Author: Chris

Hierarchy in Properties Using Semantic Mediawiki #arraydefine and #arraymap Magic

At work, I’ve been working with our HR reps to develop something we’re calling Career Learning Maps.  The Learning Maps are a way of determining the various opportunities via career level (Positions such as Associate, Senior, Lead) and the skills required at each level. For each skill we’d have a list of suggested Learning Resources such as books, videos, seminars, etc. that assist in developing those skills.

For example, today you might be a Senior Database Engineer, but you’re looking to move up to Lead Database Engineer. What skills are required at this new level? Or what if you’re currently an Associate Technician, but want to move toward a Management position?

The Learning maps can help you understand the various job descriptions and associated attributes available to co-workers.

Since we already have Semantic Mediawiki as our knowledge repository for our department we decided to leverage its capabilities to deliver the Learning Maps.

One of the issues I was running into was figuring out a way to develop a hierarchical list of skills. Each skill is related to what we’re calling a Skill Dimension. Think of the dimension as parent groupings of skills. Business Acumen is a skill dimension that includes the skills of General Healthcare Industry Knowledge, Process Improvement and Business Process Management among others.

I figured out a cool way to use #arraydefine and #arraymaptemplate to create a hierarchical list based upon properties. This list appears on each individual Job Description and shows the relationship between the career level, related skill dimensions (to that career level) and individual skills organized by skill dimension. Check this out:


Each job description is semantic form/template driven, as is each career skill dimension and career skill.

 

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Here’s the code – in case you might want to use it in the future.

In the Job Description Article Template (after building your form/template to include these properties!):

{{#arraydefine:levelsman|{{#arraymap:{{{Career_Level|}}}|,|@@|@@}}|,}}

(This defines an array using the specific job description’s career level.)

{{#arraymaptemplate:{{{Skill_Group|}}}|Career_Skills_Dimension_Template|,|
}}

(Ok, how many Skill Dimension apply to this job description? Give me a list and format it with a template.)

In a template called “Career_Dimension_Group_Template”

'''[[{{{1}}}]]'''<br>
{{#ask: [[Category:Career Skills]]
[[Skill Dimension::{{{1}}}]]
[[Career Level::{{#arrayprint:levelsman | }}]]
|link=none
|order=ASC
|format=ul
|default=No Skills Listed
|searchlabel=
}}

(Spit out a Skill Dimension, followed by an #ask that lists all relevant skills that are both part of that group and at the same level as this job description.).

The best part is that each property value is a page, so I can change “link=none” to “link=all” to have each skill in each group be a link to that individual skill’s article as well!

—-

To expand this even further, I’ve created a matrix listing all Skill Dimensions by Career Stream (Management, Professional, Technician) in a matrix with each Career Level. Co-workers can use this to easily see which skills are relevant across a given level or dimension.

While not very attractive, each cell in the matrix has a variation of the following #ask query.

{{#ask: [[Category:MTS Career Skills]]
[[MTS_Career_Level::Executive Director]]
[[MTS_Skill_Dimension::Strategic Focus (Management)]]
|link=all
|order=ASC
|format=ul
|default=
|searchlabel=
}}
The MTS_Career_Level and MTS_Skill_Dimension property values change for each cell.

—-

All together this creates a responsive and scalable solution to manage these relationships. Adding a new skill, resource or job description automatically updates all areas. In fact, most updating happens via forms and templates so our HR and Training folks can easily develop these resources with minimal Semantic Mediawiki development.

I hope this might be helpful for folks using Semantic Mediawiki to show relationships between properties in an interesting or unique way. Let me know if you have any questions or feedback.

Big Data, Mining, and (Musical) Recommendation Engines

As a side project in my free time I’m helping a small business setup an e-commerce store front. One of the things we’ve discussed is the idea of a recommendation engine to suggest other items to purchase. This lead down an Internet rabbit hole where I ended up reading about The Echo Nest.
The Echo Nest is a self-described “music intelligence platform that synthesizes billions of data points and transforms it into musical understanding.“. It is widely herald as one of the largest and most comprehensive uses of data mining (to find the language and culture around music across the web) and big data (to store and present those relationships) within the music recommendation industry.
Yes! There is an industry. A substantial one. Apple’s Genius feature in iTunes, Pandora, Last.fm, Spotify – all are trying to provide relevant music based upon your listening tastes. Why? So you’ll buy more music of course!
Brian Whitman, one of the co-founders of The Echo Nest, talks in great length about the how and why behind what makes their product so unique – and so incredibly accurate. I won’t steal the thunder of the article, but needless to say, dedication and refinement are key.
This is totally sausage-making, behind-the-scenes stuff, but I encourage you to at least look it over.
Ok, so now the really fun stuff. Here’s something called The Infinite Jukebox. It uses some of the data points within the Echo Nest to create a version of a given song that never ends. It uses references within a song that are similar to other points within the song, makes some minor adjustments when needed (like tempo) and then plays the song forever. The presentation is neat as well, you can view the branches within the song where things loop and even click around the song to find points where things can loop.

At work we’re looking at ways of using the topics of big data, mining, and recommendation engines to provide better healthcare. Reading about The Echo Nest gives me some ideas on how these technologies could impact the care we give! If you have your own ideas or suggestions, please leave a note below.

Guns.

I’m getting this off my chest as a parent and citizen. I grew up around guns and those that favor them as a thing to own.
1. Getting help for mental issues should not make people, especially men, feel ostracized.
2. These services should be abundant and readily available.
3. No one should say “not my problem” when introduced to individuals who visibly need help.
4. Assault rifles are unnecessary for civilians. This is 2012, not the 1700’s. The tyranny of any government is a self-induced fantasy.
5. The argument that we need guns to protect us from our democratically elected government is absurd. Even an assault rifle is useless against jets, missiles, rockets, etc. You might as well argue we should be able to strap those to our trucks, cause you know THE EVIL GOVERNMENT.
6. You’re right, signs don’t stop criminals from doing crazy things. Neither does gun locks, the cost of bullets, gun laws or a society that encourages violence and fear to perpetuate the celebrity of asshats that do stuff like this.
7. You know what does help? Removing access to portable metal contraptions whose sole purpose is the killing of living things. Make it as hard as PHYSICALLY possible for people.
8. You know you don’t need that gun. You just want it to be cool and live out some lame male Rambo fantasy of being a badass – knowing, of course, that truthfully our lives are more safe and predictable than any other time in human history.