@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}}, } @inproceedings{46364, abstract = {{Automated algorithm configuration procedures play an increasingly important role in the development and application of algorithms for a wide range of computationally challenging problems. Until very recently, these configuration procedures were limited to optimising a single performance objective, such as the running time or solution quality achieved by the algorithm being configured. However, in many applications there is more than one performance objective of interest. This gives rise to the multi-objective automatic algorithm configuration problem, which involves finding a Pareto set of configurations of a given target algorithm that characterises trade-offs between multiple performance objectives. In this work, we introduce MO-ParamILS, a multi-objective extension of the state-of-the-art single-objective algorithm configuration framework ParamILS, and demonstrate that it produces good results on several challenging bi-objective algorithm configuration scenarios compared to a base-line obtained from using a state-of-the-art single-objective algorithm configurator.}}, author = {{Blot, A and Hoos, H and Jourdan, L and Marmion, M and Trautmann, Heike}}, booktitle = {{LION 2016: Learning and Intelligent Optimization}}, editor = {{et al. Joaquin, Vanschooren}}, pages = {{32–47}}, publisher = {{Springer International Publishing}}, title = {{{MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework}}}, doi = {{10.1007/978-3-319-50349-3_3}}, volume = {{10079}}, year = {{2016}}, } @inbook{46363, abstract = {{The averaged Hausdorff distance has been proposed as an indicator for assessing the quality of finitely sized approximations of the Pareto front of a multiobjective problem. Since many set-based, iterative optimization algorithms store their currently best approximation in an internal archive these approximations are also termed archives. In case of two objectives and continuous variables it is known that the best approximations in terms of averaged Hausdorff distance are subsets of the Pareto front if it is concave. If it is linear or circularly concave the points of the best approximation are equally spaced. Here, it is proven that the optimal averaged Hausdorff approximation and the Pareto front have an empty intersection if the Pareto front is circularly convex. But the points of the best approximation are equally spaced and they rapidly approach the Pareto front for increasing size of the approximation.}}, author = {{Rudolph, G and Schütze, O and Trautmann, Heike}}, booktitle = {{Applications of Evolutionary Computation: 19$^th$ European Conference, EvoApplications 2016, Porto, Portugal, March 30 — April 1, 2016, Proceedings, Part II}}, editor = {{Squillero, G and Burelli, P}}, isbn = {{978-3-319-31153-1}}, pages = {{42–55}}, publisher = {{Springer International Publishing}}, title = {{{On the Closest Averaged Hausdorff Archive for a Circularly Convex Pareto Front}}}, doi = {{10.1007/978-3-319-31153-1_4}}, year = {{2016}}, } @inproceedings{46369, abstract = {{This paper formally defines multimodality in multiobjective optimization (MO). We introduce a test-bed in which multimodal MO problems with known properties can be constructed as well as numerical characteristics of the resulting landscape. Gradient- and local search based strategies are compared on exemplary problems together with specific performance indicators in the multimodal MO setting. By this means the foundation for Exploratory Landscape Analysis in MO is provided.}}, author = {{Kerschke, Pascal and Wang, Hao and Preuss, Mike and Grimme, Christian and Deutz, André and Trautmann, Heike and Emmerich, Michael}}, booktitle = {{Proceedings of the 14$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XIV)}}, pages = {{962–972}}, publisher = {{Springer}}, title = {{{Towards Analyzing Multimodality of Multiobjective Landscapes}}}, doi = {{10.1007/978-3-319-45823-6_90}}, year = {{2016}}, } @inproceedings{46367, abstract = {{When selecting the best suited algorithm for an unknown optimization problem, it is useful to possess some a priori knowledge of the problem at hand. In the context of single-objective, continuous optimization problems such knowledge can be retrieved by means of Exploratory Landscape Analysis (ELA), which automatically identifies properties of a landscape, e.g., the so-called funnel structures, based on an initial sample. In this paper, we extract the relevant features (for detecting funnels) out of a large set of landscape features when only given a small initial sample consisting of 50 x D observations, where D is the number of decision space dimensions. This is already in the range of the start population sizes of many evolutionary algorithms. The new Multiple Peaks Model Generator (MPM2) is used for training the classifier, and the approach is then very successfully validated on the Black-Box Optimization Benchmark (BBOB) and a subset of the CEC 2013 niching competition problems.