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Promising automation of repetitive tasks and release of manpower, Robotic Process Automation (RPA) continues to be a fast-growing market in the IT industry. The industry-driven advancement also comes with disadvantages. Each vendor established ...

Process discovery aims to learn a process model from observed process behavior. From a user’s perspective, most discovery algorithms work like a black box. Besides parameter tuning, there is no interaction between the user and the algorithm. ...

Information from social media can be leveraged by social scientists to support effective decision making. However, such data sources are often characterised by high volumes and noisy information, therefore data analysis should be always preceded ...

In theory, ontology-based process modelling (OBPM) bares great potential to extend business process management. Many works have studied OBPM and are clear on the potential amenities, such as eliminating ambiguities or enabling advanced reasoning ...

Process analytics is an umbrella of data-driven techniques which includes making predictions for individual process instances or overall process models. At the instance level, various novel techniques have been recently devised, tackling next ...

Process models automatically discovered from event logs represent business process behavior in a compact graphical way. To compare process variants, e.g., to explore how the system’s behavior changes over time or between customer segments, ...

Business process modeling (BPMo) is of primary importance for assessing the current state of an organizations’ practices to discover inefficiencies, redesign business processes, and build software solutions. High-quality representations best ...

Managing data-intensive systems has long been recognized as an expensive and error-prone process. This is mainly due to the often implicit consistency relationships that hold between applications and their database. As new technologies emerged for ...

We study the usage of negation in JSON Schema data modeling. Negation is a logical operator rarely present in type systems and schema description languages, since it complicates decision problems: many software tools, but also formal frameworks ...

An increasing number of organisations rely on NoSQL technologies to manage their mission-critical data. However, those technologies were not intended to replace relational database management systems, but rather to complement them. Hence the ...

Trajectories of moving objects are usually modeled as sequences of space-time points or, in case of semantic trajectories, as labelled stops and moves. Data analytics methods on these kinds of trajectories tend to discover geometrical and temporal ...

Forward engineering relational schemas based on conceptual models is an established practice, with a number of commercial tools available and widely used in production settings. These tools employ automated transformation strategies to address the ...

During the development of NoSQL-backed software, the database schema evolves naturally alongside the application code. Especially in agile development, new application releases are deployed frequently. Eventually, decisions have to be made ...

Temporal keyword search enables non-expert users to query temporal relational databases with time conditions. However, aggregates and group-by are currently not supported in temporal keyword search, which hinders querying of statistical ...

The emergence of NoSQL databases and polyglot persistence demands to address classical research topics in the context of new data models and database systems. Schema evolution is a crucial aspect in database management to which limited attention ...

For many companies, information and communication technology (ICT) is an essential part of the value proposition. Netflix and Spotify would not have been possible without internet technology.

Business model up-scaling often requires a different ICT ...

A core strength of enterprise architecture (EA) models is their holistic and integrative nature. With ArchiMate, a de-facto industry standard for modeling EAs is available and widely adopted. However, with the growing complexity of enterprise ...

IT departments in Multi-Business Organizations (MBOs) face challenges when providing services to satisfy business needs. In many cases, the services provided by an IT department do not address all the requirements of the relevant business units ...

Most computer science curricula include a compulsory course on data structures. Students are prone to memorise facts about data structures instead of understanding the essence of underlying concepts. This can be explained by the fact that learning ...

As knowledge increases tremendously each and every day, there is a need for means to manage and organize it, so as to utilize it when needed. For example, for finding solutions to technical/engineering problems. An alternative for achieving this ...

The advent of socio-technical, cyber-physical and artificial intelligence systems has broadened the scope of requirements engineering, which must now deal with new classes of requirements, concerning ethics, privacy and trust. This brings new ...

Smart manufacturing demands to process data in domain-specific real-time. Engineering models created for constructing, commissioning, planning, or simulating manufacturing systems can facilitate aggregating and abstracting the wealth of ...

The rapid advancements in autonomous driving enable the formation of vehicle groups with small distances between vehicles, known as platooning. This technology has attracted research interest as it offers great benefits for future transportation, ...${}_{\mathrm{}}$