Dirk Neumann

University of Freiburg, Freiburg, Baden-Württemberg, Germany

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Publications (154)37.28 Total impact

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    ABSTRACT: In this paper we demonstrate the potential of data analytics methods for location-based services. We develop a support system that enables user-based relocation of vehicles in free-floating carsharing models. In these businesses, customers can rent and leave cars anywhere within a predefined operational area. However, due to this flexibility, free-floating carsharing is prone to supply and demand imbalance. The support system detects imbalances by analyzing patterns in vehicle idle times. Alternative rental destinations are proposed to customers in exchange for a discount. Using data on 250,000 rentals in the city of Vancouver, we evaluate the relocation system through a simulation. The results show that our approach decreases the average vehicle idle time by up to 16 percent, suggesting a more balanced state of supply and demand. Employing the system results in a higher degree of vehicle utilization and leads to a substantial increase of profits for providers.
    Full-text · Conference Paper · Dec 2015
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    Johannes Bendler · Tobias Brandt · Dirk Neumann
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    ABSTRACT: The rapid and ongoing evolution of mobile devices allows for increasing ubiquity of online handhelds, yet boosting the recent growth of social platforms. This development facilitates participation in social media for an enormous amount of individuals independently from time and location. When navigating through a city and especially when following activities worthy to be shared with others, people uncover their traces in both geographical and temporal dimension. Using these traces to spot popular areas in a metropolitan region is valuable to a broad variety of applications, reaching from city planning to venue recommendation and investment. We propose a density-based method to determine the attractiveness of areas based solely on spatial and contentual characteristics of Twitter activity. Furthermore, we show the relation of attached images, videos, or linked places to the activity users are engaged in and assess the explanatory power of Twitter messages in a geographical context.
    Full-text · Conference Paper · Dec 2015
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    ABSTRACT: The large-scale integration of intermittent resources of power generation leads to unprecedented fluctuations on the supply side. An electricity retailer can tackle these challenges by pursuing strategies of flexible load shifting — so-called demand response mechanisms. This work addresses the associated trade-off between ICT deployment and economic benefits. The ICT design of a demand response system serves as the basis of a cost-value model, which incorporates all relevant cost components and compares them to the expected savings of an electricity retailer. Our analysis is based on a typical German electricity retailer to determine the optimal read-out frequency of smart meters. For our set of parameters, a positive information value of smart meter read-outs is achieved within an interval of 21 to 57 min regarding variable costs. Electricity retailers can achieve a profitable setting by restricting smart meter roll-out to large customers.
    No preview · Article · Nov 2015
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    Tobias Brandt · Dirk Neumann
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    ABSTRACT: Flash crashes, perceived as sharp drops in market prices that rebound shortly after, have turned the public eye toward the vulnerability of information technology-based stock trading. In this paper, we explain flash crashes as the result of actions made by rational agents. We argue that the advancement of information technology (IT), which has long been associated with competitive advantages, may cause ambiguities with respect to the game form that give rise to a hypergame. We employ hypergame theory to demonstrate that a market crash constitutes an equilibrium state if players misperceive the true game. Once the ambiguity is resolved, prices readjust to the appropriate level, creating the characteristic flash-crash effect. By analyzing the interaction with herd behavior, we find that flash crashes may be an unavoidable systemic problem of modern financial markets.
    Full-text · Article · Apr 2015 · Journal of Management Information Systems
  • Sebastian Wagner · Tobias Brandt · Dirk Neumann
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    ABSTRACT: As a rapidly expanding market, carsharing presents a possible remedy for traffic congestion in urban centers. Especially free-floating carsharing, which allows customers to leave their car anywhere within the operator’s business area, provides users with flexibility, and complements public transportation. We present a novel method that provides strategic and operational decision support to companies maneuvering this competitive and constantly changing market environment. Using an extensive set of customer data in a zero-inflated regression model, we explain spatial variation in carsharing activity through the proximity of particular points of interests, such as movie theaters and airports. As an application case, as well as a validation of the model, we use the resulting indicators to predict the number of rentals before an expansion of the business area and compare it to the actual demand post-expansion. We find that our approach correctly identifies areas with a high carsharing activity and can be easily adapted to other cities.
    No preview · Article · Mar 2015 · Omega
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    Tim Püschel · Guido Schryen · Diana Hristova · Dirk Neumann
