One month ago, I started thinking to "open" author a paper based on some concepts which I would like to further develop. I think I need to make a start now.
In a previous, paper, we defined:
Type 1 Data: These are resources that end users are interested in. (In certain circumstances, Type 2 and Type 3 data are themselves Type 1 data). Type 1 Data may include lesson plans, teaching strategies, curriculum ideas, web pages, software, information data sources, dynamic data (such as stock price quotes, weather conditions), media (sound and video), software modules, or Computer Assisted Learning packages, etc.
Type 2 Data: These data are derived directly from Type 1 data and sometimes function as surrogate data for it (especially for non-text based Type 1 data) in order to conserve computation or cognitive load while performing the resource discovery function of the Search Sites. Examples include metadata and indexes of websites. The comprehensiveness of Type 2 data and the relevancy of the collection to the Sites' users are part of the primary asset of Search Sites.
Type 3 data: This is data that are not captured by Type 1 and Type 2 data, and which typically cannot be derived directly from a single Type 1 or Type 2 data. A little more explanation may help make this concept clear.
What I have in mind is to improve on this model:
Say we have a set of data, let call it S1 with elements s11, s12, s13, ... s1n. These elements was referred to as type 1 data in the previous paper.
Now, apply a "meta" operation* on each of the element in S1 which will produce a set S2 with elements s21, s22, s23, ... s2n where s21 is the metadata of s11. These elements ( s21, s22, s23, ... s2n) was referred to as type 2 data in the previous paper.
Note that elements of S2 are data as well. These elements are themselves type 1 data and hence we can apply "meta" operation on these as well to produce another set of type 2 data. This is infinitely recursive.
What is interesting, and perhaps confusing, is that there exist more than one meta operation. In fact, there are infinite number of meta operations. Each meta operation will produce a set of Type 2 carrying the implicit characteristics of the meta operation. We further define a meta-meta operation as an operation on a set S which will extract the common characteristics all elements in the set S2 to produce M1. Since there are infinite number of possible meta operations, there exists infinite number of characteristic, M1, M2, M3,... Mn,... Each of these characteristics, when expressed in as data, is what we refer to as type 3 data in the previous paper.
An example of Mi may be the Dublin Core specification, which defines a particular meta operation. The process of producing M1 is the meta-meta operation. Different community of practice will obviously have their own variations of meta operation (adoption and extension of DC) producing Mi.
Meta-meta operation applies on data elements. Since type 1 data is data, we can also meta-meta operation on type 1 data. One of the possible characteristics of type 1 data is the link information among the elements. This link information has been an important information to determine the "page-rank" in Google's search result. Again, there are other meta-meta operation which can be applied to type 1 as well as type 2 data.
The utility of this data model may be used to understand the work in metadata....
*A meta operation may be the extraction of metadata from the type 1 data, such as assigning dc.creator (in type 2 data of this post) to the value "Albert Ip" (for this particular post, which is type 1 data in this case). Another meta operation may be to create the frequency count of all the words used in this post.
No comments:
Post a Comment