There has been a growing interest in the graph-streaming setting where a continuous
stream of graph updates is mixed with graph queries. In principle, purely-functional trees are …

Graph processing has become an important part of various areas of computing, including
machine learning, medical applications, social network analysis, computational sciences …

Graph processing has become an important part of various areas of computing, including
machine learning, medical applications, social network analysis, computational sciences …

Over the last few years, data analytics shifted from adescriptive era, confined to the
explanation of past events, to the emergence of predictive techniques. Nonetheless, existing …

Most of today's graph analytics systems model static graphs and do not support business
use cases that require the ability to:(i) query the dynamic graph data for a time-evolving …

The integration of a model composition operator into a system is a challenging task: the
properties associated with such operators can drastically change how the developers will be …

Recent years have witnessed a massive increase in the amount of data generated by the
Internet of Things (IoT) and social media. Processing huge amounts of this data poses non …

Graph processing is a fundamental tool in many computational disciplines due to the
widespread availability of graph data. However, processing large graphs quickly and cost-e …

동시성 제어는 하나의 메모리에 2 개 이상의 개체가 접근할 때 Race Condition 을 방지하기
위하여 사용하는 방식이다. 물리적으로 분리된 다중 노드가 공유 메모리에 접근할 때 Race …

Parallel graph algorithms are important to a variety of computational disciplines today due to
the widespread availability of large-scale graph-based data. Existing work that processes …