PLM, RDBMS and Future Data Management Challenges

It is not unusual to hear about problems with PLM systems. It is costly, complicated, hard to implement and non-intuitive. However, I want to raise a voice and speak about data management (yes, data management). Most of PDM/PLM software is running on top of data-management technologies developed and invented 30-40 years ago. The RDBM history is going back to the invention made by Edgar Codd at IBM back in 1970.

I was reading Design News article – Top automotive trends to watch in 2012. Have a read and make your opinion. One of trends was about growing complexity of electrical control units. Here is the quote:

As consumers demand more features and engineers comply, automakers face a dilemma: The number of electronic control units is reaching the point of unmanageability. Vehicles now employ 35 to 80 microcontrollers and 45 to 70 pounds of onboard wiring. And there’s more on the horizon as cameras, vision sensors, radar systems, lanekeeping, and collision avoidance systems creep into the vehicle.

It made me think about potential alternatives. Even if I cannot see any technology these days that can compete on the level of cost, maturity and availability with RDBMS, in my view, now it is a right time to think about future challenges and possible options.

Key-Value Store

These types of stores became popular over the past few years. Navigate to the following article by Read Write Enterprise -Is the Relational Database Doomed? Have a read. The article (even if it a bit dated) provides a good review of key-value stores as a technological alternative to RDBMS. It obviously includes pros and cons. One of the biggest "pro" to use key-value store is scalability. Obvious bad is an absence of a good integrity control.

NoSQL (Graph databases)

Another interesting example of RDBMS alternative is so-called noSQL databases. The definition and classification of noSQL databases is not stable. Before jumping into noSQL bandwagon, analyze the potential impact of immaturity, complexity and absence of standards. However, over the last 1-2 year, I can see a growing interest into this type of technology. Neo4j is a good example you can experiment with in case you are interested.

Semantic Web

Semantic web (or web of data) is not a database technology. Opposite to RDBMS, Key-value stores and graph databases, semantic web is more about how to provide a logical and scalable way to represent data (I wanted to say in "semantic way", but understand the potential of tautology :)). Semantic web relies on a set of W3C standard and combines set of specification describing ways to represent and model data such as RDF and OWL. You can read more by navigating to the following link.

What is my conclusion? I think, the weak point of existing RDBMS technologies in the context of PLM is a growing complexity of data – both from structural and unstructured aspects. The amount of data will raise lots of questions in front of enterprise IT in manufacturing companies and PLM vendors. Just my thoughts…

Best, Oleg

About these ads

6 Responses to PLM, RDBMS and Future Data Management Challenges

  1. Lars Taxén says:

    Oleg,

    Interesting topic!

    In my view, you need to bring in the issue of “context” into data modeling in order to cope with increasing complexity. You need some mechanism that recognizes data with respect to their relevance for a certain community or domain. Consider, for example, the lifecycle of a piano. First, it must be built, then moved to the concert hall, played on in concert and maybe maintained. In all these domains, the identity of the piano remains the same, but the relevant properties and relationships are quite different. In the building context, things like material properties, sustain structures, building traditions, etc., are relevant. When moving it, weight and size matters; in concert tuning, attack, sustain, timbre, and so on are important; when maintaining it tuning mechanism may be most relevant. Each domain should be given complete freedom to model the piano in the way it suits the domain the best. In this way you will get a reduction of complexity by loose coupling between domains, and the data model would grow “organically”.

    I do not know enough about the different techniques of data modeling you mention, but it would be interesting to find out if anyone of them includes a contextual thinking in the way described above.

    Lars

  2. Rahul Deshpande says:

    Oleg,

    In my view, current PLM platform needs revamp first on two aspects.
    1. They need to make full use of RDBMS capabilities of existing database (many are using them as dump or equivalent to a more efficient excel sheet)
    2. The way data is stored structured it doesn’t support lot of existing data types for eg. There are issues with data management when it comes to a sheet metal or piping information in a ship or aircraft. it also includes the electrical data (it can’t be managed).

    The basic PDM capabilities which were developed keeping in mind a “Car” need to broaden its scope

    My conclusion: the PLM data structure needs a full revamp for all major PLM systems (even though these companies claim they can manage all mentioned data above).

  3. Lars,thanks for commenting! You’re spot on when talking about your “piano” example. One of the most fascinating elements of modern data modeling concepts (especially in semantic web) are bases on the concept of Open World assumption (http://en.wikipedia.org/wiki/Open_world_assumption) opposite to Close World assumption. The data about piano needs to be gathered during the piano-lifecycle. Most of the solutions built today are not capable of support such a type of modeling. This is the challenge. I’d encourage you to have a look on what we do at Inforbix with product data semantics –> http://www.inforbix.com/inforbix-product-data-semantics-explained-2/. Best, Oleg

  4. Rahul, I cannot agree more with you. The object / relational data modeling is challenging. Many vendors are abandoning RDBMS capabilities to develop flexible object modeling solutions. The performance and scalability become a bottleneck for some of them. Remember, most of PDM/PLM platforms were developed 10-20 years ago… Thanks for commenting. Best, Oleg

  5. [...] the first truly radical change since it began almost thirty years ago.  In the blogpost titled, PLM, RDBMS and Future Data Management Challenges, on PLM Think Tank, author Oleg Shilovitsky points out the effect PLM’s out-of-date technology is [...]

  6. El matrimonio es una ordenanza de la creación…

    [...]PLM, RDBMS and Future Data Management Challenges « Daily PLM Think Tank Blog[...]…

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

Follow

Get every new post delivered to your Inbox.

Join 252 other followers

%d bloggers like this: