Algorithms for clustering data
Algorithms for clustering data
HDM—a model-based approach to hypertext application design
ACM Transactions on Information Systems (TOIS)
RMM: a methodology for structured hypermedia design
Communications of the ACM
The object-oriented hypermedia design model
Communications of the ACM
Communications of the ACM
A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
Data mining: concepts and techniques
Data mining: concepts and techniques
From adaptive hypermedia to the adaptive web
Communications of the ACM - The Adaptive Web
Improving web performance by client characterization driven server adaptation
Proceedings of the 11th international conference on World Wide Web
Designing Data-Intensive Web Applications
Designing Data-Intensive Web Applications
Adaptive Hypermedia: An Attempt to Analyze and Generalize
MHVR '94 Selected papers from the First International Conference on Hypermedia, Multimedia, and Virtual Reality: Models, Systems, and Applications
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Modelling Ubiquitous Web Applications - The WUML Approach
Revised Papers from the HUMACS, DASWIS, ECOMO, and DAMA on ER 2001 Workshops
Using Clustering Algorithms in Legacy Systems Remodularization
WCRE '97 Proceedings of the Fourth Working Conference on Reverse Engineering (WCRE '97)
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A Survey of Adaptive Applications in Mobile Computing
ICDCSW '01 Proceedings of the 21st International Conference on Distributed Computing Systems
OOHDM-Web: an environment for implementation of hypermedia applications in the WWW
ACM SIGWEB Newsletter
Design time support for adaptive behavior in Web sites
Proceedings of the 2003 ACM symposium on Applied computing
An Efficient and Scalable Algorithm for Clustering XML Documents by Structure
IEEE Transactions on Knowledge and Data Engineering
Ontology Based Context Modeling and Reasoning using OWL
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
A general methodology for context-aware data access
Proceedings of the 4th ACM international workshop on Data engineering for wireless and mobile access
A Rule-based Approach to Content Delivery Adaptation in Web Information Systems
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Modeling heterogeneous context information in adaptive web based applications
ICWE '06 Proceedings of the 6th international conference on Web engineering
Foundations of preferences in database systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Aspect-oriented software development
Aspect-oriented software development
An optimization model for Web content adaptation
Computer Networks: The International Journal of Computer and Telecommunications Networking
Context Management for Adaptive Information Systems
Electronic Notes in Theoretical Computer Science (ENTCS)
Modelling dynamic personalization in web applications
ICWE'03 Proceedings of the 2003 international conference on Web engineering
Towards a common metamodel for the development of web applications
ICWE'03 Proceedings of the 2003 international conference on Web engineering
An approach to user-behavior-aware web applications
ICWE'05 Proceedings of the 5th international conference on Web Engineering
AML: a modeling language for designing adaptive web applications
Personal and Ubiquitous Computing
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For mobile Web applications one needs to take into account context characteristics (such as the device capabilities, the network QoS, the user preferences, and the location) to meet the constraints of the client and guarantee a satisfying interaction with the user. A major issue in this framework is that, in real world scenarios, the number of adaptation requirements can change and increase very rapidly. Therefore, a relevant problem is the definition of effective methods for choosing efficiently the most suitable adaptation for a given context. To this aim, we propose in this paper a new cluster-based approach that automatically classifies the contexts on the basis of their characteristics: at a logical level, each class corresponds to contexts that require similar adaptations. We show that this classification strongly alleviates the adaptation process. The approach relies on a metric distance that is used to compare contexts and on a threshold that provides a reference to group them. Each context in a cluster is associated with the adaptation that best matches with the context requirements. We also illustrate an implementation of our approach and a number of experimental results that support its validity. Semantic Web design principles and enabling technologies are important ingredients of the overall framework.