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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.

Digitization relies on emerging technologies driven by Internet of Things. This not only affects businesses, but also the entire society: smart homes, smart agriculture, and intelligent transportation. The connection of “things” under the same umbrella applies also in education through massive open online courses (MOOCs). Each industry should adapt to the changes brought by Digital Era. This paper makes an introduction to the challenges that come together with Industry 4.0 and proposes Process Mining techniques as a new approach for business process analysis in order to support companies to rapidly adapt to market changes by offering customized solutions for customers. The study provides a brief overview of Industry 4.0 and gradually introduces Process Mining as a purview of Industry 4.0. A case study approach is used to confirm the advantages brought by Process Mining. Different types of graphical visualization describing both control-flow and resource perspectives are also presented.

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.