… popular graph partitioning and computing systems. … systems are presented. Finally, we
discuss future challenges and research directions in graph partitioning and computing systems…

… -based models, and graph partitioning in a distributed setup. Separately, graph databases
with native graph … We show how to directly train a GNN on a graph DB, by retrieving minimal …

… of actual partitioning, Grep uses a graph learning model to predict database performance. …
Search-based works address limitations of prior learningbased methods cannot recover the …

… To this end, we propose a graph model based priority scheduling scheme in Promi. In
this scheme, we model the migrated mini-partitions as a graph, where mini-partitions are …

… based on the generated partitions in a bottom-up manner with a reinforcement learning-based …
graphs that allows it to capture latent graph structures more precisely than existing GNN-…

… This information is integrated into two graph structures, and we develop a dual-branch graph
… We incorporated DISEncoder into two machine learning models for database optimization …

… preprocessing data, splitting it, selecting embedding models, storing it in a vector database,
and … This study introduces the KG2data system, which enables the intelligent and automated …

… Since dataflow graphs are executed in a data-parallel fashion… are split into one or more
stream partitions (one partition per … records in a simple streaming grep job on 30 machines (120 …

… such as grep, sort and head. Dryad is notable for allowing … (Q18) from a published study
based on this database [23]. … literature [18] and include horizontally partitioning the datasets…

… based, knowledge based, or machine learning based … for machine learning algorithms,
GraphX for graph processing, … RDD is a collection of objects partitioned across the machines of …