88 research outputs found

    Using Visualization to Support Data Mining of Large Existing Databases

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    In this paper. we present ideas how visualization technology can be used to improve the difficult process of querying very large databases. With our VisDB system, we try to provide visual support not only for the query specification process. but also for evaluating query results and. thereafter, refining the query accordingly. The main idea of our system is to represent as many data items as possible by the pixels of the display device. By arranging and coloring the pixels according to the relevance for the query, the user gets a visual impression of the resulting data set and of its relevance for the query. Using an interactive query interface, the user may change the query dynamically and receives immediate feedback by the visual representation of the resulting data set. By using multiple windows for different parts of the query, the user gets visual feedback for each part of the query and, therefore, may easier understand the overall result. To support complex queries, we introduce the notion of approximate joins which allow the user to find data items that only approximately fulfill join conditions. We also present ideas how our technique may be extended to support the interoperation of heterogeneous databases. Finally, we discuss the performance problems that are caused by interfacing to existing database systems and present ideas to solve these problems by using data structures supporting a multidimensional search of the database

    Self-adaptive unobtrusive interactions of mobile computing systems

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    [EN] In Pervasive Computing environments, people are surrounded by a lot of embedded services. Since pervasive devices, such as mobile devices, have become a key part of our everyday life, they enable users to always be connected to the environment, making demands on one of the most valuable resources of users: human attention. A challenge of the mobile computing systems is regulating the request for usersĀæ attention. In other words, service interactions should behave in a considerate manner by taking into account the degree to which each service intrudes on the userĀæs mind (i.e., the degree of obtrusiveness). The main goal of this paper is to introduce self-adaptive capabilities in mobile computing systems in order to provide non-disturbing interactions. We achieve this by means of an software infrastructure that automatically adapts the service interaction obtrusiveness according to the userĀæs context. This infrastructure works from a set of high-level models that define the unobtrusive adaptation behavior and its implication with the interaction resources in a technology-independent way. Our infrastructure has been validated through several experiments to assess its correctness, performance, and the achieved user experience through a user study.This work has been developed with the support of MINECO under the project SMART-ADAPT TIN2013-42981-P, and co-financed by the Generalitat Valenciana under the postdoctoral fellowship APOSTD/2016/042.Gil Pascual, M.; Pelechano Ferragud, V. (2017). Self-adaptive unobtrusive interactions of mobile computing systems. Journal of Ambient Intelligence and Smart Environments. 9(6):659-688. https://doi.org/10.3233/AIS-170463S65968896Aleksy, M., Butter, T., & Schader, M. (2008). Context-Aware Loading for Mobile Applications. 