Marko A. Dimitrijevic

dblp:31/8241 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
—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
Query processing and optimization · 60% Database system architecture and tuning · 40%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Cloud and datacenter computing · 100%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Database system architecture and tuning › database tuning
automatic database tuning
0.312018
FusionInsight LibrA: Huawei's Enterprise Cloud Data Analytics Platform · Proc. VLDB Endow. 2018
Query processing and optimization › query compilation
just-in-time compilation
0.312018
FusionInsight LibrA: Huawei's Enterprise Cloud Data Analytics Platform · Proc. VLDB Endow. 2018
Query processing and optimization
query compilation
0.312018
FusionInsight LibrA: Huawei's Enterprise Cloud Data Analytics Platform · Proc. VLDB Endow. 2018
Query processing and optimization
query optimization
0.312018
FusionInsight LibrA: Huawei's Enterprise Cloud Data Analytics Platform · Proc. VLDB Endow. 2018
Cloud and datacenter computing
cloud data analytics
0.312018
FusionInsight LibrA: Huawei's Enterprise Cloud Data Analytics Platform · Proc. VLDB Endow. 2018
YearPublicationVenuePosition
2018 FusionInsight LibrA: Huawei's Enterprise Cloud Data Analytics Platform
abstract
Huawei Fusion Insight Libr A (FI-MPPDB) is a petabyte scale enterprise analytics platform developed by the Huawei data-base group. It started as a prototype more than five years ago, and is now being used by many enterprise customers over the globe, including some of the world's largest financial institutions. Our product direction and enhancements have been mainly driven by customer requirements in the fast evolving Chinese market. This paper describes the architecture of FI-MPPDB and some of its major enhancements. In particular, we focus on top four requirements from our customers related to data analytics on the cloud: system availability, auto tuning, query over heterogeneous data models on the cloud, and the ability to utilize powerful modern hardware for good performance. We present our latest advancements in the above areas including online expansion, auto tuning in query optimizer, SQL on HDFS, and intelligent JIT compiled execution. Finally, we present some experimental results to demonstrate the effectiveness of these technologies.
Le Cai, Jianjun Chen 0001, Kuorong Chiang, Marko A. Dimitrijevic, Yonghua Ding, Ahmad Ghazal, Jacques Hebert, Kamini Jagtiani, Suzhen Lin, Demai Ni, Chunfeng Pei, Jason Sun, Li Zhang 0132, Mingyi Zhang 0001
Proc. VLDB Endow.6