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 …

In recent years, significant advances have been made in the design and evaluation of
balanced (hyper) graph partitioning algorithms. We survey trends of the past decade in …

We observe significant overlaps in the computations performed by user jobs in modern
shared analytics clusters. Naïvely computing the same subexpressions multiple times results …

The world is becoming a more conjunct place and the number of data sources such as
social networks, online transactions, web search engines, and mobile devices is increasing …

Graph edge partitioning is an important prepro-cessing step to optimize distributed
computing jobs on graph-structured data. The edge set of a given graph is split into k equally …

The performance optimization of database systems has been widely studied for years. From
the perspective of the operation and maintenance personnel, it mainly includes three topics …

This paper studies incremental graph partitioning. Given a (vertex-cut or edge-cut) partition
C (G) of a graph G and updates ΔG to G, it is to compute changes ΔO to C (G), yielding a …

We propose Distributed Neighbor Expansion (Distributed NE), a parallel and distributed
graph partitioning method that can scale to trillion-edge graphs while providing high …

Graphs are a tremendously suitable data representations that model the relationships of
entities in many application domains, such as recommendation systems, machine learning …

Distributed systems that manage and process graph-structured data internally solve a graph
partitioning problem to minimize their communication overhead and query run-time. Besides …