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Foundations of multimodal representations: a taxonomy of representational modalities. Interacting with Computers, 6(4), 347-371. doi:10.1016/0953-5438(94)90008-6Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., & Riboni, D. (2010). A survey of context modelling and reasoning techniques. Pervasive and Mobile Computing, 6(2), 161-180. doi:10.1016/j.pmcj.2009.06.002Blumendorf, M., Lehmann, G., & Albayrak, S. (2010). Bridging models and systems at runtime to build adaptive user interfaces. Proceedings of the 2nd ACM SIGCHI symposium on Engineering interactive computing systems - EICS ā€™10. doi:10.1145/1822018.1822022D.M.Ā Brown, Communicating Design: Developing Web Site Documentation for Design and Planning, 2nd edn, New Riders Press, 2010.J.Ā Bruin, Statistical Analyses Using SPSS, 2011, http://www.ats.ucla.edu/stat/spss/whatstat/whatstat.htm#1sampt.J.Ā CĆ”mara, G.Ā Moreno and D.Ā Garlan, Reasoning about human participation in self-adaptive systems, in: SEAMS 2015, 2015, pp.Ā 146ā€“156.Campbell, A., & Choudhury, T. (2012). From Smart to Cognitive Phones. IEEE Pervasive Computing, 11(3), 7-11. doi:10.1109/mprv.2012.41Y.Ā Cao, M.Ā Theune and A.Ā Nijholt, Modality effects on cognitive load and performance in high-load information presentation, in: Proceedings of the 14th International Conference on Intelligent User Interfaces, IUIā€™09, ACM, New York, 2009, pp.Ā 335ā€“344.Chang, F., & Ren, J. (2007). Validating system properties exhibited in execution traces. Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering - ASE ā€™07. doi:10.1145/1321631.1321723H.Ā Chen and J.P.Ā Black, AĀ quantitative approach to non-intrusive computing, in: Mobiquitousā€™08: Proceedings of the 5th Annual International Conference on Mobile and Ubiquitous Systems, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), ICST, Brussels, 2008, pp.Ā 1ā€“10.Chittaro, L. (2010). Distinctive aspects of mobile interaction and their implications forĀ theĀ design of multimodal interfaces. Journal on Multimodal User Interfaces, 3(3), 157-165. doi:10.1007/s12193-010-0036-2Clerckx, T., Vandervelpen, C., & Coninx, K. (2008). Task-Based Design and Runtime Support for Multimodal User Interface Distribution. Lecture Notes in Computer Science, 89-105. doi:10.1007/978-3-540-92698-6_6Cook, D. J., & Das, S. K. (2012). Pervasive computing at scale: Transforming the state of the art. Pervasive and Mobile Computing, 8(1), 22-35. doi:10.1016/j.pmcj.2011.10.004Cornelissen, B., Zaidman, A., van Deursen, A., Moonen, L., & Koschke, R. (2009). A Systematic Survey of Program Comprehension through Dynamic Analysis. IEEE Transactions on Software Engineering, 35(5), 684-702. doi:10.1109/tse.2009.28Czarnecki, K. (2004). Generative Software Development. Lecture Notes in Computer Science, 321-321. doi:10.1007/978-3-540-28630-1_33M.Ā deĀ SĆ”, C.Ā Duarte, L.Ā CarriƧo and T.Ā Reis, Designing mobile multimodal applications, in: Information Science Reference, 2010, pp.Ā 106ā€“136, ChapterĀ 5.C.Ā Duarte and L.Ā CarriƧo, AĀ conceptual framework for developing adaptive multimodal applications, in: Proceedings of the 11th International Conference on Intelligent User Interfaces, IUIā€™06, ACM, New York, 2006, pp.Ā 132ā€“139.Evers, C., Kniewel, R., Geihs, K., & Schmidt, L. (2014). The user in the loop: Enabling user participation for self-adaptive applications. Future Generation Computer Systems, 34, 110-123. doi:10.1016/j.future.2013.12.010Fagin, R., Halpern, J. Y., & Megiddo, N. (1990). A logic for reasoning about probabilities. Information and Computation, 87(1-2), 78-128. doi:10.1016/0890-5401(90)90060-uFerscha, A. (2012). 