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Online Nonstop Task Management for Storm-Based Distributed Stream Processing Engines
Most distributed stream processing engines (DSPEs) do not support online task management and cannot adapt to time-varying data flows. Recently, some...
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A Time-Series-Based Sample Amplification Model for Data Stream with Sparse Samples
The data stream is a dynamic collection of data that changes over time, and predicting the data class can be challenging due to sparse samples,...
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Micro-batch and data frequency for stream processing on multi-cores
Latency or throughput is often critical performance metrics in stream processing. Applications’ performance can fluctuate depending on the input...
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Adaptive Data Stream Mining (DSM) Systems
Dynamic, data-driven methods are key to enabling the deployment of accurate and efficient data stream mining (DSM) systems, by invoking suitably...
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A data stream-based approach for anomaly detection in surveillance videos
Anomaly detection is a challenging task in surveillance videos. Nowadays, deep learning-based approaches have been developed for this issue. Although...
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Parallel continuous skyline query over high-dimensional data stream windows
Real-time multi-criteria decision-making applications in fields like high-speed algorithmic trading, emergency response, and disaster management have...
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Trends in Data Stream Mining
Learning from data streams is a hot topic in machine learning and data mining. This article presents our recent work on the topic of learning from...
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Data Stream Processing in Reconciliation Testing: Industrial Experience
The paper focuses on a research area encompassing tools and methods of reconciliation testing, an approach to software testing that relies on the...
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Data Stream Analysis
Data stream is a typical big data. Data stream can be founded in many real-life applications, such as wireless sensor networks, power consumption,...
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A smart intelligent approach based on hybrid group search and pelican optimization algorithm for data stream clustering
Big data applications generate a huge range of evolving, real-time, and high-dimensional streaming data. In many applications, data stream clustering...
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A Real-Time Semantic Annotation to the Sensor Stream Data for the Water Quality Monitoring
Currently, billions of interconnected IoT devices continuously generate a huge amount of various sensed data in the stream called sensor stream data....
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A survey on the evolution of stream processing systems
Stream processing has been an active research field for more than 20 years, but it is now witnessing its prime time due to recent successful efforts...
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Network security AIOps for online stream data monitoring
In cybersecurity, live production data for predictive analysis pose a significant challenge due to the inherently secure nature of the domain....
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Latency and Energy-Awareness in Data Stream Processing for Edge Based IoT Systems
LE-STREAM is a framework for IoT data stream processing. Data processing in IoT is challenging due to its dynamic and heterogeneous nature, and the...
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A Systematic Approach to Consuming Data in Complex Data Management Landscapes Using Data Consumption Patterns
Through data analytics, enterprises can exploit the value their data hold. However, there are still various challenges to be solved, one of them...
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Time series big data: a survey on data stream frameworks, analysis and algorithms
Big data has a substantial role nowadays, and its importance has significantly increased over the last decade. Big data’s biggest advantages are...
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Electrical Big Data’s Stream Management for Efficient Energy Control
Energy is crucial for any activity of a country. Therefore, electrical organizations need to analyze the data generated in the form of data streams...
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Data stream classification using a deep transfer learning method based on extreme learning machine and recurrent neural network
Deep learning-based approaches have gained popularity for many applications in recent years and have become the state-of-the-art method in machine...
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A Docker-based federated learning framework design and deployment for multi-modal data stream classification
In the high-performance computing (HPC) domain, federated learning has gained immense popularity. Especially in emotional and physical health...
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SAR-BSO meta-heuristic hybridization for feature selection and classification using DBNover stream data
Advancements in cloud technologies have increased the infrastructural needs of data centers due to storage needs and processing of extensive...
