PKC Workflow/Knowledge

Time Management

We can abstract and list the purpose for async and sync communication. For sync meeting, I found it efficient to demonstrate something which one already have, like a wiki function, technical issue. The listener can output all of their thoughts and start discussion base on the rich context

1. See Timeline Template on Wikipedia:Template:Timeline
2. The MediaWiki:TimelineTable extension can set the depth to either year, month, or date.
3. The MediaWiki:EasyTimeline extension seems to be more complex but complete.

Communication Management

From Synchronous to Asynchronous Communication

By specifying different communication scenarios, collaborators can have better expectation of one another's reply time.

• Face-to-face meeting: participants communicate in realtime by either audio or video.
• 1st level: participants are focusing on the message; the turn taking period is 5 to 60 seconds.
• 2nd level: participants work on other things and talk at the same time; the turn taking period is 1-10 minutes.
• 3rd level: the turn taking period is 10mins to 1hr
• 4th level: the turn taking period is several hours to days.

Structuring Talks

This process is at the core of Ideation, where users try to make the best use of each piece of information during cognitive process. Communication data is an important data source. To best utilize communication, each piece of information could be further transformed into content, actions, or any types of data and managed in PKC. This process could be iteratively improved by some rules of a team. The core of the rules is the abstraction of data. For each piece of information, if its type is well-defined, users can categorize it and trigger manual or automatic actions. If the type isn't defined or only weakly defined, users should choose whether to define its type, optimize them, or do nothing. (Universal data abstraction, evolving abstraction). So there are two levels of communication:

• Basic level: the communication goes on by its pragmatics.
• Meta level: the communication triggers actions to improve the system.

For example:

• For an undefined pattern, define a new page.
• For a set of messages, create a summary and locate in Channel namespace.
• For some actions, create an action item and locate in Project namespace.

Formalizing the abstraction rule

Improvement of data abstraction

• Data abstraction in given Design Space
• Defining new structure and expanding the Design Space
• The minimal complexity of data abstraction is to only categorize data and execute the defined process without conducting extra sense-making.

Universal Abstraction and Context Awareness

Context awareness refers, in information and communication technologies, to a capability to take into account the situation of data entities. Dey and Abowd (1999) distinguish between the context types location, identity, activity and time. Kaltz et al. (2005) identified the categories user&role, process&task, location, time and device to cover a broad variety of mobile and web scenarios. With the proper methodology(UDA) and tools (PKC), it is possible to optimize the context awareness given the computational and cognitive power. Narrowing down to project management scenario, the working progress itself and the data engaged in it are important types of data source. The abstraction rule of context management is listed below:

• For each activities in work, categorize it into Activity Namespace and give proper summarization. It is the generalization of git commit.
• The activity can relate to a task in the project's Work Breakdown Structure
• For researching or other knowledge related activities, the information could be locate in a Knowledge management.
• The activities could be distribute into channel for collaborators with authorization.

Namespace Management

The namespace of a project is constantly built up by the abstraction process.

• Structure of different namespace

Basic Logic

Subcategory versus Concretization

Observation

As we are working in an expressive system, we have versatile ways to structure data. The process provides more operational data which then feedback to the design process, resulting in a more complex and optimized structure.