=Paper=
{{Paper
|id=None
|storemode=property
|title=Adaptive Hypermedia Systems Analysis Approach by Means of the GAF Framework
|pdfUrl=https://ceur-ws.org/Vol-823/dah2011_paper_5.pdf
|volume=Vol-823
|dblpUrl=https://dblp.org/rec/conf/ht/KnutovBP11
}}
==Adaptive Hypermedia Systems Analysis Approach by Means of the GAF Framework==
https://ceur-ws.org/Vol-823/dah2011_paper_5.pdf
Adaptive Hypermedia Systems Analysis Approach by
Means of the GAF Framework
Evgeny Knutov, Paul De Bra, and Mykola Pechenizkiy
Department of Computer Science, Eindhoven University of Technology,
P.O. Box 513, 5600 MB, Eindhoven, the Netherlands
e.knutov@tue.nl, debra@win.tue.nl, m.pechenizkiy@tue.nl
Abstract. Adaptive Hypermedia Systems (AHS) have long been concentrating
on adaptive guidance of links between domain concepts with lots of custom de-
velopments and ad-hoc implementations. Here we consider a formalization ap-
proach to AHS composition and design by defining building blocks’ interfaces
and presenting corresponding dependencies by means of the GAF framework.
This helps to identify system design guidelines and start building adaptive sys-
tem from scratch as well as analyze adaptive system behaviour, architecture and
risks involved.
1 Introduction
Since the most cited Adaptive Hypermedia (AH) model AHAM [1] new terms, defi-
nitions and models have been introduced and realized in prototypes. Most AH models
focus on a layered architecture and concentrate on adaptation to the linking and naviga-
tion between concepts of a domain. With the exploding popularity of the Web search-
ing rather than linking, or Recommender systems (RS) to rank relevant content and
provide personalized information the area of AHS has gained a lot. The Generic Adap-
tation Framework (GAF)1 research project aims to develop a new reference model for
the adaptive hypermedia research field. The new model considers new developments,
techniques and methodologies in the areas of adaptive hypermedia and adjacent fields.
Besides GAF concerns the detailed system analysis in terms of AHS building blocks,
connections and dependencies, approaches that can be used to implement such a sys-
tem.
GAF conceptual scheme of the layered structure is presented in Figure 1. It aligns
the order of the layers in the system according to the classification of AH methods and
techniques [5]. Though this order represents the basic understanding of the adaptation
questions, every particular system may vary or even omit some of these, thus leading to
a different composition of the system layers determined by the different adaptation idea
behind this (adaptive eLearning application, Recommender System, etc.). We believe
that in order to couple, align, sort and arrange the layers of such a system (both the
generic model or some particular domain focused implementation) one should keep in
mind an adaptation process scenario (partially considered as use-cases in [4]) that will
partially determine the layer arrangement and to some extent will define the mandatory
and optional elements and drive the system design.
1 http://www.win.tue.nl/ eknutov/gaf.html
˜
Classification of
AH Methods and Techniques;
adaptation process
Goal Model
Why?
Domain Model
What?
Resource Model
User Model
Group Model To What?
User Context
Context Model
Usage Context
When?
Application Model Where?
Adaptation Model
Higher Order Adaptation
Presentation Model How?
Fig. 1. Conceptual scheme of GAF layered structure
2 AHS Analysis Approach
As thoroughly investigated in [7] the evaluation of AH systems plays an important role.
The described layered evaluation provides the description of the system functionality
and helps to solve many related problems. In our work we consider a more formalized
and specific system analysis approach by taking up systems’ block composition sce-
narios, interfaces. Thus we define dependencies between models, methods they use to
communicate with each other and particular implementations (based on usage scenar-
ios). As a reference we took the approach from [3]. The main steps of such an analysis
are presented in Figure 2. By scenarios here we mean framework use-cases (adaptive
search, adaptive eLearning, recommender system, etc.), mostly covered in [4]. These
scenarios are represented by ‘sequence charts’ and are constructed using GAF layers.