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    ABSTRACT: Cloud computing promises the flexible delivery of computing services in a pay-as-you-go manner. It allows customers to easily scale their infrastructure and save on the overall cost of operation. However Cloud service offerings can only thrive if customers are satisfied with service performance. Allow-ing instantaneous access and flexible scaling while maintaining the service levels and offering competitive prices poses a significant challenge to Cloud Computing providers. Furthermore services will remain available in the long run only if this business generates a stable revenue stream. To address these challenges we introduce novel policy-based service admission control mod-els that aim at maximizing the revenue of Cloud providers while taking in-formational uncertainty regarding resource requirements into account. Our evaluation shows that policy-based approaches statistically significantly out-perform first come first serve approaches, which are still state of the art. Furthermore the results give insights in how and to what extent uncertainty has a negative impact on revenue.
    Full-text · Article · Jan 2015 · European Journal of Operational Research
  • Nicole Ludwig · Stefan Feuerriegel · Dirk Neumann
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    ABSTRACT: Successful companies are increasingly those companies that excel in the task of extracting knowledge from data. Tapping the source of ‘Big Data’ requires powerful algorithms combined with a strong understanding of the data used. One of the key challenges in predictive analytics is the identification of relevant factors that may explain the variables of interest. In this paper, we present a case study in predictive analytics in which we focus on the selection of relevant exogenous variables. More specifically, we attempt to predict the German electricity spot prices with reference to historical prices and a deep set of weather variables. In order to choose the relevant weather stations, we use the least absolute shrinkage selection operation (LASSO) and random forests to implicitly execute a variable selection. Overall, in our case study of German weather data, we manage to improve forecasting accuracy by up to 16.9% in terms of mean average error.
    No preview · Article · Jan 2015 · Journal of Decision System
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    ABSTRACT: Free-floating carsharing is a young and rapidly expanding market that allows customers to end their rentals anywhere within the business area of the provider. Through this flexibility it complements public transportation and reduces the environmental footprint of the transportation sector. We present a novel data analytics methodology that supports companies – from local start-ups to global players – in maneuvering this constantly growing and changing market environment. Using a large set of rental data, we derive indicators for the attractiveness of certain areas based on points of interest in their vicinity, such as shopping malls, movie theaters, or train stations. In a case study of Berlin we use these indicators to accurately identify promising regions for an expansion of the business area. However, the methodology introduced in this paper can also improve operational decisions of the service provider and is applicable to a wide range of other location-based services.
    Full-text · Conference Paper · Dec 2014
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    ABSTRACT: While the classic definition of Big Data included the dimensions volume, velocity, and variety, a fourth dimension, veracity, has recently come to the attention of researchers and practitioners. The increasing amount of user-generated data associated with the rise of social media emphasizes the need for methods to deal with the uncertainty inherent to these data sources. In this paper we address one aspect of uncertainty by developing a new methodology to establish the reliability of user-generated data based upon causal links with recurring patterns. We associate a large data set of geo-tagged Twitter messages in San Francisco with points of interest, such as bars, restaurants, or museums, within the city. This model is validated by causal relationships between a point of interest and the amount of messages in its vicinity. We subsequently analyze the behavior of these messages over time using a jackknifing procedure to identify categories of points of interest that exhibit consistent patterns over time. Ultimately, we condense this analysis into an indicator that gives evidence on the certainty of a data set based on these causal relationships and recurring patterns in temporal and spatial dimensions.
    No preview · Article · Oct 2014 · Business & Information Systems Engineering
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    ABSTRACT: While the classic definition of Big Data included the dimensions volume, velocity, and variety, a fourth dimension, veracity, has recently come to the attention of researchers and practitioners. The increasing amount of user-generated data associated with the rise of social media emphasizes the need for methods to deal with the uncertainty inherent to these data sources. In this paper we address one aspect of uncertainty by developing a new methodology to establish the reliability of user-generated data based upon causal links with recurring patterns. We associate a large data set of geo-tagged Twitter messages in San Francisco with points of interest, such as bars, restaurants, or museums, within the city. This model is validated by causal relationships between a point of interest and the amount of messages in its vicinity. We subsequently analyze the behavior of these messages over time using a jackknifing procedure to identify categories of points of interest that exhibit consistent patterns over time. Ultimately, we condense this analysis into an indicator that gives evidence on the certainty of a data set based on these causal relationships and recurring patterns in temporal and spatial dimensions.