20 Years Past Weiser: Whatā€™s Next? IEEE Pervasive Computing, 11(1), 52-61. doi:10.1109/mprv.2011.78Floch, J., FrĆ , C., Fricke, R., Geihs, K., Wagner, M., Lorenzo, J., ā€¦ Scholz, U. (2012). Playing MUSIC - building context-aware and self-adaptive mobile applications. Software: Practice and Experience, 43(3), 359-388. doi:10.1002/spe.2116Gibbs, W. W. (2005). Considerate Computing. Scientific American, 292(1), 54-61. doi:10.1038/scientificamerican0105-54Gil, M., Giner, P., & Pelechano, V. (2011). Personalization for unobtrusive service interaction. Personal and Ubiquitous Computing, 16(5), 543-561. doi:10.1007/s00779-011-0414-0Gil Pascual, M. (s.Ā f.). Adapting Interaction Obtrusiveness: Making Ubiquitous Interactions Less Obnoxious. A Model Driven Engineering approach. doi:10.4995/thesis/10251/31660Haapalainen, E., Kim, S., Forlizzi, J. F., & Dey, A. K. (2010). Psycho-physiological measures for assessing cognitive load. Proceedings of the 12th ACM international conference on Ubiquitous computing - Ubicomp ā€™10. doi:10.1145/1864349.1864395Hallsteinsen, S., Geihs, K., Paspallis, N., Eliassen, F., Horn, G., Lorenzo, J., ā€¦ Papadopoulos, G. A. (2012). A development framework and methodology for self-adapting applications in ubiquitous computing environments. Journal of Systems and Software, 85(12), 2840-2859. doi:10.1016/j.jss.2012.07.052Hassenzahl, M. (2004). The Interplay of Beauty, Goodness, and Usability in Interactive Products. Human-Computer Interaction, 19(4), 319-349. doi:10.1207/s15327051hci1904_2Hassenzahl, M., & Tractinsky, N. (2006). User experienceĀ -Ā a research agenda. Behaviour & Information Technology, 25(2), 91-97. doi:10.1080/01449290500330331Ho, J., & Intille, S. S. (2005). Using context-aware computing to reduce the perceived burden of interruptions from mobile devices. Proceedings of the SIGCHI conference on Human factors in computing systems - CHI ā€™05. doi:10.1145/1054972.1055100Horvitz, E., Kadie, C., Paek, T., & Hovel, D. (2003). Models of attention in computing and communication. Communications of the ACM, 46(3), 52. doi:10.1145/636772.636798Horvitz, E., Koch, P., Sarin, R., Apacible, J., & Subramani, M. (2005). Bayesphone: Precomputation of Context-Sensitive Policies for Inquiry and Action in Mobile Devices. Lecture Notes in Computer Science, 251-260. doi:10.1007/11527886_33Kephart, J. O., & Chess, D. M. (2003). The vision of autonomic computing. Computer, 36(1), 41-50. doi:10.1109/mc.2003.1160055Korpipaa, P., Malm, E.-J., Rantakokko, T., Kyllonen, V., Kela, J., Mantyjarvi, J., ā€¦ Kansala, I. (2006). Customizing User Interaction in Smart Phones. IEEE Pervasive Computing, 5(3), 82-90. doi:10.1109/mprv.2006.49S.Ā LemmelƤ, A.Ā Vetek, K.Ā MƤkelƤ and D.Ā Trendafilov, Designing and evaluating multimodal interaction for mobile contexts, in: Proceedings of the 10th International Conference on Multimodal Interfaces, ICMIā€™08, ACM, New York, 2008, pp.Ā 265ā€“272.Lim, B. Y. (2010). Improving trust in context-aware applications with intelligibility. Proceedings of the 12th ACM international conference adjunct papers on Ubiquitous computing - Ubicomp ā€™10. doi:10.1145/1864431.1864491J.-Y.Ā Mao, K.Ā Vredenburg, P.W.Ā Smith and T.Ā Carey, User-centered design methods in practice: A survey of the state of the art, in: Proceedings of the 2001 Conference of the Centre for Advanced Studies on Collaborative Research, CASCONā€™01, IBM Press, 2001, p.Ā 12.Maoz, S. (2009). Using Model-Based Traces as Runtime Models. Computer, 42(10), 28-36. doi:10.1109/mc.2009.336Mayer, R. E., & Moreno, R. (2003). Nine Ways to Reduce Cognitive Load in Multimedia Learning. Educational Psychologist, 38(1), 43-52. doi:10.1207/s15326985ep3801_6Motti, V. G., & Vanderdonckt, J. (2013). A computational framework for context-aware adaptation of user interfaces. IEEE 7th International Conference on Research Challenges in Information Science (RCIS). doi:10.1109/rcis.2013.6577709R.