The combination of ubiquitous and social computing is an emerging
esearch area which integrates different but complementary methods,
echniques and tools. In this paper, we focus on the Ubicon platform,
ts applications, and a large spectrum of analysis results.

bicon provides an extensible framework for building and hosting applications
argeting both ubiquitous and social environments. We summarize the
rchitecture and exemplify its implementation using four real-world
pplications built on top of Ubicon. In addition, we discuss several
cientific experiments in the context of these applications in order
o give a better picture of the potential of the framework, and discuss
nalysis results using several real-world data sets collected utilizing
bicon.

@article{atzmueller2014ubicon, author = {Atzmueller, Martin and Becker, Martin and Kibanov, Mark and Scholz, Christoph and Doerfel, Stephan and Hotho, Andreas and Macek, Bjoern-Elmar and Mitzlaff, Folke and Mueller, Juergen and Stumme, Gerd}, title = {Ubicon and its Applications for Ubiquitous Social Computing}, journal = {New Review of Hypermedia and Multimedia}, year = {2014}, volume = {20}, number = {1}, pages = {53--77}, url = {http://www.tandfonline.com/doi/abs/10.1080/13614568.2013.873488}, doi = {10.1080/13614568.2013.873488}, keywords = {2014, analytics, mining, myown, social, ubicon, ubiquitous}, abstract = {The combination of ubiquitous and social computing is an emerging research area which integrates different but complementary methods, techniques and tools. In this paper, we focus on the Ubicon platform, its applications, and a large spectrum of analysis results. Ubicon provides an extensible framework for building and hosting applications targeting both ubiquitous and social environments. We summarize the architecture and exemplify its implementation using four real-world applications built on top of Ubicon. In addition, we discuss several scientific experiments in the context of these applications in order to give a better picture of the potential of the framework, and discuss analysis results using several real-world data sets collected utilizing Ubicon.} }

%0 = article %A = Atzmueller, Martin and Becker, Martin and Kibanov, Mark and Scholz, Christoph and Doerfel, Stephan and Hotho, Andreas and Macek, Bjoern-Elmar and Mitzlaff, Folke and Mueller, Juergen and Stumme, Gerd %D = 2014 %T = Ubicon and its Applications for Ubiquitous Social Computing %U = http://www.tandfonline.com/doi/abs/10.1080/13614568.2013.873488

@proceedings{cellier2014proceedings,, title = {Proceedings of the 1st International Workshop on Interactions between
Data Mining and Natural Language Processing co-located with The European
Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases, DMNLP@PKDD/ECML 2014, Nancy, France, September
15, 2014}, editor = {Cellier, Peggy and Charnois, Thierry and Hotho, Andreas and Matwin, Stan and Moens, Marie-Francine and Toussaint, Yannick}, series = {CEUR Workshop Proceedings}, publisher = {CEUR-WS.org}, year = {2014}, volume = {1202}, url = {http://ceur-ws.org/Vol-1202}, keywords = {2014, data, mining, myown, nlp, workshop} }

%0 = proceedings %B = } %C = %D = 2014 %I = CEUR-WS.org %T = Proceedings of the 1st International Workshop on Interactions between
Data Mining and Natural Language Processing co-located with The European
Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases, DMNLP@PKDD/ECML 2014, Nancy, France, September
15, 2014} %U = http://ceur-ws.org/Vol-1202

@book{ACHH:12,, title = {Modeling and Mining Ubiquitous Social Media}, editor = {Atzmueller, Martin and Chin, Alvin and Helic, Denis and Hotho, Andreas}, series = {Lecture Notes in Computer Science}, publisher = {Springer Verlag}, address = {Heidelberg, Germany}, year = {2012}, volume = {7472}, url = {http://www.springer.com/computer/ai/book/978-3-642-33683-6}, keywords = {2012, everyaware, media, mining, modeling, myown, social, ubiquitous} }

%0 = book %C = Heidelberg, Germany %D = 2012 %I = Springer Verlag %T = Modeling and Mining Ubiquitous Social Media %U = http://www.springer.com/computer/ai/book/978-3-642-33683-6

