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Modern data processing systems are designed to be general purpose, in that they can …

Modern key-value stores typically rely on an LSM-tree in storage (SSD) to handle writes and
Bloom filters in memory (DRAM) to optimize reads. With ongoing advances in SSD …

Data-intensive key-value stores based on the Log-Structured Merge-Tree are used in
numerous modern applications ranging from social media and data science to cloud …

We introduce the concept of design continuums for the data layout of key-value stores. A
design continuum unifies major distinct data structure designs under the same model. The …

Data discovery refers to a set of tasks that enable users and downstream applications to
explore and gain insights from massive collections of data sources such as data lakes. In …

Modern storage engines and key-value stores have come to rely on the log-structured
merge-tree (LSM-tree) as their core data structure. LSM-tree operates by gradually merge …

Despite the wide adoption of graph processing across many different application domains,
there is no underlying data structure that can serve a variety of graph workloads (analytics …

Data-intensive analytical applications need to support both efficient reads and writes.
However, what is usually a good data layout for an update-heavy workload, is not well …

Log-structured merge (LSM) trees have emerged as one of the most commonly used disk-
based data structures in modern data systems. LSM-trees employ out-of-place ingestion to …

Log-structured merge (LSM) trees have emerged as one of the most commonly used storage-
based data structures in modern data systems as they offer high throughput for writes and …