    No preview · Article · Oct 2014 · Business and Information Systems Engineering the international journal of Wirtschaftsinformatik
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    ABSTRACT: Natural disasters, such as earthquakes, tsunamis and hurricanes, cause tremendous harm each year. In order to reduce casualties and economic losses during the response phase, rescue units must be allocated and scheduled efficiently. As this problem is one of the key issues in emergency response and has been addressed only rarely in literature, this paper develops a corresponding decision support model that minimizes the sum of completion times of incidents weighted by their severity. The presented problem is a generalization of the parallel-machine scheduling problem with unrelated machines, non-batch sequence-dependent setup times and a weighted sum of completion times – thus, it is NP-hard. Using literature on scheduling and routing, we propose and computationally compare several heuristics, including a Monte Carlo-based heuristic, the joint application of 8 construction heuristics and 5 improvement heuristics, and GRASP metaheuristics. Our results show that problem instances (with up to 40 incidents and 40 rescue units) can be solved in less than a second, with results being at most 10.9% up to 33.9% higher than optimal values. Compared to current best practice solutions, the overall harm can be reduced by up to 81.8%.
    Full-text · Article · Jun 2014 · European Journal of Operational Research
  • Stefan Feuerriegel · Dirk Neumann
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    ABSTRACT: Due to the integration of intermittent resources of power generation such as wind and solar, the amount of supplied electricity will exhibit unprecedented fluctuations. Electricity retailers can partially meet the challenge of matching demand and volatile supply by shifting power demand according to the fluctuating supply side. The necessary technology infrastructure such as Advanced Metering Infrastructures for this so-called Demand Response (DR) has advanced. However, little is known about the economic dimension and further effort is strongly needed to realistically quantify the financial impact. To succeed in this goal, we derive an optimization problem that minimizes procurement costs of an electricity retailer in order to control Demand Response usage. The evaluation with historic data shows that cost volatility can be reduced by 7.74%; peak costs drop by 14.35%; and expenditures of retailers can be significantly decreased by 3.52%.
    No preview · Article · Feb 2014 · Energy Policy
  • Xiaoqiu Qiu · Dirk Neumann
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    ABSTRACT: Auctions are important tools for resource allocation and price negotiations, while combinatorial auctions are perceived to achieve higher efficiency when allocating multiple items. In the recent decade, many auction designs are proposed and proven to be efficient, incentive compatible, and tractable. However most of the results hinge on quasi-linear preference bidders with ultimate patience, which is not quite realistic. In reality human bidders cannot engage in hundreds of auction rounds evaluating thousands of package combinations simultaneously. They either withdraw early or bid only on a limited subset of valuable packages. In this paper, we introduce bid ranges, with an additional sealed-bid phase before a Combinatorial Clock auction for information elicitation. With range information, the auction can start at higher prices with fewer rounds, and bidders are informed with the most relevant packages. Our design reduces the complexity both for the bidders and auctioneer, and is verified with computational simulations.
    No preview · Conference Paper · Jan 2014
  • Stefan Feuerriegel · Max W. Lampe · Dirk Neumann
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    ABSTRACT: Speculative bubbles are commonly referred to situations where stock prices considerably deviate from their fundamentals until the bubbles bust. Bursting of bubbles such as the dot-com or U.S. housing bubble is very costly, so there is a need for mechanisms to detect them. In this paper, we attempt to predict when bubbles may bust using the sentiment of news announcements. Accordingly, we first try to understand how news reception evolves depending on the market phase (boom or bust). The probability of bubble bursts are calculated on the basis of a Markov-regime switching model. The approach is applied and validated using the oil market which appears to be one of the most important markets in the globalized world. Our methodology can be similarly extended to other markets such as gold or wheat.
    No preview · Conference Paper · Jan 2014
  • Tobias Brandt · Dirk Neumann
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    ABSTRACT: Flash crashes, perceived as sharp drops in market prices that rebound shortly after, have turned the public eye towards the vulnerability of IT-based stock trading. In this paper we explain flash crashes as the result of actions made by rational agents. We argue that information technology, which has long been associated with competitive advantages, may cause ambiguities with respect to the game form that give rise to a Hyper game. We employ Hyper game Theory to demonstrate that a market crash constitutes an equilibrium state if players misperceive the true game. Once the ambiguity is resolved, prices readjust to the appropriate level, creating the characteristic flash crash effect. We also discuss endogenous and exogenous mechanisms that may alleviate the threat of a flash crash and present possible options for future research.
    No preview · Conference Paper · Jan 2014
  • Michael Liebmann · Alexei G. Orlov · Dirk Neumann
