Developers often prefer flexibility over upfront schema design, making semi-structured data
formats such as JSON increasingly popular. Large amounts of JSON data are therefore …

Queries to detect isomorphic subgraphs are important in graph-based data management.
While the problem of subgraph isomorphism search has received considerable attention for …

In this paper we define a research agenda to develop a general framework supporting
online autonomous tuning of data partitioning and layouts with a reinforcement learning …

The data science revolution and growing popularity of data lakes make efficient processing
of raw data increasingly important. To address this, we propose the ACCelerated Operators …

Scientific experiments, simulations, and modern applications generate large amounts of
data. Analysing resources required to process such big datasets is essential to identify …

Many applications ingest data in an intermittent, yet largely predictable, pattern. Existing
systems tend to ignore how data arrives when making decisions about how to update (or …

Database applications are often developed with object-oriented languages while using
relational databases as the backend. To accelerate these applications, developers would …

Parsing is essential for a wide range of use cases, such as stream processing, bulk loading,
and in-situ querying of raw data. Yet, the compute-intense step often constitutes a major …

In modern large-scale distributed systems, analytics jobs submitted by various users often
share similar work. Instead of optimizing jobs independently, multi-query optimization …

Modern big data frameworks (such as Hadoop and Spark) allow multiple users to do large-
scale analysis simultaneously, by deploying data-intensive workflows (DIWs). These DIWs of …