Curvaceous Shop – Parallel Data Investigator (PDI)

Available now!

Curvaceous PDI Light – One Year License 

 Includes up to 20 variables

Special Introductory Price – $160

*Excluding VAT, price may vary slightly in other currencies


 View Cart

 View Cart

 View Cart


Curvaceous PDI Medium – One Year License

Includes up to 50 variables. 

Special Introductory Price – $240

*Excluding VAT, price may vary slightly in other currencies


 View Cart


Curvaceous PDI Heavy One Year License

Includes up to 120 variables

Special Introductory Price – $320

*Excluding VAT, price may vary slightly in other currencies


 View Cart


*These are all introductory prices liable to change without warning.


Interrogative Visualisation

Discover Interrogative Visualisation as a new multivariate  analysis method that allows you to explore, find cause-and-effect relationships, improve your continuous improvement methods and results and does not require you to learn statistics or have a maths specialisation to do much more analysis in much less time. You need knowledge of the domain you are exploring but given that who knows what you might discover.

We have been developing Interrogative Visualization methods for many years but restricted them until now to the process industry domain (power plants, paper mills, chemical plants, oil refineries etc) where they have enabled chemical and control engineers (like us) to uncover great wealth and make processes much safer. as we are mostly chemical engineers. But we always believed that there was great value waiting to be harvested in numerous other domains and have produced PDI out of PPCL’s CVE as a low-cost starter product so that whether you are a student, a  research scientist, an oceanographer, a market research analyst or a finance wizard you can discover for yourself the value in your domain.

There are examples supplied with PDI for manufacturing process improvement, multi-variable visual cluster analysis and of analysis of Covid data from freely available data published weekly by the UK Government (and many other Governments). These will help you learn the techniques of Interrogative Visualization.

At Curvaceous, we believe that Interrogative Visualization methods will allow PDI to provide exceptional value to those outside of process industries. In fact anyone working with spreadsheets full of mostly numeric data can utilise PDI to carry out the following;

  • Test Hypotheses
  • Discover more about multi-variable cause-and-effect relationships between variables
  • Find recurring patterns of performance or operation through entirely visual cluster analysis
  • Simplify analysis of response surfaces
  • Identify the independent variables contributing most towards variability and give direction to improvement programs
  • Gain insight into solutions offered by algorithmic methods such as AI

Interrogative Visualization provided by PDI enables the user to visually understand the whole of the data without dimensionality reduction, and empowers the all-powerful abilities of the human mind and human intelligence (HI) that are not brought into play with traditional purely numeric analysis. Sometimes a picture is worth a thousand numbers! Discover for yourself the Blessings of High Dimensionality!


PDI Features

PDI is a workstation/laptop product supported under Windows 8 and 10 and utilizing the Parallel Coordinate Transformation.

  • PDI Data sources are flat files in csv format where the rows are different sets of related variable values and the columns are the variables and their labels. Files below 50 million cells (rows x columns) are recommended  as above this size some PDI functions may not be able to obtain the amount of temporary memory some PDI functions need.
  • PDI includes sophisticated Join and Append functions for combining multiple data files including alignment of any time-based files at different data frequencies.
  • PDI recognises blank or non-numeric fields as invalid or missing values and provides several methods for substituting alternative values. It also allows the user to mark data values he knows from his domain knowledge to be invalid
  • PDI includes the powerful query features of CVE allowing high dimensionality queries to be created in a number of steps
  • PDI includes visual clustering of very high dimensionality and can identify the genuine multivariable outliers
  • PDI includes the unique Box Variable and its associated Pareto plot for identifying variables contributing most to variability
  • PDI comes with a 200-page User Guide in pdf format
  • PDI supports use on multiple displays through the extended Windows desktop feature and includes capture of images of PDI windows in png format
  • PDI is available with a one-year licence in three different tiers defined by maximum numbers of variables.


Geometric Process Control

Perhaps the deepest insight from this display is that the selected data define a closed shape in multi-dimensional space which, in the process industries, is called an Operating Envelope. This is the foundation of PPCL’s technology of Geometric Process Control which, in the process industries, can control a process into a chosen multi-variable Operating Envelope or use the Envelope to predict fault conditions in time for them to be avoided or mitigated. This is delivering to the process industry its long-held ambition for much better and more meaningful operator alarms and  ‘predictive alarming’.

In manufacturing industry GPC is seen as the successor to the highly mathematical Multi-variable Statistical Process Control (MSPC) which in turn is the long-running attempt to widen the applicability of the very successful univariate Statistical Process Control (SPC) of the 1950’s to today’s more complex and data-rich manufacturing environments. Click here to read more about Geometric Process Control.


What are Parallel Co-ordinates?

In a parallel coordinate graph such as the one below all the variable axes are vertical and parallel, and a single data point is a polyline connecting the values of all the variables. The polyline is actually a representation of a point in a high dimensionality space with the coordinates of the point being the values of the individual variables.

A large dataset plotted in parallel co-ordinates generally suffers from the well-known ‘over-plotting’ problem and conveys little information. What makes CVE and PDI so outstandingly powerful is the capability to query the data using combinations of simple 2-d shapes such as lines and planes all brought together with Boolean logic to allow the creation of queries in as many dimensions as the user could wish for. We named this “Interrogative Visualization” and it provides an astonishingly powerful “no-maths” method for analysing numerical data. CVE and PDI contain sophisticated Join functions to facilitate combination of time-series and/or parameter data from other databases.

Many types of query have been found useful and implemented in CVE and PDI, but even the simplest query can provide immediate insight into cause-and-effect relationships to the user with domain-knowledge. The below examples shows a dataset from a cake-making plant with process variables such as weight of flour, size and number of eggs, dimensions of baking tins, mixing time, cooking time and oven temperature combined with quality variables of the product from a human cake-testing panel. The user of CVE or PDI can put specification ranges (the yellow triangles in the example) defining what constitutes a good cake on the quality variables and all the data points or polylines with quality values inside their ranges are shown in yellow. The ranges within which the process variables can be used (for example, how many small eggs should the cooks use to give the same ‘good-cake’ result as the large eggs that are usually used?) are immediately visible. This is immeasurably more informative than putting in numbers for ranges on the quality variables and getting back numbers for ranges on the process variables.


Parallel Co-ordinate Plot
The yellow triangles are the product specification limits in this cake-making example. Each polyline is one cake. Cakes that met all of the tasting panels specifications ( taste, texture, shape, wetness etc) are in yellow so looking at the manufacturing variables (how much flour, number and size of eggs, cooking time and temperature etc. one can see in yellow how to make good cakes and in black how to make cakes that tasting panel will reject.