Results 151 to 160 of about 2,924 (186)

Approximate Stream Analytics in Apache Flink

, 2017
Quoc, Do Le, Bhatotia, Pramod, Chen, Ruichuan, Fetzer, Christof, Hilt, Volker, Strufe, Thorsten   +5 more
openaire   +1 more source

Credit risk prediction model for listed companies based on improved reinforcement learning and Bayesian optimization hyperband. [PDF]

PLoS One
Yuanqing C, Gao Z, Jian Z, Alizadehsani R, Pławiak P.   +4 more
europepmc   +1 more source

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Apache Flink in current research

IT - Information Technology, 2016
Abstract Recent trends in data collection and the decreasing prices of storage result in constantly growing amounts of analyzable data. These masses of data cannot easily be processed by traditional database systems as these do not allow for a sufficient degree of scalability.
Tilmann Rabl, Jonas Traub, Volker Markl   +2 more
exaly   +2 more sources

Big data multi-query optimisation with Apache Flink

International Journal of Web Engineering and Technology, 2018
Big data analytic frameworks, such as MapReduce, Spark and Flink, have recently gained more popularity to process large data. Flink is an open-source Apache-hosted big data analytic framework for processing batch and streaming data. For historical data processing (batch), Flink's query optimiser is built based on techniques which have been used in the ...
Radhya Sahal, Mohamed H Khafagy, Fatma A Omara   +2 more
exaly   +2 more sources

HYAS: Hybrid Autoscaler Agent for Apache Flink

Lecture Notes in Computer Science, 2023
Euripides G M Petrakis
exaly   +2 more sources

V2F: Real Time Video Segmentation with Apache Flink

Lecture Notes in Computer Science, 2022
Dimitrios Kastrinakis, Euripides G M Petrakis   +1 more
exaly   +2 more sources

TALOS: Task Level Autoscaler for Apache Flink

Lecture Notes in Computer Science
Euripides G M Petrakis
exaly   +2 more sources

Q-Flink: A QoS-Aware Controller for Apache Flink

2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), 2020
Modern stream-data processing platforms are required to execute processing pipelines over high-volume, yet high-velocity, datasets under tight latency constraints. Apache Flink has emerged as an important new technology of large-scale platform that can distribute processing over a large number of computing nodes in a cluster (i.e., scale-out processing)
M. Reza Hoseinyfarahabady, Ali Jannesari, Javid Taheri, Wei Bao 0001, Albert Y. Zomaya, Zahir Tari   +5 more
openaire   +1 more source

Adaptive Distributed Partitioning in Apache Flink

2020 IEEE 36th International Conference on Data Engineering Workshops (ICDEW), 2020
Dynamically adapting the workload of each worker in Flink is a challenging issue. In this work, we deal with a special case, where the data are conceptually split in contiguous overlapping regions. This scenario is encountered in several streaming applications, such as those employing nearest neighbor queries.
Theodoros Toliopoulos, Anastasios Gounaris   +1 more
openaire   +1 more source

Anomaly detection for NILM task with Apache Flink

Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems, 2020
The topic of the 2020 DEBS Grand Challenge is to develop a solution for Non Intrusive Load Monitoring (NILM). Sensors continuously send voltage and current data into a stream processing application that would detect the pattern of power data based on the data characteristics.
Zongshun Zhang, Ethan Timoteo Go
openaire   +1 more source