Navatec Applied Research   

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Accumulogs, the forerunners of blockchain technology
In 1986, at the time of the initial development of Locational State Theory, Hector McNeill identified the need for cumulative memories or accumulogs to help support more rapid learning and coherent decision making to improve the performance of the personal acquisition of knowledge and economic processes in projects and agriculture, industry and services.

Today this is also known as blockchain technology.

SEEL has worked on the development of Accumulogs since 1986, in a dedicated Accumulog unit.

A significant development has been it's integration with real time audit (RTA) within the Navatec System.

We are implementing an advanced Accumulog as a Plasma System option based on an extension1 to the normal object elements profile.

The Accumulog contains all of the standard object profile consisting of the data elements: object, properties and methods but also provides access to a validator that can be used to reconfirm or revalidate the object profile by generating the associated resultant states and their probabilities.


This represents an major addition to the object-oriented configuration and a valuable extension to object representation to include operational objectives. This is to enhance the ability of Accumulogs to serve their original purpose of providing the most powerful learning and recall system in existence today. As a result rather than representing text, data, graphs and sound the Accumulog permits users to test the method through simulation to generate output states and probabilities. The original data cannot be lost but a user can assess new data or ideas by manipulating the validator so as to "probe" the system and conduct highly informative instructional simulation exercises. This is a powerful tool for teams to understand in some depth the inter-relationships that exist in any decision analysis model of a project.

Portfolio data warehouses

The operation of this system can take data elements from different project records in a portfolio to build a portfolio data warehouse. Any project design process can therefore draw upon existing knowledge, benchmarks and other reference data from an ever-growing portfolio data warehouse. This represents one of the most powerful project design system frameworks available because it promotes lower risk innovation.

Any simulations carried out are captured and recorded as Logical Process Options (also called Logical Project Options or LPOs) in Accumulogs based on the input profile. Users can run the re-validate datasets through simulation any time by accessing the Accumulog and selecting any recorded simulation or raw data can be fed into the simulator to generate more LPOs.

Adding Locational State elements to Accumulogs

We are completing bench tests of prototypes to assess the ease of adding locational state elements to Accumulogs. This represents a leading edge development which will take decision analysis to the next generation of applications that provide exceptional clarity of multi-factor determinant relationships which cannot be captured by state-of-the-art statistical analysis.

What is the main difference between a blockchain and an Accumulog?

Amongst the most difficult data to collect in some parts of the world, is locational state data, especially that relating to phenomena such as weather patterns or even political and economic events. Standard blockchains collect secure transactional data in a sequence and the transactional data recorded is predefined. These transactional records are a static ledger. Accumulogs, on the other hand, also collect new data which is validated, and especially locational state data. This enables Accumulog records that were input in the past to be related to increasingly refined locational state data as a basis for improving the understanding of the nature and relationships between data elements input in the past. This provides a powerful vector for learning based on instructional simulation using more refined and complete data. This does not alter the original data records but it enables layers of more in-depth interpretative analysis to be added helping to reveal previously "hidden" relationships. As a result, Accumulogs are not static ledgers in the common sense of these terms but they extend blockchains with additional non-intrusive data that can expose valuable data relationships based on the OPEE approach and simulation.

Evolving applications

Currently two developments are underway to extend the advantages for users of project cycle and portfolio management systems that use Accumulogs. These are:
  • PDW-Portfolio data warehouses
  • ADD-Advanced domain diagnostics
PDW-Portfolio Data Warehouses

Portfolio data warehouses are essentially the aggregated data collected on all projects in a portfolio where OPEE and Accumulogs have been used to record information on the whole project cycle. The high levels of granularity of the standardised datasets across all projects provides an excellent knowledge base and foundation for advanced analysis of portfolio perfromance and the relative contributions of projects to that performance.

ADD-Advanced domain diagnostics

Some US$215 billion is spent on projects each year across many sectors to support research, innovation and economic development. In the quest for meaningful results-based analysis that informs the professions involved in economic development. The following assessments are of importance:
  • Monitoring and evaluation functions
  • Results and accountability functions
  • Project quality of entry
  • Implementation phase resilience
  • Improvements in investment quality
Most funding organizations have multidomain portfolios within countries. The purpose of ADD is to use Portfolio Data Warehouses based on OPEE and Accumulogs to provide highly detailed datasets to enable an improved level of discrimination between sector or domain-specific analysis so as to identify causes of variance in performance. This information has a significant potential contribution to make to the improvement of project design procedures where the nature of a domain requires additional attention to identified factors. This can also contribute to a more refined appreciation of details that should be taken into account in desiging policy frameworks for providing incentives for projects in specific domains.

Hector McNeill
August 2018

1  OPEE - Object profile elements extension. OPEE is an extension to the normal object oriented element profile developed by SEEL. It adds additional elements to enable users to validate an object profile based on an operational implementation of the object method which can generate the related states (where these exist) and the probability profile of these states. This provides a means of adjusting the object profile to approximate known ranges in benchmarks or other evidence. Where no such benchmarks or evidence exist resort can be made to locational state theory which, in many cases, can determine the degree to which an object profile approximates reality.