@inproceedings{46350,
  abstract     = {{The ubiquity of WiFi access points and the sharp increase in WiFi-enabled devices carried by humans have paved the way for WiFi-based indoor positioning and location analysis. Locating people in indoor environments has numerous applications in robotics, crowd control, indoor facility optimization, and automated environment mapping. However, existing WiFi-based positioning systems suffer from two major problems: (1) their accuracy and precision is limited due to inherent noise induced by indoor obstacles, and (2) they only occasionally provide location estimates, namely when a WiFi-equipped device emits a signal. To mitigate these two issues, we propose a novel Gaussian process (GP) model for WiFi signal strength measurements. It allows for simultaneous smoothing (increasing accuracy and precision of estimators) and interpolation (enabling continuous sampling of location estimates). Furthermore, simple and efficient smoothing methods for location estimates are introduced to improve localization performance in real-time settings. Experiments are conducted on two data sets from a large real-world commercial indoor retail environment. Results demonstrate that our approach provides significant improvements in terms of precision and accuracy with respect to unfiltered data. Ultimately, the GP model realizes continuous location sampling with consistently high quality location estimates.}},
  author       = {{van Engelen, J.E. and van Lier, J.J. and Takes, F.W. and Trautmann, Heike}},
  booktitle    = {{Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD)}},
  pages        = {{524–540}},
  publisher    = {{Springer}},
  title        = {{{Accurate WiFi based indoor positioning with continuous location sampling}}},
  year         = {{2018}},
}

@article{46351,
  abstract     = {{Clustering is an important field in data mining that aims to reveal hidden patterns in data sets. It is widely popular in marketing or medical applications and used to identify groups of similar objects. Clustering possibly unbounded and evolving data streams is of particular interest due to the widespread deployment of large and fast data sources such as sensors. The vast majority of stream clustering algorithms employ a two-phase approach where the stream is first summarized in an online phase. Upon request, an offline phase reclusters the aggregations into the final clusters. In this setup, the online component will idle and wait for the next observation in times where the stream is slow. This paper proposes a new stream clustering algorithm called evoStream which performs evolutionary optimization in the idle times of the online phase to incrementally build and refine the final clusters. Since the online phase would idle otherwise, our approach does not reduce the processing speed while effectively removing the computational overhead of the offline phase. In extensive experiments on real data streams we show that the proposed algorithm allows to output clusters of high quality at any time within the stream without the need for additional computational resources.}},
  author       = {{Carnein, Matthias and Trautmann, Heike}},
  journal      = {{Big Data Research}},
  pages        = {{101–111}},
  title        = {{{evoStream — Evolutionary Stream Clustering Utilizing Idle Times}}},
  doi          = {{10.1016/j.bdr.2018.05.005}},
  volume       = {{14}},
  year         = {{2018}},
}

@article{46353,
  abstract     = {{Incorporating decision makers' preferences is of great significance in multiobjective optimization. Target region-based multiobjective evolutionary algorithms (TMOEAs), aiming at a well-distributed subset of Pareto optimal solutions within the user-provided region(s), are extensively investigated in this paper. An empirical comparison is performed among three TMOEA instantiations: T-NSGA-II, T-SMS-EMOA and T-R2-EMOA. Experimental results show that T-SMS-EMOA has the best overall performance regarding the hypervolume indicator within the target region, while T-NSGA-II is the fastest algorithm. We also compare TMOEAs with other state-of-the-art preference-based approaches, i.e., DF-SMS-EMOA, RVEA, AS-EMOA and R-NSGA-II to show the advantages of TMOEAs. A case study in the mission planning of earth observation satellite is carried out to verify the capabilities of TMOEAs in the real-world application. Experimental results indicate that preferences can improve the searching ability of MOEAs, and TMOEAs can successfully find nondominated solutions preferred by the decision maker.}},
  author       = {{Li, L and Wang, Y and Trautmann, Heike and Jing, N and Emmerich, M}},
  journal      = {{Swarm and Evolutionary Computation}},
  pages        = {{196–215}},
  title        = {{{Multiobjective evolutionary algorithms based on target region preferences}}},
  doi          = {{10.1016/j.swevo.2018.02.006}},
  volume       = {{40}},
  year         = {{2018}},
}

