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Auflistung nach Schlagwort "Predictive analytics"

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  • Zeitschriftenartikel
    An ETA Prediction Model for Intermodal Transport Networks Based on Machine Learning
    (Business & Information Systems Engineering: Vol. 62, No. 5, 2020) Balster, Andreas; Hansen, Ole; Friedrich, Hanno; Ludwig, André
    Transparency in transport processes is becoming increasingly important for transport companies to improve internal processes and to be able to compete for customers. One important element to increase transparency is reliable, up-to-date and accurate arrival time prediction, commonly referred to as estimated time of arrival (ETA). ETAs are not easy to determine, especially for intermodal freight transports, in which freight is transported in an intermodal container, using multiple modes of transportation. This computational study describes the structure of an ETA prediction model for intermodal freight transport networks (IFTN), in which schedule-based and non-schedule-based transports are combined, based on machine learning (ML). For each leg of the intermodal freight transport, an individual ML prediction model is developed and trained using the corresponding historical transport data and external data. The research presented in this study shows that the ML approach produces reliable ETA predictions for intermodal freight transport. These predictions comprise processing times at logistics nodes such as inland terminals and transport times on road and rail. Consequently, the outcome of this research allows decision makers to proactively communicate disruption effects to actors along the intermodal transportation chain. These actors can then initiate measures to counteract potential critical delays at subsequent stages of transport. This approach leads to increased process efficiency for all actors in the realization of complex transport operations and thus has a positive effect on the resilience and profitability of IFTNs.
  • Textdokument
    A Comparison of Distributed Stream Processing Systems for Time Series Analysis
    (BTW 2019 – Workshopband, 2019) Gehring, Melissa; Charfuelan, Marcela; Markl, Volker
    Given the vast number of data processing systems available today, in this paper, we aim to identify, select, and evaluate systems to determine the one that is better suited to use in conducting time series analysis. Published studies of performance are used to compare several open-source systems, and two systems are further selected for qualitative comparison and evaluation regarding the development of a time series analytics task. The main interest of this work lies in the investigation of the Ease of development. As a test scenario, a discrete Kalman filter is implemented to predict the closing price of stock market data in real-time. Basic functionality coverage is considered, and advanced functionality is evaluated using several qualitative comparison criteria.
  • Zeitschriftenartikel
    Prescriptive Control of Business Processes
    (Business & Information Systems Engineering: Vol. 58, No. 4, 2016) Krumeich, Julian; Werth, Dirk; Loos, Peter
    This paper proposes a concept for a prescriptive control of business processes by using event-based process predictions. In this regard, it explores new potentials through the application of predictive analytics to big data while focusing on production planning and control in the context of the process manufacturing industry. This type of industry is an adequate application domain for the conceived concept, since it features several characteristics that are opposed to conventional industries such as assembling ones. These specifics include divergent and cyclic material flows, high diversity in end products’ qualities, as well as non-linear production processes that are not fully controllable. Based on a case study of a German steel producing company – a typical example of the process industry – the work at hand outlines which data becomes available when using state-of-the-art sensor technology and thus providing the required basis to realize the proposed concept. However, a consideration of the data size reveals that dedicated methods of big data analytics are required to tap the full potential of this data. Consequently, the paper derives seven requirements that need to be addressed for a successful implementation of the concept. Additionally, the paper proposes a generic architecture of prescriptive enterprise systems. This architecture comprises five building blocks of a system that is capable to detect complex event patterns within a multi-sensor environment, to correlate them with historical data and to calculate predictions that are finally used to recommend the best course of action during process execution in order to minimize or maximize certain key performance indicators.
  • Zeitschriftenartikel
    The Price of Privacy
    (Business & Information Systems Engineering: Vol. 61, No. 4, 2019) Baumann, Annika; Haupt, Johannes; Gebert, Fabian; Lessmann, Stefan
    The analysis of clickstream data facilitates the understanding and prediction of customer behavior in e-commerce. Companies can leverage such data to increase revenue. For customers and website users, on the other hand, the collection of behavioral data entails privacy invasion. The objective of the paper is to shed light on the trade-off between privacy and the business value of customer information. To that end, the authors review approaches to convert clickstream data into behavioral traits, which we call clickstream features, and propose a categorization of these features according to the potential threat they pose to user privacy. The authors then examine the extent to which different categories of clickstream features facilitate predictions of online user shopping patterns and approximate the marginal utility of using more privacy adverse information in behavioral prediction models. Thus, the paper links the literature on user privacy to that on e-commerce analytics and takes a step toward an economic analysis of privacy costs and benefits. In particular, the results of empirical experimentation with large real-world e-commerce data suggest that the inclusion of short-term customer behavior based on session-related information leads to large gains in predictive accuracy and business performance, while storing and aggregating usage behavior over longer horizons has comparably less value.