Understanding and Designing Situation-Aware Mobile and Ubiquitous Computing Systems
Using spatial models as a shared common basis of
information about the environment for different kinds of contextaware
systems has been a heavily researched topic in the last years.
Thereby the research focused on how to create, to update, and to
merge spatial models so as to enable highly dynamic, consistent and
coherent spatial models at large scale. In this paper however, we
want to concentrate on how context-aware applications could use this
information so as to adapt their behavior according to the situation
they are in. The main idea is to provide the spatial model
infrastructure with a situation recognition component based on
generic situation templates. A situation template is – as part of a
much larger situation template library – an abstract, machinereadable
description of a certain basic situation type, which could be
used by different applications to evaluate their situation. In this
paper, different theoretical and practical issues – technical, ethical
and philosophical ones – are discussed important for understanding
and developing situation dependent systems based on situation
templates. A basic system design is presented which allows for the
reasoning with uncertain data using an improved version of a
learning algorithm for the automatic adaption of situation templates.
Finally, for supporting the development of adaptive applications, we
present a new situation-aware adaptation concept based on
workflows.
[1] R. Lange, N. Cipriani, L. Geiger, M. Großmann, H. Weinschrott, A.
Brodt, M. Wieland, S. Rizou, and K. Rothermel. Making the world wide
space happen: New challenges for the nexus context platform. In
Proceedings of the 7th Annual IEEE International Conference on
Pervasive Computing and Communications (PerCom 2009), Galveston,
TX, USA, 2009.
[2] M. Großmann, M. Bauer, N. Hoenle, U.-P. Kaeppeler, D. Nicklas, and T.
Schwarz. Efficiently managing context information for large-scale
scenarios. In Proceedings of the 3rd IEEE International Conference on
Pervasive Computing and Communications, 2005.
[3] K. Henricksen and J. Indulska. A software engineering framework for
context-aware pervasive computing. In Proceedings of the 2nd IEEE
International Conference on Pervasive Computing and Communications,
2004.
[4] M. Roman and R. H. Campbell. Gaia: Enabling active spaces. In
Proceedings of the 9th ACM SIGOPS European Workshop, Kolding,
2000.
[5] D. Salber, A., K. Dey, and G. D. Abowd. The Context Toolkit: Aiding
the development of context-enabled applications. In Proceedings of the
SIGCHI Conference on Human Factors in Computing Systems: The CHI
is the Limit, 1999.
[6] A.K. Dey and G. D. Abowd. Towards a better understanding of context
and context-awareness. In Proceedings of the Workshop on The What,
Who, Where, When, and How of Context-Awareness, as part of the
2000 Conference on Human Factors in Computing Systems (CHI 2000),
The Hague, The Netherlands, April 3, 2000.
[7] L. Wetzel. Types and Tokens. In the Stanford Encyclopedia of
Philosophy (Winter 2008 Edition), Edward N. Zalta (ed.),
URL=http://plato.stanford.edu/archives/win2008/entries/types-tokens/
[8] C.S. Peirce. Collected Papers. Harvard University Press.
[9] C. Hubig. Die Kunst des Möglichen. Band 1: Technikphilosophie als
Reflexion der Medialität. Transcript.
[10] C. Hubig. Abduktion - Das implizite Voraussetzen von Regeln. In G.
J├╝ttemann (ed.), Individuelle und soziale Regeln des Handelns, pp. 157-
167, Asanger.
[11] C. Hubig. Interdisziplinarität und Abduktionenwirrwarr. In N.
Gottschalk-Mazouz/N. Mazouz (eds.), Nachhaltigkeit und globaler
Wandel. pp. 319-340. Campus.
[12] G. Schurz. Patterns of Abduction. In Synthese Vol. 164, No. 2, 2008, pp.
201-234
[13] K.R. Popper. The Logic of Scientific Discovery. Routledge.
[14] G.I. Rochlin. Iran Air Flight 655 and the USS Vincent. In T.R. La Porte
(ed.), Social Responses to large scale technical systems. pp. 99-125.
Kluwer.
