@article {224,
	title = {Editorial preface: Special issue on context-aware and mobile multimedia databases and services},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Export Date: 10 July 2014},
	month = {2010},
	pages = {2 - 3},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84861465089\&partnerID=40\&md5=7b82f006c7ffa103ca31e784f7cd8bf8},
	author = {Grigoras, R. and Charvillat, V. and Chbeir, R. and Kosch, H. and Bailer, W. and Boll, S. and Brunie, L. and Cao, Y. and Delgado, J. and Granitzer, M. and Hausenblas, M. and Eidenberger, H. and Klamma, R. and Lux, M. and Marques, O. and Oria, V. and Plesca, C. and Pr{\'e}teux, F. and Sedes, F. and Spaniol, M. and Ternier, S. and Wolpers, M.}
}
@article {175,
	title = {Distributed Web 2.0 crawling for ontology evolution},
	journal = {Journal of Digital Information Management},
	volume = {7},
	year = {2009},
	note = {Export Date: 10 July 2014},
	month = {2009},
	pages = {114 - 119},
	abstract = {The World Wide Web as a social network reflects changes of interest in certain domains. It has been shown that free online content available through blogs, wikis, news media and online forums is a valuable source of information to identify trends in certain domains. Utilizing this data, one can construct ontologies that describe this information and provide a semantically correct overview of a domain. Tracked over time this also enables a user to identify trends and hypes. The decentralised structure of the Internet, the huge amount of data and upcoming Web2.0 technologies pose several challenges to a crawling system for ontology learning, evolution and trend analysis. This paper presents a distributed crawling system with browser integration for Web2.0. The proposed crawler is a high performance Web data retrieval system aimed to gather browser-equivalent textual Web content and prepare it for ontology learning.},
	keywords = {Distributed crawling, Executed crawling, Ontology evolution, Ontology learning, Web 2.0 crawling},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-70350630600\&partnerID=40\&md5=7b88c38575eca72b620a0a6e113a8f55},
	author = {Juffinger, A. and Neidhart, T. and Granitzer, M. and Kern, R. and Weichselbraun, A. and Wohlgenannt, G. and Scharl, A.}
}
@article {197,
	title = {Machine learning based work task classification},
	journal = {Journal of Digital Information Management},
	volume = {7},
	year = {2009},
	note = {Cited By (since 1996):6Export Date: 10 July 2014},
	month = {2009},
	pages = {305 - 312},
	abstract = {Increasing the productivity of a knowledge worker via intelligent applications requires the identification of a user{\textquoteright}s current work task, i.e. the current work context a user resides in. In this work we present and evaluate machine learning based work task detection methods. By viewing a work task as sequence of digital interaction patterns of mouse clicks and key strokes, we present (i) a methodology for recording those user interactions and (ii) an in-depth analysis of supervised classification models for classifying work tasks in two different scenarios: a task centric scenario and a user centric scenario. We analyze different supervised classification models, feature types and feature selection methods on a laboratory as well as a real world data set. Results show satisfiable accuracy and high user acceptance by using relatively simple types of features.},
	keywords = {Data model, Data structure, Work task classification user interaction},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77953256434\&partnerID=40\&md5=ad1b8dc83a81694afe23a15ad90c66af},
	author = {Granitzer, M. and Rath, A.S. and Kr{\"o}ll, M. and Seifert, C. and Ipsmiller, D. and Devaurs, D. and Weber, N. and Lindstaedt, S.}
}
@article {147,
	title = {A generic framework for visualizing the news article domain and its application to real-world data},
	journal = {Journal of Digital Information Management},
	volume = {6},
	year = {2008},
	note = {Cited By (since 1996):3Export Date: 10 July 2014},
	month = {2008},
	pages = {434 - 441},
	abstract = {In this work we present APA Labs, a generic framework for visualizing the news article domain. APA Labs is a web-based platform enabling retrieval and analysis of news repositories provided by the Austrian Press Agency. APA Labs is designed as a rich internet application combined with a modular system of interactive visualizations. News articles are analyzed using domain specific named entity extraction methods combined with language specific heuristics. The proposed methods were subject to an evaluation procedure outlined in this contribution. This article illustrates the domain, the underlying concepts and implementation details. Several visualization modules are presented and an outlook on planned modules is given. Being online for around six months the community feedback as well as the easy integration of new modules shows the success of the underlying concept and the platform itself.},
	keywords = {Business process, Collaboration, Email, Interoperability, SME},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77950335223\&partnerID=40\&md5=e07a8280eb1e93146ec05ab62b545f5a},
	author = {Lex, E. and Seifert, C. and Kienreich, W. and Granitzer, M.}
}
