ENBIS-13 in Ankara15 – 19 September 2013 Abstract submission: 5 February – 5 June 2013
The following abstracts have been accepted for this event:
Managing IT Infrastructure and Service Capacity through Data Analysis
Authors: Lance Mitchell (Greenfield Research and Allen Systems Group), Michel Lutz (ST MicroElectronics and École Nationale Supérieure des Mines)
Primary area of focus / application: Other: IT Infrastructure and Data Management
Keywords: IT, Infrastructure, Capacity Planning, Big Data, ITIL
Submitted at 3-Jun-2013 16:04 by Lance Mitchell
Accepted (view paper)
Nowadays, IT architectures are very complex and are composed of multiple connected and interdependent components. To cope with such complexity, IT executives more and more rely on data analysis to manage the capacity of information systems (Gunther, 2007; Allspaw, 2008). IT management best practices, like ITIL framework, recommend setting up information systems, dedicated to facilitate the access to all historical data necessary to manage capacity (Lloyd & Rudd, 2007).
Managing potentially millions of Configuration Items (CIs) and all of their relationships and dependencies is obviously very complex, and requires simplification to understand both the relevance and the significance of the collected data.
The presenters will suggest that the future solution might be a Big Data approach, and will consider the 4 'V's of Big Data: Value, Variety, Velocity and Volume. This may become a topic for post-session discussion.
Real use cases will be shown to bring these concepts to life.
Does Risk Diversification Always Work? The Answer through Simple Modelling
Authors: Marie Kratz (ESSEC Business School)
Primary area of focus / application: Other: French special session
Keywords: financial risk, insurance risk, risk analysis, risk diversification, quantitative risk management, stochastic risk modelling
Submitted at 3-Jun-2013 16:07 by Marie Kratz
Accepted (view paper)
This work has been done in collaboration with Marc Busse and Michel Dacorogna, SCOR.
Regression Models to Identify Extreme Risk Factors
Authors: Meitner Cadena (ESSEC & UPMC)
Primary area of focus / application: Modelling
Keywords: Extreme values, Regression model, Generalized linear model, Regression trees, Health insurance portfolio, Retired people
Submitted at 4-Jun-2013 16:37 by MEITNER CADENA
Accepted (view paper)
development. This method is general, thus may be applied to different fields. An application will be developed when considering risk factors in an health insurance portfolio of retired people. This sample population is nowadays of particular interest due to its high increase.
Pareto-optimal Designs - Computer Simulation Experiments for Alternatives to G-optimal Designs
Authors: Helmut Waldl (Johannes Kepler University Linz)
Primary area of focus / application: Design and analysis of experiments
Keywords: optimal design, Pareto surface, G-optimality, D-optimality, computer simulation experiment
Submitted at 5-Jun-2013 11:46 by Helmut Waldl
D-optimality is another design criterion. A D-optimal design maximizes the determinant of the information matrix of the estimates. D-optimality in terms of trend parameter estimation and D-optimality in terms of covariance parameter estimation yield basically different designs. The Pareto frontier of these two determinant criteria corresponds with designs that perform well under both criteria.
Under certain conditions searching the G-optimal design on the above Pareto frontier yields almost as good results as searching the G-optimal design in the whole design region. In doing so the maximal kriging variance has to be computed only a few times though.
The method is demonstrated by means of a computer simulation experiment based on data provided by the Belgian institute Management Unit of the North Sea Mathematical Models (MUMM).
Different Classification Techniques Used to Determine the Change Causes in Control Charts
Authors: Esteban Alfaro Cortés (University of Castilla-La Mancha), José-Luis Alfaro Navarro (University of Castilla-La Mancha), Matías Gámez Martínez (University of Castilla-La Mancha), Noelia García Rubio (University of Castilla-La Mancha)
Primary area of focus / application: Process
Keywords: Hotelling T2, linear and quadratic discriminant analysis, artificial neural networks, classification trees, random forest, boosting
Submitted at 5-Jun-2013 20:15 by Noelia García Rubio
Traditional procedures consisted in analysing a univariate chart for each quality characteristic (Alt, 1985; Hayter and Tsui, 1994) but it is not an appropriate approach under the hypothesis of correlated characteristics. Due to this and other drawbacks some methods based on multivariate analysis have been developed, being the most well-known the T2 decomposition into components that reflect the contribution of every variable (Mason et al., 1995; Mason et al., 1996 and 1997; Doganaksoy et al., 1991; Timm, 1996; Runger et al., 1996).
A more recent alternative is the application of classification methods (Murphy, 1987; Cheng, 1995 and 1997; Chang, 1996; Zorriassatine and Tannock, 1998; Guh and Hsieh, 1999, Guh and Tannock, 1999a and 1999b; Ho y Chang, 1999; Cook and Chiu, 1998; Cook et al., 2001; Guh, 2003; Noorosana and Vaghefi, 2003; Niaki and Abassi, 2005; Aparisi et al., 2006; Guh, 2007; Gámez et al., 2009; Alfaro et al., 2009).
In this work we use Hotelling T2 decomposition (although the procedure can be extended to MEWMA or CUSUM) to compare the performance of Linear and Quadratic Discriminant Analysis, Artificial Neural Networks, Classification Trees, Random Forest and Boosted Trees, depending on the correlation level and the kind of shift, trying to offer a guide for the correct use of each classification technique.
Sampling for Nonconformities and Other Issues in the Forthcoming Revision of ISO 2859-2
Authors: Rainer Göb (University of Würzburg)
Primary area of focus / application: Quality
Keywords: Attributes sampling, type A sampling, sampling for nonconformities, limiting quality, consumer's risk, producer's risk
Submitted at 5-Jun-2013 20:15 by Rainer Göb