The variety of data is one of the most challenging issues for the research and practice in
data management systems. The data are naturally organized in different formats and …

The last decade of database research has led to the prevalence of specialized systems for
different workloads. Consequently, organizations often rely on a combination of specialized …

In many use-cases, information is stored in text but not available in structured data.
However, extracting data from natural language text to precisely fit a schema, and thus …

Over the past decade, distributed dataflow systems (DDS) have become a standard
technology. In these systems, users write programs in restricted dataflow programming …

Today's users of data processing systems come from different domains, have different levels
of expertise, and prefer different programming languages. As a result, analytical workload …

In the metaverse the physical space and the virtual space co-exist, and interact
simultaneously. While the physical space is virtually enhanced with information, the virtual …

Moving data analysis and processing to the cloud is no longer reserved for a few companies
with petabytes of data. Instead, the flexibility of on-demand resources is attracting an …

Machine Learning (ML) is quickly becoming a prominent method in many data management
components, including query optimizers which have recently shown very promising results …

Data science and artificial intelligence are driven by a plethora of diverse data-related
assets, including datasets, data streams, algorithms, processing software, compute …

Today's organizations utilize a plethora of heterogeneous and autonomous DBMSes, many
of those being spread across different geo-locations. It is therefore crucial to have effective …