We also consider system specific aspects and AHS building blocks composition which
impacts the system architecture, such as event-driven system or service oriented or these
two together.
As a result of this approach we would have elementary base concerns of AHS,
which would explain mandatory and optional building blocks of the system, trade-off
available, mostly concerning optional elements of AHS, and the dependencies involved
presented as table. We will elaborate the approach further and explain it through the
example of the Domain Model (DM).
3 AHS Models Analysis Approach: DM example
Hereafter we elaborate the analysis approach and consider the AHS DM. In Figure 3
we show an example of DM interface dependencies. Analyzing it down further we
comprise the dependency table of building blocks’ interfaces (such as Domain, Use,
Resource, Context models), scenarios of how these models are used and which type
system scenarios
description (mandatory and e.g. service based
optional), e.g. AHS, architecture against
RecSys. Adapt.Search, etc. event-driven or DB
Scenarios Specific questions Arch. approaches
GAF AHS Analysis
Sensitivity points Trade-offs Risks
elementary base concerns alternative blocks / dependencies
(mandatory vs. optional implementations involved, implement.
elements compositiopn) Optional elements complexity, etc.
Fig. 2. AHS analysis approach
User Model
Overlay
Mapped Ontology DM-UM systainability
(access, value-pairs,
Index of related terms relational DB querying)
Custom (e.g. Stak in “HS”)
+ Group Model
constructing UM
(implicitly
Resource Model
look up DM e.g. RecSys) Construct DM Type of the Data (needed for
based on the AE and Present. Model)
user pref
Domain Model Actual Resource Data
Open Data (URI, Query)
properties
methods
technologies
scenarios
modify link/ query
available data resources
refine
(retrieve Res)
Adaptation Engine Concept structure
access Content pointers
Rules
- types mapping
goals on DM
- data usage (e.g. author
- inferences alignment) Extensions
Logs
constructing Versioning (structure)
HigherOrderAdaptation
goals (e.g. TOC) Provenance (relationships)
Recursions (relationships)
Indexing (descript and content)
Goal Model Instantiation (DM cache)
Overlay (incl. sub-trees,
sequences, single concepts)
Ontol. alignment
Fig. 3. Domain Model interface dependencies
of system is being described (AHS, Adaptive eLearning, Recommender System, etc.),
possible technologies to implement it (Data Bases, OWL ontologies for semantic web
enabled systems, TF-IDF index for search, etc.). As a result we’ll have a detailed picture
of the system components, evaluated against the reference model (GAF), which will
help to identify all pros and cons.
Considering any arbitrary DM properties and interfaces we analyze them against
the following properties and methods of the reference structure (see Figure 4 for de-
Classes Classes
Sets constructor
Collections Construct/author(manual, automatic, semi-automatic))
Indices Maintain/Update/Refine
Trees
Prerequisites Relationships methods
Entity relat. Access/Retrieve (next, sequence, subset, relation,
- same type)
- parent Map (UM, GM, Group luster, Rules)
- etc. Merge/Split/Extract
Properties Attributes
Features
technologies
Character.
DB / OWL/ DAML / XML / Index / etc. / custom
Parameters
Aspects
Functional
Complex terms
scenarios
structures
AHS, AeLearning, RecSys,
accepted as
SemWeb, AdaptSearch, WIS
a single term
Restrictions
Assertions
available data
Domain
DublinCode, var. DB,
Rules
WordNet, FOAF
Etc.