Ā Murch, Autonomic Computing, IBM Press, 2004.Obrenovic, Z., Abascal, J., & Starcevic, D. (2007). Universal accessibility as a multimodal design issue. Communications of the ACM, 50(5), 83-88. doi:10.1145/1230819.1241668Patterson, D. J., Baker, C., Ding, X., Kaufman, S. J., Liu, K., & Zaldivar, A. (2008). Online everywhere. Proceedings of the 10th international conference on Ubiquitous computing - UbiComp ā€™08. doi:10.1145/1409635.1409645Pielot, M., de Oliveira, R., Kwak, H., & Oliver, N. (2014). Didnā€™t you see my message? Proceedings of the 32nd annual ACM conference on Human factors in computing systems - CHI ā€™14. doi:10.1145/2556288.2556973Poppinga, B., Heuten, W., & Boll, S. (2014). Sensor-Based Identification of Opportune Moments for Triggering Notifications. IEEE Pervasive Computing, 13(1), 22-29. doi:10.1109/mprv.2014.15S.Ā Ramchurn, B.Ā Deitch, M.Ā Thompson, D.Ā De Roure, N.Ā Jennings and M.Ā Luck, Minimising intrusiveness in pervasive computing environments using multi-agent negotiation, in: Mobile and Ubiquitous Systems: Networking and Services, MOBIQUITOUS 2004. The First Annual International Conference on, 2004, pp.Ā 364ā€“371.C.Ā Roda, Human Attention and Its Implications for Human-Computer Interaction, Cambridge University Press, 2011.S.Ā Rosenthal, A.K.Ā Dey and M.Ā Veloso, Using decision-theoretic experience sampling to build personalized mobile phone interruption models, in: Proceedings of the 9th International Conference on Pervasive Computing, Pervasive 2011, Springer-Verlag, Berlin, 2011, pp.Ā 170ā€“187.E.Ā Rukzio, K.Ā Leichtenstern and V.Ā Callaghan, An experimental comparison of physical mobile interaction techniques: Touching, pointing and scanning, in: 8th International Conference on Ubiquitous Computing, UbiComp 2006, Orange County, California, 2006.Serral, E., Valderas, P., & Pelechano, V. (2010). Towards the Model Driven Development of context-aware pervasive systems. Pervasive and Mobile Computing, 6(2), 254-280. doi:10.1016/j.pmcj.2009.07.006D.Ā Siewiorek, A.Ā Smailagic, J.Ā Furukawa, A.Ā Krause, N.Ā Moraveji, K.Ā Reiger, J.Ā Shaffer and F.L.Ā Wong, Sensay: A context-aware mobile phone, in: Proceedings of the 7th IEEE International Symposium on Wearable Computers, ISWCā€™03, IEEE Computer Society, Washington, 2003, p.Ā 248.Tedre, M. (2006). What should be automated? Proceedings of the 1st ACM international workshop on Human-centered multimedia - HCM ā€™06. doi:10.1145/1178745.1178753M.Ā Valtonen, A.-M.Ā Vainio and J.Ā Vanhala, Proactive and adaptive fuzzy profile control for mobile phones, in: IEEE International Conference on Pervasive Computing and Communications, 2009, PerCom, 2009, pp.Ā 1ā€“3.Vastenburg, M. H., Keyson, D. V., & de Ridder, H. (2007). Considerate home notification systems: a field study of acceptability of notifications in the home. Personal and Ubiquitous Computing, 12(8), 555-566. doi:10.1007/s00779-007-0176-xWarnock, D., McGee-Lennon, M., & Brewster, S. (2011). The Role of Modality in Notification Performance. Lecture Notes in Computer Science, 572-588. doi:10.1007/978-3-642-23771-3_43Weiser, M., & Brown, J. S. (1997). The Coming Age of Calm Technology. Beyond Calculation, 75-85. doi:10.1007/978-1-4612-0685-9_6Van Woensel, W., Gil, M., Casteleyn, S., Serral, E., & Pelechano, V. (2013). Adapting the Obtrusiveness of Service Interactions in Dynamically Discovered Environments. Mobile and Ubiquitous Systems: Computing, Networking, and Services, 250-262. doi:10.1007/978-3-642-40238-8_2

