Semantic sensor web data exploration and visualization for intelligent decision support: position paper
Article No.: 44, Pages 1 - 4
Abstract
To date, Semantic Sensor Web research and development has focused on establishing common techniques and practices that homogenize how to discover sensors, collect their data, integrate them, extract information from them, etc. However, as these issues are overcome and huge data bases of sensor data begin to emerge, the focus should change to improve the data management and the information overload, discarding the non relevant information from the relevant one, and on the other hand, allow easy and intuitive navigation through it. The objective is to move up the wisdom hierarchy and empower users so they can start discovering new relevant knowledge and making decissions based on that. In this position paper, we start drafting an architecture, aligned with current practices and standards, which facilitates the whole process: from data collecting and storing, to wisdom generation and navigation. Efforts will focus on empower users to spot trends or events in data. Moreover, the system will learn from the discoveries made by users so it can later automatise the detection of similar situations and integrate users wisdom.
References
[1]
P. Barnaghi, W. Wang, C. Henson, and K. Taylor. Semantics for the internet of things. International Journal on Semantic Web and Information Systems, 8(1):1--21, 2012.
[2]
C. Bizer, T. Heath, and T. Berners-Lee. Linked data - the story so far. International Journal on Semantic Web and Information Systems, 5(3):1--22, 2009.
[3]
J. Bock, U. Lösch, and H. Wang. Automatic reasoner selection using machine learning. In Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, WIMS '12, pages 23:1--23:12, New York, NY, USA, 2012. ACM.
[4]
J. M. Brunetti, R. Gil, and R. García. Facets and pivoting for flexible and usable linked data exploration. In Interacting with Linked Data Workshop, ILD'12, volume 913, pages 22--35, Heraklion, Crete, Greece, 2012. CEUR Workshop Proceedings.
[5]
A. Bröring, J. Echterhoff, S. Jirka, I. Simonis, T. Everding, C. Stasch, S. Liang, and R. Lemmens. New generation sensor web enablement. Sensors, 11(3):2652--2699, Mar. 2011.
[6]
M. BĂdescu. Visualization of the european environmental data. In Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, WIMS '12, pages 53:1--53:4, New York, NY, USA, 2012. ACM.
[7]
M. Compton, P. Barnaghi, L. Bermudez, R. García-Castro, O. Corcho, S. Cox, J. Graybeal, M. Hauswirth, C. Henson, A. Herzog, V. Huang, K. Janowicz, W. D. Kelsey, D. Le Phuoc, L. Lefort, M. Leggieri, H. Neuhaus, A. Nikolov, K. Page, A. Passant, A. Sheth, and K. Taylor. The SSN ontology of the W3C semantic sensor network incubator group. Web Semantics: Science, Services and Agents on the World Wide Web, 2012.
[8]
O. Corcho and R. García-Castro. Five challenges for the semantic sensor web. Semantic Web Journal, 1(1,2):121--125, 2010.
[9]
C. Henson, A. Sheth, and K. Thirunarayan. Semantic perception: Converting sensory observations to abstractions. IEEE Internet Computing, 16(2):26--34, Apr. 2012.
[10]
D. Le-Phuoc, J. Xavier Parreira, and M. Hauswirth. Linked stream data processing. In D. Hutchison, T. Kanade, J. Kittler, J. M. Kleinberg, F. Mattern, J. C. Mitchell, M. Naor, O. Nierstrasz, C. Pandu Rangan, B. Steffen, M. Sudan, D. Terzopoulos, D. Tygar, M. Y. Vardi, G. Weikum, T. Eiter, and T. Krennwallner, editors, Reasoning Web. Semantic Technologies for Advanced Query Answering, volume 7487, pages 245--289. Springer Berlin Heidelberg, Berlin, Heidelberg, 2012.
[11]
T. Myers and I. Atkinson. Eco-informatics modelling via semantic inference. Information Systems, 38(1):16--32, Mar. 2013.
Index Terms
- Semantic sensor web data exploration and visualization for intelligent decision support: position paper
Recommendations
Multi Visualization and Dynamic Query for Effective Exploration of Semantic Data
ISWC '09: Proceedings of the 8th International Semantic Web ConferenceSemantic formalisms represent content in a uniform way according to ontologies. This enables manipulation and reasoning via automated means (e.g. Semantic Web services), but limits the user's ability to explore the semantic data from a point of view ...
Design and evaluation of overview components for effective semantic data exploration
WIMS '13: Proceedings of the 3rd International Conference on Web Intelligence, Mining and SemanticsThe growing volumes of semantic data available in the Web result in the need for handling the Information Overload phenomenon. The potential of this amount of data is enormous but in most cases it is very difficult for users to visualize, explore and ...
Using the Rhizomer Platform for Semantic Decision Support Systems Development
Decision support systems get more useful as they manage to make decisions more informed. However, the cost of information and of combining and making it available in the appropriate context make this a tricky trade-off. Fortunately, Semantic Web ...
Comments
Information & Contributors
Information
Published In
June 2013
408 pages
ISBN:9781450318501
DOI:10.1145/2479787
- Conference Chair:
- David Camacho,
- Program Chairs:
- Rajendra Akerkar,
- Maria D. Rodriguez Moreno
Copyright © 2013 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
- UAM: Autonomous University of Madrid
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 12 June 2013
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- Spanish Government
Conference
WIMS '13
Sponsor:
- UAM
WIMS '13: 3rd International Conference on Web Intelligence, Mining and Semantics
June 12 - 14, 2013
Madrid, Spain
Acceptance Rates
WIMS '13 Paper Acceptance Rate 28 of 72 submissions, 39%;
Overall Acceptance Rate 140 of 278 submissions, 50%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 182Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)0
Reflects downloads up to 23 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in