@book{AH:12,, title = {Proceedings of the Third International Workshop on Mining Ubiquitous and Social Environments (MUSE 2012)}, editor = {Atzmueller, Martin and Hotho, Andreas}, publisher = {Workshop Notes}, address = {Bristol, UK}, year = {2012}, url = {http://www.kde.cs.uni-kassel.de/ws/muse2012/proceedings.pdf}, keywords = {2012, mining, muse, myown} }

%0 = book %C = Bristol, UK %D = 2012 %I = Workshop Notes %T = Proceedings of the Third International Workshop on Mining Ubiquitous and Social Environments (MUSE 2012) %U = http://www.kde.cs.uni-kassel.de/ws/muse2012/proceedings.pdf

@electronic{han2012mining, author = {Han, Jiawei and Kamber, Micheline and Pei, Jian}, title = {Data mining concepts and techniques, third edition}, publisher = {Morgan Kaufmann Publishers}, address = {Waltham, Mass.}, year = {2012}, url = {http://www.amazon.de/Data-Mining-Concepts-Techniques-Management/dp/0123814790/ref=tmm_hrd_title_0?ie=UTF8&qid=1366039033&sr=1-1}, isbn = {0123814790}, keywords = {data, mining, tobuy} }

%0 = electronic %A = Han, Jiawei and Kamber, Micheline and Pei, Jian %B = } %C = Waltham, Mass. %D = 2012 %I = Morgan Kaufmann Publishers %T = Data mining concepts and techniques, third edition} %U = http://www.amazon.de/Data-Mining-Concepts-Techniques-Management/dp/0123814790/ref=tmm_hrd_title_0?ie=UTF8&qid=1366039033&sr=1-1

@book{north2012mining, author = {North, Matthew}, title = {Data mining for the masses}, year = {2012}, url = {http://www.amazon.de/Data-Mining-Masses-Matthew-North/dp/0615684378/ref=sr_1_1?s=books-intl-de&ie=UTF8&qid=1366038800&sr=1-1&keywords=rapidminer}, isbn = {9780615684376 0615684378}, keywords = {data, mining, tobuy} }

%0 = book %A = North, Matthew %D = 2012 %T = Data mining for the masses %U = http://www.amazon.de/Data-Mining-Masses-Matthew-North/dp/0615684378/ref=sr_1_1?s=books-intl-de&ie=UTF8&qid=1366038800&sr=1-1&keywords=rapidminer

@book{AH:11,, title = {Proceedings of the 2011 International Workshop on Mining Ubiquitous and Social Environments (MUSE 2011)}, editor = {Atzmueller, Martin and Hotho, Andreas}, publisher = {ECML/PKDD 2011}, address = {Athens, Greece}, year = {2011}, keywords = {2011, mining, muse, myown} }

%0 = book %C = Athens, Greece %D = 2011 %I = ECML/PKDD 2011 %T = Proceedings of the 2011 International Workshop on Mining Ubiquitous and Social Environments (MUSE 2011)

@inproceedings{atzmueller2011towards, author = {Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, title = {Towards Mining Semantic Maturity in Social Bookmarking Systems}, editor = {Passant, Alexandre and Fernández, Sergio and Breslin, John and Bojārs, Uldis}, booktitle = {Proceedings of the 4th international workshop on Social Data on the Web (SDoW2011)}, year = {2011}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/atzmueller2011towards.pdf}, keywords = {2011, bookmarking, mining, myown, pattern, semantic, social} }

%0 = inproceedings %A = Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd %B = Proceedings of the 4th international workshop on Social Data on the Web (SDoW2011) %D = 2011 %T = Towards Mining Semantic Maturity in Social Bookmarking Systems %U = http://www.kde.cs.uni-kassel.de/pub/pdf/atzmueller2011towards.pdf

@inproceedings{conf/birthday/BloehdornBCGHLMMSSV11, author = {Bloehdorn, Stephan and Blohm, Sebastian and Cimiano, Philipp and Giesbrecht, Eugenie and Hotho, Andreas and Lösch, Uta and Mädche, Alexander and Mönch, Eddie and Sorg, Philipp and Staab, Steffen and Völker, Johanna}, title = {Combining Data-Driven and Semantic Approaches for Text Mining.}, editor = {Fensel, Dieter}, booktitle = {Foundations for the Web of Information and Services}, publisher = {Springer}, year = {2011}, pages = {115-142}, url = {http://dblp.uni-trier.de/db/conf/birthday/studer2011.html#BloehdornBCGHLMMSSV11}, isbn = {978-3-642-19796-3}, keywords = {2011, data, mining, myown, semantic, text} }

