@article{atzmueller2014ubicon, 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.}, 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}, doi = {10.1080/13614568.2013.873488}, interhash = {6364e034fa868644b30618dc887c0270}, intrahash = {176e4f2816af5fe1630ed65e062900ce}, journal = {New Review of Hypermedia and Multimedia}, number = 1, pages = {53--77}, title = {{Ubicon and its Applications for Ubiquitous Social Computing}}, url = {http://www.tandfonline.com/doi/abs/10.1080/13614568.2013.873488}, volume = 20, year = 2014 } @article{liu2012crowdsourcing, abstract = {Some complex problems, such as image tagging and natural language processing, are very challenging for computers, where even state-of-the-art technology is yet able to provide satisfactory accuracy. Therefore, rather than relying solely on developing new and better algorithms to handle such tasks, we look to the crowdsourcing solution -- employing human participation -- to make good the shortfall in current technology. Crowdsourcing is a good supplement to many computer tasks. A complex job may be divided into computer-oriented tasks and human-oriented tasks, which are then assigned to machines and humans respectively.

To leverage the power of crowdsourcing, we design and implement a Crowdsourcing Data Analytics System, CDAS. CDAS is a framework designed to support the deployment of various crowdsourcing applications. The core part of CDAS is a quality-sensitive answering model, which guides the crowdsourcing engine to process and monitor the human tasks. In this paper, we introduce the principles of our quality-sensitive model. To satisfy user required accuracy, the model guides the crowdsourcing query engine for the design and processing of the corresponding crowdsourcing jobs. It provides an estimated accuracy for each generated result based on the human workers' historical performances. When verifying the quality of the result, the model employs an online strategy to reduce waiting time. To show the effectiveness of the model, we implement and deploy two analytics jobs on CDAS, a twitter sentiment analytics job and an image tagging job. We use real Twitter and Flickr data as our queries respectively. We compare our approaches with state-of-the-art classification and image annotation techniques. The results show that the human-assisted methods can indeed achieve a much higher accuracy. By embedding the quality-sensitive model into crowdsourcing query engine, we effectively reduce the processing cost while maintaining the required query answer quality.}, acmid = {2336676}, author = {Liu, Xuan and Lu, Meiyu and Ooi, Beng Chin and Shen, Yanyan and Wu, Sai and Zhang, Meihui}, interhash = {41ad6e73b03373d76d3164ba248335d7}, intrahash = {2091967734f96c4afbc09319d48a8c65}, issn = {2150-8097}, issue_date = {June 2012}, journal = {Proceedings of the VLDB Endowment}, month = jun, number = 10, numpages = {12}, pages = {1040--1051}, publisher = {VLDB Endowment}, title = {CDAS: a crowdsourcing data analytics system}, url = {http://dl.acm.org/citation.cfm?id=2336664.2336676}, volume = 5, year = 2012 } @inproceedings{AP:08a, author = {Atzmueller, Martin and Puppe, Frank}, booktitle = {Proc. 21th International Florida Artificial Intelligence Research Society Conference (FLAIRS-2008)}, interhash = {e1aaffd9c5f2211b522819a26d237f7b}, intrahash = {a912321c6df86a4989eb5d88adf39bd1}, optpages = {518--523}, publisher = {AAAI Press}, title = {{Semi-Automatic Refinement and Assessment of Subgroup Patterns}}, year = 2008 } @phdthesis{Atzmueller:06, author = {Atzmueller, Martin}, interhash = {f0a13a0e19eb1d888645b88d62a7f678}, intrahash = {4527cce722540a59b8c9435c4a513f76}, school = {Department of Computer Science, University of Würzburg}, title = {{Knowledge-Intensive Subgroup Mining -- Techniques for Automatic and Interactive Discovery}}, year = 2006 } @inproceedings{AS:08a, author = {Atzmueller, Martin and Seipel, Dietmar}, booktitle = {Proc. 18th International Conference on Applications of Declarative Programming and Knowledge Management, accepted}, interhash = {094ce683ca7a8b8bc06b02d7f4f4a848}, intrahash = {06df7a1e43864bd50cf2c6011e25c03a}, title = {{Declarative Specification of Ontological Domain Knowledge for Descriptive Data Mining (Extended version)}}, year = 2008 } @inproceedings{AS:08b, author = {Atzmueller, Martin and Seipel, Dietmar}, booktitle = {Proc. 18th International Conference on Applications of Declarative Programming and Knowledge Management, accepted}, interhash = {d1dbda136a427047193aea59338c4aee}, intrahash = {dd84981f691d4383862579b50800016d}, title = {{Causal Subgroup Analysis for Detecting Confounding (Extended version)}}, year = 2008 } @article{APB:09, author = {Atzmueller, Martin and Puppe, Frank and Buscher, Hans-Peter}, interhash = {7035d39f90827971b2b5dbd63ec950bf}, intrahash = {818608d57145cfc07a75e9608d059d23}, journal = {International Journal on Artificial Intelligence Tools (IJAIT)}, number = 1, pages = {1 -- 18}, title = {{A Semi-Automatic Approach for Confounding-Aware Subgroup Discovery}}, volume = 18, year = 2009 } @article{AN:09b, author = {Atzmueller, Martin and Nalepa, Grzegorz J.}, interhash = {c7058098395da81311dcd5bc23f331db}, intrahash = {e35aa5117c5a636c45f980c9bbab2fb3}, journal = {Technical Report 458, Institute of Computer Science, University of Würzburg}, title = {{Towards Rapid Knowledge Capture using Textual Subgroup Mining for Rule Prototyping}}, year = 2009 } @inproceedings{ABP:03, address = {Luzern, Switzerland}, author = {Atzmueller, Martin and Baumeister, Joachim and Puppe, Frank}, booktitle = {Proc. 2nd Conf. Professional Knowledge Management (WM2003)}, interhash = {4d8e15d1a910865cc2e0b4c96e72a60d}, intrahash = {dff1b95ee0b123f588541964cf5c1904}, title = {{Evaluation of two Strategies for Case-Based Diagnosis handling Multiple Faults}}, year = 2003 } @inproceedings{ASBPB:04, author = {Atzmueller, Martin and Shi, Wenqi and Baumeister, Joachim and Puppe, Frank and Barnden, John A.}, booktitle = {Proc. 17th Intl. Florida Artificial Intelligence Research Society Conference 2004 (FLAIRS-2004)}, editor = {Barr, Valerie and Markov, Zdravko}, interhash = {9e4f3354e9cbe98e236c2657ebef689c}, intrahash = {97fecd065b682b474b168f9baeb51121}, pages = {154--159}, publisher = {AAAI Press}, title = {{Case-Based Approaches for Diagnosing Multiple Disorders}}, year = 2004 } @inproceedings{BAP:02, author = {Baumeister, Joachim and Atzmueller, Martin and Puppe, Frank}, booktitle = {Advances in Case-Based Reasoning}, interhash = {bfae82181f17ea7b88fd9d52d9bdd931}, intrahash = {032258a6dda14093880c3c8bff63ac62}, note = {Proc. 6th European Conference on Case-Based Reasoning (ECCBR 2002)}, pages = {28-42}, series = {LNAI}, title = {{Inductive Learning for Case-Based Diagnosis with Multiple Faults}}, volume = 2416, year = 2002 } @inproceedings{ABP:03Score, author = {Atzmueller, Martin and Baumeister, Joachim and Puppe, Frank}, booktitle = {Medical Data Analysis, Proc. 4th Intl. Symposium on Medical Data Analysis (ISMDA 2003), LNCS 2868}, interhash = {5cbf05acbafc5076859768f7de9ef8d3}, intrahash = {5af72bbce786c6101690ac0addae1b50}, pages = {23-30}, title = {{Inductive Learning of Simple