Case Studies in Developing Contextualising Information Systems Roland Klemke, Achim Nick GMD – German National Research Center for Information Technology Schloß Birlinghoven D-53754 Sankt Augustin
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Abstract. Contextualisation of information is done for at least two different purposes: (1) contextualisation approaches address problems of information overload by filtering information appropriate in a certain context. (2) The presentation of information enriched with contextual information supports the recognition of its relevance and guides its understanding. We review four approaches to contextualisation and develop a framework of guidelines for the development of information systems that make use of contextualisation.
1.
Introduction
Increasing amounts of information available online lead to problems of information overload and to a perceived decrease of information quality. However, the availability of high quality information as a key factor for the success of an organisation is more important than ever. Information contextualisation is an important technique to cope with information overload as contextualised information is better comprehensible and may be presented in appropriate contexts only. We present four approaches performed focussing on the role of contextualisation, compare these and derive guidelines helping to use contextualisation techniques.
2.
Contextualisation Approaches
COBRA. During the COBRA project we developed an information brokering environment supporting brokers at the Economic Information Centre of Milan Chambers of Commerce (EIC). These brokers provide customers with information about organisations in the Milan area. Their work is a mixture of routine tasks (querying online information sources and databases), and intellectual tasks (understanding ambiguous client needs and transforming them into formal queries using complex categorisation and classification schemes). It was our goal to automate routine tasks (see [4]) and support the intellectual tasks.
One of the core aspects of the brokers situation is that they have to work for several clients simultaneously, meaning, that they are often forced to switch contexts. We support this situation by contextualising all information objects along a visualisation of the brokering processes performed for clients. When a broker switches from one client to another, she can see, in which stage the corresponding process is and how many open requests are waiting at which respective stages. Our evaluation of the system showed that the contextualisation of information guided retrieval and reuse of information objects. Also, the permanent availability of an overview over the state of the work simplified the work of the brokers. ELFI. ELFI is an information brokering system for research funding. Funding agencies offer information about funding programs to researchers with a funding need. About 2000 German researchers currently use the system (see [6]). ELFI’s brokering process is in three stages: (1) the ELFI service provider sets up the initial ELFI domain model, resulting in domain concepts and classification terms. (2) Automatic processes contextualise documents gathered from funding agencies and a human broker conceptualises and categorises the contextualised documents updating the domain model. (3) Funding agents at the different universities personalise this information to the researcher’s need specifying interest profiles that filter appropriate items out of the available information. We can observe two contextualisation steps: (1) incoming documents are contextualised along the domain model, i.e. they are enriched with occurrences of domain terms. This contextualisation helps the broker to decide about information relevance. (2) The domain model is contextualised along interest profiles filtering information items relevant to a specific researcher. Thus, the first contextualisation step enriches information while the second one reduces its amount. A survey of ELFI users yielded that interest-based information contextualisation helps to save time in the searching process. The ELFI service provider team reports, that document contextualisation accelerates the specific domain modelling tasks. CRUMPET. CRUMPET realises services for nomadic users targeting towards tourists visiting a town. Tourists explore cities guided and supported by personalised handheld devices combined with GPS or infrared sensors determining their position. Information is tailored to the position and interests. E.g. for a tourist interested in churches the CRUMPET system may generate a description of a trip to a nearby cathedral, showing a map with the actual position and the path to follow. Users access the system with different devices (e.g. mobile phones, handheld devices, desktop PC) to which the presentation is adapted. The actual network bandwidth is used for further adaptation. CRUMPET uses three forms of contextualisation: the topical filtering context, the actual physical context of the user and device characteristics. WINDS. The WINDS project contributes to the reorganisation of the pedagogical, cultural and functional aspects of design education at universities. The traditional approach to design teaching shows some frequent problems which increase learning time and reduce knowledge retention. WINDS aims at creating support instruments favouring a pedagogically more adequate approach to design teaching.
