Machine learning has become a critical component of modern data-driven online services.
Typically, the training phase of machine learning techniques requires to process large-scale …

We present Stacked Filters, a new probabilistic filter which is fast and robust similar to query-
agnostic filters (such as Bloom and Cuckoo filters), and at the same time brings low false …

Bloom filter is an efficient data structure for filtering negative keys (keys not in a given set)
with substantially small space. However, in real-world applications, there widely exist …

Despite decades of research on approximate query processing (AQP), our understanding of
sample-based joins has remained limited and, to some extent, even superficial. The …

Modern applications are increasingly generating and persisting data across geo-distributed
data centers or edge clusters rather than a single cloud. This paradigm introduces …

Bloom filters, cuckoo filters, and other approximate set membership sketches have a wide
range of applications. Oftentimes, expensive operations can be skipped if an item is not in a …

Bloom filter is an efficient data structure for filtering negative keys (keys not in a given set)
with substantially small space. However, in real-world applications, there widely exist …

This paper proposes Adaptive-Multistage-Join (AM-Join) for scalable and fast equi-joins in
distributed shared-nothing architectures. AM-Join utilizes (a) Tree-Join, a novel algorithm …

Geo-distributed analytics (GDA) involves processing of data stored across geographically
distributed sites. Such analytics involves data transfer over the wide area network (WAN) …

Data stream processing is an increasingly important topic due to the prevalence of smart
devices and the demand for real-time analytics. Geo-distributed streaming systems, where …