MapReduce is a framework for processing and managing large-scale datasets in a
distributed cluster, which has been used for applications such as generating search indexes …

In many important applications—such as search engines and relational database systems—
data are stored in the form of arrays of integers. Encoding and, most importantly, decoding of …

MapReduce-based data warehouse systems are playing important roles of supporting big
data analytics to understand quickly the dynamics of user behavior trends and their needs in …

Invented about 40 years ago and called ubiquitous less than 10 years later, B-tree indexes
have been used in a wide variety of computing systems from handheld devices to …

Abstract Database Management Systems (DBMSs) are a ubiquitous and critical component
of modern computing, and the result of decades of research and development in both …

Large-scale data analysis has become increasingly important for many enterprises.
Recently, a new distributed computing paradigm, called MapReduce, and its open source …

Causality analysis automates attack forensic and facilitates behavioral detection by
associating causally related but temporally distant system events. Despite its proven …

The open-source FastLanes project aims to improve big data formats, such as Parquet, ORC
and columnar database formats, in multiple ways. In this paper, we significantly accelerate …

Large-scale machine learning (ML) algorithms are often iterative, using repeated read-only
data access and I/O-bound matrix-vector multiplications to converge to an optimal model. It …

Query performance in current systems depends significantly on tuning: how well the query
matches the available indexes, materialized views etc. Even in a well tuned system, there …