Johannes Gaertner

Publications (117) View all

• Conference Proceeding: A Taxonomy of Dirty Time-Oriented Data

  Theresia Gschwandtner, Johannes Gärtner, Wolfgang Aigner, Silvia Miksch
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  ABSTRACT: Data quality is a vital topic for business analytics in order to gain accurate insight and make correct decisions in many data-intensive industries. Albeit systematic approaches to categorize, detect, and avoid data quality problems exist, the special characteristics of time-oriented data are hardly considered. However, time is an important data dimension with distinct characteristics which a�ffords special consideration in the context of dirty data. Building upon existing taxonomies of general data quality problems, we address textquoteleftdirty&$#$39; time-oriented data, i.e., time-oriented data with potential quality problems. In particular, we investigated empirically derived problems that emerge with di�fferent types of time-oriented data (e.g., time points, time intervals) and provide various examples of quality problems of time-oriented data. By providing categorized information related to existing taxonomies, we establish a basis for further research in the field of dirty time-oriented data, and for the formulation of essential quality checks when preprocessing time-oriented data.
  Proceedings of the CD-ARES 2012, Prague, Czech Republic; 01/2012
• Article: Hierarchical Temporal Patterns and Interactive Aggregated Views for Pixel-Based Visualizations

  Tim Lammarsch, Wolfgang Aigner, Alessio Bertone, Johannes Gärtner, Eva Mayr, Silvia Miksch, Michael Smuc
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  ABSTRACT: Many real-world problems involve time-oriented data. Time data is different from other kinds of data--explicitly harnessing the structures of time in visualizations can guide and support users’ visual analysis processes. State-of-the-art visualizations hardly take advantage of the structures of time to aid users in understanding and exploring the data. To bring more flexibility to the analysis process, we have developed interactive visual methods incorporating the structures of time within a pixel-based visualization called GROOVE (granular overview overlay). GROOVE uses different techniques to visualize time-oriented data by overlaying several time granularities in one visualization and provides interactive operators, which utilize the structures of time in different ways to capture and explore time-oriented data.
  2010 14th International Conference Information Visualisation. 07/2009;
• Article: To Score or Not to Score? Tripling Insights for Participatory Design

  M. Smuc, E. Mayr, T. Lammarsch, W. Aigner, S. Miksch, J. Gartner
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  ABSTRACT: For evaluating visual-analytics tools, many studies confine to scoring user insights into data. For participatory design of those tools, we propose a three-level methodology to make more out of users' insights. The relational insight organizer (RIO) helps to understand how insights emerge and build on one each other. In recent years, computers have also been used to develop visual methods and tools that further support the data analysis process. With the advent of the emerging field of visual analytics (VA), the underlying concept of visual tools is taken a step further. In essence, VA combines human analytical capabilities with computer processing capacities. In the human-computer interaction process, the user generates new knowledge and gains insights.
  IEEE Computer Graphics and Applications 07/2009; · 1.41 Impact Factor
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  Available from: Johannes Gaertner

  Article: The minimum shift design problem

  Luca Di Gaspero, Johannes Gärtner, Guy Kortsarz, Nysret Musliu, Andrea Schaerf, Wolfgang Slany
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  ABSTRACT: The min-Shift Design problem (MSD) is an important scheduling problem that needs to be solved in many industrial contexts. The issue is to find a minimum number of shifts and the number of employees to be assigned to these shifts in order to minimize the deviation from workforce requirements. Our research considers both theoretical and practical aspects of the min-Shift Design problem. This problem is closely related to the minimum edge-cost flow problem (MECF), anetwork flow variant that has many applications beyond shift scheduling. We show that MSD reduces to a special case of MECF and, exploiting this reduction, we prove a logarithmic hardness of approximation lower bound for MSD. On the basis of these results, we propose a hybrid heuristic for the problem, which relies on a greedy heuristic followed by a local search algorithm. The greedy part is based on the network flow analogy, and the local search algorithm makes use of multiple neighborhood relations. An experimental analysis on structured random instances shows that the hybrid heuristic clearly outperforms our previous commercial implementation. Furthermore, it highlights the respective merits of the composing heuristics for different performance parameters.
  Annals of Operations Research 04/2012; 155(1):79-105. · 0.84 Impact Factor
• Article: Reducing understaffing and shift work with Temporal Profile Optimization (TPO).

  Karin Boonstra-Hörwein, Dieter Punzengruber, Johannes Gärtner
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  ABSTRACT: The ergonomic quality of shift schedules can be improved by reducing time periods with understaffing (resulting in work-pressure, poor quality, etc.) and evening, night and/or weekend work. Improving the quality of forecasts regarding future workforce requirements as well as the optimization of work processes by moving as much work as possible to more suitable time zones are two approaches to this. We introduce and propose Temporal Profile Optimization (TPO) as a systematic approach to question the demand as well as its translation to workforce planning. Temporal profiles describe the number of employees needed over time (e.g. for different days of the week, times of day, for different calendar days) as well as the shift-times and staffing levels planned to meet this workforce demand. With Temporal Profile Forecasts we introduce a forecasting method that is based on time-stamped historical data and methodologically supplements traditional time series models like SARIMA in many ways. With Temporal Profile Reengineering we use systematic and often participatory methods from business process reengineering to identify moveable work and streamline the load lines by (re-)distributing movable work such that shifts and schedules are improved. The approach is illustrated along two business cases. Using TP-Forecasts for air traffic controllers increased forecasting accuracy whereby a different shift design was possible resulting in 3-4% less shift work. In a warehouse of an Austrian freight carrier a TP-Forecast together with TP-Reengineering helped to rearrange work processes such that the resulting workforce requirements curve had a more even form. This allowed for shorter shifts than before (thereby decreasing overtime). Experiences made so far stress the potential of Temporal Profile Optimization.
  Applied ergonomics 01/2011; 42(2):233-7. · 1.11 Impact Factor