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Evan Ye

dblp:199/6354 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Graph data management · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Storage systems · 50% Distributed systems · 50%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Graph data management
graph query processing
0.312017
ZipG: A Memory-efficient Graph Store for Interactive Queries · SIGMOD Conference 2017
Graph data management
graph storage
0.312017
ZipG: A Memory-efficient Graph Store for Interactive Queries · SIGMOD Conference 2017
Storage systems
compressed data storage
0.112017
ZipG: A Memory-efficient Graph Store for Interactive Queries · SIGMOD Conference 2017

Methods — techniques the papers use, named apart from their topics

graph compression · 0.6
YearPublicationVenuePosition
2017 ZipG: A Memory-efficient Graph Store for Interactive Queries
abstract
We present ZipG, a distributed memory-efficient graph store for serving interactive graph queries. ZipG achieves memory efficiency by storing the input graph data using a compressed representation. What differentiates ZipG from other graph stores is its ability to execute a wide range of graph queries directly on this compressed representation. ZipG can thus execute a larger fraction of queries in main memory, achieving query interactivity. ZipG exposes a minimal API that is functionally rich enough to implement published functionalities from several industrial graph stores. We demonstrate this by implementing and evaluating graph queries from Facebook TAO, LinkBench, Graph Search and several other workloads on top of ZipG. On a single server with 244GB memory, ZipG executes tens of thousands of queries from these workloads for raw graph data over half a TB; this leads to an order of magnitude (sometimes as much as 23×) higher throughput than Neo4j and Titan. We get similar gains in distributed settings compared to Titan.
Anurag Khandelwal, Zongheng Yang, Evan Ye, Rachit Agarwal 0001, Ion Stoica
SIGMOD Conference3