This article shows a novel approach to semantically align two domain contexts in a distributed system based on the theory of Information Flow [1], also known as Channel Theory. In this article, we propose a 2-step approach to cope with the increasing complexity in constructing the channels, when the channel theory is applied in a complex environment, for example in the area of smart manufacturing. We describe why the methods that had been used so far for constructing the channel might not be suitable for such a complex environment and introduce the main components of our approach. Furthermore, we are explaining how these components work together by using an example from the manufacturing area where product specifications have to be aligned with the production capabilities of manufacturing equipment. Within this example, in the first step a high-level description of production steps is mapped to production processes, and in the second step, a detailed description of the production steps in question is mapped to available equipment and tooling that is related to the filtered production processes from step 1.

As databases become widely used, there is a growing need to translate information between multiple databases. Semantic interoperability and integration has been a long standing challenge for the database community and has now become a prominent area of database research. In this paper, we aim to answer the question how semantic interoperability between two databases can be achieved by using Formal Concept Analysis (FCA for short) and Information Flow (IF for short) theories. For our purposes, firstly we discover knowledge from different databases by using FCA, and then align what is discovered by using IF and FCA. The development of FCA has led to some software systems such as TOSCANA and TUPLEWARE, which can be used as a tool for discovering knowledge in databases. A prototype based on the IF and FCA has been developed. Our method is tested and verified by using this prototype and TUPLEWARE.

Datalog is a widely recognised language for a certain class of deductive databases. Information Content Inclusion Relation (IIR) formulates a general, information theoretic relationship between: data constructs; between data constructs and real world objects, and between real world objects. IIR is particularly concerned with the information that data carry. It would therefore seem desirable to find out whether IIR and reasoning based on IIR may be implemented by using 'safe' Datalog. We present and prove the following theorem:
Any database system that can be modelled using IIR can be represented as a 'safe' Datalog program.}
This paper explores the nature of the relationship between the two frameworks for representing domains of application, in order that such representations of IIR by 'safe' Datalog can then be used as a tool for the analysis of any site that can be approached with the notion of information content, and in particular any given database, and hence how a database works may be approached in terms of information content of events.

Representation is a key concept for information systems (IS). IS is designated to provide information. However, the most available information systems such as databases using a computer are actually data storage and management systems because all they handle are data, which may be used to represent information. Therefore, a good understanding on the relationships between data, information and representation is vital for our study of information systems. And yet, it would seem that in the literature of information systems, the notion of representation has not been well studied, and in particular, how a possible representational relationship between two sets of data constructs may be systematically identified and how this representational relation may be explored for bearing on IS problems would seem worth looking at. The results of our investigation show that representation is a special type of signs created or being used by human agents to carry information about other states of affairs. Any representational relationship is realized and underpinned by information flow between components within an information channel. Moreover, representation can be quantitatively measured as long as certain conditions are met. The information content of representation reveals what you could possibly learn from it and the semantic content of representation is the proposition that a human originator intended to convey by using the representation. In addition, schema connections (i.e., connections between elements within a data schema) can be identified by examining the representational relationship between data entities.