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RDBMS

What is the right data model for PLM?

by Oleg on August 17, 2012 · 11 comments

I think the agreement about importance of the data model among all implementers of PDM / PLM is almost absolute. Data drives everything PDM / PLM system is doing. Therefore, to define the data model is the first step in many implementations. It sounds as something simple. However, there is implied complexity. In most cases, you will be limited by the data model capabilities of PLM system you have. This is a time, I want to take you back in history.

Spreadsheet Data Model

Historically, it became the most commonly used data model. And the reason is not only because Excel is available to everybody. In my view, it happened also, because tables (aka spreadsheets) is a simple way to think about your data. You can think about table of drawings, parts, ECOs. Since almost everything in engineering starts from Bill of Material, to think about BOM table is also very simple. The key reason why in many cases spreadsheet model became so wide-accepted are simplicity and absolute flexibility. Engineers love flexibility, and this data model became widely popular.

Relational Data Model

This data model was developed by Edgar Codd back more than 50 years ago. Database software runs on top of this model, and we got what known today as RDBMS. Until second half of the last decade, it was the solution all PDM /PLM developers were relying. First PDM systems were developed based on RDBMS. However, they had low flexibility. Because of rigorous rules of this model, making changes was considered as not a simple task. One of the innovations of late 1990s was to develop a flexible data model as an abstraction on top of RDBS. Almost all PDM/PLM systems in production today are using object abstractions developed on top of the relational data model.

The challenges of Spreadsheets and Relational Databases

Despite these technologies are proven and used by many mainstream applications, it is far from perfection. One of the product development demands is flexibility. Spreadsheet model can deliver that, but gets very costly within the time. Relational data model can combine flexibility and support manageability of data. However, it becomes to make a change in these models is costly. Identification, openness and expandability is problematic in relational data models opposite to some other web-based solutions.

Future data models – NoSQL, RDF, etc.

Thinking about what comes in the future, I want to spell to buzzwords – NoSQL and Semantic Web. I can see a growing amount of solutions trying to adopt a variety of new data platforms. NoSQL comes to the place as an alternative solution to Relational Database. If this is a first time you’re hearing this buzzword, navigate to the following Wikipedia link. NoSQL is not all the same. It combined the whole group of solutions such a key-value stores, object databases, graph databases, triple store. Semantic web is collaborative movement led by W3C. The collection of Semantic Web technologies (RDF, OWL, SKOS, SPARQL, etc.) provides an environment where application can query that data, draw inferences using vocabularies, etc. Part of these standards something called Linked Data – a collection of data set in open formats (RDF) that shared on the web.

What is my conclusion? Many of the technologies used by PLM companies these days are outdated and came from the past 20-25 years. There is nothing wrong in these technologies. They are proven and successfully used for many applications. However, in order to achieve the next level of efficiency and embrace future of PLM, new horizons need to be explored. Data flexibility, openness and interoperability – these elements are absolutely important in the future of PLM. Options to use future data models coming from past 10 years of web experience need to be explored. Important. Just my thoughts…

Best, Oleg

Image: FreeDigitalPhotos.net

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Will PLM vendors dig into Big Data?

by Oleg on May 18, 2012 · 9 comments

Big data is hyping trend these days. Many people is using the term of big data for different purposes and situations. Here is a problem of big data in a nutshell, how I see it. The data is growing. It is growing in organizations and outside of organizational boundaries. It is growing because of application complexity and implementation complexity. My take is that each time we face “data problems” that cannot be solved in a traditional phase, the case of “big data” discussion comes up. To confirm that, take a look on the definition of Big Data you can find in Wikipedia:

In information technology, big data consists of data sets that grows so large and complex that they become awkward to work with using on-hand database management tools. Difficulties include capture, storage,[3] search, sharing, analytics,[4] and visualizing.

So, I wanted to come with some examples of situations where “big data use case” is real and can bring a significant value to manufacturing organizations. My attention caught by the report made by SAS – Data Equity: Unlocking the Value of Big Data. You can grab a copy of the report by registering via this link. Download your copy. I’m sure you find it interesting. Here is a very good explanation about why big data becomes important.

Big data is becoming an increasingly important asset to draw upon: large volumes of highly detailed data from the various strands of a business provide the opportunity to deliver significant financial and economic benefits to firms and consumers. The advent of big data analytics in recent years has made it easier to capitalise on the wealth of historic and real-time data generated through supply chains, production processes and customer behaviours.

Big data can bring value. This is what you can learn in the SAS article. You can see it on the chart SAS presented to show BigData forecast to 2017 (see below).

Thinking about PLM and the impact on specific industry sectors, the example of a supply chain is very appealing. The data in a supply chain is getting really messy. Here is a very insightful take on supply chain and big data from the same SAS report.

Optimal inventory levels may be computed, through analytics accounting for product lifecycles, lead times, location attributes and forecasted demand levels. The sharing of big data with upstream and downstream units in the supply chain, or vertical data agglomeration, can guide enterprises seeking to avoid inefficiencies arising from incomplete information, helping to achieve demand-driven supply and just-in-time (JIT) delivery processes.

Why big data is complicated and why software vendors may consider it? Here is the interesting quote from the report that actually explains that:

A major obstacle to undertaking big data analytics is the level of technical skill required to operate such systems effectively. Although software solutions for tackling big data continue to become more user-friendly, they have not yet reached the stage where no specialist knowledge is necessary. The requisite skills for big data analysis are above those required for traditional data mining, and the cost of hiring big data specialists can be prohibitive for many firms.

Big Data and PLM vendors

I haven’t seen PLM vendors providing examples and mentioning big data.  I think the fundamental problem is technology. The majority of PLM software vendors are running PLM products based on platforms developed 10-15 years ago. All these solutions are relying on RBDMS. As we learned, RDBMS doesn’t scale at the level of big data. An interesting exclusion is Dassault System, which decided to acquire Exalead back 2010 and improve their semantic indexing and search. However, I haven’t seen any implementation of Exalead applied to manufacturing and big data domain.

What is my conclusion? The value of big data is undoubted. To adopt “big data”, PLM vendor needs to go to “unknown” place characterized by a different technological stack. It is not clear how they will do so. The time is running. The ability to dig into big data problem will become an imperative very soon. Just my thoughts…

Best, Oleg

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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

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Will Database in the Cloud supercharge PLM for Small Companies?

October 23, 2011

I want to talk about an interesting segment of cloud technologies – cloud SQL Database. For the last months, I’ve seen multiple announcements of vendors in this space. Overall, it seems as an interesting trend. In a nutshell, cloud SQL database is a service that allows you to have your SQL database running “somewhere” on […]

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