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    ABSTRACT: This paper applies novel sentiment analyses to Reuters news to study stock and CDS traders' differential interpretations of financial news. We construct sentiment measures to identify which news content influences investors' behavior and create dynamic word lists that reflect the divergent viewpoints of CDS and equity investors. We find that (1) equity and CDS traders focus on different content within the same news; (2) traders particularly disagree with respect to news concerning debt topics, especially regarding M&A activity; (3) the subprime crisis impacted debt news content and altered the typical inverse relationship between equity and CDS markets on news days.
    No preview · Article · Jan 2014 · SSRN Electronic Journal
  • J. Bendler · A. Ratku · D. Neumann
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    ABSTRACT: The presence of crime is one of the major challenges for societies all over the World, especially in metropolitan areas. As indicated by prior research, Information Systems can contribute greatly to cope with the complex factors that influence the emergence and location of delinquencies. In this work, we combine commonly used approaches of static environmental characteristics with Social Media. We expect that blending in such dynamic information of public behavior is a valuable addition to explain and predict criminal activity. Consequently, we employ Zero-Inflated Poisson Regressions and Geographically Weighted Regressions to examine how suitable Social Media data actually is for this purpose. Our results unveil geographic variation of explanatory power throughout a metropolitan area. Furthermore, we find that Social Media works exceptionally well for description of certain crime types and thus is also likely to enhance the accuracy of delinquency prediction.
    No preview · Article · Jan 2014
  • J. Bendler · T. Brandt · S. Wagner · D. Neumann
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    ABSTRACT: Social networks offer vast potential for marketing agencies, as members freely provide private information, for instance on their current situation, opinions, tastes, and feelings. The use of social networks to feed into crime platforms has been acknowledged to build a kind of a virtual neighborhood watch. Current attempts that tried to automatically connect news from social networks with crime platforms have concentrated on documentation of past events, but neglected the opportunity to use Twitter data as a decision support system to detect future crimes. In this work, we attempt to unleash the wisdom of crowds materialized in tweets from Twitter. This requires to look at Tweets that have been sent within a vicinity of each other. Based on the aggregated Tweets traffic we correlate them with crime types. Apparently, crimes such as disturbing the peace or homicide exhibit different Tweet patterns before the crime has been committed. We show that these tweet patterns can strengthen the explanation of criminal activity in urban areas. On top of that, we go beyond pure explanatory approaches and use predictive analytics to provide evidence that Twitter data can improve the prediction of crimes.
    No preview · Article · Jan 2014
  • A. Beckhaus · D. Neumann · L.M. Karg
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    ABSTRACT: We contemplate the concept of intra-organizational communities of operation to account for the organizational design that has recently emerged. Intra-organizational communities of operation address operative tasks of information workers within the boundaries of a firm. By relying on community principles such as self-coordination and intrinsic motivation, this design is believed to be highly scalable and efficient. In order to understand the mechanisms of how these communities function, we develop a research framework aiming to explain performance by means of constructs adapted from conventional group research. We evaluate our model in an empirical study at a large software vendor in its bug tracking process. We find that community centrality, informal roles, and heterogeneity are associated with performance while sub-network centralization and size are not. Our findings will motivate managers to benefit from intra-organizational communities' flexibility and scalability and assist them in the design process by unveiling the mechanisms that influence performance.
    No preview · Article · Jan 2014
  • Source
    Tobias Brandt · Stefan Feuerriegel · Dirk Neumann
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    ABSTRACT: In this paper we show how IS research can contribute to two major issues many societies face today – dealing with the decreasing availability of easily accessible fossil fuels and the threat of climate change. We introduce an IS artifact that uses historical data on energy consumption, energy generation, and mobility behavior to unleash synergies between residential photovoltaic panels and electric vehicles. Using real world data we show in simulations that our residential information system allows households to decrease annual net energy costs by up to 68 percent. This results in an earlier break-even point of green technologies and clears a path towards a more sustainable society. Following up with an investment analysis we show that the information system has the same financial impact as the subsidy for plugin-hybrid electric vehicles by the U.S. federal government following the American Recovery and Reinvestment Act – at no cost to the taxpayer.
    Full-text · Conference Paper · Dec 2013

Publication Stats

904 Citations
37.28 Total Impact Points

Institutions

  • 2008-2015
    • University of Freiburg
      • Department of Information Systems Research
      Freiburg, Baden-Württemberg, Germany
  • 2002-2010
    • Karlsruhe Institute of Technology
      • • Institute of Information Management in Engineering
      • • Institute of Information Systems and Marketing
      Karlsruhe, Baden-Wuerttemberg, Germany