Related papers: ONDA: ONline Database Architect
Computer-based information technologies have been extensively used to help many organizations, private companies, and academic and education institutions manage their processes and information systems hereby become their nervous centre. The…
Dropout is attracting intensive research interest in deep learning as an efficient approach to prevent overfitting. Recently incorporating structural information when deciding which units to drop out produced promising results comparing to…
While there exist a plethora of deep learning tools and frameworks, the fast-growing complexity of the field brings new demands and challenges, such as more flexible network design, speedy computation on distributed setting, and…
In the past few years, the number of OLAP applications increased quickly. These applications use two significantly different DB structures: multidimensional (MD) and table-based. One can show that the traditional model of relational…
Data-driven research is increasingly ubiquitous and data itself is a defining asset for researchers, particularly in the computational social sciences and humanities. Entire careers and research communities are built around valuable,…
Ontology-based data access (OBDA) is a popular paradigm for querying heterogeneous data sources by connecting them through mappings to an ontology. In OBDA, it is often difficult to reconstruct why a tuple occurs in the answer of a query.…
Relational databases (DBs) are ideal tools to manage bulky and structured data archives. In particular for Astronomy they can be used to fulfill all the requirements of a complex project, i.e. the management of: documents, software (s/w)…
Although Cooperative Driving Automation (CDA) has attracted considerable attention in recent years, there remain numerous open challenges in this field. The gap between existing simulation platforms that mainly concentrate on single-vehicle…
Querying databases for the right information is a time consuming and error-prone task and often requires experienced professionals for the job. Furthermore, the user needs to have some prior knowledge about the database. There have been…
Neural Networks are notoriously difficult to inspect. We introduce comgra, an open source python library for use with PyTorch. Comgra extracts data about the internal activations of a model and organizes it in a GUI (graphical user…
When using multiple software tools to analyze, visualize, or optimize models in MBSE, it is often tedious and error-prone to manually coordinate the execution of these tools and to retain their respective input and output data for later…
Recent standardization work for database languages has reflected the growing use of typed graph models (TGM) in application development. Such data models are frequently only used early in the design process, and not reflected directly in…
This paper provides an overview of the CODA framework for modelling and refinement of component-based embedded systems. CODA is an extension of Event-B and UML-B and is supported by a plug-in for the Rodin toolset. CODA augments Event-B…
Model Driven Engineering (MDE) is an emerging approach of software engineering. MDE emphasizes the construction of models from which the implementation should be derived by applying model transformations. The Ontology Definition Meta-model…
In the last decades, data have become a cornerstone component in many business decisions, and copious resources are being poured into production and acquisition of the high-quality data. This emerging market possesses unique features, and…
In the manufacturing context, there have been numerous efforts to use modeling and simulation tools and techniques to improve manufacturing efficiency over the last four decades. While an increasing number of manufacturing system decisions…
Analyzing origin-destination flows is an important problem that has been extensively investigated in several scientific fields, particularly by the visualization community. The problem becomes especially challenging when involving massive…
Relational learning is a challenging problem that has motivated a wide range of approaches, including graph-based models (e.g., graph neural networks, graph transformers), tabular methods (e.g., tabular foundation models), and…
Distributed systems can be very large and complex. The various considerations that influence their design can result in a substantial specification, which requires a structured framework that has to be managed successfully. The purpose of…
Codd [Codd 1970] wrote the first paper in which the model of a relational database was proposed. Adleman [Adleman 1994] wrote the first paper in which DNA strands in a test tube were used to solve an instance of the Hamiltonian path…