[15] G.I. Rochlin. Trapped in the net. The unanticipated consequences of
computerization. Princeton University Press.
[16] J Weyer. Die Risiken der Automatisierungsarbeit. Mensch-Maschine-
Interaktion und Störfallmanagement in hochautomatisierten
Verkehrsflugzeugen. In Zeitschrift f├╝r Soziologie, Vol. 25, 1997, pp.
239-257.
[17] C. Hubig. Selbstständige Nutzer oder verselbstständigte Medien. Die
neue Qualität der Vernetzung. In F. Mattern (ed.), Total vernetzt.
Szenarien einer informatisierten Welt. pp. 211-230. Springer.
[18] C. Hubig. Die Kunst des Möglichen. Band 2: Ethik der Technik als
provisorische Moral. Transcript.
[19] G. D. Abowd, A. K. Dey, P. J. Brown, N. Davies, M. Smith, and P.
Steggles. Towards a better understanding of context and
contextawareness. In HUC -99: Proceedings of the 1st international
symposium on Handheld and Ubiquitous Computing, pages 304-307,
London, UK, 1999. Springer-Verlag.
[20] S. McKeever, J. Ye, L. Coyle, and S. Dobson. A context quality model
to support transparent reasoning with uncertain context. In 1st
International Workshop on Quality of Context (QuaCon), Stuttgart,
Germany, 2009.
[21] Zweigle, Oliver; Häussermann, Kai; Käppeler, Uwe-Philipp; Levi, Paul:
Extended TA Algorithm for adapting a Situation Ontology. In:
Proceedings of the FIRA RoboWorld Congress 2009, Progress in
Robotics. Communications in Computer and Information Science; 44,
pp. 364-371, Incheon, Korea: Springer Verlag, August 18, 2009.
[22] de Kleer, J. Using crude probability estimates to guide diagnosis. pages
118-123, 1992.
[23] de Kleer, J. and Williams, B.C. Diagnosing multiple faults. pages 372-
388, 1987.
[24] Mayrhofer, Rene; Radi, Harald and Ferscha, Alois. A.: Recognizing and
predicting context by learning from user behavior. pages 25-35, 2003.
[25] Reiter, R. A theory of diagnosis from first principles. Artif. Intell.,
32(1):57-95,1987.
[26] Hou, Aimin. A theory of measurement in diagnosis from first principles.
Artif.Intell., 65(2):281-328, 1994.
[27] Cheng, R; Prabhakar, S: Managing uncertainty in sensor database.
SIGMOD Rec., 32(4):41-46, 2003.
[28] Chen, H. and Finin, T. and Joshi, A.: An ontology for context-aware
pervasive computing environments, In: The Knowledge Engineering
Review, vol. 18, no 3, pages 197-207, 2004
[29] Wang, X.H. and Zhang, D.Q. and Gu, T. and Pung, H.K.: Ontology
based context modeling and reasoning using OWL, In: Proceedings of
the second IEEE annual conference on pervasive computing and
communications workshops, Vol. 18, 2004
[30] Henricksen, K. and Indulska, J. and Rakotonirainy, A.: Modeling
context information in pervasive computing systems, in: Lecture notes in
computer science, pages 167-180,2002
[31] B. Brumitt, B. Meyers, J. Krumm, A. Kern, and S. Shafer. Easyliving:
Technologies for intelligent environments. pages 12-29. Springer
Verlag, 2000.
[32] S. Pado and M. Lapata. Constructing semantic space models from parsed
corpora, 2003.
[33] J. Pearl. Probabilistic Reasoning in Intelligent Systems : Networks of
Plausible Inference. Morgan Kaufmann, September 1988.
[34] Sowmya Ramachandran, Raymond J. Mooney: Revising Bayesian
Network Parameters Using Backpropagation, IN: Proceedings of the
1996 IEEE International Conference on Neural Networks, pp.82-87,
Washington D.C., June 1996.
[35] R.M. Neal: Connectionist learning of belief networks, In: Artificial
Intelligence, vol. 56, pp. 71-113, 1992.
