Streaming Analytics and Workflow Automation for DFS

Jayawardana, Yasith; Jayarathna, Sampath

doi:10.1145/3383583.3398589

Cited by 6 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, to describe data, one should preferably use FAIR, domain-agnostic metadata standards that are extensible for domain-specific use cases. In this work, we use the Data Description System (DDS) [34,35] metadata format to describe data sets, data streams, and data analytics.…”

Section: Metadata Standardsmentioning

confidence: 99%

StreamingHub: Interactive Stream Analysis Workflows

Jayawardana,

Ashok,

Jayarathna

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Reusable data/code and reproducible analyses are foundational to quality research. This aspect, however, is often overlooked when designing interactive stream analysis workflows for time-series data (e.g., eye-tracking data). A mechanism to transmit informative metadata alongside data may allow such workflows to intelligently consume data, propagate metadata to downstream tasks, and thereby auto-generate reusable, reproducible analytic outputs with zero supervision. Moreover, a visual programming interface to design, develop, and execute such workflows may allow rapid prototyping for interdisciplinary research. Capitalizing on these ideas, we propose StreamingHub, a framework to build metadata propagating, interactive stream analysis workflows using visual programming. We conduct two case studies to evaluate the generalizability of our framework. Simultaneously, we use two heuristics to evaluate their computational fluidity and data growth. Results show that our framework generalizes to multiple tasks with a minimal performance overhead.

show abstract

Section: Metadata Standardsmentioning

confidence: 99%

StreamingHub: Interactive Stream Analysis Workflows

Jayawardana,

Ashok,

Jayarathna

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The robust Cybersecurity Infrastructure safeguards the digital environment, employing secure communication protocols and a responsive Intrusion Detection System (IDS) that swiftly addresses potential threats [8][9][10]. A central hub for data analytics contributes to continuous improvement by analyzing substantial volumes of 3D printing data daily, generating actionable insights that refine designs and optimize production workflows [11][12][13]. The framework is designed with a user-centric approach, ensuring accessibility and empowerment through intuitive interfaces and responsive help desks [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Optimizing extreme manufacturing framework: a secure and efficient 3D printing integration framework

Palanisamy

2024

Eng. Res. Express

View full text Add to dashboard Cite

This study presents a comprehensive framework for extended manufacturing with integrated 3D printing technologies, exemplifying a paradigm shift in the manufacturing landscape. The Digital Thread Integration establishes a dynamic foundation, enabling real-time collaboration and data flow throughout the product lifecycle. Leveraging advanced AI-driven optimization, Digital Design Platforms streamline designs, processing 1,000 iterations per hour, and recommending materials based on component requirements. On-Demand Manufacturing Hubs strategically placed globally achieve substantial reductions in lead times (48 hours) and material waste (15%). The Cybersecurity Infrastructure ensures the sanctity of the digital environment, employing secure communication protocols and an Intrusion Detection System (IDS) responding to threats in milliseconds. The Data Analytics Hub contributes to continual improvement by analysing 100 GB of 3D printing data daily, generating 50 actionable insights weekly. User Interface and Accessibility initiatives empower the workforce through intuitive training modules and responsive help desks. In conclusion, this framework exemplifies secure, efficient, and data-driven extended manufacturing, positioning the industry at the forefront of technological advancement.

show abstract

A Serverless Electroencephalogram Data Retrieval and Preprocessing Framework

Farrow,

Jayarathna

2023

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)

View full text Add to dashboard Cite

Streaming Analytics and Workflow Automation for DFS

Cited by 6 publications

References 3 publications

StreamingHub: Interactive Stream Analysis Workflows

StreamingHub: Interactive Stream Analysis Workflows

Optimizing extreme manufacturing framework: a secure and efficient 3D printing integration framework

A Serverless Electroencephalogram Data Retrieval and Preprocessing Framework

Contact Info

Product

Resources

About