@inproceedings{48839,
  abstract     = {{We analyze the effects of including local search techniques into a multi-objective evolutionary algorithm for solving a bi-objective orienteering problem with a single vehicle while the two conflicting objectives are minimization of travel time and maximization of the number of visited customer locations. Experiments are based on a large set of specifically designed problem instances with different characteristics and it is shown that local search techniques focusing on one of the objectives only improve the performance of the evolutionary algorithm in terms of both objectives. The analysis also shows that local search techniques are capable of sending locally optimal solutions to foremost fronts of the multi-objective optimization process, and that these solutions then become the leading factors of the evolutionary process.}},
  author       = {{Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Günter and Trautmann, Heike}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference}},
  isbn         = {{978-1-4503-5618-3}},
  keywords     = {{combinatorial optimization, metaheuristics, multi-objective optimization, orienteering, transportation}},
  pages        = {{585–592}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Local Search Effects in Bi-Objective Orienteering}}},
  doi          = {{10.1145/3205455.3205548}},
  year         = {{2018}},
}

@inproceedings{48867,
  abstract     = {{Assessing the performance of stochastic optimization algorithms in the field of multi-objective optimization is of utmost importance. Besides the visual comparison of the obtained approximation sets, more sophisticated methods have been proposed in the last decade, e. g., a variety of quantitative performance indicators or statistical tests. In this paper, we present tools implemented in the R package ecr, which assist in performing comprehensive and sound comparison and evaluation of multi-objective evolutionary algorithms following recommendations from the literature.}},
  author       = {{Bossek, Jakob}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}},
  isbn         = {{978-1-4503-5764-7}},
  keywords     = {{evolutionary optimization, performance assessment, software-tools}},
  pages        = {{1350–1356}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Performance Assessment of Multi-Objective Evolutionary Algorithms with the R Package ecr}}},
  doi          = {{10.1145/3205651.3208312}},
  year         = {{2018}},
}

@inproceedings{48885,
  abstract     = {{Performance comparisons of optimization algorithms are heavily influenced by the underlying indicator(s). In this paper we investigate commonly used performance indicators for single-objective stochastic solvers, such as the Penalized Average Runtime (e.g., PAR10) or the Expected Running Time (ERT), based on exemplary benchmark performances of state-of-the-art inexact TSP solvers. Thereby, we introduce a methodology for analyzing the effects of (usually heuristically set) indicator parametrizations - such as the penalty factor and the method used for aggregating across multiple runs - w.r.t. the robustness of the considered optimization algorithms.}},
  author       = {{Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}},
  isbn         = {{978-1-4503-5764-7}},
  keywords     = {{algorithm selection, optimization, performance measures, transportation, travelling salesperson problem}},
  pages        = {{1737–1744}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers}}},
  doi          = {{10.1145/3205651.3208233}},
  year         = {{2018}},
}

@book{48880,
  author       = {{Grimme, Christian and Bossek, Jakob}},
  isbn         = {{978-3-658-21150-9}},
  publisher    = {{Springer Vieweg}},
  title        = {{{Einführung in die Optimierung - Konzepte, Methoden und Anwendungen}}},
  doi          = {{10.1007/978-3-658-21151-6}},
  year         = {{2018}},
}

@article{48884,
  abstract     = {{The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard problems. Over the years, many different solution approaches and solvers have been developed. For the first time, we directly compare five state-of-the-art inexact solvers\textemdash namely, LKH, EAX, restart variants of those, and MAOS\textemdash on a large set of well-known benchmark instances and demonstrate complementary performance, in that different instances may be solved most effectively by different algorithms. We leverage this complementarity to build an algorithm selector, which selects the best TSP solver on a per-instance basis and thus achieves significantly improved performance compared to the single best solver, representing an advance in the state of the art in solving the Euclidean TSP. Our in-depth analysis of the selectors provides insight into what drives this performance improvement.}},
  author       = {{Kerschke, Pascal and Kotthoff, Lars and Bossek, Jakob and Hoos, Holger H. and Trautmann, Heike}},
  issn         = {{1063-6560}},
  journal      = {{Evolutionary Computation}},
  keywords     = {{automated algorithm selection, machine learning., performance modeling, Travelling Salesperson Problem}},
  number       = {{4}},
  pages        = {{597–620}},
  title        = {{{Leveraging TSP Solver Complementarity through Machine Learning}}},
  doi          = {{10.1162/evco_a_00215}},
  volume       = {{26}},
  year         = {{2018}},
}

