The complexity of an (ontology-driven) conceptual model highly correlates with the complexity of the domain and software for which it is designed. With that in mind, an algorithm for producing ontology-driven conceptual model abstractions was ...

The terms ‘semantics’ and ‘ontology’ are increasingly appearing together with ‘explanation’, not only in the scientific literature, but also in everyday social interactions, in particular, within organizations. Ontologies have been shown to play ...

In many contexts where data is streamed on a large scale, such as video surveillance systems, there is a dual requirement: secure data storage and continuous access to audio and video content by third parties, such as human operators or specific ...

Security risk management (SRM) is crucial for protecting valuable assets from malicious harm. While blockchain technology has been proposed to mitigate security threats in traditional applications, it is not a perfect solution, and its security ...

Deep reinforcement learning holds significant potential for application in industrial decision-making, offering a promising alternative to traditional physical models. However, its black-box learning approach presents challenges for real-world ...

• Segmentation of the state–space through probabilistic modeling.
• Initializing baseline policy for guided exploration using Behavioral Cloning (BC).
• Identifying segmented abnormal states with probabilistic modeling and DRL.
• ...

The increasing RDF (Resource Description Framework) data volume requires a Hadoop platform for processing queries over large datasets. In this work, SPARQL (Simple Protocol and Rdf Query Language) queries are evaluated with Hadoop based on the ...

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• Extended vertical partitioning is proposed for distributed data stores.
• Hybrid monarch butterfly with beetle swarm load balancing optimization for accessing the RDF data.
• The proposed SPARQL query processing is evaluated over large ...

Most NoSQL systems are schema-on-read: data can be stored without first having to declare a schema that imposes a structure. This schemaless feature offers flexibility to evolve data-intensive applications when data change frequently. However, ...

Process mining (PM) aims to construct, from event logs, process maps that can help discover, automate, improve, and monitor organizational processes. Robotic process automation (RPA) uses software robots to perform some tasks usually executed by ...

• Combining process mining (PM) and RPA offers unique process management opportunities.
• PM techniques must better support discovering processes based on UI logs for RPA.
• Tools need to better integrate PM and RPA features together, in ...

Software testing is the most used technique for quality assurance in industry. However, in computer science education software testing is still treated as a second-class citizen and students are unable to test their software well enough. ...

A Product Configuration System (PCS) is a software system that facilitates the sales and production processes of defined customizable products. PCS are specific software developments in the sense that they are knowledge-intensive so that they ...

Virtual Knowledge Graphs (VKGs) constitute one of the most promising paradigms for integrating and accessing legacy data sources. A critical bottleneck in the integration process involves the definition, validation, and maintenance of mapping ...

In the last few years, the concept of data lake has become trendy for data storage and analysis. Thus, several approaches have been proposed to build data lake systems. However, such proposals are difficult to evaluate as there are no commonly ...