%0 = inproceedings %A = Bloehdorn, Stephan and Blohm, Sebastian and Cimiano, Philipp and Giesbrecht, Eugenie and Hotho, Andreas and Lösch, Uta and Mädche, Alexander and Mönch, Eddie and Sorg, Philipp and Staab, Steffen and Völker, Johanna %B = Foundations for the Web of Information and Services %D = 2011 %I = Springer %T = Combining Data-Driven and Semantic Approaches for Text Mining. %U = http://dblp.uni-trier.de/db/conf/birthday/studer2011.html#BloehdornBCGHLMMSSV11

@book{han2011mining, author = {Han, Jiawei and Kamber, Micheline}, title = {Data mining : concepts and techniques}, publisher = {Elsevier/Morgan Kaufmann}, address = {Amsterdam [u.a.]}, year = {2011}, url = {http://www.amazon.de/Data-Mining-Practical-Techniques-Management/dp/0123748569/ref=sr_1_2?ie=UTF8&qid=1366038862&sr=8-2&keywords=Data+mining}, isbn = {9780123814791 0123814790}, keywords = {data, mining, tobuy, weka} }

%0 = book %A = Han, Jiawei and Kamber, Micheline %C = Amsterdam [u.a.] %D = 2011 %I = Elsevier/Morgan Kaufmann %T = Data mining : concepts and techniques %U = http://www.amazon.de/Data-Mining-Practical-Techniques-Management/dp/0123748569/ref=sr_1_2?ie=UTF8&qid=1366038862&sr=8-2&keywords=Data+mining

@inproceedings{conf/birthday/HothoS11, author = {Hotho, Andreas and Stumme, Gerd}, title = {From Semantic Web Mining to Social and Ubiquitous Mining - A Subjective View on Past, Current, and Future Research.}, editor = {Fensel, Dieter}, booktitle = {Foundations for the Web of Information and Services}, publisher = {Springer}, year = {2011}, pages = {143-153}, url = {http://dblp.uni-trier.de/db/conf/birthday/studer2011.html#HothoS11}, isbn = {978-3-642-19796-3}, keywords = {2011, mining, myown, social, ubiquitous, web} }

%0 = inproceedings %A = Hotho, Andreas and Stumme, Gerd %B = Foundations for the Web of Information and Services %D = 2011 %I = Springer %T = From Semantic Web Mining to Social and Ubiquitous Mining - A Subjective View on Past, Current, and Future Research. %U = http://dblp.uni-trier.de/db/conf/birthday/studer2011.html#HothoS11

@inproceedings{mitzlaff2011community, author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, title = {Community Assessment using Evidence Networks}, booktitle = {Analysis of Social Media and Ubiquitous Data}, series = {LNAI}, year = {2011}, volume = {6904}, keywords = {2011, community, evaluation, knowledge, mining, myown} }

%0 = inproceedings %A = Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd %B = Analysis of Social Media and Ubiquitous Data %D = 2011 %T = Community Assessment using Evidence Networks

@inproceedings{MASH:13b, author = {Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas}, title = {On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission)}, booktitle = {Proc. LWA 2013 (KDML Special Track)}, publisher = {University of Bamberg}, address = {Bamberg, Germany}, year = {2011}, keywords = {2013, mining, myown, social, ubiquitous} }

%0 = inproceedings %A = Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas %B = Proc. LWA 2013 (KDML Special Track) %C = Bamberg, Germany %D = 2011 %I = University of Bamberg %T = On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission)

Machine learning approaches to multi-label document classification have (to date) largely relied on discriminative modeling techniques such as support vector machines. A drawback of these approaches is that performance rapidly drops off as the total number of labels and the number of labels per document increase. This problem is amplified when the label frequencies exhibit the type of highly skewed distributions that are often observed in real-world datasets. In this paper we investigate a class of generative statistical topic models for multi-label documents that associate individual word tokens with different labels. We investigate the advantages of this approach relative to discriminative models, particularly with respect to classification problems involving large numbers of relatively rare labels. We compare the performance of generative and discriminative approaches on document labeling tasks ranging from datasets with several thousand labels to datasets with tens of labels. The experimental results indicate that generative models can achieve competitive multi-label classification performance compared to discriminative methods, and have advantages for datasets with many labels and skewed label frequencies.