@article{48866,
  abstract     = {{Bossek, (2018). grapherator: A Modular Multi-Step Graph Generator. Journal of Open Source Software, 3(22), 528, https://doi.org/10.21105/joss.00528}},
  author       = {{Bossek, Jakob}},
  issn         = {{2475-9066}},
  journal      = {{Journal of Open Source Software}},
  number       = {{22}},
  pages        = {{528}},
  title        = {{{Grapherator: A Modular Multi-Step Graph Generator}}},
  doi          = {{10.21105/joss.00528}},
  volume       = {{3}},
  year         = {{2018}},
}

@inproceedings{46348,
  abstract     = {{We analyze the effects of including local search techniques into a multi-objective evolutionary algorithm for solving a bi-objective orienteering problem with a single vehicle while the two conflicting objectives are minimization of travel time and maximization of the number of visited customer locations. Experiments are based on a large set of specifically designed problem instances with different characteristics and it is shown that local search techniques focusing on one of the objectives only improve the performance of the evolutionary algorithm in terms of both objectives. The analysis also shows that local search techniques are capable of sending locally optimal solutions to foremost fronts of the multi-objective optimization process, and that these solutions then become the leading factors of the evolutionary process.}},
  author       = {{Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Guenter and Trautmann, Heike}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference}},
  isbn         = {{978-1-4503-5618-3}},
  pages        = {{585–592}},
  publisher    = {{ACM}},
  title        = {{{Local Search Effects in Bi-Objective Orienteering}}},
  doi          = {{10.1145/3205455.3205548}},
  year         = {{2018}},
}

@article{46352,
  abstract     = {{The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard problems. Over the years, many different solution approaches and solvers have been developed. For the first time, we directly compare five state-of-the-art inexact solvers—namely, LKH, EAX, restart variants of those, and MAOS—on a large set of well-known benchmark instances and demonstrate complementary performance, in that different instances may be solved most effectively by different algorithms. We leverage this complementarity to build an algorithm selector, which selects the best TSP solver on a per-instance basis and thus achieves significantly improved performance compared to the single best solver, representing an advance in the state of the art in solving the Euclidean TSP. Our in-depth analysis of the selectors provides insight into what drives this performance improvement.}},
  author       = {{Kerschke, Pascal and Kotthoff, Lars and Bossek, Jakob and Hoos, Holger H. and Trautmann, Heike}},
  journal      = {{Evolutionary Computation (ECJ)}},
  number       = {{4}},
  pages        = {{597–620}},
  title        = {{{Leveraging TSP Solver Complementarity through Machine Learning}}},
  doi          = {{10.1162/evco_a_00215}},
  volume       = {{26}},
  year         = {{2018}},
}

@inproceedings{46349,
  abstract     = {{Performance comparisons of optimization algorithms are heavily influenced by the underlying indicator(s). In this paper we investigate commonly used performance indicators for single-objective stochastic solvers, such as the Penalized Average Runtime (e.g., PAR10) or the Expected Running Time (ERT), based on exemplary benchmark performances of state-of-the-art inexact TSP solvers. Thereby, we introduce a methodology for analyzing the effects of (usually heuristically set) indicator parametrizations - such as the penalty factor and the method used for aggregating across multiple runs - w.r.t. the robustness of the considered optimization algorithms.}},
  author       = {{Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion}},
  isbn         = {{978-1-4503-5764-7/18/07}},
  pages        = {{1737–1744}},
  title        = {{{Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers}}},
  doi          = {{10.1145/3205651.3208233}},
  year         = {{2018}},
}

@inbook{46355,
  abstract     = {{In this chapter we present the adaptions of the recently proposed Directed Search method to the context of unconstrained parameter dependent multi-objective optimization problems (PMOPs). The new method, called 𝜆-DS, is capable of performing a movement both toward and along the solution set of a given differentiable PMOP. We first discuss the basic variants of the method that use gradient information and describe subsequently modifications that allow for a gradient free realization. Finally, we show that 𝜆-DS can be used to understand the behavior of stochastic local search within PMOPs to a certain extent which might be interesting for the development of future local search engines, or evolutionary strategies, for the treatment of such problems. We underline all our statements with several numerical results indicating the strength of the novel approach.}},
  author       = {{Adrián, Sosa Hernández V and Lara, A and Trautmann, Heike and Rudolph, G and Schütze, O}},
  booktitle    = {{NEO 15}},
  editor       = {{Schütze, O and Trujillo, L and Legrand, P and Maldonado, Y}},
  isbn         = {{978-3-319-44003-3}},
  pages        = {{281–330}},
  publisher    = {{Springer International Publishing}},
  title        = {{{The Directed Search Method for Unconstrained Parameter Dependent Multi-objective Optimization Problems}}},
  doi          = {{10.1007/978-3-319-44003-3_12}},
  year         = {{2017}},
}

