2012: the year of the mashup, based on search, multi-facet representation and semantics (Part 1)

At the beginning of this year, I planned to write a classical post regarding my predictions for 2012. For the first time. But time was missing and I gave up.

Nevertheless, the idea was to talk about Mobile UI, more and more successfully aggregating information from different sources, and not only linking data to dedicated app:

  • Flipboard, google currents: aggregating several information sources, rss feeds, Facebook and twitter accounts and many more.
  • Mango interface from windows mobile: not focused on application, but data

With those application, the purpose is not about the application used, but about the data itself. Simply.

But what about PLM apps? Did vendor conscious about the need to aggregate data from and inside their application? To build dedicated visual dashboards, that the user can define and share?

Some time ago, I posted about specialized search available to quickly access data. The idea was t provide simplify access to data, designing specific queries, and defining associated specific layouts. But this approach is still designed by implementers, and not the users themselves.

So my prediction was that 2012 were the start do development of visual dashboards. But what are the base building blocks to be able to offer such capability?

Then, earlier this year, DASSAULT SYSTEMES acquired Netvibes. It was surprising, as this application is dedicated to monitoring data on the web, like monitoring e-reputation. Far from the PLM world. But Netvibes should provide the technology to build dashboard, may be in combination with Exalead.

Then came an internal request at FAURECIA to provide a new layout in Enovia, leveraging visual data management, in order to reproduce physical dashboards in projects rooms, where projects teams have regular meetings and can visually display the project progress. All data is existing in the PLM application, simply not organized like it is needed at a given time of the project life cycle. Again implementers are needed.

Then came Active workspace by Siemems. A kind of SBA, allowing the user to navigate, through search, inside the 3D data.

So after simply 3 months after the start of 2012, my prediction seems going to reality!

But how such dashboards can be built, which technology can unable highly configurable visual dashboards?

Let’s see that in the second part!

And you, do you need personal dashboard about your product data?

Semantic search, Classification and Data migration: the winning team

Seven years ago, I had a mission to perform data migration from one system to another. One of the major challenges was to import parts inside a hierarchy of categories, which have been designed in the new system. Analyzing the legacy data, another hierarchy had been set, but this hierarchy had more than 900 entries, so users had mostly used a wrong category, making this information totally unreliable.

So we estimated we could try to use the description field of parts to classify the objects, guessing that the users had used meaningful words to describe their objects. The method had to be found.

So I imagined an algorithm to do so. The method was to analyze the words of the description, and to compare those words to a dictionary, providing as well a multiplication factor to each word depending on its position in the description. In parallel, I built the technical dictionary analyzing the description of roughly 500 000 parts, founding the most used words.

I shown that more than 75% of the parts could be automatically migrated using this algorithm. For the remaining 25%, I built an application which was providing the list of parts to classify, and the possible categories available in the new system, and we asked to experts to manually classify the remaining parts. Having done that, I enriched my dictionary with some new words that I had not been able to imagine the meaning (including some funny ones…). With the new dictionnary, we could be able to automatically classify more than 90% of the parts.

Then we set up an automatic procedure using this algorithm in order to migrate data at night from the legacy system to the new one, as both systems were decided to run in parallel for a given period of time. This system ran for one year, until all project data was migrated to the new system. Then the migration system was stopped, and put on archive. I created a semantic search engine without knowing it.

Years after, I have now to implement a search engine based on Exalead search engine. This technology implements semantic options, and hopefully I can reuse the dictionary I built seven years ago to provide more value this new technology.

My conclusion today is that there are several lessons I learnt from this experience:

  • semantic search can help migrate data
  • semantic search can help classify data
  • data migration activity can bring value for future activities
  • companies should pay attention building technical dictionaries, compiling words that users are using everyday