@misc{Rubin2011, author = {Rubin, Timothy N. and Chambers, America and Smyth, Padhraic and Steyvers, Mark}, title = {Statistical Topic Models for Multi-Label Document Classification}, year = {2011}, note = {cite arxiv:1107.2462}, url = {http://arxiv.org/abs/1107.2462}, keywords = {mining, model, text, tm, topic, toread}, abstract = { Machine learning approaches to multi-label document classification have (to date) largely relied on discriminative modeling techniques such as support vector machines. A drawback of these approaches is that performance rapidly drops off as the total number of labels and the number of labels per document increase. This problem is amplified when the label frequencies exhibit the type of highly skewed distributions that are often observed in real-world datasets. In this paper we investigate a class of generative statistical topic models for multi-label documents that associate individual word tokens with different labels. We investigate the advantages of this approach relative to discriminative models, particularly with respect to classification problems involving large numbers of relatively rare labels. We compare the performance of generative and discriminative approaches on document labeling tasks ranging from datasets with several thousand labels to datasets with tens of labels. The experimental results indicate that generative models can achieve competitive multi-label classification performance compared to discriminative methods, and have advantages for datasets with many labels and skewed label frequencies. } }

%0 = misc %A = Rubin, Timothy N. and Chambers, America and Smyth, Padhraic and Steyvers, Mark %B = } %C = %D = 2011 %I = %T = Statistical Topic Models for Multi-Label Document Classification} %U = http://arxiv.org/abs/1107.2462

@article{hotho2010ubiquitous, author = {Hotho, Andreas and Ulslev Pedersen, Rasmus and Wurst, Michael}, title = {Ubiquitous Data}, journal = {Lecture Notes in Computer Science}, publisher = {Springer}, year = {2010}, number = {6202}, pages = {61--74}, url = {http://rd.springer.com/content/pdf/10.1007%2F978-3-642-16392-0_4.pdf}, issn = {0302-9743}, keywords = {2010, data, dm, mining, myown, social, ubiquitous} }

%0 = article %A = Hotho, Andreas and Ulslev Pedersen, Rasmus and Wurst, Michael %D = 2010 %I = Springer %T = Ubiquitous Data %U = http://rd.springer.com/content/pdf/10.1007%2F978-3-642-16392-0_4.pdf

@inproceedings{conf/icdm/YassineH10, author = {Yassine, Mohamed and Hajj, Hazem}, title = {A Framework for Emotion Mining from Text in Online Social Networks.}, editor = {Fan, Wei and Hsu, Wynne and Webb, Geoffrey I. and Liu, Bing and Zhang, Chengqi and Gunopulos, Dimitrios and Wu, Xindong}, booktitle = {ICDM Workshops}, publisher = {IEEE Computer Society}, year = {2010}, pages = {1136-1142}, url = {http://dblp.uni-trier.de/db/conf/icdm/icdmw2010.html#YassineH10}, keywords = {emotion, everyaware, mining, text, toread} }

%0 = inproceedings %A = Yassine, Mohamed and Hajj, Hazem %B = ICDM Workshops %D = 2010 %I = IEEE Computer Society %T = A Framework for Emotion Mining from Text in Online Social Networks. %U = http://dblp.uni-trier.de/db/conf/icdm/icdmw2010.html#YassineH10

Giving a broad perspective of the field from numerous vantage points, 'Text Mining' focuses on statistical methods for text mining and analysis. It examines methods to automatically cluster and classify text documents and applies these methods in a variety of areas.

