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Data commons collate data with cloud computing infrastructure and commonly used software services, tools and applications to create biomedical resources for the large-scale management, analysis, harmonization, and sharing of biomedical…

Recent work on database application development platforms has sought to include a declarative formulation of a conceptual data model in the application code, using annotations or attributes. Some recent work has used metadata to include the…

Data Lake (DL) is a Big Data analysis solution which ingests raw data in their native format and allows users to process these data upon usage. Data ingestion is not a simple copy and paste of data, it is a complicated and important phase…

Large organizations are seeking to create new architectures and scalable platforms to effectively handle data management challenges due to the explosive nature of data rarely seen in the past. These data management challenges are largely…

In recent past, big data opportunities have gained much momentum to enhance knowledge management in organizations. However, big data due to its various properties like high volume, variety, and velocity can no longer be effectively stored…

Since decades, the modelling of metadata has been core to the functioning of any academic library. Its importance has only enhanced with the increasing pervasiveness of Generative Artificial Intelligence (AI)-driven information activities…

Designing effective data manipulation methods is a long standing problem in data lakes. Traditional methods, which rely on rules or machine learning models, require extensive human efforts on training data collection and tuning models.…

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…

Within enterprises, there is a growing need to intelligently navigate data lakes, specifically focusing on data discovery. Of particular importance to enterprises is the ability to find related tables in data repositories. These tables can…

The Big Data landscape poses challenges in managing diverse data formats, requiring efficient storage and processing for high-quality analysis. Effective metadata management is crucial for organizing, accessing, and reusing data within…

Recent technology breakthroughs have enabled data collection of unprecedented scale, rate, variety and complexity that has led to an explosion in data management requirements. Existing theories and techniques are not adequate to fulfil…

Metadata are like the steam engine of the 21st century, driving businesses and offer multiple enhancements. Nevertheless, many companies are unaware that these data can be used efficiently to improve their own operation. This is where the…

The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of…

Traditional data in Digital Humanities projects bear various formats (structured, semi-structured, textual) and need substantial transformations (encoding and tagging, stemming, lemmatization, etc.) to be managed and analyzed. To fully…

Evaluating the quality of open-domain chatbots has become increasingly reliant on LLMs acting as automatic judges. However, existing meta-evaluation benchmarks are static, outdated, and lacking in multilingual coverage, limiting their…

Data warehousing enables performant access to high-quality data integrated from dynamic data sources. The medallion architecture, a standard for data warehousing, addresses these goals by organizing data into bronze, silver and gold layers,…

High-quality datasets are typically required for accomplishing data-driven tasks, such as training medical diagnosis models, predicting real-time traffic conditions, or conducting experiments to validate research hypotheses. Consequently,…

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…

Machine Learning (ML) is continuously permeating a growing amount of application domains. Generative AI such as Large Language Models (LLMs) also sees broad adoption to process multi-modal data such as text, images, audio, and video. While…

Recent and forthcoming advances in instrumentation, and giant new surveys, are creating astronomical data sets that are not amenable to the methods of analysis familiar to astronomers. Traditional methods are often inadequate not merely…