    Ontology Engineering: a Survey and a Return on Experience

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    Ontology is a new object of IA that recently came to maturity and a powerful conceptual tool of Knowledge Modeling. It provides a coherent base to build on, and a shared reference to align with, in the form of a consensual conceptual vocabulary, on which one can build descriptions and communication acts. This report presents the object that is called "an ontology" and a state of the art of engineering techniques for ontologies. Then it describes a project for which we developed an ontology and used it to improve knowledge management. Finally it describes the design process and discuss the resulting ontology

    24th International Conference on Information Modelling and Knowledge Bases

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    In the last three decades information modelling and knowledge bases have become essentially important subjects not only in academic communities related to information systems and computer science but also in the business area where information technology is applied. The series of European ā€“ Japanese Conference on Information Modelling and Knowledge Bases (EJC) originally started as a co-operation initiative between Japan and Finland in 1982. The practical operations were then organised by professor Ohsuga in Japan and professors Hannu Kangassalo and Hannu Jaakkola in Finland (Nordic countries). Geographical scope has expanded to cover Europe and also other countries. Workshop characteristic - discussion, enough time for presentations and limited number of participants (50) / papers (30) - is typical for the conference. Suggested topics include, but are not limited to: 1. Conceptual modelling: Modelling and specification languages; Domain-specific conceptual modelling; Concepts, concept theories and ontologies; Conceptual modelling of large and heterogeneous systems; Conceptual modelling of spatial, temporal and biological data; Methods for developing, validating and communicating conceptual models. 2. Knowledge and information modelling and discovery: Knowledge discovery, knowledge representation and knowledge management; Advanced data mining and analysis methods; Conceptions of knowledge and information; Modelling information requirements; Intelligent information systems; Information recognition and information modelling. 3. Linguistic modelling: Models of HCI; Information delivery to users; Intelligent informal querying; Linguistic foundation of information and knowledge; Fuzzy linguistic models; Philosophical and linguistic foundations of conceptual models. 4. Cross-cultural communication and social computing: Cross-cultural support systems; Integration, evolution and migration of systems; Collaborative societies; Multicultural web-based software systems; Intercultural collaboration and support systems; Social computing, behavioral modeling and prediction. 5. Environmental modelling and engineering: Environmental information systems (architecture); Spatial, temporal and observational information systems; Large-scale environmental systems; Collaborative knowledge base systems; Agent concepts and conceptualisation; Hazard prediction, prevention and steering systems. 6. Multimedia data modelling and systems: Modelling multimedia information and knowledge; Contentbased multimedia data management; Content-based multimedia retrieval; Privacy and context enhancing technologies; Semantics and pragmatics of multimedia data; Metadata for multimedia information systems. Overall we received 56 submissions. After careful evaluation, 16 papers have been selected as long paper, 17 papers as short papers, 5 papers as position papers, and 3 papers for presentation of perspective challenges. We thank all colleagues for their support of this issue of the EJC conference, especially the program committee, the organising committee, and the programme coordination team. The long and the short papers presented in the conference are revised after the conference and published in the Series of ā€œFrontiers in Artificial Intelligenceā€ by IOS Press (Amsterdam). The books ā€œInformation Modelling and Knowledge Basesā€ are edited by the Editing Committee of the conference. We believe that the conference will be productive and fruitful in the advance of research and application of information modelling and knowledge bases. Bernhard Thalheim Hannu Jaakkola Yasushi Kiyok

    The Sixth Annual Workshop on Space Operations Applications and Research (SOAR 1992)

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    This document contains papers presented at the Space Operations, Applications, and Research Symposium (SOAR) hosted by the U.S. Air Force (USAF) on 4-6 Aug. 1992 and held at the JSC Gilruth Recreation Center. The symposium was cosponsored by the Air Force Material Command and by NASA/JSC. Key technical areas covered during the symposium were robotic and telepresence, automation and intelligent systems, human factors, life sciences, and space maintenance and servicing. The SOAR differed from most other conferences in that it was concerned with Government-sponsored research and development relevant to aerospace operations. The symposium's proceedings include papers covering various disciplines presented by experts from NASA, the USAF, universities, and industry