@book{srivastava2009mining, author = {Srivastava, Asho and Sahami, Mehran.}, title = {Text mining : classification, clustering, and applications}, publisher = {CRC Press}, address = {Boca Raton, FL}, year = {2009}, pages = {--}, url = {http://www.worldcat.org/search?qt=worldcat_org_all&q=9781420059403}, isbn = {9781420059403 1420059408}, keywords = {mining, text, toread}, abstract = {Giving a broad perspective of the field from numerous vantage points, 'Text Mining' focuses on statistical methods for text mining and analysis. It examines methods to automatically cluster and classify text documents and applies these methods in a variety of areas.} }

%0 = book %A = Srivastava, Asho and Sahami, Mehran. %C = Boca Raton, FL %D = 2009 %I = CRC Press %T = Text mining : classification, clustering, and applications %U = http://www.worldcat.org/search?qt=worldcat_org_all&q=9781420059403

@article{march06crane, author = {Crane, Gregory}, title = {What Do You Do with a Million Books?}, journal = {D-Lib Magazine}, year = {2006}, volume = {12}, number = {3}, url = {http://www.dlib.org/dlib/march06/crane/03crane.html}, doi = {10.1045/march2006-crane}, issn = {1082-9873}, keywords = {Book, Mining, Text, google, tm, toread} }

%0 = article %A = Crane, Gregory %D = 2006 %T = What Do You Do with a Million Books? %U = http://www.dlib.org/dlib/march06/crane/03crane.html

Abstract. This survey reviews the research related to PageRank computing. Components of a PageRank vector serve as authority weights for web pages independent of their textual content, solely based on the hyperlink structure of the web. PageRank is typically used as a web search ranking component. This defines the importance of the model and the data structures that underly PageRank processing. Computing even a single PageRank is a difficult computational task. Computing many PageRanks is a much more complex challenge. Recently, significant effort has been invested in building sets of personalized PageRank vectors. PageRank is also used in many diverse applications other than ranking. We are interested in the theoretical foundations of the PageRank formulation, in the acceleration of PageRank computing, in the effects of particular aspects of web graph structure on the optimal organization of computations, and in PageRank stability. We also review alternative models that lead to authority indices similar to PageRank and the role of such indices in applications other than web search. We also discuss linkbased search personalization and outline some aspects of PageRank infrastructure from associated measures of convergence to link preprocessing. 1.

@article{Berkhin05asurvey, author = {Berkhin, Pavel}, title = {A survey on pagerank computing}, journal = {Internet Mathematics}, year = {2005}, volume = {2}, pages = {73--120}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.2294}, keywords = {data, mining, pagerank, survey}, abstract = {Abstract. This survey reviews the research related to PageRank computing. Components of a PageRank vector serve as authority weights for web pages independent of their textual content, solely based on the hyperlink structure of the web. PageRank is typically used as a web search ranking component. This defines the importance of the model and the data structures that underly PageRank processing. Computing even a single PageRank is a difficult computational task. Computing many PageRanks is a much more complex challenge. Recently, significant effort has been invested in building sets of personalized PageRank vectors. PageRank is also used in many diverse applications other than ranking. We are interested in the theoretical foundations of the PageRank formulation, in the acceleration of PageRank computing, in the effects of particular aspects of web graph structure on the optimal organization of computations, and in PageRank stability. We also review alternative models that lead to authority indices similar to PageRank and the role of such indices in applications other than web search. We also discuss linkbased search personalization and outline some aspects of PageRank infrastructure from associated measures of convergence to link preprocessing. 1.} }

%0 = article %A = Berkhin, Pavel %D = 2005 %T = A survey on pagerank computing %U = http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.2294

@incollection{Fayyad:1996:DMK:257938.257942, author = {Fayyad, Usama M. and Piatetsky-Shapiro, Gregory and Smyth, Padhraic}, title = {Advances in knowledge discovery and data mining}, editor = {Fayyad, Usama M. and Piatetsky-Shapiro, Gregory and Smyth, Padhraic and Uthurusamy, Ramasamy}, publisher = {American Association for Artificial Intelligence}, address = {Menlo Park, CA, USA}, year = {1996}, pages = {1--34}, url = {http://portal.acm.org/citation.cfm?id=257938.257942}, isbn = {0-262-56097-6}, keywords = {data, definition, discovery, knowledge, mining} }

%0 = incollection %A = Fayyad, Usama M. and Piatetsky-Shapiro, Gregory and Smyth, Padhraic %C = Menlo Park, CA, USA %D = 1996 %I = American Association for Artificial Intelligence %T = Advances in knowledge discovery and data mining %U = http://portal.acm.org/citation.cfm?id=257938.257942