@inproceedings{46360,
  abstract     = {{Nowadays customers expect a seamless interaction with companies throughout all available communication channels. However, many companies rely on different software solutions to handle each channel, which leads to heterogeneous IT infrastructures and isolated data sources. Omni-Channel CRM is a holistic approach towards a unified view on the customer across all channels. This paper introduces three case studies which demonstrate challenges of omni-channel CRM and the value it can provide. The first case study shows how to integrate and visualise data from different sources which can support operational and strategic decision. In the second case study, a social media analysis approach is discussed which provides benefits by offering reports of service performance across channels. The third case study applies customer segmentation to an online fashion retailer in order to identify customer profiles.}},
  author       = {{Carnein, Matthias and Heuchert, Markus and Homann, Leschek and Trautmann, Heike and Vossen, Gottfried and Becker, Jörg and Kraume, Karsten}},
  booktitle    = {{Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17)}},
  editor       = {{de Cesare, Sergio and Ulrich, Frank}},
  isbn         = {{978-3-319-70625-2}},
  pages        = {{69–78}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Towards Efficient and Informative Omni-Channel Customer Relationship Management}}},
  doi          = {{10.1007/978-3-319-70625-2_7}},
  volume       = {{10651}},
  year         = {{2017}},
}

@inproceedings{46361,
  abstract     = {{Until recently, customer service was exclusively provided over traditional channels. Cus- tomers could write an email or call a service center if they had questions or problems with a product or service. In recent times, this has changed dramatically as companies explore new channels to offer customer service. With the increasing popularity of social media, more companies thrive to provide customer service also over Facebook and Twitter. Companies aim to provide a better customer ex- perience by offering more convenient channels to contact a company. In addition, this unburdens traditional channels which are costly to maintain. This paper empirically evaluates the performance of customer service in social media by analysing a multitude of companies in the airline industry. We have collected several million customer service requests from Twitter and Facebook and auto- matically analyzed how efficient the service strategies of the respective companies are in terms of response rate and time.}},
  author       = {{Carnein, Matthias and Homann, Leschek and Trautmann, Heike and Vossen, Gottfried and Kraume, Karsten}},
  booktitle    = {{Proceedings of the 17$^th$ Conference on Database Systems for Business, Technology, and Web (BTW ’17)}},
  editor       = {{Ritter, Norbert and Schwarz, Holger and Klettke, Meike and Thor, Andreas and Kopp, Oliver and Bernhard, Matthias Wieland}},
  issn         = {{978-3-88579-660-2}},
  pages        = {{33–40}},
  publisher    = {{Gesellschaft für Informatik}},
  title        = {{{Customer Service in Social Media — An Empirical Study of the Airline Industry}}},
  volume       = {{P-266}},
  year         = {{2017}},
}

@inbook{46356,
  abstract     = {{Integrating user preferences in Evolutionary Multiobjective Optimization (EMO) is currently a prevalent research topic. There is a large variety of preference handling methods (originated from Multicriteria decision making, MCDM) and EMO methods, which have been combined in various ways. This paper proposes a Web Ontology Language (OWL) ontology to model and systematize the knowledge of preference-based multiobjective evolutionary algorithms (PMOEAs). Detailed procedure is given on how to build and use the ontology with the help of Protégé. Different use-cases, including training new learners, querying and reasoning are exemplified and show remarkable benefit for both EMO and MCDM communities.}},
  author       = {{Li, L and Yevseyeva, I and Basto-Fernandes, V and Trautmann, Heike and Jing, N and Emmerich, M}},
  booktitle    = {{Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings}},
  editor       = {{Trautmann, H and Rudolph, G and Klamroth, K and Schütze, O and Wiecek, M and Jin, Y and Grimme, C}},
  isbn         = {{978-3-319-54157-0}},
  pages        = {{406–421}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms}}},
  doi          = {{10.1007/978-3-319-54157-0_28}},
  year         = {{2017}},
}

