Part I: Smart Information Aggregation Services -- Intelligent News Aggregator for German with Sentiment Analysis -- Twitter Sentiment Tracking for Predicting Marketing Trends -- Health Assistance for Immigrants -- Information Aggregation in an Enterprise -- Part II: Personalization and Recommendation Services -- Semantic Movie Recommendations -- News Recommendation in Real-Time -- Personalized Information Access using Semantic Knowledge -- Personalized Fashion Advice -- Gamification of Workplace Activities -- Part III: Sensor-Based Knowledge Acquisition and Signal Processing Services -- Optimization of In-House Energy Demand -- Detecting Violent Content in Hollywood Movies and User-Generated Videos -- Discovery of Driving Behavior Patterns -- Intermodal Mobility Assistance for Megacities.

This must-read text/reference presents an overview of smart information systems for both the private and public sector, highlighting the research questions that can be studied by applying computational intelligence. The book demonstrates how to transform raw data into effective smart information services, covering the challenges and potential of this approach. Each chapter describes the algorithms, tools, measures and evaluations used to answer important questions. This is then further illustrated by a diverse selection of case studies reflecting genuine problems faced by SMEs, multinational manufacturers, service companies, and the public sector. Topics and features: Provides a state-of-the-art introduction to the field, integrating contributions from both academia and industry Includes numerous individual use cases, which outline the need for smart information systems that help in typical situations faced by many on a daily basis Reviews novel information aggregation services that illustrate how textual data can be employed to generate smart information services Discusses personalization and recommendation systems that tailor information based on users' individual preferences Examines sensor-based knowledge acquisition services, describing how the analysis of sensor data can be used to provide a clear picture of our world Offering practical and accessible guidance for the application of computational intelligence to solving real-world problems, this volume will prove invaluable to researchers and professionals involved in information retrieval, machine learning and data analysis. Dr. Frank Hopfgartner is Director of the Competence Center Information Retrieval and Machine Learning of the Distributed Artificial Intelligence Laboratory, and lecturer at the Berlin Institute of Technology, Berlin, Germany.