Related papers: A Repository for Formal Contexts
Recently, increasingly large amounts of data are generated from a variety of sources. Existing data processing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on Big Data, a…
In open-source software development environments; textual, numerical and relationship-based data generated are of interest to researchers. Various data sets are available for this data, which is frequently used in areas such as software…
The performance of machine learning models relies heavily on the quality of input data, yet real-world applications often face significant data-related challenges. A common issue arises when curating training data or deploying models: two…
In response to the increasing volume and sensitivity of data, traditional centralized computing models face challenges, such as data security breaches and regulatory hurdles. Federated Computing (FC) addresses these concerns by enabling…
Social media has become a popular means for people to consume news. Meanwhile, it also enables the wide dissemination of fake news, i.e., news with intentionally false information, which brings significant negative effects to the society.…
In this paper we are interested in studying concise representations of concepts and dependencies, i.e., implications and association rules. Such representations are based on equivalence classes and their elements, i.e., minimal generators,…
Generating value from data requires the ability to find, access and make sense of datasets. There are many efforts underway to encourage data sharing and reuse, from scientific publishers asking authors to submit data alongside manuscripts…
Context is a rich concept and is an elusive concept to define. The concept of context has been studied by philosophers, linguists, psychologists, and recently by computer scientists. Within each research community the term context was…
We posit that striving for distributed systems that provide "single system image" semantics is fundamentally flawed and at odds with how systems operate in the physical world. We realize the database as an optimization of this system: a…
Formal Concept Analysis "FCA" is a data analysis method which enables to discover hidden knowledge existing in data. A kind of hidden knowledge extracted from data is association rules. Different quality measures were reported in the…
Nowadays, many decision support applications need to exploit data that are not only numerical or symbolic, but also multimedia, multistructure, multisource, multimodal, and/or multiversion. We term such data complex data. Managing and…
Formal Concept Analysis (FCA) offers several tools for qualitative data analysis. One possibility is to group objects that share common attributes together and get a concept lattice that describes the data. Quite often the size of this…
We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from a text corpus. The approach is based on Formal Concept Analysis (FCA), a method mainly used for the analysis of data, i.e. for investigating…
Centralized and distributed systems are two main approaches to organizing ICT infrastructure, each with its pros and cons. Centralized systems concentrate resources in one location, making management easier but creating single points of…
Reproducibility in research remains hindered by complex systems involving data, models, tools, and algorithms. Studies highlight a reproducibility crisis due to a lack of standardized reporting, code and data sharing, and rigorous…
The Resource Description Framework (RDF) is continuing to grow outside the bounds of its initial function as a metadata framework and into the domain of general-purpose data modeling. This expansion has been facilitated by the continued…
Data is the most powerful decision-making tool at our disposal. However, despite the exponentially growing volumes of data generated in the world, putting it to effective use still presents many challenges. Relevant data seems to be never…
Every digital process needs to consume some data in order to work properly. It is very common for applications to use some external data in their processes, getting them by sources such as external APIs. Therefore, trusting the received…
Large-scale datasets are foundational for research and development in natural language processing. However, current approaches face three key challenges: (1) reliance on ambiguously licensed sources restricting use, sharing, and derivative…
Data collected by large-scale instruments, observatories, and sensor networks are key enablers of scientific discoveries in many disciplines. However, ensuring that these data can be accessed, integrated, and analyzed in a democratized and…