The Jaccard similarity index is an important measure of the overlap of two sets, widely used
in machine learning, computational genomics, information retrieval, and many other areas …

Modern database systems are growing increasingly distributed and struggle to reduce query
completion time with a large volume of data. In this paper, we leverage programmable …

How can we perform similarity joins of multi-dimensional streams in a distributed fashion,
achieving low latency? Can we adaptively repartition those streams in order to retain high …

Due to the rapid development of massively parallel data processing systems such as
MapReduce and Spark, there have been revived interests in designing algorithms in a …

This paper studies communication-oblivious algorithms under the massively parallel
computation (MPC) model. The communication patterns of these algorithms follow a …

This paper considers the worst-case complexity of multi-round join evaluation in the
Massively Parallel Computation (MPC) model. Unlike the sequential RAM model, in which …

We study equi-join computation in the massively parallel computation (MPC) model.
Currently, a main open question under this topic is whether it is possible to design an …

We present a constant-round algorithm in the massively parallel computation (MPC) model
for evaluating a natural join where every input relation has two attributes. Our algorithm …

Intersection joins over interval data are relevant in spatial and temporal data settings. A set
of intervals join if their intersection is non-empty. In case of point intervals, the intersection …

Computations on irregular graph structures are important for many fields, including social
sciences, bioinformatics, chemistry, medicine, cybersecurity, healthcare, web graph …