Abstract: A method and a system for enabling storing of genealogically related data within a blockchain network, in which the network is configured to generate one or more blockchains, includes enabling a data structure of a transaction of a given blockchain to include a data field, referred to as a genealogical data field. The genealogical data field is configured to include a set of directed links to one or more other blockchains foreseen to be in a genealogical relationship with the given blockchain. A method to enable a GUI screen to display a graphical representation of genealogically related data within a blockchain network is also provided.

Abstract: A method for automatically distributing asset pool shares among actors in a supply chain blockchain network includes: receiving measured values of contribution/obtainment parameters and of obtainment parameters of all network actors based on an amount of actor data inflow/outflow to/from the network in a given time interval; determining, for each actor ai, a net asset balance NBi based on a product between an asset pool to be distributed among actors and the difference between a contribution ratio and an obtainment ratio. Asset pool shares are distributed at defined times among the actors by transferring asset amounts directly between actor pairs via a plurality of corresponding blockchain transferal transactions where a first actor receives an amount of assets from a second actor having a higher net balance. The asset amounts are determined according to a predefined distribution rule based on the net asset balances NB1, NBi, NBN of the N actors.

Abstract: A <<dynamic data cache module>> is inserted between the archiving subsystem (e.g. relational database writing API) and the tag data flow from the acquisition server. Then client data requests must be routed always through the <<dynamic data cache module>>. The dynamic data cache module>> is able to manage tag data that is not only coming from real-time acquisition (i.e. keeping the last n values of tag data in the cache) but also <<chunk>> of data in a different time span. For this usage, the cache will be size-limited and a last recently used (LRU) algorithm may be used to free up space when needed.