}}, author = {{Kerschke, Pascal and Preuss, Mike and Wessing, Simon and Trautmann, Heike}}, booktitle = {{Proceedings of the 18$^th$ Annual Conference on Genetic and Evolutionary Computation}}, isbn = {{978-1-4503-4206-3}}, pages = {{229–236}}, title = {{{Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models}}}, doi = {{10.1145/2908812.2908845}}, year = {{2016}}, } @article{46371, abstract = {{One main task in evolutionary multiobjective optimization (EMO) is to obtain a suitable finite size approximation of the Pareto front which is the image of the solution set, termed the Pareto set, of a given multiobjective optimization problem. In the technical literature, the characteristic of the desired approximation is commonly expressed by closeness to the Pareto front and a sufficient spread of the solutions obtained. In this paper, we first make an effort to show by theoretical and empirical findings that the recently proposed Averaged Hausdorff (or Δ𝑝-) indicator indeed aims at fulfilling both performance criteria for bi-objective optimization problems. In the second part of this paper, standard EMO algorithms combined with a specialized archiver and a postprocessing step based on the Δ𝑝 indicator are introduced which sufficiently approximate the Δ𝑝-optimal archives and generate solutions evenly spread along the Pareto front.}}, author = {{Rudolph, G and Schütze, O and Grimme, C and Domínguez-Medina, C and Trautmann, Heike}}, journal = {{Computational Optimization and Applications (Comput. Optim. Appl.)}}, number = {{2}}, pages = {{589–618}}, title = {{{Optimal averaged Hausdorff archives for bi-objective problems: theoretical and numerical results}}}, doi = {{10.1007/s10589-015-9815-8}}, volume = {{64}}, year = {{2016}}, } @article{46372, abstract = {{We present a new hybrid evolutionary algorithm for the effective hypervolume approximation of the Pareto front of a given differentiable multi-objective optimization problem. Starting point for the local search (LS) mechanism is a new division of the decision space as we will argue that in each of these regions a different LS strategy seems to be most promising. For the LS in two out of the three regions we will utilize and adapt the Directed Search method which is capable of steering the search into any direction given in objective space and which is thus well suited for the problem at hand. We further on integrate the resulting LS mechanism into SMS-EMOA, a state-of-the-art evolutionary algorithm for hypervolume approximations. Finally, we will present some numerical results on several benchmark problems with two and three objectives indicating the strength and competitiveness of the novel hybrid.}}, author = {{Schütze, O and Sosa, Hernandez VA and Trautmann, Heike and Rudolph, G}}, journal = {{Journal of Heuristics}}, number = {{3}}, pages = {{273–300}}, title = {{{The Hypervolume based Directed Search Method for Multi-Objective Optimization Problems}}}, doi = {{10.1007/s10732-016-9310-0}}, volume = {{22}}, year = {{2016}}, } @inproceedings{46368, abstract = {{Exploratory Landscape Analysis (ELA) aims at understanding characteristics of single-objective continuous (black-box) optimization problems in an automated way. Moreover, the approach provides the basis for constructing algorithm selection models for unseen problem instances. Recently, it has gained increasing attention and numerical features have been designed by various research groups. This paper introduces the R-Package FLACCO which makes all relevant features available in a unified framework together with efficient helper functions. Moreover, a case study which gives perspectives to ELA for multi-objective optimization problems is presented.}}, author = {{Kerschke, Pascal and Trautmann, Heike}}, booktitle = {{Proceedings of the IEEE Congress on Evolutionary Computation (CEC)}}, title = {{{The R-Package FLACCO for Exploratory Landscape Analysis with Applications to Multi-Objective Optimization Problems}}}, doi = {{10.1109/CEC.2016.7748359}}, year = {{2016}}, } @article{46370, abstract = {{This report documents the talks and discussions at the Dagstuhl Seminar 15211 "Theory of Evolutionary Algorithms". This seminar, now in its 8th edition, is the main meeting point of the highly active theory of randomized search heuristics subcommunities in Australia, Asia, North America, and Europe. Topics intensively discussed include rigorous runtime analysis and computational complexity theory for randomised search heuristics, information geometry of randomised search, and synergies between the theory of evolutionary algorithms and theories of natural evolution.}}, author = {{Neumann, F and Trautmann, Heike}}, journal = {{Dagstuhl Reports}}, number = {{5}}, pages = {{78–79}}, title = {{{Working Group Report: Bridging the Gap Between Experiments and Theory Using Feature-Based Run-Time Analysis; Theory of Evolutionary Algorithms (Dagstuhl Seminar 15211)}}}, doi = {{10.4230/DagRep.5.5.57}}, volume = {{5}}, year = {{2016}}, } @inproceedings{46365, abstract = {{Despite the intrinsic hardness of the Traveling Salesperson Problem (TSP) heuristic solvers, e.g., LKH+restart and EAX+restart, are remarkably successful in generating satisfactory or even optimal solutions. However, the reasons for their success are not yet fully understood. Recent approaches take an analytical viewpoint and try to identify instance features, which make an instance hard or easy to solve. We contribute to this area by generating instance sets for couples of TSP algorithms A and B by maximizing/minimizing their performance difference in order to generate instances which are easier to solve for one solver and much harder to solve for the other. This instance set offers the potential to identify key features which allow to distinguish between the problem hardness classes of both algorithms.