Fig. 4. Domain Model abstraction class
Table 1. partial GAF blocks high-level dependencies: DM example
DM Scenario Resource Model Adaptation Engine User Modelling
properties
and methods
concept tree conventional AHS content ECA reasoning, UM overlay
eLearning pages/frames prerequisites
relations
feature space recommender datasets promotions implicit
system and ranking user profiling
mechanisms
index adaptive search WWW ranking implicit
user profiling
tails). The major division here concerns methods and properties of the abstract Domain
Model class. Further we distinguish classes (like sets or collections of concepts or con-
cept maps, indices, trees, etc.), relationships (which are conventionally constituted by
the ontology relationships), attributes of the concepts (e.g feature space, properties,
characteristics, etc.), then functional terms which are denoted by complex structures
usually treated as a single term, and restrictions defined by assertions or some specific
domain rules.
Methods can be defined by constructors used to author DM as well as refine, main-
tain or update it. Major DM methods describe the access and retrieve procedures mainly
called by User Model (UM), Resource model (RM) and Adaptation Engine (AE) to ac-
cess the conceptual structure and query corresponding content. We also define mapping
methods which are used to maintain structure sustainability especially in overlay type
of models or ontology mapping for instance. These mappings (or alignments) can be
done between DM and User, Goals, Groups models and Rules sets. Additionally we
have methods to merge, split and extract sub-models of DM, which can be used in
distributed domain modelling or open corpus adaptation.
DM scenarios describe the system behaviour in terms of functional flow and user
interaction. We have described most prominent use-cases of such a framework compli-
ance with different types of systems in [4]. Thus the DM usage in different cases could
be analyzed against these reference scenarios.
Finally we have a number of particular technologies to work with DM and associ-
ated or cross-technology data available to start modelling (e.g Dublin Core to devise
adaptive eLearning application or a dataset feature list to devise recommender system
or adaptive search portal). This may remind us of the UML notion used in [6] to for-
malize the AHS modelling, however we define more strict dependencies in the GAF
formalization through defining interfaces, methods and scenarios, besides we use it to
analyze system, identify alternatives and be able to compare and assess other systems
in terms of the GAF framework. Table 1 presents high-level dependencies between DM
properties and methods, scenarios and other AHS’ models. This is just to give an idea
of our approach, ideally these dependencies would be described in meticulous details,
parametrizing abstract DM interfaces and to some extent show concrete technology or
implementation approach for each of these models’ interfaces.
4 Summarizing Implications of the Analysis Approach
Here we would like to summarize the major implications of our approach and antici-
pated benefits.
– Reference structures — being a reference model GAF and detailed dependencies
of its layers will serve as an ideal starting point for AH system designers and re-
searches in the field.
– Complexity and Performance — defining a number of dependencies and known
technologies would give an impression of the system complexity.
– Compatibility and Compliance — compliance description ([4]) provides the de-
scription of use-cases and application scenarios of the GAF framework.
– Modifiability — trade-off between blocks or modules’ alternatives will show the
modification possibilities, or further system extensions.
5 Conclusions and Future Work
The coming years will bring more use-cases of how AHS can provide adaptation and
personalization, what techniques will be introduced, and what research areas will in-
troduce new technologies in its evolution. So far a study of existing adaptation and
personalization approaches was done to comply with the layered structure of adap-
tive information systems, which raised the problem of system composition and design
analysis. We try to solve this problem using a classical software architecture analysis
approach extending it with adaptation framework specific questions and interface de-
pendencies in order to meticulously analyze any adaptive system in terms of the GAF
framework.
At the same time evaluating the proposed general-purpose AHS architecture (GAF
framework) against recommender systems [2] has shown that the GAF architecture is
sufficiently generic to accommodate the description of different personalization ap-
proaches including recommenders, as well as provide the flexibility of both AH and
RS in one go by building a custom system with the GAF building blocks. The real
though not very meticulous case study has proven our points. It has given us new chal-
lenges to investigate the applicability of new approaches, as well as new developments
in adaptive information systems which will allow to decide on the system composition
at the implementation level and this is where one would need the AHS analysis.
6 Acknowledgements
This work has been supported by the NWO GAF: Generic Adaptation Framework
project.
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