We present a data mining framework that can be applied for analyzing patient arrivals into healthcare centers. The sequentially applied methods are association mining, text cloud analysis, Pareto analysis, cross-tabular analysis, and regression analysis. We applied our framework using real-world data from a one of the largest public hospitals in the U.A.E., demonstrating its applicability and possible benefits. The dataset used was eventually 110,608 rows in total for the regression models, covering the most utilized 14 hospital units. The dataset is at least 10-fold larger than datasets used in closely-related research. The developed data mining framework can provide the input for a subsequent optimization model, which can be used to optimally assign appointments for patients, based on their arrival patterns.

Abdallah, S., Malik, M., Ertek, G. (2017) A Data Mining Framework for the Analysis of Patient Arrivals into Healthcare Centers. ICIT 2017 Proceedings of the 2017 International Conference on Information Technology. Pages 52-61. Singapore. December 27 – 29, 2017. ACM.

The published paper can be accessed from the following URL:

https://dl.acm.org/citation.cfm?id=3176740

The presentation for this paper received the Best Presentation Award from among 11 presentations in its session at ICIT 2017.

Dr. Gürdal Ertek recommends the following related books:

Essentials of Business Analytics

Information Visualization: An Introduction by Robert Spence (2014-11-04) 

Download this paper.

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.

Please cite this paper as follows:

Asian, S., Ertek, G., Haksoz, C., Pakter, S. and Ulun, S., 2017. Wind turbine accidents: A data mining study. IEEE Systems Journal, 11(3), pp.1567-1578.

Dr. Gürdal Ertek recommends the following related books:

Essentials of Business Analytics

Information Visualization: An Introduction by Robert Spence (2014-11-04) 

Wind Energy Explained: Theory, Design and Application

Data collected and generated through and posterior to projects, such as data residing in project management software and post-project review documents, can be a major source of actionable insights and competitive advantage. This paper presents a rigorous methodological analysis of the applied research published in academic literature, on the application of data mining (DM) for project management (PM). The objective of the paper is to provide a comprehensive analysis and discussion of where and how data mining is applied for project management data and to provide practical insights for future research in the field.

Ertek, G., Tunc, M.M., Zhang, A.N., Tanrikulu, O., Asian, S. (2017) Data Mining of Project Management Data: An Analysis of Applied Research Studies. ICIT 2017 Proceedings of the 2017 International Conference on Information Technology. Pages 35-41. Singapore. December 27 – 29, 2017. ACM.

The published paper can be accessed from the following url:
https://dl.acm.org/citation.cfm?id=3176714

Download the paper & the presentation.

Dr. Gürdal Ertek recommends the following related books:

Essentials of Business Analytics

Rapid Miner

 

 

Download the final draft of this paper.

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., Sevinç, M., & Tunç, M. M. (2017). New knowledge in strategic management through visually mining semantic networks. Information Systems Frontiers, 19(1), 165-185.

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New Knowledge in Strategic Management through Visually Mining Semantic Networks

Dr. Gürdal Ertek recommends the following related books:

Information Visualization: An Introduction by Robert Spence (2014-11-04) 

Strategic Management: Concepts 3rd Edition

 

 

Depolar, ürünlerin dağıtımı sırasında kullanılan geçici stok noktalarıdır. Depolar, tedarik zincirlerinin hedeflenen amaçlar doğrultusunda çalışmasına ve lojistik faaliyetlerinin etkin yürütülmesine önemli katkıda bulunurlar. Depolar, üretim tesislerinin içinde veya yanında bulunabileceği gibi, ayrı, özel olarak inşa edilmiş yapılar halinde de kurulabilirler. Şekil 4.1’de, tipik bir deponun genel görünüşü sunulmaktadır. Malzeme/ürünler, bu tipik depoda raflarda depolanmakta, malzeme giriş çıkışları depo rampaları üzerinden gerçekleşmekte, yükleme/boşaltma işlemleri forklift olarak adlandırılan araçlar kullanılarak gerçekleştirilmektedir. Deponun yönetimi, Depo Yöneticisi (Warehouse Manager) ya da Depo Müdürüunvanını taşıyan bir lojistik uzmanı tarafından yürütülmektedir.

Ertek, G., (2012) “Depolama Sistemleri (Warehousing Systems)”, Uluslararası Lojistik, Anadolu Üniversitesi Yayınları, Açıköğretim Fakültesi Yayını No: 1593. Eds. Bülent Çatay and Gürkan Öztürk.

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Depolama Sistemleri

Dr. Gurdal Ertek’in onerdigi kitaplar:

World-Class Warehousing and Material Handling, Second Edition

 

 

 

 

 

As the global energy demand is increasing, the share of renewable energy and specifically wind energy in the supply is growing. While vast literature exists on the design and operation of wind turbines, there exists a gap in the literature with regards to the investigation and analysis of wind turbine accidents. This paper describes the application of text mining and machine learning techniques for discovering actionable insights and knowledge from news articles on wind turbine accidents. The applied analysis methods are text processing, clustering, and multidimensional scaling (MDS). These methods have been combined under a single analysis framework, and new insights have been discovered for the domain. The results of our research can be used by wind turbine manufacturers, engineering companies, insurance companies, and government institutions to address problem areas and enhance systems and processes throughout the wind energy value chain.

Ertek, G., Chi, X., Zhang, A. N., & Asian, S. (2017, December). Text mining analysis of wind turbine accidents: An ontology-based framework. In Big Data (Big Data), 2017 IEEE International Conference on (pp. 3233-3241). IEEE.

The published paper can be accessed from the following url:
http://ieeexplore.ieee.org/document/8258305/

Download the paper.

Dr. Gürdal Ertek recommends the following related books:

Essentials of Business Analytics

Information Visualization: An Introduction by Robert Spence (2014-11-04) 

Wind Energy Explained: Theory, Design and Application

Download this paper.

A schedule-based system is a system that operates on or contains within a schedule of events and breaks at particular time intervals. Given RFID data from a schedule-based system, what set of actions and computations, and what type of data mining methods can be applied so that one can obtain actionable insights regarding the system and domain? The research goal of this paper is to answer this posed research question through the development of a framework that systematically produces actionable insights for a given schedule-based system. We show that through integrating appropriate data analysis methodologies as a unified framework, one can obtain many insights from even a very simple RFID dataset, which contains only very few fields. The developed framework is general, and is applicable to any schedule-based system, as long as it operates under a few basic assumptions. The types of insights are also general, and are formulated in the most abstract possible way. The applicability of the developed framework is illustrated through a case study, where real world data from a schedule-based system is analyzed using the introduced framework. Insights obtained include the profiling of entities and events, the interactions between entity and events, and the relations between events.

Please cite this paper as follows:

Ertek, G., Chi, X., & Zhang, A. N. (2017). A Framework for Mining RFID Data From Schedule-Based Systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(11), 2967-2984. DOI: 10.1109/TSMC.2016.2557762

Dr. Gürdal Ertek recommends the following related books:

Essentials of Business Analytics

Information Visualization: An Introduction by Robert Spence (2014-11-04)