Good morning, folks! Today we posted a new Just KNIME It! challenge extraordinarily early: we will go back to our normal posting hours next week.
This week our challenge is on movies! Let’s leverage KNIME’s reporting capabilities to thoroughly investigate what genres and movies were the highlights of many different decades.
Here is the challenge. Let’s use this thread to post our solutions to it, which should be uploaded to your public KNIME Hub spaces with tag JKISeason3-20 .
Need help with tags? To add tag JKISeason3-20 to your workflow, go to the description panel in KNIME Analytics Platform, click the pencil to edit it, and you will see the option for adding tags right there. Let us know if you have any problems!
In between my sessions this week, where I’m sharing knowledge and experiences on using the KNIME Analytics Platform at several high schools outside Jakarta, I’ve just uploaded the solution for the challenge tonight (Jakarta time). I’ve also attached the report in a PDF document.
I have a lot to learn, but here is my contribution, I used Python to organize the decades, I found it simpler this way. My report is pretty ugly (Laughs) but it was my first contact with the component.
TL:DR: Report is done and llama3.2b (3bn) sorted out the top 5 features for me
I had a pretty successful week so felt to have a bit of a laugh today whilst doing this.
As I wasn’t 100% clear what to do about those features, I reverted back to what you do these days when you don’t know how to proceed… I asked AI to work out the features for me
In my WF there’s one Metanode which encapsulates the data processing. It includes sending top 5 movie titles and descriptions to a locally run llama3.2 (3bn) LLM hosted via Ollama. I used this as it supposedly is good at returning structured outputs and I can now definitely confirm this.
As I asked for JSON which then needed to be parsed into columns I took a bit of a risk for things to fail inside the loop so I built in safeguards to save the data each iteration… I was very surprised when the entire loop ran through without one issue at first go.
In order for people to generate the report I saved all data into .table files, which the second meta node picks up to generate the report.
There’s two pages per decade - the second one contains the top 5 features the top 5 movies share according to llama3.2b incl. an explanation :D.
Have to admit that the reporting extension has come a fair way then just a few months ago, but some things are still a bit clunky:
getting table views to fit properly
generating headers… the header components did not work so I had a go with eCharts. This seemed to work ok although the background color gets ignored, adding a b64 encoded image works on some pages and not on others (left it out in the end)
==> great features with some more room for improvement
I also once again was positively surprised by my body K-AI - he/she/it sorted out the decades topic with one prompt :D:
I think this reporting extension is genious! You can do enterprise level reporting and distribution with it (combined for example with the send email node)!
As I saw MartinDDDD gave a very detailed download help (Thank you!), but if that doesn’t work for some reason I would gladly send you the workflow on different channel!
Hi all,
Here is my solution. As I browsed through the bar charts, the war genre from the 1930s caught my eye because of its distinctive features. This was largely due to the famous “Gone with the Wind”. Thanks.
Good morning, folks! Today we posted a new Just KNIME It! challenge extraordinarily early: we will go back to our normal posting hours next week.
This week our challenge is on movies! 
Let’s leverage KNIME’s reporting capabilities to thoroughly investigate what genres and movies were the highlights of many different decades.
Need help with tags? To add tag JKISeason3-20 to your workflow, go to the description panel in KNIME Analytics Platform, click the pencil to edit it, and you will see the option for adding tags right there. 
Let us know if you have any problems!
