Semantic function module pipeline generation

With modular automation, modular industrial plants use a functional engineering approach, and modules enable plug & produce plant engineering. However, plant configuration is still a largely manual process and often not optimized with respect to the available information. In this contribution, we propose a system and algorithm to support the automation engineer in the process of joining together modules into process pipelines and in their optimization. Our solution is built upon an abstract semantic data model that facilitates the automated matching of pre- and post-condition of modules and of the things that are processed by these modules. The pipeline generation engine is further extended by means of an optimization and ranking algorithm, and thus enables automated inter-module pipeline generation and plant optimization. We evaluate our system by means of a simple fictional use case scenario and prove feasibility, applicability as well as the huge potential for time and cost savings.

Towards evolutionary knowledge representation under the big data circumstance

Purpose The purpose of this paper is to propose a graph-based representation approach for evolutionary knowledge under the big data circumstance, aiming to gradually build conceptual models from data. Design/methodology/approach A semantic data model named meaning graph (MGraph) is introduced to represent knowledge concepts to organize the knowledge instances in a graph-based knowledge base. MGraph uses directed acyclic graph–like types as concept schemas to specify the structural features of knowledge with intention variety. It also proposes several specialization mechanisms to enable knowledge evolution. Based on MGraph, a paradigm is introduced to model the evolutionary concept schemas, and a scenario on video semantics modeling is introduced in detail. Findings MGraph is fit for the evolution features of representing knowledge from big data and lays the foundation for building a knowledge base under the big data circumstance. Originality/value The representation approach based on MGraph can effectively and coherently address the major issues of evolutionary knowledge from big data. The new approach is promising in building a big knowledge base.

Your Spreadsheets Can Be FAIR: A Tool and FAIRification Workflow for the eNanoMapper Database

The field of nanoinformatics is rapidly developing and provides data driven solutions in the area of nanomaterials (NM) safety. Safe by Design approaches are encouraged and promoted through regulatory initiatives and multiple scientific projects. Experimental data is at the core of nanoinformatics processing workflows for risk assessment. The nanosafety data is predominantly recorded in Excel spreadsheet files. Although the spreadsheets are quite convenient for the experimentalists, they also pose great challenges for the consequent processing into databases due to variability of the templates used, specific details provided by each laboratory and the need for proper metadata documentation and formatting. In this paper, we present a workflow to facilitate the conversion of spreadsheets into a FAIR (Findable, Accessible, Interoperable, and Reusable) database, with the pivotal aid of the NMDataParser tool, developed to streamline the mapping of the original file layout into the eNanoMapper semantic data model. The NMDataParser is an open source Java library and application, making use of a JSON configuration to define the mapping. We describe the JSON configuration syntax and the approaches applied for parsing different spreadsheet layouts used by the nanosafety community. Examples of using the NMDataParser tool in nanoinformatics workflows are given. Challenging cases are discussed and appropriate solutions are proposed.

A Semantic Data Model: Meaning Making from Data Structures in the SQL Server

Information systems designs are increasingly concerned with entity relationships and technical programmatic approaches to solutions architecture as opposed to semantic based, business focused information architecture that places business definitions at the centre of the information system design and implementation. The disconnect between information technology and business is perpetuated by an overly prescriptive information technology technical design method that fails to incorporate qualitative and normative aspects of business, where information is structured and delivered according to business. The paper will discuss various decision support and semantic approaches to information design and delivery and argue that the traditional modes of solution delivery do not include meaning making of the data elements which are essential to business information reporting and analytics. The meaning making aspect identified is linked to data dictionary or business data glossary that allows for the discovery of semantic meaning from the SQL Server. Using Christian Fürber’s methodology on semantic programming, the analytics team developed a semantic model that enabled detailed definition of fields and the discovery of information using semantic search functionality embedded in the SQL Server. The project provided semantic data framework that provided business with the capability for semantic reconciliation and data sets that were further integrated with Tableau visualization and SQL auto processes.