http://dl.gi.de/handle/20.500.12116/10605 2026-07-21T13:56:08Z 2026-07-21T13:56:08Z Ionescu, Lucian Gwiggner, Claus Kliewer, Natalia http://dl.gi.de/handle/20.500.12116/10675 2018-03-26T09:09:41Z 2016-01-01T00:00:00Z

Data Analysis of Delays in Airline Networks Ionescu, Lucian; Gwiggner, Claus; Kliewer, Natalia Cost-optimized airline resource schedules often imply a lack of delay tolerance in case of unforeseen disruptions, e.g. late check-ins, technical defects or airport and airspace congestion. Therefore, the consideration of timeliness and robustness has become an important topic in robust resource scheduling and a wide range of sophisticated scheduling approaches has been developed in recent years. However, these approaches depend on assumptions made concerning delay occurrences. A better understanding of delay mechanisms may lead to a better trade-off between cost-efficiency and robustness and is therefore the purpose of this paper. We provide a data-driven detection of decision rules for daytime delay trends, depending on spatio-temporal attributes. The focus is on interpretable rules whose prediction accuracy is compared to random forests as a non-parametric, automated modeling approach. The obtained results give an insight into both the nature of primary delay occurrence and the methodical potential of delay prediction in the context of robust resource scheduling.

2016-01-01T00:00:00Z Alpar, Paul Schulz, Michael http://dl.gi.de/handle/20.500.12116/10672 2018-03-26T09:09:41Z 2016-01-01T00:00:00Z

Self-Service Business Intelligence Alpar, Paul; Schulz, Michael

2016-01-01T00:00:00Z Bichler, Martin Heinzl, Armin Aalst, Wil http://dl.gi.de/handle/20.500.12116/10671 2018-03-26T09:09:41Z 2016-01-01T00:00:00Z

BISE and the Engineering Sciences Bichler, Martin; Heinzl, Armin; Aalst, Wil

2016-01-01T00:00:00Z Voigt, Sebastian Hinz, Oliver http://dl.gi.de/handle/20.500.12116/10674 2018-03-26T09:09:41Z 2016-01-01T00:00:00Z

Making Digital Freemium Business Models a Success: Predicting Customers’ Lifetime Value via Initial Purchase Information Voigt, Sebastian; Hinz, Oliver In digital freemium business models such as those of online games or social apps, a large share of overall revenue derives from a small portion of the user base. Companies operating in these and similar businesses are increasingly constructing forecasting models with which to identify potential heavy users as early as possible and create special retention measures to suit those users’ needs. In our study, we observe three digital freemium companies that sell virtual credits and investigate to what extent initial purchase information can be used to determine a given customer’s lifetime value. We find that customers represent higher future lifetime values if they (a) make a purchase early after registration, (b) spend a significant amount on their initial purchase, and (c) use credit cards to purchase credits. In addition, we see that users tend to spend increasing amounts on subsequent purchases.

2016-01-01T00:00:00Z