A Preference-based Stream Analyzer

L. Rudenko, M. Endres, P. Roocks, W. Kießling

erschienen 2016 ECML PKDD 2016 Workshop on Large-scale Learning from Data Streams in Evolving Environments, Riva del Garda, Italien, Sep. 2016


Stream query processing is becoming increasingly important as more time-oriented data is produced and analyzed nowadays. Examples for stream based applications include sensor networks and infrastructure monitoring, electronic trading on Wall Street, or social networks. In the last decade several technologies have emerged to address the challenges of processing such high-volume and real-time data streams, which do not take the form of persistent database relations, but rather arrive in continuous, rapid and time-varying data objects. In this paper we focus on the new problem of preference-based query processing to analyze data streams. Preferences allow us to filter out only relevant information of the continuous data flow. In addition we provide a database approach that stores only the most important information in the database w.r.t. the user’s preference. For this we introduce novel preference-based integrity constraints to keep the data right and consistent.