@inbook{46357,
  abstract     = {{The liner shipping fleet repositioning problem (LSFRP) is a central optimization problem within the container shipping industry. Several approaches exist for solving this problem using exact and heuristic techniques, however all of them use a single objective function for determining an optimal solution. We propose a multi-objective approach based on a simulated annealing heuristic so that repositioning coordinators can better balance profit making with cost-savings and environmental sustainability. As the first multi-objective approach in the area of liner shipping routing, we show that giving more options to decision makers need not be costly. Indeed, our approach requires no extra runtime than a weighted objective heuristic and provides a rich set of solutions along the Pareto front.}},
  author       = {{Tierney, K and Handali, J and Grimme, C and Trautmann, Heike}},
  booktitle    = {{Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings}},
  editor       = {{Trautmann, H and Rudolph, G and Klamroth, K and Schütze, O and Wiecek, M and Jin, Y and Grimme, C}},
  isbn         = {{978-3-319-54157-0}},
  pages        = {{622–638}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Multi-objective Optimization for Liner Shipping Fleet Repositioning}}},
  doi          = {{10.1007/978-3-319-54157-0_42}},
  year         = {{2017}},
}

@inproceedings{46359,
  abstract     = {{This paper proposes a new stream clustering algorithm for text streams. The algorithm combines concepts from stream clustering and text analysis in order to incrementally maintain a number of text droplets that represent topics within the stream. Our algorithm adapts to changes of topic over time and can handle noise and outliers gracefully by decaying the importance of irrelevant clusters. We demonstrate the performance of our approach by using more than one million real-world texts from the video streaming platform Twitch.tv.}},
  author       = {{Carnein, Matthias and Assenmacher, Dennis and Trautmann, Heike}},
  booktitle    = {{Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17)}},
  editor       = {{de Cesare, Sergio and Ulrich, Frank}},
  isbn         = {{978-3-319-70625-2}},
  pages        = {{79–88}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Stream Clustering of Chat Messages with Applications to Twitch Streams}}},
  doi          = {{10.1007/978-3-319-70625-2_8}},
  year         = {{2017}},
}

@article{46362,
  abstract     = {{Social bots are currently regarded an influential but also somewhat mysterious factor in public discourse and opinion making. They are considered to be capable of massively distributing propaganda in social and online media, and their application is even suspected to be partly responsible for recent election results. Astonishingly, the term social bot is not well defined and different scientific disciplines use divergent definitions. This work starts with a balanced definition attempt, before providing an overview of how social bots actually work (taking the example of Twitter) and what their current technical limitations are. Despite recent research progress in Deep Learning and Big Data, there are many activities bots cannot handle well. We then discuss how bot capabilities can be extended and controlled by integrating humans into the process and reason that this is currently the most promising way to realize meaningful interactions with other humans. This finally leads to the conclusion that hybridization is a challenge for current detection mechanisms and has to be handled with more sophisticated approaches to identify political propaganda distributed with social bots.}},
  author       = {{Grimme, C and Preuss, M and Adam, L and Trautmann, Heike}},
  journal      = {{Big Data}},
  number       = {{4}},
  pages        = {{279–293}},
  title        = {{{Social Bots: Human-Like by Means of Human Control?}}},
  doi          = {{10.1089/big.2017.0044}},
  volume       = {{5}},
  year         = {{2017}},
}

@inproceedings{46358,
  abstract     = {{Analysing streaming data has received considerable attention over the recent years. A key research area in this field is stream clustering which aims to recognize patterns in a possibly unbounded data stream of varying speed and structure. Over the past decades a multitude of new stream clustering algorithms have been proposed. However, to the best of our knowledge, no rigorous analysis and comparison of the different approaches has been performed. Our paper fills this gap and provides extensive experiments for a total of ten popular algorithms. We utilize a number of standard data sets of both, real and synthetic data and identify key weaknesses and strengths of the existing algorithms.}},
  author       = {{Carnein, Matthias and Assenmacher, Dennis and Trautmann, Heike}},
  booktitle    = {{Proceedings of the ACM International Conference on Computing Frontiers (CF ’17)}},
  isbn         = {{978-1-4503-4487-6/17/05}},
  pages        = {{361–365}},
  title        = {{{An Empirical Comparison of Stream Clustering Algorithms}}},
  doi          = {{10.1145/3075564.3078887}},
  year         = {{2017}},
}