KNOWLEDGE TOOLS Evolution has not optimized our brains for the amounts and kinds of information that we have to deal with nowadays. For one thing, we cannot hold more than a hand full of objects in our “working memory,” i. e., we cannot focus on more than a few things at a time. For dealing with today’s complex problems, this is a severe limitation. Our long-term memory, however, does not have such limitations, fortunately. The number of things we can learn is virtually unlimited. But, why do we still forget things then? When we forget something, it is not because it is really lost from our memory, but because our brain currently does not find a way to access this knowledge. Knowledge media of all kinds are there to help us remember what we once knew. There are many kinds of knowledge media: note books, post-its, sketches, mind-maps, wikis, calendars, to-do lists and many more. And often, one picture or a simple keyword is enough to make us remember – be it an item on our shopping list or an important aspect of a complex problem. It is a common mistake to think that we can manage knowledge by means of media, as the term “knowledge management” seems to suggest. Genuine knowledge is something that resides in people’s heads only. What we can manage through media though, are knowledge cues: pictures, text, symbols, information that help us remember what we already knew. WHY VISUAL? Human sense of orientation has developed over millions of years. It is highly optimized for orientation in a three dimensional world and on large plains and not for finding a way through complex hypertexts (like the web) or abstract formal structures (like data bases or many other kinds of software). Using graphical environments for structuring externalized knowledge enables knowledge workers to use their highly efficient sense of spatial orientation on their personal knowledge- and information space. From the 1970s on, a number of visual mapping techniques has evolved, some of which have found wide-spread use and have proven their usefulness in numerous studies, e. g. as learning aids. According to their basic structure, most of them can be related to the following three fundamentally different visual mapping approaches, and they all have their specific pros and cons: MIND-MAPS
Unfortunately, none of the approaches combines their main advantages: a hierarchical overall structure, the ability to represent interrelations and allowing constructive ambiguity. Also, when you make really large maps with hundreds of nodes, they always get very messy and hard to read. Moreover, since most of these visual mapping approaches come from the paper world, they are limited in some ways: A paper cannot grow in size as needed and interactive animation is impossible. (� Fig. 3) iMAPPING iMapping is a new visual mapping technique, which has been invented in order to combine the benefits of the different visual mapping paradigms and additionally allow to handle large amounts of items without loosing overview. (� Figs. 4, 5) Like spatial hypertext, an iMap is like a large pin board, but one where you can zoom in on any item. Items can be freely positioned on the map, even inside other items. Like that, any item can contain other items and thus be like a map of its own. Since this nesting can go very deep, it allows to bring structure to large collections of items: The author’s own big iMap, e. g., contains more than 5,000 note items, which can still easily be overviewed because they are organized in more than ten levels of hierarchy. Compared to the iMap as a whole, some deep down parts of this iMap are so small, that they occupy less than a pixel when zoomed out, and that it would take more than 90,000 square meters to print out the whole iMap at a readable size. This is a good thing because, from an overview perspective, details become so small that they do not distract from seeing the big picture. Through a smooth zooming function, any part can be enlarged without leaving the overall map and without loosing orientation. So, like mind-maps, iMaps have a hierarchical structure, but maybe a more natural one: The big picture surrounds the details. Details are parts of the whole instead of being scattered around a central topic connected via branches. This allows for cross-links in iMaps, which would not work in mind maps. (� Figs. 6, 7) Like in concept maps, any item can be linked to any other item: Links can even go to far away items that rest in other levels of hierarchy or other sub-maps. However, in order to avoid visual clutter from too many links, usually, links are not all shown at once. Consistent with Ben Shneiderman’s famous Information Seeking Mantra: “Overview first, zoom and filter, then details-on-demand,” only those links are made visible, which connect to the currently selected items. REDUCING COMPLEXITY
A mind-map always has one central topic in the middle, with extending labeled branches and sub-branches. Instead of distinct nodes and links, mind-maps only have labeled branches. Relations between these branches cannot be specified. Structurally, a mind-map is a simple tree of labeled items, which provide an easy-to-understand hierarchical structure. Mind-mapping is an established technique for brainstorming, outlining, note taking and clustering. Mind-maps are, however, not suitable for interrelated structures, because they are constrained to the hierarchical model. (� Fig. 1)
In knowledge-intensive activities, like complex problem solving or personal knowledge management, it is even more crucial than otherwise, to unburden the user of all cognitive overhead in order to leave as much of the user’s limited working memory to the actual task at hand. Cognitive overhead is the part of a user’s cognitive load that is not directly related to the intended action, but rather to dealing with side issues, distractions or the software as such. To that end, iMapping reduces visual complexity in several ways: Organizing hierarchy by nesting instead of with branches, vastly reduces the number of lines needed to depict the same structure. Also, the spatial hypertext approach often makes explicit links unnecessary. Visual complexity is further reduced by only showing Links on demand. (� Figs. 8, 9) The number of explicit long-distance links needed is further reduced by the fact that in an iMap, one and the same logical item can have several visual occurrences. So, e. g., there can be several items representing the same person Max: One in Friends and one in Calpano.com. But the iMapping Tool knows that they mean the same. If one of them is changed, so is the other. (� Fig. 10) The iMapping Tool is a software application made for iMapping. Apart from the abovementioned, it also features a semantic data model, which allows to query the iMap’s content in a structured way.
CONCEPT MAPS In contrary to that, concept maps emphasize the interrelations bet ween items. Concept maps consist of labeled boxes and labeled arrows linking all boxes to a kind of network. Concept maps have proven useful to represent complex subject matters, especially with a focus on the interrelations of items. This basic node-and-link network structure also forms the basis of many other modeling approaches. However, these more general network-structured maps are not as easy to handle as mind-maps because it is more laborious to explicitly specify all the relations. This makes them less suitable for simple tasks like note taking or brainstorming. (� Fig. 2) SPATIAL HYPERTEXT A so-called spatial hypertext can be seen like a pin board. It is a set of text snippets that are not explicitly connected but implicitly related through their spatial layout: Things that belong together are simply put close to one another. To relate two items in an informal way, they are simply placed near to each other, but maybe not quite as near as to a third object. This allows for so-called constructive ambiguity and is an intuitive way to deal with vague relations and orders. On the other hand, relations between things cannot be made clear like this.
For more information, more examples and to download the free iMapping Tool: imapping.info To see the author presenting his own big iMap, search Heiko’s Big Map on YouTube. For a scientific description of iMapping including references, see the author’s PhD thesis: diss.heikohaller.de
Slanted 18 — Essays and Reports
Heiko Haller, P 153
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