[36] L.K. Saul, T. Jaakkola, and M.I. Jordan: "Mean field theory for sigmoid
belief networks", Technical Report 9501, Computational Cognitive
Science, MIT, 1995.
[37] W. Buntine: Operations for learning with graphical models, In: Journal
of Artificial Intelligence Research, vol. 2, pp. 159-225, 1994.
[38] M.A. Tanner, Tools for Statistical Inference, Springer-Verlag, New
York, second edition, 1993.
[39] R.E. Kass and A.E. Raftery, \Bayes factors and model uncertainty",
Journal of the American Statistical Association, vol. 90, pp. 773-795,
1995.
[40] D. Edwards, \Hierarchical interaction models", Journal of the Royal
Statistical Society B, vol. 51, no. 3, 1989.
[41] R. Jirousek and S. Preucil: On the effective implementation of the
iterative proportional fitting procedure", Computational Statistics and
Data Analysis, vol. 19, no. 2, pp. 177-189, 1995.
[42] S.L. Lauritzen: The EM algorithm for graphical association models with
missing data, Computational Statistics and Data Analysis, vol. 19, no. 2,
pp. 191-201, 1995.
[43] A.P. Dempster, N.M. Laird, and D.B. Rubin: Maximum likelihood from
incomplete data via the EM algorithm, In: Journal of the Royal
Statistical Society B, vol. 39, pp. 1-38, 1977.
[44] Myers, J.W. and Laskey, K.B. and DeJong, K.A.: Learning bayesian
networks from incomplete data using evolutionary algorithms, In:
Proceedings of the Genetic and Evolutionary Computation Conference,
vol 1,pp:458-465, 1999
[45] Tian, F. and Lu, Y. and Shi, C.: Learning Bayesian networks with
hidden variables using the combination of EM and evolutionary
algorithms,In: Lecture notes in computer science,pp:568-574, 2001
[1] R. Lange, N. Cipriani, L. Geiger, M. Großmann, H. Weinschrott, A.
Brodt, M. Wieland, S. Rizou, and K. Rothermel. Making the world wide
space happen: New challenges for the nexus context platform. In
Proceedings of the 7th Annual IEEE International Conference on
Pervasive Computing and Communications (PerCom 2009), Galveston,
TX, USA, 2009.
[2] M. Großmann, M. Bauer, N. Hoenle, U.-P. Kaeppeler, D. Nicklas, and T.
Schwarz. Efficiently managing context information for large-scale
scenarios. In Proceedings of the 3rd IEEE International Conference on
Pervasive Computing and Communications, 2005.
[3] K. Henricksen and J. Indulska. A software engineering framework for
context-aware pervasive computing. In Proceedings of the 2nd IEEE
International Conference on Pervasive Computing and Communications,
2004.
[4] M. Roman and R. H. Campbell. Gaia: Enabling active spaces. In
Proceedings of the 9th ACM SIGOPS European Workshop, Kolding,
2000.
[5] D. Salber, A., K. Dey, and G. D. Abowd. The Context Toolkit: Aiding
the development of context-enabled applications. In Proceedings of the
SIGCHI Conference on Human Factors in Computing Systems: The CHI
is the Limit, 1999.
[6] A.K. Dey and G. D. Abowd. Towards a better understanding of context
and context-awareness. In Proceedings of the Workshop on The What,
Who, Where, When, and How of Context-Awareness, as part of the
2000 Conference on Human Factors in Computing Systems (CHI 2000),
The Hague, The Netherlands, April 3, 2000.
[7] L. Wetzel. Types and Tokens. In the Stanford Encyclopedia of
Philosophy (Winter 2008 Edition), Edward N. Zalta (ed.),
URL=http://plato.stanford.edu/archives/win2008/entries/types-tokens/
[8] C.S. Peirce. Collected Papers. Harvard University Press.
[9] C. Hubig. Die Kunst des Möglichen. Band 1: Technikphilosophie als
Reflexion der Medialität. Transcript.
[10] C. Hubig. Abduktion - Das implizite Voraussetzen von Regeln. In G.
J├╝ttemann (ed.), Individuelle und soziale Regeln des Handelns, pp. 157-
167, Asanger.
