A checklist for what to store when importing data into a system, with a silly acronym: CLOGS.
- Content: the data as a sequence of symbols, i.e. Shannon's definition of information.
- Language: the step from the content to its (hopefully unambiguous) meaning.
- Origin: which human or organisation sent this data?
- Grounding: the step from fiction to non-fiction. The link to reality or to common ideas.
- Speech-act: the intended or expected effect of this data on its audience.
Keeping this checklist in mind helps me think about for instance the Web of Data. There, the response of a http request is the content. The Content-Type header helps point at the language, although there are several layers there: a document might be JSON with a UTF-8 encoding, and on top of that it might be JSON-LD. If it is then the data itself points to ontologies that help the receiver to interpret it unambiguously. The language of this web page is HTML - instructions for displaying a web page. But at a deeper layer, the language of its "title" tag is English. At an even deeper layer the abbreviation 'CLOGS' is a pun on wooden shoes which (at least in tourist shops) are a symbol for the Netherlands. Language is all those layers - as long as it's a common understanding and it's reasonably clear and unambiguous to the receiver that the meaning was intended.
The web architecture has a good definition of origin, for instance when it comes to trusting the publisher of executable code, or trusting (the data on) an e-commerce or online banking website based on its TLS certificate. Also in electronic data internchange, blockchain systems, self-sovereign identity, gossip networks, etc, digital signatures are often used to sign data. The receiver might know the author of the data (and their domain name or public key), or trust a data publishing platform on which a piece of data appears with some level of recommendations. Sometimes the meaning of a piece of data is related to origin. For instance, a message saying that some stranger wants to buy your guitar is only trustworthy if it also comes from that stranger. Like language, and like identity, origin has multiple layers.
Grounding is a term we used in 1990s bottom-up AI research, claiming that intelligence in small robots or autonomous agents is more meaningful than the expert systems that came before it in top-down AI. It provides the link between the story and actual entities that the receiver already knew from elsewhere, or that at least exist in the physical world. Grounding is also essential for deduplicating data when it is synced from one system to another.
The speech-act behind a piece of data can be a promise, a request, or any way in which the sender of the data is trying to affect the audience. When you send a timesheet, a receipt or an invoice to your manager or to a client, you are not just sharing data, you are also asking to get paid, and both sender and receiver understand this even though it's not part of the meaning of the content. Even when data is just on the web, there is some meaning to the fact itself that that data appears there: it implies that this data has met the requirements for appearing on whichever website it appears on.
The actual enforcement of access control would be done by the data hosting service involved in transporting the data, but the information needed to carry out access control can be part of the scope of the CLOGS checklist. The speech act is where consent for resharing would be signalled, which gets close to access control. And there may be access control information included in the content of the data, for instance when storing data on a Solid pod and adding ACL files to it with Web Access Control instructions.
One piece of data can also change through multiple versions over time, in which case we need to keep track of versioning information. In that case you should probably apply the CLOGS checklist to each changeset. For instance, an invoice can be seen as a document that changes in the status of an order. This approach implies that the "current state" of a dataset is built by replaying the change log, and each node in a federation does that calculation deterministically. There may be other valid ways to share versioned data between systems though, so I'm not entirely sure yet about this point.
To interpret the speech-act of a piece of data, it's obviously important to understand its origin first. In Federated Bookkeeping we try to interpret incoming data from other systems to automatically update our own records and trigger routine business actions automatically. This is a form of Master Data Management, although maybe instead of a single source of truth, Federated Bookkeeping aims to create a distributed source of truth. When doing so, it's vitally important to not only understand the Contents of a business document through the Language in which it is written, but also be aware of the Origin, the Grounding and the Speech-act of each incoming and outgoing message. I hope the CLOGS acronym can help us reason about that.