Recent demands for storing and querying big data have revealed various shortcomings of
traditional relational database systems. This, in turn, has led to the emergence of a new kind …

The rise of big data has led to new demands for machine learning (ML) systems to learn
complex models, with millions to billions of parameters, that promise adequate capacity to …

We propose a parameter server system for distributed ML, which follows a Stale
Synchronous Parallel (SSP) model of computation that maximizes the time computational …

Healthcare data management has been gaining a lot of attention in recent years because of
its high potential to provide more accurate and cost-efficient patient care. The traditional …

In this paper we present BlockchainDB, which leverages blockchains as a storage layer and
introduces a database layer on top that extends blockchains by classical data management …

Choosing a cloud storage system and specific operations for reading and writing data
requires developers to make decisions that trade off consistency for availability and …

Over the years, different meanings have been associated with the word consistency in the
distributed systems community. While in the'80s “consistency” typically meant strong …

Large-scale distributed systems often rely on replicated databases that allow a programmer
to request different data consistency guarantees for different operations, and thereby control …

In globally distributed systems, shared state is never perfect. When communication is neither
fast nor reliable, we cannot achieve strong consistency, low latency, and availability at the …

Many modern machine learning (ML) algorithms are iterative, converging on a final solution
via many iterations over the input data. This paper explores approaches to exploiting these …