Diagnostic Scores}}, year = 2003 } @book{Atzmueller:07, author = {Atzmueller, Martin}, interhash = {cbe5f4fa5eea8677465c6046bf267c4e}, intrahash = {2e7517d39c2152d4f27d6bb6ba60d694}, month = {March}, publisher = {IOS Press}, series = {Dissertations in Artificial Intelligence-Infix (Diski)}, title = {{Knowledge-Intensive Subgroup Mining -- Techniques for Automatic and Interactive Discovery}}, volume = 307, year = 2007 } @article{ABP:05Score, author = {Atzmueller, Martin and Baumeister, Joachim and Puppe, Frank}, interhash = {b00411fb6f0dd77c3ec3c11d46eb73e3}, intrahash = {0b6a82c2ed7a48da6566e32a6eed8d30}, journal = {Artificial Intelligence in Medicine. Special Issue on Intelligent Data Analysis in Medicine}, number = 1, pages = {19--30}, title = {{Semi-Automatic Learning of Simple Diagnostic Scores Utilizing Complexity Measures}}, volume = 37, year = 2006 } @inproceedings{ABP:04, address = {Potsdam, Germany}, author = {Atzmueller, Martin and Baumeister, Joachim and Puppe, Frank}, booktitle = {Proc. 15th Intl. Conference on Applications of Declarative Programming and Knowledge Management (INAP 2004)}, interhash = {24f3dc2569736288fa8b0306e3027781}, intrahash = {d09f6f4325e6e7edbd07d24596916a40}, pages = {203--213}, title = {{Quality Measures for Semi-Automatic Learning of Simple Diagnostic Rule Bases}}, year = 2004 } @inproceedings{APB:04, author = {Atzmueller, Martin and Puppe, Frank and and Hans-Peter Buscher}, booktitle = {Proc. LWA 2004 Workshop, Germany}, interhash = {264a093e8334e6d1914d47c606a87264}, intrahash = {7238e196f4e10da65a5eff7ca245450c}, pages = {117--123}, title = {{Towards Knowledge-Intensive Subgroup Discovery}}, year = 2004 } @inproceedings{PBAHB:05, author = {Puppe, Frank and Buscher, Georg and Atzmueller, Martin and Huettig, Matthias and Buscher, Hans-Peter}, booktitle = {{Professional Knowledge Management}}, interhash = {1832749125ac669565d67fb109e01a4b}, intrahash = {a1107f4eb901bba8e62a8b98f38b906f}, number = 3782, opteditor = {Klaus-Dieter Althoff and Andreas Dengel and Ralph Bergmann and Markus Nick and Thomas Roth-Berghofer}, pages = {319–-329}, series = {LNAI}, title = {{Clinical Experiences with a Knowledge-Based System in Sonography (SonoConsult)}}, year = 2005 } @inproceedings{APB:05a, address = {Edinburgh, Scotland}, author = {Atzmueller, Martin and Puppe, Frank and Buscher, Hans-Peter}, booktitle = {Proc. 19th International Joint Conference on Artificial Intelligence (IJCAI-05)}, interhash = {16411ee398a5fd417482325974b4d90d}, intrahash = {8279c387181f14f0df818f4ea234ea10}, pages = {647--652}, title = {{Exploiting Background Knowledge for Knowledge-Intensive Subgroup Discovery}}, year = 2005 } @inproceedings{APB:05b, address = {Aberdeen, Scotland}, author = {Atzmueller, Martin and Puppe, Frank and Buscher, Hans-Peter}, booktitle = {Proc. 10th International Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-2005)}, interhash = {28d541adcbdd65b028f20a6e0a9cde9d}, intrahash = {a00eca98602c968903b66bb08f2816e3}, pages = {46--51}, title = {{Profiling Examiners using Intelligent Subgroup Mining}}, year = 2005 } @inproceedings{ABP:09, author = {Atzmueller, Martin and Beer, Stephanie and Puppe, Frank}, booktitle = {Proc. 22nd International Florida Artificial Intelligence Research Society Conference (FLAIRS), accepted}, interhash = {ab3138620625200cb818970bc1615925}, intrahash = {5b8c0b2e4bd3d4380591a603b9dccc73}, pages = {372-377}, publisher = {AAAI Press}, title = {{A Data Warehouse-Based Approach for Quality Management, Evaluation and Analysis of Intelligent Systems using Subgroup Mining}}, year = 2009 }