[11] C. Hubig. Interdisziplinarität und Abduktionenwirrwarr. In N.
Gottschalk-Mazouz/N. Mazouz (eds.), Nachhaltigkeit und globaler
Wandel. pp. 319-340. Campus.
[12] G. Schurz. Patterns of Abduction. In Synthese Vol. 164, No. 2, 2008, pp.
201-234
[13] K.R. Popper. The Logic of Scientific Discovery. Routledge.
[14] G.I. Rochlin. Iran Air Flight 655 and the USS Vincent. In T.R. La Porte
(ed.), Social Responses to large scale technical systems. pp. 99-125.
Kluwer.
[15] G.I. Rochlin. Trapped in the net. The unanticipated consequences of
computerization. Princeton University Press.
[16] J Weyer. Die Risiken der Automatisierungsarbeit. Mensch-Maschine-
Interaktion und Störfallmanagement in hochautomatisierten
Verkehrsflugzeugen. In Zeitschrift f├╝r Soziologie, Vol. 25, 1997, pp.
239-257.
[17] C. Hubig. Selbstständige Nutzer oder verselbstständigte Medien. Die
neue Qualität der Vernetzung. In F. Mattern (ed.), Total vernetzt.
Szenarien einer informatisierten Welt. pp. 211-230. Springer.
[18] C. Hubig. Die Kunst des Möglichen. Band 2: Ethik der Technik als
provisorische Moral. Transcript.
[19] G. D. Abowd, A. K. Dey, P. J. Brown, N. Davies, M. Smith, and P.
Steggles. Towards a better understanding of context and
contextawareness. In HUC -99: Proceedings of the 1st international
symposium on Handheld and Ubiquitous Computing, pages 304-307,
London, UK, 1999. Springer-Verlag.
[20] S. McKeever, J. Ye, L. Coyle, and S. Dobson. A context quality model
to support transparent reasoning with uncertain context. In 1st
International Workshop on Quality of Context (QuaCon), Stuttgart,
Germany, 2009.
[21] Zweigle, Oliver; Häussermann, Kai; Käppeler, Uwe-Philipp; Levi, Paul:
Extended TA Algorithm for adapting a Situation Ontology. In:
Proceedings of the FIRA RoboWorld Congress 2009, Progress in
Robotics. Communications in Computer and Information Science; 44,
pp. 364-371, Incheon, Korea: Springer Verlag, August 18, 2009.
[22] de Kleer, J. Using crude probability estimates to guide diagnosis. pages
118-123, 1992.
[23] de Kleer, J. and Williams, B.C. Diagnosing multiple faults. pages 372-
388, 1987.
[24] Mayrhofer, Rene; Radi, Harald and Ferscha, Alois. A.: Recognizing and
predicting context by learning from user behavior. pages 25-35, 2003.
[25] Reiter, R. A theory of diagnosis from first principles. Artif. Intell.,
32(1):57-95,1987.
[26] Hou, Aimin. A theory of measurement in diagnosis from first principles.
Artif.Intell., 65(2):281-328, 1994.
[27] Cheng, R; Prabhakar, S: Managing uncertainty in sensor database.
SIGMOD Rec., 32(4):41-46, 2003.
[28] Chen, H. and Finin, T. and Joshi, A.: An ontology for context-aware
pervasive computing environments, In: The Knowledge Engineering
Review, vol. 18, no 3, pages 197-207, 2004
[29] Wang, X.H. and Zhang, D.Q. and Gu, T. and Pung, H.K.: Ontology
based context modeling and reasoning using OWL, In: Proceedings of
the second IEEE annual conference on pervasive computing and
communications workshops, Vol. 18, 2004
[30] Henricksen, K. and Indulska, J. and Rakotonirainy, A.: Modeling
context information in pervasive computing systems, in: Lecture notes in
computer science, pages 167-180,2002
[31] B. Brumitt, B. Meyers, J. Krumm, A. Kern, and S. Shafer. Easyliving:
Technologies for intelligent environments. pages 12-29. Springer
Verlag, 2000.
