Technical database applications for Big Data

Do you have lots of complex data and wish to store it in a standardized way for speedy access? Do you wish to take advantage of your data directly from within your applications? At CIM we benefit from our extensive experience with other database applications like SPC and Remote Monitoring enabling us to assist with your company's advanced database application. 

Valuable experience used in new contexts

Most of the solutions CIM are involved in, are built around databases acting as the interfacing centre between equipment, systems and users. Often, the databases store lots of data, originating from many different sources in many different formats. Our involvemens in these projects has given us extensive experience in designing and implementing database applications for technical use. Using this experience when helping our customers in similar solutions, ensures the right direction from the beginning and making the right design decisions.

Databases are the key to standardizing data

When dealing with technical data from different sources, the data content is most often not standardized. By introducing a database in your design, the database can act as the stadardized centre of the solution. Anything comming into or going out of the database is standardized. This approach makes your solution far easier to expand when adding new data sources, designing new user interfaces or adding new data dependent functionality.

Some examples of where we can help

Some examples of technical database applications, where CIM can assist you:

• Handling of test parameters • Report generator 
• Standardization of measurement data        • Product configurator 

Your benefits when choosing CIM

When choosing CIM to assist with your company's next technical database application, you will benefit from:

  • Extensive experience with similar solutions
  • Making the right design decisions to save time and money
  • Scalable solutions which are easy to maintain and expand

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Customer cases:

Additional information:

White paper @

Defining the five V's of Big Data:

Volume System is gathering large amounts or
volumes of data
Variety Data gathered by the system and analyzed is
varied in structure and format
Velocity Data is gathered at a high speed with
high sample rates
Value Significant value is derived from the analysis
of data; this was previously limited by technology
Visibility Data is accessed or visible from disparate or
multiple geographic locations


To qualify as Big Data, data does not have to exhibit all five
of the characteristics above
—just a mix of a few of these characteristics.