}}, author = {{Bossek, Jakob and Trautmann, Heike}}, booktitle = {{Learning and Intelligent Optimization}}, editor = {{Festa, P and Sellmann, M and Vanschoren, J}}, isbn = {{978-3-319-50348-6}}, pages = {{48–59}}, publisher = {{Springer International Publishing}}, title = {{{Evolving Instances for Maximizing Performance Differences of State-of-The-Art Inexact TSP Solvers}}}, doi = {{10.1007/978-3-319-50349-3_4}}, volume = {{10079}}, year = {{2016}}, } @inproceedings{46366, abstract = {{State of the Art inexact solvers of the NP-hard Traveling Salesperson Problem (TSP) are known to mostly yield high-quality solutions in reasonable computation times. With the purpose of understanding different levels of instance difficulties, instances for the current State of the Art heuristic TSP solvers LKH+restart and EAX+restart are presented which are evolved using a sophisticated evolutionary algorithm. More specifically, the performance differences of the respective solvers are maximized resulting in instances which are easier to solve for one solver and much more difficult for the other. Focusing on both optimization directions, instance features are identified which characterize both types of instances and increase the understanding of solver performance differences.}}, author = {{Bossek, Jakob and Trautmann, Heike}}, booktitle = {{AI*IA 2016 Advances in Artificial Intelligence}}, editor = {{Adorni, G and Cagnoni, S and Gori, M and Maratea, M}}, isbn = {{978-3-319-49129-5}}, pages = {{3–12}}, publisher = {{Springer}}, title = {{{Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference}}}, doi = {{10.1007/978-3-319-49130-1_1}}, volume = {{10037}}, year = {{2016}}, } @inproceedings{46373, abstract = {{The need for automatic methods of topic discovery in the Internet grows exponentially with the amount of available textual information. Nowadays it becomes impossible to manually read even a small part of the information in order to reveal the underlying topics. Social media provide us with a great pool of user generated content, where topic discovery may be extremely useful for businesses, politicians, researchers, and other stakeholders. However, conventional topic discovery methods, which are widely used in large text corpora, face several challenges when they are applied in social media and particularly in Twitter – the most popular microblogging platform. To the best of our knowledge no comprehensive overview of these challenges and of the methods dedicated to address these challenges does exist in IS literature until now. Therefore, this paper provides an overview of these challenges, matching methods and their expected usefulness for social media analytics.}}, author = {{Chinnov, Andrey and Kerschke, Pascal and Meske, Christian and Stieglitz, Stefan and Trautmann, Heike}}, booktitle = {{Proceedings of the 20$^th$ Americas Conference on Information Systems (AMCIS ’15)}}, isbn = {{978-0-9966831-0-4}}, pages = {{1–10}}, title = {{{An Overview of Topic Discovery in Twitter Communication through Social Media Analytics}}}, year = {{2015}}, } @inproceedings{46375, abstract = {{In single-objective optimization different optimization strategies exist depending on the structure and characteristics of the underlying problem. In particular, the presence of so-called funnels in multimodal problems offers the possibility of applying techniques exploiting the global structure of the function. The recently proposed Exploratory Landscape Analysis approach automatically identifies problem characteristics based on a moderately small initial sample of the objective function and proved to be effective for algorithm selection problems in continuous black-box optimization. In this paper, specific features for detecting funnel structures are introduced and combined with the existing ones in order to classify optimization problems regarding the funnel property. The effectiveness of the approach is shown by experiments on specifically generated test instances and validation experiments on standard benchmark problems.}}, author = {{Kerschke, Pascal and Preuss, Mike and Wessing, Simon and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’15)}}, editor = {{Silva, Sara}}, isbn = {{978-1-4503-3472-3}}, pages = {{265–272}}, publisher = {{ACM}}, title = {{{Detecting Funnel Structures by Means of Exploratory Landscape Analysis}}}, doi = {{10.1145/2739480.2754642}}, year = {{2015}}, } @inproceedings{46376, abstract = {{We investigate per-instance algorithm selection techniques for solving the Travelling Salesman Problem (TSP), based on the two state-of-the-art inexact TSP solvers, LKH and EAX. Our comprehensive experiments demonstrate that the solvers exhibit complementary performance across a diverse set of instances, and the potential for improving the state of the art by selecting between them is significant. Using TSP features from the literature as well as a set of novel features, we show that we can capitalise on this potential by building an efficient selector that achieves significant performance improvements in practice. Our selectors represent a significant improvement in the state-of-the-art in inexact TSP solving, and hence in the ability to find optimal solutions (without proof of optimality) for challenging TSP instances in practice.}}, author = {{Kotthoff, Lars and Kerschke, Pascal and Hoos, Holger and Trautmann, Heike}}, booktitle = {{Learning and Intelligent Optimization}}, editor = {{Dhaenens, Clarisse and Jourdan, Laetitia and Marmion, Marie-Eléonore}}, isbn = {{978-3-319-19084-6}}, pages = {{202–217}}, publisher = {{Springer International Publishing}}, title = {{{Improving the State of the Art in Inexact TSP Solving Using Per-Instance Algorithm Selection}}}, year = {{2015}}, }