Note about CFAR-m

contact@cfar-m.com Skype: CFAR-m Mob.: +33 (0)3 30 72 90 13
Tel.: +33 (0)9 75183 180 website: www/cfar-m.com
Note about CFAR-m
In summary, CFAR-m helps to describe complex reality with many interacting fields; to
extract knowledge, deliver metrics, simulations, and build powerful models. Extracting more
objective information from datasets helps to provide a better analysis and understanding and
forms the foundations to building advanced solutions to problems, issues or situations.
It can be applied in known fields or used to investigate clusters coming from patterns
discovery tools.
Main features of CFAR-m
1°) automatic extraction of weightings
2°) no reduction of variables (it accounts for all the variables for processing the calculus and
delivering the results)
3°) each item has its own vector of weight
4°) it shows the contribution of each variable (or group of variables) to the ranking
(sensitivity)
5°) by taking into consideration all the variables without exception, CFAR-m is able to
determine the level of influence (lots, some, none) of each one. Variables are used to build
simplified models that work in real-time. That said, in an ever-changing world, we need to be
able to detect and anticipate any changes. That is why CFAR-m is reused periodically to
check that no major changes have occurred and that the influence of any previously non-
influential variables has not grown exponentially in the interim. If that is the case, then those
variables are integrated into the simplified model.
6°) whilst CFAR-m can be used for aggregation and ranking purposes, CFAR-m is better as
a tool to build advanced and sophisticated applications.
___________________
CFAR-m is fully working for R&D and consulting. For industrial solutions or volumes of data
exceeding our current resources it needs specific developments that need time and
investment.
CFAR-m works only as a service and is never delivered as a software protection in order to
protect our 10 years of investment in its development. However, any solution that would
guarantee protecting the absolute confidentiality of CFAR-m can be envisaged.
CFAR-m works with quantitative structured data. We can look into converting other forms of
data when possible.

As previously stated, CFAR-m is an algorithm that allows, from recorded data, to describe
very highly complex situations with many sectors interacting with one another. Then, with
complete neutrality (no presupposition), CFAR-m can extract deep knowledge/information
and build models to make decisions. It is a powerful tool to find the root causes and so treat
the origins of a problem and not to react “off the cuff” to consequences with unnecessary
costs and generally for mixed results.
However, you call the techniques: analytic, root causes, complexity, or any other ... If you
want to manage a problem you have to first understand it and then generate metrics to
manage it.
There are tools that offer similar functionalities to CFAR-m, but unlike CFAR-m they use
different theoretical backgrounds and algorithms, which don’t have the same level of
coherence and thus cannot deliver the same level of quality and efficiency.
CFAR-m is a powerful algorithm that can be used in a number of domains to provide added
value and a competitive advantage (IP). Away from its use for simple aggregation and
ranking, CFAR-m can play a key role in the development of “revolutionary” applications and
therefore new products, services and even company start-ups.
Different stages of R&D and Consultancy:
1) What do we want to measure (risk, governance etc.). This confirms which theoretical
framework is relevant. Even though CFAR-m is a powerful tool, it must be correctly used. If
you are not able to accurately describe what you want, specialists may potentially be
required or even R&D conducted.
2) What are the “dimensions” to take into account to get the results you want? If you don't
know, specialists may be required or even R&D conducted.
3) What are the variables that represent each dimension? If you don't know, specialists may
be required or even R&D conducted.
4) Information to provide in order to use CFAR-m: Sense of contribution of each variable
to the ranking. That means that for each variable we have to clarify whether a higher value of
a variable will push the ranking of the item towards the first ranking position (positive
contribution) or have the opposite effect. CFAR-m delivers ranking and if we want to rank we
must be able to clarify the contribution of each variable. If we do not know, further
investigations will be needed or R&D must be conducted.
5) Output: What results do you want to obtain? Ranking, contribution of variables, index,
partial index, weightings, or/and simulations?
Once all these points defined, a quote can be provided.
CFAR-m: a tool for Composites Indicators

An indicator is the mathematical aggregation of a set of variables that describe a concept,
phenomena, or a dimension. Usually indicators have no common units of measurement;
generally an indicator has no dimension it just attempts to give the best indication about the
observed fields that it tries to describe.
A composite indicator (CI) is a mathematical aggregation of a set of individual indicators that
measure multi-dimensional concepts.
There is no universally agreed methodology and the arbitrary nature of the weighting process
by which components are combined constitutes the main weakness of composite indicators
which CFAR-m overcomes using Artificial Neural Network (ANN) models, which contains
proven qualities in modelling complex relationships...
CFAR-m can model complex relationships without needing any a priori assumptions about
the distribution of variables (a major constraint of conventional statistical techniques).
CFAR-m is an original method of aggregation based on neural networks which can
summarize with great objectivity the information contained in a large number of variables
emanating from many different fields. It avoids the adoption of an equal weighting or a
weighting based on exogenous, arbitrary, or presupposed criteria.
CFAR-m can help to describe and model complex concepts and phenomena with interacting
dimensions whilst preserving all the informational value/influence of each variable (no
reduction of variables). Thanks to that capability, CFAR-m can go on to deliver metrics
emanating directly from the prepared datasets, which allows the advanced understanding
and management of complex studied problems.
That said, CFAR-m is often compared to data mining, data analysis and other methods,
however the technology offers much more.
Precisions:
CFAR-m is a powerful tool, but it has to be used by specialists. That said, it is possible to
create tools and models based on CFAR-m’s results in order to provide solutions that can be
very simple to use.
CFAR-m is an ANN (Artificial Neural Network) based algorithm and ANN is a time-consuming
process.
This time depends on the convergence speed that is specific to each dataset (small datasets
can take a long time to process and bigger amounts of data less time).
CFAR-m can be integrated into real time processing requirements through the creation of
solutions adapted for those needs. We can build complex event processing solutions, with

CFAR-m surveying and taking any data environment changes into account. For each
dimension the number of items must be superior to the number of variables (the system must
be free).
All dimensions being considered must relate to the same items.
NB:
- For dynamic simulations taking in account uncertainty (i.e. for C.I and so on) we partner
with http://www.youtube.com/watch?v=x4zAniSP0FY
- We also have a partner on the issue of “Complexity” to obtain interesting change indicators.
How to work together
We are looking to find partners that have their own uniqueness and know-how, thus
proposing competitive advantages. With that base, we can see if CFAR-m can bring further
competitive advantages and added value and vice-versa. By combining our strengths in
order to share the generated added value between the end user and us.
We have currently 4 ways of development
This is not for hiring but business partnering with companies or individuals as independents.
1°) Consulting
Consultants can use CFAR-m for their missions when it is relevant. Consulting is defined
here as a one shot intervention.
2°) Ventures and Joint-Venture Projects
When a start-up or an established firm can see applications where CFAR-m has a potential
role to play; we can partner on new products or services.
3°) R&D
If CFAR-m has a potential role to play in a research project we can consider participating if
the development strategy in place is compatible with our goals.
4°) OPEN PROJECT
If you have a project idea involving CFAR-m (alone or with other technologies) and you are
an expert in the project field, you can propose or submit it to us and we will study it and how
it would be possible to implement it in partnership.
We are open-minded about all contact in this area.

Conclusion:
These features bring us to:
1°) better describe known phenomena
2°) better describe discovered new patterns (pattern discovery)
3°) participate to R&D.
4°) initiate new projects where CFAR-m is a key technology.
Contact:
If you are interested in discussing this further, I invite you to contact us to plan a call/Skype
for example.

Note about CFAR-m

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