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\workshopHeader{New Frontiers in Mining Complex Patterns}
{Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, \break Giuseppe Manco, Elio Masciari and Zbigniew W.\ Ras} % organisers
{09:00--16:00} % please use this time format everywhere
{Room: Wills, 3.32}
{Monday}

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\workshopIntro{This workshop is concerned with emerging technologies and applications where complex patterns in expressive languages are extracted from new prominent data sources like blogs, event or log data, biological data, spatio-temporal data, social networks, mobility data, sensor data and streams, and so on. Advanced techniques, which preserve the informative richness of data and allow us to efficiently and efficaciously identify complex information units present in such data, will be presented.}


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\timeTitle{09:00--09:10}{WELCOME}

%\timeTitlePeopleAbstract{09:10--10:00}
\timeTitlePeople{09:10--10:00}
{Invited Talk: Advances in Semantic Data  Mining}
{Nada Lavra\v{c}}
%{In data mining and knowledge discovery, the expert is usually confronted with a task of constructing a predictive model or a set of descriptive patterns from the available data. In early approaches to data mining, simple tabular data was used as input to data analysis. Recent approaches, however, address much more complex data mining tasks, including relational data mining, text mining and mining of heterogeneous information networks. The availability of large amounts of semantically annotated data in all domains of science, and biology in particular, poses requirements for new data mining approaches which need to deal with increased data complexity, the relational character of semantic representations, as well as the reasoning capacities of the underlying ontologies. This talk addresses semantic data mining, an emerging data mining task in which semantic data in the form of domain ontologies is used as background knowledge in data analytics. The talk introduces a general framework for semantic data mining, followed by the presentation of the SEGS method for semantic subgroup discovery from microarray data, and two general methods for semantic subgroup discovery SDM-SEGS and SDM-Aleph implemented as reusable workflows in the Orange4WS data mining environment. The use of described methods and tools is illustrated on selected biomedical applications.}


\workshopSection{THEORY}

\timeTitlePeople{10:00--10:15}{Towards the Definition of Learning Systems with Configurable Operators and Heuristics}
{Fernando Mart{\mbox{\'{\i}}}nez-Plumed, C\`{e}sar Ferri, Jos\'{e} Hern\'{a}ndez-Orallo and  Mar{\mbox{\'{\i}}}a Jos\'{e} Ram{\mbox{\'{\i}}}rez-Quintana}

\timeTitlePeople{10:15--10:30}{Reducing Examples in Relational Learning with Bounded-Treewidth Hypotheses}
{Ond\v{r}ej Ku\v{z}elka, Andrea Szab\'{o}ov\'{a} and Filip \v{Z}elezn\'{y}}

\timeTitle{10:30--11:00}{Coffee Break}

\workshopSection{MISC COMPLEX DATA}

\timeTitlePeople{11:00--11:15}{Mining Complex Event Patterns in Computer Networks}
{Dietmar Seipel, Philipp Neubeck, Stefan K\"ohler and Martin Atzmueller}

\timeTitlePeople{11:15--11:30}{Learning with Aggregation and Correlation in the Presence of Large Fluctuations}
{Eric Paquet, Herna Lydia Viktor and Hongyu Guo}




\timeTitlePeople{11:30--11:45}{Machine Learning as an Objective Approach to Understanding Musical Origin}
{Claire Q and Ross King}

\timeTitlePeople{11:45--12:00}{Object-driven Action Rules and their Application to Hypernasality Treatment} 
{Ayman Hajja, Alicja Wieczorkowska, Zbigniew Ras and Ryszard Gubrynowicz}

\timeTitlePeople{12:00--12:10}{Pre-filtering Features in Random Forests for Microarray Data Classification} {Nicoletta Dess\`{i}, Gabriele Milia and Barbara Pes}

\timeTitle{12:10--13:25}{  Lunch (not provided)}

\workshopSection{TRAJECTORIES AND TIME SERIES}

\timeTitlePeople{13:25--13:40}{MatArcs: An Exploratory Data Analysis of Recurring Patterns in Multivariate Time Series}
{Julia Gaebler, Stephan Spiegel and Sahin Albayrak}


\timeTitlePeople{13:40--13:55}{Effectively Grouping Trajectory Streams}{Gianni Costa, Giuseppe Manco and Elio Masciari}

\timeTitlePeople{13:55--14:10}{Healthcare Trajectory Mining by Combining Multi-dimensional Component and Itemsets} {Elias Egho, Dino Ienco, Nicolas Jay, Amedeo Napoli, Pascal Poncelet, Catherine Quantin, Chedy Raissi and Maguelonne Teisseire}

\timeTitlePeople{14:10--14:25}{Graph-Based Approaches to Clustering Network-Constrained Trajectory Data}{Mohamed Khalil El Mahrsi and Fabrice Rossi}


\timeTitle{14:25--14:30}{Break}

\newpage
\workshopSection{GRAPHS AND NETWORKS}

\timeTitlePeople{14:30--14:45}{Finding the Most Descriptive Substructures in Graphs with Numeric Labels}{Michael Davis, Weiru Liu and Paul Miller}

\timeTitlePeople{14:45--15:00}{Learning in Probabilistic Graphs exploiting Language-Constrained Patterns}{Claudio Taranto, Nicola Di Mauro and Floriana Esposito}

\timeTitlePeople{15:00--15:15}{Improving Robustness and Flexibility of Concept Taxonomy Learning from Text}
{Fabio Leuzzi, Stefano Ferilli, Claudio Taranto and Fulvio Rotella}

\timeTitlePeople{15:15--15:30}{Discovering Evolution Chains for Link Mining in Dynamic Networks}{Corrado Loglisci, Michelangelo Ceci and Donato Malerba}

\timeTitlePeople{15:30--15:45}{SISO: a conceptual framework for the construction of ``stereotypical maps'' in a\hfill\break Social Internetworking}
{Scenario Francesco Buccafurri, Gianluca Lax, Antonino Nocera and Domenico Ursino}

\timeTitlePeople{15:45--15:55}{Context-Aware Prediction on Business Process Executions}{Francesco Folino, Massimo Guarascio and Luigi Pontieri}


\timeTitle{15:55--16:00}{FINAL REMARKS}



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