting oxides (TCO) to solar absorber materials. As a test bed, we report on examples of our current data management system for PV research and advanced data mining to improve the performance of solar cells such as CuInxGa1-xSe2 (CIGS) aiming at low-cost and high-rate processes. For the PV data management, we show recent developments of a strategy for data modeling, collection and aggregation methods, and construction of data interfaces, which enable proper archiving and data handling for data mining. For scientific data mining, the value of high-dimensional visualizations and non-linear dimensionality reduction is demonstrated to quantitatively assess how process conditions or properties are interconnected in the context of the development of Al-doped ZnO (AZO) thin films as the TCO layers for CIGS devices. Such relationships between processing and property of TCOs lead to optimal process design toward enhanced performance of CIGS cells/devices.

scholarly journals Understanding the Investors’ Investment Decisions through Some Data Mining Tools- A Study in Dhaka Stock Exchange (DSE)

2018 ◽  
pp. 9-19
Author(s):  
Md. Khashrul Alam ◽  
S. M. Towhidur Rahman ◽  
Afifa Khanom
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Stock Market ◽  
Stock Exchange ◽  
Investment Decision ◽  
Scientific Data ◽  
Wrong Decision ◽  
Secondary Sources ◽  
Money Flow ◽  
Mining Tools

Purpose: Decision making is the process of choosing a particular alternative from a number of alternatives. Decision making is very much important in investment in the stock market. As it is enormously sensitive, a wrong decision may put the investor back to the street. Modern scientific data mining tools can play important role in making investment decision in the stock market. The purpose of the study is to find out the effectiveness of investors’ decision in buying and selling stock and the efficiency of some data mining tools in aiding investor’s decision. Methodology: This paper used several data mining techniques such as beta, Chaikin money flow indicator (CMI) and Bollinger band to analyze investors’ decision in buying and selling stocks. Data for the study were taken both from primary and secondary sources specially, from website of Dhaka Stock Exchange. Findings: The result shows that in most cases majority of investors failed to take right decision in right time in terms of the estimation derived from data mining tools used in the study. It was also found that Bollinger band was found to be more efficient than CMI in making prediction.

ADaM Services: Scientific Data Mining in the Service-Oriented Architecture Paradigm

2009 ◽  
pp. 57-70
Author(s):  
Rahul Ramachandran ◽  
Sara Graves ◽  
John Rushing ◽  
Ken Keizer ◽  
Manil Maskey ◽  
...  
Keyword(s):  
Data Mining ◽  
Service Oriented Architecture ◽  
Scientific Data ◽  
Service Oriented ◽  
Scientific Data Mining

Tools for Aggregating, Analyzing and Mining Combinatorial Data

2009 ◽  
Vol 1159 ◽  
Author(s):  
Wesley Jones ◽  
Changwon Suh ◽  
Peter A Graf ◽  
Daniel Korytina ◽  
Craig Swank ◽  
...  
Keyword(s):  
Data Mining ◽  
Principal Component ◽  
Analysis Data ◽  
Scientific Data ◽  
Data Management System ◽  
Multiple Sources ◽  
Data Mining Techniques ◽  
Conducting Oxides ◽  
Structure And Property ◽  
Combinatorial Data

AbstractWe demonstrate how data mining techniques can be applied to complex combinatorial data sets and how data from multiple sources can be aggregated via the developed scientific data management system. An example is shown for the case of aggregated combinatorial data for the study of composition, processing, structure, and property relationships of transparent conducting oxides by applying data mining techniques such as principal component analysis. Data mappings of mined results are shown to effectively enable visualization of data trends, identification of anomalies in Fourier transform infrared spectroscopy patterns, and scientifically interesting libraries and spectral regions.

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