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 …

The open-source FastLanes project aims to improve big data formats, such as Parquet, ORC
and columnar database formats, in multiple ways. In this paper, we significantly accelerate …

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 …

Large-scale machine learning (ML) algorithms are often iterative, using repeated read-only
data access and I/O-bound matrix-vector multiplications to converge to an optimal model. It …

Strings are prevalent in real-world data sets. They often occupy a large fraction of the data
and are slow to process. In this work, we present Fast Static Symbol Table (FSST), a …

Lightweight integer compression algorithms are frequently applied in in-memory database
systems to tackle the growing gap between processor speed and main memory bandwidth …

Data encoding has been applied to database systems for decades as it mitigates bandwidth
bottlenecks and reduces storage requirements. But even in the presence of these …

In this paper, we present MorphStore, an open-source in-memory columnar analytical query
engine with a novel holistic compression-enabled processing model. Basically, compression …

Vectorization based on the Single Instruction Multiple Data (SIMD) parallel paradigm is a
core technique to improve query processing performance especially in state-of-the-art in …

Large-scale machine learning algorithms are often iterative, using repeated read-only data
access and I/O-bound matrix-vector multiplications to converge to an optimal model. It is …