TY - GEN
T1 - AdaptMX
T2 - 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018
AU - Wang, Xiaotong
AU - Jiang, Cheng
AU - Fang, Junhua
AU - Wang, Xiangfeng
AU - Zhang, Rong
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - Stream join is a fundamental and important processing in many real-world applications. Due to the complexity of join operation and the inherent characteristic of streaming data (e.g., skewed distribution and dynamics), though massive research has been conducted, adaptivity and load-balancing are still urgent problems. In this paper, an enhanced adaptive join-matrix system AdaptMX for stream theta-join is presented, which combines the key-based and tuple-based join approaches well: (i) at outer level, it modifies the well-known join-matrix model to allocate resource on demand, improving the adaptivity of tuple-based parititoning scheme; (ii) at inner level, it adopts a key-based routing policy among grouped processing tasks to maintain the join semantics and cost-effective load balancing strategies to remove the stragglers. For demonstration, we present a transparent processing of distributed stream theta-join and compare the performance of our AdaptMX system with other baselines, with 3 × higher throughput.
AB - Stream join is a fundamental and important processing in many real-world applications. Due to the complexity of join operation and the inherent characteristic of streaming data (e.g., skewed distribution and dynamics), though massive research has been conducted, adaptivity and load-balancing are still urgent problems. In this paper, an enhanced adaptive join-matrix system AdaptMX for stream theta-join is presented, which combines the key-based and tuple-based join approaches well: (i) at outer level, it modifies the well-known join-matrix model to allocate resource on demand, improving the adaptivity of tuple-based parititoning scheme; (ii) at inner level, it adopts a key-based routing policy among grouped processing tasks to maintain the join semantics and cost-effective load balancing strategies to remove the stragglers. For demonstration, we present a transparent processing of distributed stream theta-join and compare the performance of our AdaptMX system with other baselines, with 3 × higher throughput.
UR - https://www.scopus.com/pages/publications/85048945305
U2 - 10.1007/978-3-319-91458-9_52
DO - 10.1007/978-3-319-91458-9_52
M3 - 会议稿件
AN - SCOPUS:85048945305
SN - 9783319914572
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 802
EP - 806
BT - Database Systems for Advanced Applications - 23rd International Conference, DASFAA 2018, Proceedings
A2 - Pei, Jian
A2 - Sadiq, Shazia
A2 - Li, Jianxin
A2 - Manolopoulos, Yannis
PB - Springer Verlag
Y2 - 21 May 2018 through 24 May 2018
ER -