相关论文: Using Felis to Represent the Semantics and Metadat…

Data discovery in data lakes with ever increasing datasets has long been recognized as a big challenge in the realm of data management, especially for semantic search of and hierarchical global catalog generation of tables. While large…

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)…

We introduce DataTales, a novel benchmark designed to assess the proficiency of language models in data narration, a task crucial for transforming complex tabular data into accessible narratives. Existing benchmarks often fall short in…

Nowadays medium-large size astronomical projects have to face the management of a large amount of information and data. Dedicated data centres manage the collection of raw and processed data and consequently make them accessible, typically…

In the implementation and use of research information systems (RIS) in scientific institutions, text data mining and semantic technologies are a key technology for the meaningful use of large amounts of data. It is not the collection of…

The Resource Description Framework is well-established as a lingua franca for data modeling and is designed to integrate heterogeneous data at instance and schema level using statements. While RDF is conceptually simple, data models…

We present the Rubin Observatory system for data storage/retrieval and pipelined code execution. The layer for data storage and retrieval is named the Butler. It consists of a relational database, known as the registry, to keep track of…

The visual inspection of image and catalog data continues to be a valuable aspect of astronomical data analysis. As the scale of astronomical image and catalog data continues to grow, visualizing the data becomes increasingly difficult. In…

Helix is an open-source, extensible, Python-based software framework to facilitate reproducible and interpretable machine learning workflows for tabular data. It addresses the growing need for transparent experimental data analytics…

Machine learning (ML) practitioners and organizations are building model zoos of pre-trained models, containing metadata describing properties of the ML models and datasets that are useful for reporting, auditing, reproducibility, and…

In the coming era of data-intensive science, it will be increasingly important to be able to seamlessly move between scientific results, the data analyzed in them, and the processes used to produce them. As observations, derived data…

A public database of astrophysical (radio and other) catalogs (CATS), has been created at Special Astrophysical Observatory (SAO). It allows to execute a number of operations in batch or interactive mode, e.g. to obtain a list and…

Meta-evaluation studies of system performances in controlled offline evaluation campaigns, like TREC and CLEF, show a need for innovation in evaluating IR-systems. The field of academic search is no exception to this. This might be related…

The scientific community use PDEs to model a range of problems. The people in this domain are interested in visualizing their results, but existing mechanisms for visualization can not handle the full richness of computations in the domain.…

The development of temporal extensions of Answer Set Programming (ASP) has led to the emergence of non-monotonic linear-time (TEL), dynamic (DEL), and metric (MEL) temporal equilibrium logics. However, the inherent rigidity of highly…

Word Sense Disambiguation (WSD) has been widely evaluated using the semantic frameworks of WordNet, BabelNet, and the Oxford Dictionary of English. However, for the UCREL Semantic Analysis System (USAS) framework, no open extensive…

Machine learning algorithms are highly useful for the classification of time series data in astronomy in this era of peta-scale public survey data releases. These methods can facilitate the discovery of new unknown events in most…

Interpretability is becoming an active research topic as machine learning (ML) models are more widely used to make critical decisions. Tabular data is one of the most commonly used modes of data in diverse applications such as healthcare…

Enterprises often own large collections of structured data in the form of large databases or an enterprise data lake. Such data collections come with limited metadata and strict access policies that could limit access to the data contents…

Dataframes are a popular abstraction to represent, prepare, and analyze data. Despite the remarkable success of dataframe libraries in Rand Python, dataframes face performance issues even on moderately large datasets. Moreover, there is…