Big Graphs: Challenges and Opportunities

被引:12
作者
Fan, Wenfei [1 ,2 ,3 ]
机构
[1] Shenzhen Inst Comp Sci, Shenzhen, Peoples R China
[2] Univ Edinburgh, Edinburgh, Midlothian, Scotland
[3] Beihang Univ, Beijing, Peoples R China
来源
PROCEEDINGS OF THE VLDB ENDOWMENT | 2022年 / 15卷 / 12期
基金
欧洲研究理事会;
关键词
ENTITY RESOLUTION; PARKINSONS-DISEASE; DRUG DEVELOPMENT; LARGE-SCALE; COMPLEXITY; RECOMMENDATION; CONSTRAINTS; INFORMATION; SIMILARITY; VERTEX;
D O I
10.14778/3554821.3554899
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Big data is typically characterized with 4V's: Volume, Velocity, Variety and Veracity. When it comes to big graphs, these challenges become even more staggering. Each and every of the 4V's raises new questions, from theory to systems and practice. Is it possible to parallelize sequential graph algorithms and guarantee the correctness of the parallelized computations? Given a computational problem, does there exist a parallel algorithm for it that guarantees to reduce parallel runtime when more machines are used? Is there a systematic method for developing incremental algorithms with effectiveness guarantees in response to frequent updates? Is it possible to write queries across relational databases and semistructured graphs in SQL? Can we unify logic rules and machine learning, to improve the quality of graph-structured data, and deduce associations between entities? This paper aims to incite interest and curiosity in these topics. It raises as many questions as it answers.
引用
收藏
页码:3782 / 3797
页数:16
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