We focus on time series visualization, classifying regular events, identifying anomalies and predicting future trends.
Extending Apache Mesos, Apache Spark, Storm, Kafka, Cassandra, Zookeeper, ... for big data processing, extensive computation, robust deployment and maintenance. Implementation of scalable AI algorithms for big data platforms (Apache Spark, Hadoop).
Our goal is to automate transformation of relational data (think about SQL database) into a single table of features, which can be used for classification, regression, clustering, outlier detection... And by this automation alleviate the biggest hurdle in the process of data mining - data preprocessing.Read more: Relational Machine Learning
Optimization and multi-objective evaluation of clustering algorithms. Time series clustering and interactive evolution of clustering.
Research and development of intelligent agent for solving problems in a complex (continuous) environments. Agent has to make decisions with incomplete information, moreover under uncertainty about consequences of his moves. (Agent used in autonomous playing of Angry Birds.)
Multi-agent systems are traditionally used for simulation of systems with a huge amount of interacting entities. Its dynamic is usually too complex but the behaviour of entities can be individually described. We analyse and extract behavioural patterns from available data and simulate complex dynamic systems in multi-agent environment.
Advanced general recommendation algorithms for items such as products, movies, songs, articles, etc. Research into ensembles of recommender systems, metalearning in recommendation, multiobjective evaluation of recommenders and domain specific problems such as periodicity of recommendations, constrains, boosting, etc.