The concept of practice connotes doing, but not just doing in and of itself. It is doing in a historical and social context that gives structure and meaning to what we do. In this sense, practice is always social practice. Such a concept of practice includes both the explicit and the tacit. It includes what is said and what is left unsaid; what is represented and what is assumed. It includes language, tools, documents, images, symbols, well-defined roles, specified criteria, codified procedures, regulations, and contracts that various practices make explicit for a variety of purposes. But it also includes all the implicit relations, tacit conventions, subtle cues, untold rules of thumb, recognisable intuitions, specific perceptions, well-tuned sensitivities, embodied understandings, underlying assumptions, and shared world views. Most of these may never be articulated, yet they are unmistakable signs of membership in communities of practice and are crucial to the success of their enterprise. (Wenger, 1998, p. 47, emphasis added)
in selecting any representation we are in the very same act unavoidably making a set of decisions about how and what to see in the world … a knowledge representation is a set of ontological commitments. It is unavoidably so because of the inevitable imperfections of representations. It is usefully so because judicious selection of commitments provides the opportunity to focus attention on aspects of the world we believe to be relevant. … In telling us what and how to see, they allow us to cope with what would otherwise be untenable complexity and detail. Hence the ontological commitment made by a representation can be one of the most important contributions it offers. (Davis et al., 1993)
Classification systems provide both a warrant and a tool for forgetting. The classification system tells you what to forget and how to forget it. The argument comes down to asking not only what gets coded in but what gets read out of a given scheme. (Bowker and Star, 1999, pp. 277, 278, 281)
Needs | What information does the group need to capture and retrieve? |
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Size | Number of stakeholders; number of subgroups |
Type | What type of project is it? |
External | What external groups does this group communicate with? |
Phase | What phase is the project in? |
Schedule | Does this group have time to learn a new tool/ language? |
Budget | Can this group purchase equipment or hire personnel? |
Personnel | Does this group have technical writers, developers, leaders, etc.? |
Communication | What mechanisms is this group currently using to share their knowledge? |
Location | Is this group co-located or geographically distributed? |
Skills | Does this group have group memory-related knowledge and skills, such as prior experience with a group memory system? |
Motivation | What is team members’ motivation to use a group memory system? |
Stability | What is likely to be the duration and stability of the team over time? |
Categories | Tasks | |||
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Setup of group memory system | Information input | Information formal isation | Information retrieval | |
Definition | What are the steps to be taken to set this group up with the group memory system tool? | What do the various users of the system need to do to enter information into the system? | What mechanisms are available to formalise the information? What can users do to help the system's automatic formalisation features work better? | Who is expected to retrieve information from this system (group members, external groups, future groups)? What mechanisms are in place for this retrieval? |
Assumptions/ requirements | What are we assuming about the group as outlined in their profile? What work procedures are required for users to begin using this system? What are the expectations of their current work practices? | What is assumed about the user community in order for them to enter information into the system? Training, motivational factors, time constraints, group size, etc. should be considered | What is assumed about the user community in order for them to enhance the formalisation of information in the system? Training, motivational factors, time constraints, group size, etc. should be considered | What does the user need to do to retrieve formalised data? What is the user willing to do to retrieve that information? Learning a new language, information overload issues are some things to consider |
Costs | What is the cost associated with setting up the system? Training, system setup, hardware requirements, etc. should be considered | What cost is associated with inputting information into the system? Things outside the existing work practices of the users should be included (extra time required, software, etc.) | What cost is associated with formalising information in the system? Things outside the existing work practices of the users should be included (extra time required, software, etc.) | What cost is associated with retrieving information from the system? Information overload, lost information, learning a new language, learning new query mechanisms, etc. should be included |
Benefits | What direct/indirect benefits does the group obtain from setting up this system? Defining a group's structure, learning new ways of communicating, solving problems, etc. should be included | What immediate benefit does the user gain by inputting information into the system? Consistency in communication, gaining a deeper understanding of a problem, learning a new way to communicate, etc. should be included | What immediate benefit do users who formalise information obtain? Clearer understanding of group tasks and goals, clearer group understanding from using structured language, etc. should be included | What benefit do users obtain from searching for and finding information in the system? What value does the memory have to group members and non-group members? What value is there in looking for information? |
