Recently, there has been a pressing need to manage high-dimensional vector data in data
science and AI applications. This trend is fueled by the proliferation of unstructured data and …

We present the first learned index that supports predecessor, range queries and updates
within provably efficient time and space bounds in the worst case. In the (static) context of …

Approximate set membership data structures (ASMDSs) are ubiquitous in computing. They
trade a tunable, often small, error rate (ϵ) for large space savings. The canonical ASMDS is …

Analytics is moving to the cloud and data is moving into data lakes. These reside on object
storage services like S3 and enable seamless data sharing and system interoperability. To …

Recent research showcased several cyber-attacks against unmodified licensed vehicles,
demonstrating the vulnerability of their internal networks. Many solutions have already been …

Compressed bitmap indexes are used in systems such as Git or Oracle to accelerate
queries. They represent sets and often support operations such as unions, intersections …

Today, businesses rely on efficiently running analytics on large amounts of operational and
historical data to gain business insights and competitive advantage. Increasingly, such …

GPUs are increasingly used for high-performance and interactive data analytics workloads
due to their capability to accelerate computation using massive parallelism. A key constraint …

In this paper, we focus on accelerating a widely employed computing pattern---set
intersection, to boost a group of graph algorithms. Graph's adjacency-lists can be naturally …

In today's data-driven world, it is critical that we use appropriate datasets for analysis and
decision-making. Datasets could be biased because they reflect existing inequalities in the …