    Hypertext Semiotics in the Commercialized Internet

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    Die Hypertext Theorie verwendet die selbe Terminologie, welche seit Jahrzehnten in der semiotischen Forschung untersucht wird, wie z.B. Zeichen, Text, Kommunikation, Code, Metapher, Paradigma, Syntax, usw. Aufbauend auf jenen Ergebnissen, welche in der Anwendung semiotischer Prinzipien und Methoden auf die Informatik erfolgreich waren, wie etwa Computer Semiotics, Computational Semiotics und Semiotic Interface Engineering, legt diese Dissertation einen systematischen Ansatz fĆ¼r all jene Forscher dar, die bereit sind, Hypertext aus einer semiotischen Perspektive zu betrachten. Durch die VerknĆ¼pfung existierender Hypertext-Modelle mit den Resultaten aus der Semiotik auf allen Sinnesebenen der textuellen, auditiven, visuellen, taktilen und geruchlichen Wahrnehmung skizziert der Autor Prolegomena einer Hypertext-Semiotik-Theorie, anstatt ein vƶllig neues Hypertext-Modell zu prƤsentieren. Eine EinfĆ¼hrung in die Geschichte der Hypertexte, von ihrer Vorgeschichte bis zum heutigen Entwicklungsstand und den gegenwƤrtigen Entwicklungen im kommerzialisierten World Wide Web bilden den Rahmen fĆ¼r diesen Ansatz, welcher als Fundierung des BrĆ¼ckenschlages zwischen Mediensemiotik und Computer-Semiotik angesehen werden darf. WƤhrend Computer-Semiotiker wissen, dass der Computer eine semiotische Maschine ist und Experten der kĆ¼nstlichen Intelligenz-Forschung die Rolle der Semiotik in der Entwicklung der nƤchsten Hypertext-Generation betonen, bedient sich diese Arbeit einer breiteren methodologischen Basis. Dementsprechend reichen die Teilgebiete von Hypertextanwendungen, -paradigmen, und -strukturen, Ć¼ber Navigation, Web Design und Web Augmentation zu einem interdisziplinƤren Spektrum detaillierter Analysen, z.B. des Zeigeinstrumentes der Web Browser, des Klammeraffen-Zeichens und der sogenannten Emoticons. Die Bezeichnung ''Icon'' wird als unpassender Name fĆ¼r jene Bildchen, welche von der graphischen BenutzeroberflƤche her bekannt sind und in Hypertexten eingesetzt werden, zurĆ¼ckgewiesen und diese Bildchen durch eine neue Generation mƤchtiger Graphic Link Markers ersetzt. Diese Ergebnisse werden im Kontext der Kommerzialisierung des Internet betrachtet. Neben der Identifizierung der Hauptprobleme des eCommerce aus der Perspektive der Hypertext Semiotik, widmet sich der Autor den InformationsgĆ¼tern und den derzeitigen Hindernissen fĆ¼r die New Economy, wie etwa der restriktiven Gesetzeslage in Sachen Copyright und Intellectual Property. Diese anachronistischen BeschrƤnkungen basieren auf der problematischen Annahme, dass auch der Informationswert durch die Knappheit bestimmt wird. Eine semiotische Analyse der iMarketing Techniken, wie z.B. Banner Werbung, Keywords und Link Injektion, sowie Exkurse Ć¼ber den Browser Krieg und den Toywar runden die Dissertation ab

    Content And Multimedia Database Management Systems

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    A database management system is a general-purpose software system that facilitates the processes of defining, constructing, and manipulating databases for various applications. The main characteristic of the ā€˜database approachā€™ is that it increases the value of data by its emphasis on data independence. DBMSs, and in particular those based on the relational data model, have been very successful at the management of administrative data in the business domain. This thesis has investigated data management in multimedia digital libraries, and its implications on the design of database management systems. The main problem of multimedia data management is providing access to the stored objects. The content structure of administrative data is easily represented in alphanumeric values. Thus, database technology has primarily focused on handling the objectsā€™ logical structure. In the case of multimedia data, representation of content is far from trivial though, and not supported by current database management systems
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