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Wind Turbine Accidents: A Data Mining Study

Wind Turbine Accidents: A Data Mining Study
Fig. 1. The cause-effect relationship and stages where an accident occurs.

Download the final draft of this paper: asian_et_al_2016_Wind_Turbine_Accidents.pdf

While the global production of wind energy is increasing, there exists a significant gap in the academic and practice literature regarding the analysis of wind turbine accidents. Our paper presents the results obtained from the analysis of 240 wind turbine accidents from around the world. The main focus of our paper is revealing the associations between several factors and deaths & injuries in wind turbine accidents. Specifically, the associations of death and injuries with the stage of the wind turbine’s life cycle (transportation, construction, operation, and maintenance) and the main cause factor categories (human, system/equipment, and nature) were investigated. To this end, we conducted a detailed investigation that integrates exploratory and statistical data analysis methods with data mining methods. The paper presents a multitude of insights regarding the accidents and discusses implications for wind turbine manufacturers, engineering and insurance companies, and government organizations.

Download the final draft of this paper: asian_et_al_2016_Wind_Turbine_Accidents.pdf

Perception gap and its impact on supply chain performance

Figure 4. SEM modelling for relationship between performance gaps and performance shortfall
Figure 4. SEM modelling for relationship between performance gaps and performance shortfall

 

Download > lu_ertek_2015_perception_gap.pdf

The main purpose of this paper is to frame the perception differences between the buyer and supplier on the supply chain’s operational delivery, and to investigate their causal relation to the overall supply chain performance. A conceptual three-level model is developed to theorise the structural existence of the perception gaps in primarily a dyadic buyer-supplier setting. Using the primary data gathered through a major survey exercise, confirmative factor analysis and structural equation modelling were conducted to test the hypotheses on the significance and relevance of the perception gaps in supply chain management. This study provides a better conceptual understanding of the perception differences on the required as well as achieved operational deliveries within the supplier-buyer dyad, and reveals their significant and negative causal impact on the overall supply chain performance.

Lu, D., Ertek, G., (2015) Perception gap and its impact on supply chain performance”, Int. J. Business Performance and Supply Chain Modelling, Vol. 7, No. 2, 122-140.

Download > lu_ertek_2015_perception_gap.pdf

 

New knowledge in strategic management through visually mining semantic networks

Fig. 5. Outlier objects.
Fig. 5. Outlier objects.

 

Download > ertek_et_al_2015_strategic_management.pdf

Today’s highly competitive business world requires that managers be able to make fast and accurate strategic decisions, as well as learn to adapt to new strategic challenges. This necessity calls for a deep experience and a dynamic understanding of strategic management. The trait of dynamic understanding is mainly the skill of generating additional knowledge and innovative solutions under the new environmental conditions. Building on the concepts of information processing, this paper aims to support managers in constructing new strategic management knowledge, through representing and mining existing knowledge through graph visualization. To this end, a three-stage framework is proposed and described. The framework can enable managers to develop a deeper understanding of the strategic management domain, and expand on existing knowledge through visual analysis. The model further supports a case study that involves unstructured knowledge of profit patterns and the related strategies to succeed using these patterns. The applicability of the framework is shown in the case study, where the unstructured knowledge in a strategic management book is first represented as a semantic network, and then visually mined for revealing new knowledge.

Ertek, G., Tokdemir, G., Sevinc, M., Tunc, M.M., (2015) New knowledge in strategic management through visually mining semantic networks”, Information Systems Frontiers, 1-21.

Download > ertek_et_al_2015_strategic_management.pdf

Profit Estimation Error Analysis in Recommender Systems based on Association Rules

Fig. 1. Association graph for the Ertek, Demiriz and Cakmak [23] dataset, when minsup = 0.01
Fig. 1. Association graph for the Ertek, Demiriz and Cakmak [23] dataset,
when minsup = 0.01

Download > ertek_et_al_2015_profit_estimation_error.pdf

It is a challenge to estimate expected benefits from recommender systems based on association rule mining. This paper aims to address this challenge and presents a study of buying preferences of a sample of retail customers. It reveals a monotonic, non-linear relationship between the expected profits (as a function of information loss) and minimum support threshold levels, when considering transactions for a recommender system based on association rules. This finding is significant for recommender systems that utilize potential profits as a decision-making criterion.

Ertek, G., Chi, X., Yee, G., Yong, O. B., Choi, B.-G., “Profit Estimation Error Analysis in Recommender Systems based on Association Rules. In Proceedings of 2015 IEEE International Conference on Big Data (Big Data). (2015) 2138 – 2142.

Download > ertek_et_al_2015_profit_estimation_error.pdf

Graph-based analysis of resource dependencies in project networks

Graph-Based Analysis of Resource Dependencies in Project Networks
Graph-Based Analysis of Resource Dependencies in Project Networks

 

Download > ertek_et_al_2015_graph_based_analysis.pdf

It is a challenge to visualize high dimensional data such as project  data to yield new and interesting types of insights. To address this, we augment the traditional PERT network diagram with additional nodes that represent resources, and with arcs from the resource nodes to the activities that use those resources. Subsequently, we apply various graph layout algorithms that can reveal the hidden patterns in the graph data. Finally, we also map various attributes of the activities to the features of activity nodes. We illustrate the applicability and usefulness of our methodology through two case studies, where we visualize data from a benchmark data library and from the real world.

Ertek, G., Choi, B.-G., Chi, X., Yang, D., Yong, O. B., “Graph-based analysis of resource dependencies in project networks. In Proceedings of 2015 IEEE International Conference on Big Data (Big Data). (2015) 2143 – 2149.

Note: This is the final draft version of this paper. Please cite this paper (or this final draft) as above.

Download > ertek_et_al_2015_graph_based_analysis.pdf