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In data management, new data storage models and technologies are gradually adopted, including graph-based data models that have been enabled by technologies such as RDF, GraphQL and LPG. These are particularly fit when dealing with the coexistence of multitude and heterogeneous data sources that require an integration architecture in a data-centric organization due to intrinsic data connectedness. Two prominent approaches are distinguished in our study: (1) embracing a unifying graph format, where, following a series of transformations, all data sources are lifted to a common format in a consolidated graph repository, (2) creating a mediator tier where legacy data sources hold their data but are accessed through a virtual graph integration layer. Each approach comes with a specific way to query data and a plethora of query languages were created for this purpose. In the context of a data integration project we investigated two approaches to data retrieval and data modeling that have gained momentum during recent years: RDF graphs holding data lifted from non-graph data sources and GraphQL acting as a proxy for retrieving virtually connected data from heterogeneous sources. The paper reports on comparative experiments with technological instantiations of the two approaches, which can inform further IT strategies in an institutional project advocating a migration from app-centricity towards data connectedness.

Digitization has subjected nowadays business models to dramatic changes. This commitment to innovation forced even the giants of technology to shift and adapt their own business models (e.g. Google developed a rival social network for Facebook, and a cloud file-storage in order to propose an inexpensive version of Dropbox or One Drive, Microsoft developed an online version of Microsoft Office). Organization’s agility must be embedded in companies’ strategies to survive in a market where everything changes with a tremendous quickness. Customer journey mapping, embedded with business process management systems, can assure an improved digital customer experience. Design thinking, a methodology used by several high-tech companies like Apple and IBM, helps the designers to better understand customers’ needs in order to provide desirable solutions. This paper proposes a new approach of generating customer traces from information systems like CRMS to improve customer experience. A germane fact is that companies should use state of the art methods and techniques to better understand customers’ needs and Process Mining algorithms are among the most suitable solutions for this type of analysis.

This paper proposes a teaching method and artifact for Conceptual Modeling education, motivated by a challenge in the authors’ university of bridging the gap between bachelor-level studies and research work on topics related to Conceptual Modeling. At bachelor-level, Conceptual Modeling is subordinated to Software Engineering or Business Process Management topics, making extensive use of available standards for graphical documentation purposes. However, at doctoral level and in project-based work, modeling methods must be scientifically framed within wider-scoped paradigms – e.g. Knowledge Management, Enterprise Modeling – or tailored for domain-specific scenarios. The teaching artifact presented in this paper is an example of an “agile modeling method” that can be iteratively evolved together with students through a metamodeling approach in support of a course flow that argues for a generalized model value proposition and modeling languages acting as “schema” that can be tailored and migrated to accommodate explicit requirements from any application domain.

Business Process Management Systems (BPMSs) are inherently model-driven, relying on machine-readable process repositories that are typically standards-based. However, a requirement for semantic agility is emerging as knowledge-driven applications become less blueprint-oriented and more context-aware. The integration of process knowledge with contextual data can be subjected to this agility requirement – i.e., having the process modelling environment customised in terms of (expanding) its knowledge space and in terms of model-data interoperability. Such customisations may capture any of the enterprise perspectives proposed by the Zachman Framework (among which the How, Who and Where facets are in our particular focus) towards the benefit of establishing a hybrid knowledge-data fabric underlying flexible, context-driven BPMSs.

[Context and motivation:] There is a variety of sources from which security requirements may be derived, typically pertaining to fields such as software engineering, information systems risk assessment, security auditing, compliance management, IT governance etc. Several approaches, especially in the software engineering domain, have already investigated security requirements within a broader scope, including results from risk management. [Question/problem:] Identifying security requirements according to just one of these fields might not suffice – opportunities of integration and enrichment must be investigated. [Principal ideas/results:] Our proposal advocates a convergence of different security requirements sources towards their richer specification, based on semantic technology. [Contribution:] Through this vision paper, we sketch the outline for a new perspective on eliciting security requirements, based on knowledge-driven integration of approaches from software engineering, risk assessment, governance and compliance.

Part 2: Short Papers; International audience; Structured Data Markup allows Web developers to embed semantics in HTML pages, thus enabling clients (search engines, client apps etc.) to distil machine-readable resource descriptions from HTML code. This approach emerged from the Semantic Web paradigm as a powerful alternative to traditional Web scraping. Its enablers are dedicated HTML extensions (e.g., RDFa) and controlled vocabularies (e.g., Schema.org). Originating in a different context, Enterprise Modelling methods rely on diagrammatic means for describing and analysing an enterprise system in terms of key properties and conceptual abstractions. Hence, both the Semantic Web and Enterprise Modelling paradigms share a common interest in machine-processable semantics towards the goal of elevating semantics-awareness in information systems and decision support. Inspired by this overlapping, the paper proposes a mechanism for streamlining semantics between Structured Data Markup and enterprise modelling methods. Towards this goal, it employs the Resource Description Framework and the Agile Modelling Method Engineering Framework.

IT audit is employed in business organizations to demonstrate they hold the control for the correct and efficient functioning of their IT infrastructure. It is slowly moving from a completely practitioners’ concern into a research domain. There is a need for identifying new methods that could facilitate an objective, real time and cost-effective assurance. This paper proposes a method to automate the IT audit process. Our approach is based on ontologies for formalizing the vast audit knowledge and on intelligent agents for real-time audit and risk assessment.

In terms of the current information society, where news and information generally run at a remarkable speed, it is imperative that the events of capital importance for the world economy (and others fields) to be available and disseminated in real time. Thus, we refer to news that may influence in a large manner the stock quotes, forex, oil quotes and so on. In view of the fact that the Internet abounds in news, in our on-going research (under grant TE 316 ”Intelligent methods for decision fundamentation on stock market transactions, based on public information”) we seek to propose a solution for extracting news vital information, its interpretation in real time, followed by suggesting possible decisional alternatives to the representatives involved in the stock exchange market. We put forward in the present paper our starting ideas for a conceptual model for a semantic Web news search, which encompasses multi-agent technologies, semantic Web, text search and mining, RSS news feeds.