Results 71 to 80 of about 2,924 (186)
Keyed watermarks: A fine-grained watermark generation for Apache Flink
Future Generation Computer Systems, 2023 Abstract Big Data Stream processing engines, exemplified by tools like Apache Flink, employ windowing techniques to manage unbounded streams of events. Aggregating relevant data within Windows is important for event-time windowing due to its impact on result accuracy. A pivotal role in this process is attributed to watermarks, unique timestamps
Tawfik Yasser +3 more
openaire +1 more source
Combining Stream Mining and Neural Networks for Short Term Delay Prediction
, 2018 The systems monitoring the location of public transport vehicles rely on wireless transmission. The location readings from GPS-based devices are received with some latency caused by periodical data transmission and temporal problems preventing data ...
A Bifet, CW Tsai, N Marz, Y Qin
core +1 more source
PlinyCompute: A Platform for High-Performance, Distributed, Data-Intensive Tool Development [PDF]
, 2017 This paper describes PlinyCompute, a system for development of high-performance, data-intensive, distributed computing tools and libraries. In the large, PlinyCompute presents the programmer with a very high-level, declarative interface, relying on ...
Barnett, R. Matthew +8 more
core
Collaborative Reuse of Streaming Dataflows in IoT Applications
, 2017 Distributed Stream Processing Systems (DSPS) like Apache Storm and Spark Streaming enable composition of continuous dataflows that execute persistently over data streams.
Chaturvedi, Shilpa +2 more
core +1 more source
Megaphone: Latency-conscious state migration for distributed streaming dataflows
, 2019 We design and implement Megaphone, a data migration mechanism for stateful distributed dataflow engines with latency objectives. When compared to existing migration mechanisms, Megaphone has the following differentiating characteristics: (i) migrations ...
Hoffmann, Moritz +5 more
core +1 more source
Benchmarking Big Data Systems: Performance and Decision-Making Implications in Emerging Technologies
TechnologiesSystems for graph processing are a key enabler for insights from large-scale graphs that are critical to many new advanced technologies such as Artificial Intelligence, Internet of Things, and blockchain.
Leonidas Theodorakopoulos +3 more
doaj +1 more source
Stream Processing Systems Benchmark: StreamBench [PDF]
, 2016 Batch processing technologies (Such as MapReduce, Hive, Pig) have matured and been widely used in the industry. These systems solved the issue processing big volumes of data successfully.
Wang, Yangjun
core
A new era of financial services: How AI enhances investment efficiency
International Studies of Economics, Volume 19, Issue 4, Page 578-588, December 2024.
Zhiyi Liu, Kai Zhang, Hongyi Zhang
wiley +1 more source
Data Optimization using Apache Flink
International Journal of Engineering and Advanced Technology, 2019 Map Reduce, Flink, and Spark, also become more popular in the processing of big data lately. Flink will be an open platform Big Data processing system for Apache-powered batch storage and streaming of data. Flink's query optimizer is constructed for historical information processing (batch) based on parallel storage systems approaches.
Vikas S, Thimmaraju S N
openaire +1 more source
RL4CEP: reinforcement learning for updating CEP rules
Complex & Intelligent SystemsThis paper presents RL4CEP, a reinforcement learning (RL) approach to dynamically update complex event processing (CEP) rules. RL4CEP uses Double Deep Q-Networks to update the threshold values used by CEP rules.
Afef Mdhaffar +4 more
doaj +1 more source