Parallel join algorithms have received much attention in recent years due to the rapid
development of massively parallel systems such as MapReduce and Spark. In the database …

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 …

Locality-sensitive hashing (LSH) is an important tool for managing high-dimensional noisy
or uncertain data, for example in connection with data cleaning (similarity join) and noise …

In this paper, we study the problem of set containment join. Given two collections RR and SS
of records, the set containment join R ⋈ _ ⊆ SR⋈⊆ S retrieves all record pairs {(r, s)\} ∈ R …

> Sampling is one of the most widely employed approximations in big data processing.
Among various challenges in sampling design, sampling for join is particularly intriguing yet …

In the era of data deluge, data analysis has become a key task for many industrial
applications, eg, master data management, and data integration. In particular, similarity join …

Massively parallel join algorithms have received much attention in recent years, while most
prior work has focused on worst-optimal algorithms. However, the worst-case optimality of …

Similarity joins are recognized to be among the most useful data processing and analysis
operations. A similarity join is used to retrieve all data pairs whose distances are smaller …

Trusted execution environment (TEE) has provided an isolated and secure environment for
building cloud-based analytic systems, but it still suffers from access pattern leakages …

In entity matching classification, we are given two sets R and S of objects where whether r
and s form a match is known for each pair (r, s) in R x S. If R and S are subsets of domains D …