[32] S. Pado and M. Lapata. Constructing semantic space models from parsed
corpora, 2003.
[33] J. Pearl. Probabilistic Reasoning in Intelligent Systems : Networks of
Plausible Inference. Morgan Kaufmann, September 1988.
[34] Sowmya Ramachandran, Raymond J. Mooney: Revising Bayesian
Network Parameters Using Backpropagation, IN: Proceedings of the
1996 IEEE International Conference on Neural Networks, pp.82-87,
Washington D.C., June 1996.
[35] R.M. Neal: Connectionist learning of belief networks, In: Artificial
Intelligence, vol. 56, pp. 71-113, 1992.
[36] L.K. Saul, T. Jaakkola, and M.I. Jordan: "Mean field theory for sigmoid
belief networks", Technical Report 9501, Computational Cognitive
Science, MIT, 1995.
[37] W. Buntine: Operations for learning with graphical models, In: Journal
of Artificial Intelligence Research, vol. 2, pp. 159-225, 1994.
[38] M.A. Tanner, Tools for Statistical Inference, Springer-Verlag, New
York, second edition, 1993.
[39] R.E. Kass and A.E. Raftery, \Bayes factors and model uncertainty",
Journal of the American Statistical Association, vol. 90, pp. 773-795,
1995.
[40] D. Edwards, \Hierarchical interaction models", Journal of the Royal
Statistical Society B, vol. 51, no. 3, 1989.
[41] R. Jirousek and S. Preucil: On the effective implementation of the
iterative proportional fitting procedure", Computational Statistics and
Data Analysis, vol. 19, no. 2, pp. 177-189, 1995.
[42] S.L. Lauritzen: The EM algorithm for graphical association models with
missing data, Computational Statistics and Data Analysis, vol. 19, no. 2,
pp. 191-201, 1995.
[43] A.P. Dempster, N.M. Laird, and D.B. Rubin: Maximum likelihood from
incomplete data via the EM algorithm, In: Journal of the Royal
Statistical Society B, vol. 39, pp. 1-38, 1977.
[44] Myers, J.W. and Laskey, K.B. and DeJong, K.A.: Learning bayesian
networks from incomplete data using evolutionary algorithms, In:
Proceedings of the Genetic and Evolutionary Computation Conference,
vol 1,pp:458-465, 1999
[45] Tian, F. and Lu, Y. and Shi, C.: Learning Bayesian networks with
hidden variables using the combination of EM and evolutionary
algorithms,In: Lecture notes in computer science,pp:568-574, 2001
@article{"International Journal of Information, Control and Computer Sciences:55577", author = "Kai Häussermann and Christoph Hubig and Paul Levi and Frank Leymann and Oliver Siemoneit and Matthias Wieland and Oliver Zweigle", title = "Understanding and Designing Situation-Aware Mobile and Ubiquitous Computing Systems", abstract = "Using spatial models as a shared common basis of
information about the environment for different kinds of contextaware
systems has been a heavily researched topic in the last years.
Thereby the research focused on how to create, to update, and to
merge spatial models so as to enable highly dynamic, consistent and
coherent spatial models at large scale. In this paper however, we
want to concentrate on how context-aware applications could use this
information so as to adapt their behavior according to the situation
they are in. The main idea is to provide the spatial model
infrastructure with a situation recognition component based on
generic situation templates. A situation template is – as part of a
much larger situation template library – an abstract, machinereadable
description of a certain basic situation type, which could be
used by different applications to evaluate their situation. In this
paper, different theoretical and practical issues – technical, ethical
and philosophical ones – are discussed important for understanding
and developing situation dependent systems based on situation
templates. A basic system design is presented which allows for the
reasoning with uncertain data using an improved version of a
learning algorithm for the automatic adaption of situation templates.
Finally, for supporting the development of adaptive applications, we
present a new situation-aware adaptation concept based on
workflows.", keywords = "context-awareness, ethics, facilitation of system use through workflows, situation recognition and learning based on situation templates and situation ontology's, theory of situationaware systems", volume = "4", number = "3", pages = "459-10", }