It is tempting when managing knowledge to create a hierarchical model or architecture for knowledge, similar to the Encyclopaedia Britannica's Propaedia, that would govern the collection and categorisation of knowledge. But most organisations are better off letting the knowledge market work, and simply providing and mapping the knowledge that its consumers seem to want. The dispersion of knowledge as described in a map may be illogical, but is still more helpful to a user than a hypothetical knowledge model that is best understood by its creators, and rarely fully implemented. Mapping organisational knowledge is the single activity most likely to yield better access. Knowledge managers can learn from the experience of data managers, whose complex models of how data would be structured in the future were seldom realised. Firms rarely created maps of the data, so they never had any guides to where the information was in the present. (Davenport, 1998, p. 189)
One of the main problems that… nurses have is that they are trying to situate their activity visibly within an informational world which has both factored them out of the equation and maintained that they should be so factored – since what nurses do can be defined precisely as that which is not measurable, finite, packaged, accountable. (Bowker and Star, 1999, p. 265)
Once war stories have been told, the stories are artefacts to circulate and preserve. Through them, experience becomes reproducible and reusable. [War stories] preserve and circulate hard-won information within the community. (Orr, 1990b, pp. 156, 157)
A good deal of new technology attends primarily to individuals and the explicit information that passes between them. To support the flow of knowledge, within or between communities and organizations, this focus must expand to encompass communities and the full richness of communication. Successful devices such as the telephone and the fax, like the book and newspaper before them, spread rapidly not simply because they carried information to individuals, but because they were easily embedded in communities. (Brown and Duguid, 1998, p. 105)
The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation. The first steps in weaving the Semantic Web into the structure of the existing Web are already under way. In the near future, these developments will usher in significant new functionality as machines become much better able to process and ‘understand’ the data that they merely display at present. (Berners-Lee et al., 2001)
The requirement for continuity and autonomy derives from our desire that an agent be able to carry out activities in a flexible and intelligent manner that is responsive to changes in the environment without requiring constant human guidance or intervention. Ideally, an agent that functions continuously in an environment over a long period of time would be able to learn from its experience. In addition, we expect an agent that inhabits an environment with other agents and processes to be able to communicate and cooperate with them, and perhaps move from place to place in doing so. (Bradshaw, 1997, p. 7)
Seeking out information which the agent thinks will be of interest: for example, InfoFinder learns a user's interests by analysing sets of messages and other online documents that the user classifies (Krulwich and Burkey, 1997). Another example is the Remembrance Agent (Rhodes and Starner, 1996), which displays a list of documents that might be relevant to the user's current context. Recreational agent systems are available to help internet users find friends and music which the agent thinks they will like, but the underlying technologies are applicable for knowledge management problems such as locating experts (for example, MIT Media Lab, agents. http://agents.media.mit.edu/projects.html). Acting as a ‘virtual participant’ in a discussion: for example, the Virtual Participant agent (Masterton, 1997) which ‘listens in’ on FirstClass electronic conferences, builds summaries of them and tries to retrieve relevant past discussions in response to questions. Managing the low-level communication and formatting of data from multiple databases when a user issues a search: for example, a network of online information resources which may vary widely in format and organisation. The end-user only cares about finding relevant material. A set of cooperating agents can be used to handle the complexity of the underlying infrastructure, requiring only one user interface, as if querying a single, static database whose terms and relations reflect the user's perspective of the knowledge domain.
Data mining is a set of techniques used in an automated approach to exhaustively explore and bring to the surface complex relationships in very large datasets … most likely implemented in relational database management technology. However, these techniques can be, have been, and will be applied to other data representations, including spatial data domains, text-based domains, and multimedia (image) domains. Data mining … uses discovery-based approaches in which pattern-matching and other algorithms are employed to determine the key relationships in the data. Data mining algorithms can look at numerous multidimensional data relationships concurrently, highlighting those that are dominant or exceptional. (Moxon, 1996)