Learning course materials following predefined structures is called expository learning as opposed to exploratory learning, where students explore learning materials on their own, based on interests and current needs. WINDS offers support for both learning styles based on contextualised learning materials. Three contextualisation strategies are used to guide the student’s learning process: structurebased, content-based, and experience-based contextualisation. The structure-based contextualisation of learning materials sets up paths through courses and is mainly used to support expository learning. Structural models put single units into the context of a course. Content-based contextualisation uses graphs of concepts and relations to offer an exploitable structure of information units. Concepts are linked to learning units and relations between different concepts support an exploratory learning style. Experience-based contextualisation uses a learner model to keep track of concepts and learning units a student worked with, offering a way to monitor individual progress and propose individual learning paths.
3.
Contextualisation Framework
Table 1 summarises the approaches, identifying the modelled contextual feature, the contextualised information, and the contextualisation purpose. From the table we can observe that the contextual dimensions modelled vary with the presented information. However, the purpose of contextualisation is either filtering and/or enrichment. Table 1. Contextual features, contextualised information and contextualisation purpose of different approaches. COBRA Feature taken as Context • “dynamic” process knowledge • “static” domain knowledge • “static” personal interest • “dynamic” location • “dynamic” device characteristics • “dynamic” user experience Contextualised Information • “dynamic” process artefacts • “dynamic” news articles and other resources • “dynamic” domain knowledge • “static” tourism related information & services • “static” learning materials Contextualisation Purpose • Presentation Enrichment / Navigation Support • Information Filtering
ELFI
CRUMPET
WINDS
X X X
X X X
X X
X X X X
X X X
X
X X
X
X X
Based on our experience from the presented approaches, we present a framework for the development of contextualising information systems. The first step in designing such a system is to understand the nature of the information dealt with. Are huge amounts of items to be presented? Is the information structured or heterogeneous? Is the amount of items growing or do we have a stable set? These
questions show whether the information systems task is to reduce the amount of information or to support its comprehension by enriching the presentation. Having clarified this, we can select contextual dimensions identifying the context of use of the information system. Are these contexts changing (and do we have to detect these changes) or is there a stable set of contexts? Which contextual dimensions are relevant? Do we need to automatically observe these dimensions? From these questions we can learn whether we can use pre-modelled contexts or whether we have to handle dynamically changing contexts. The next step is to design appropriate filtering mechanisms which reduce the available information to the amount relevant in context. How flexible shall these filter mechanisms be? Should filtering be done automatically or under user control (there is a trade-off here between comfort of use and flexibility)? Should information that is considered irrelevant be hidden or should we use ranking mechanisms? Finally, a visualisation approach is needed that allows to present information together with contextual enrichments. Here, the envisioned users of the system have to be considered: what kind of contextual information will they need? Which contextual information is obvious (and would overload the interface)? Will they need detailed contextual annotations or do they just need contextual hints?
4.
Conclusion and Future Work
The presented system development guidelines focus on contextual filtering and presentation issues and complement existing frameworks for context-aware systems, that focus on technological aspects (e.g. [1], [2]). We plan to extend the guidelines towards a context modelling framework (see [5]). An important goal is to guide designers to reflect on contextual information and to define the contextualisation purpose before developing a system.
References 1. 2. 3. 4. 5. 6.
P. J. Brown. “Some lessons for location-aware applications”, in: Proc. 1st Workshop on HCI for Mobile Devices, Glasgow University, May 1998. A. K. Dey, G. D. Abowd. “Towards a Better Understanding of Context and ContextAwareness”, Tech. Rep. GIT-GVU-99-32, College of Computing, GIT, 1999. M. Jarke, R. Klemke, A. Nick. “Broker’s Lounge - An Environment for Multi-Dimensional User-Adaptive Knowledge Management ”, in: Proc. 34th Hawaii Int’l Conf. on System Sciences, Hawaii, 2001. R. Klemke, J. Koenemann. “Supporting Information Brokers with an Organisational Memory”, in: XPS-99, 5th German Conf. on Knowledge-Based Systems, Workshop on KM, OM and Reuse, Würzburg University, March 1999. R. Klemke. "Context Framework - an Open Approach to Enhance Organisational Memory Systems with Context Modelling Techniques", in: PAKM2000: Basel, Switzerland, 2000. A. Nick, J. Koenemann, E. Schalueck. ”ELFI: Information Brokering for the domain of research fundingt”, Int’l Journal of Computer Networks and ISDN systems, 1998.
