Background and activities
Jia-Chun Lin is an assistant professor in the Department of Information Security and Communication Technology at NTNU (Norwegian University of Science and Technology).
Before this position, she was a postdoctoral researcher in the Department of Informatics, at University of Oslo and worked on an EU research project entitled "Scalable Hybrid Variability for Distributed Evolving Software Systems" (HyVar for short) from June 2015 to February 2018. She also joins SIRIUS, which is a Norwegian Centre for Research- driven Innovation that addresses the problems of scalable data access in the oil & gas industry.
Her research interests include parallel and distributed computing, cloud computing, applied machine learning, Internet of Things, big data analytics, security, and privacy. Currently she has published two IEEE transactions papers, 9 international journal papers, 17 international conference papers, and one book chapter in the above areas.
Scientific, academic and artistic work
Displaying a selection of activities. See all publications in the database
- (2020) DALC: Distributed Automatic LSTM Customization for Fine-Grained Traffic Speed Prediction. The 34th International Conference on Advanced Information Networking and Applications (AINA 2020) ; 2020-04-15 - 2020-04-17.
- (2020) Distributed Fine-Grained Traffic Speed Prediction for Large-Scale Transportation Networks based on Automatic LSTM Customization and Sharing. The 26th International European Conference on Parallel and Distributed Computing (EURO-PAR 2020) ; 2020-08-24 - 2020-08-28.
- (2020) RePAD: Real-time Proactive Anomaly Detection for Time Series. Proceedings of the 34th International Conference on Advanced Information Networking and Applications (AINA 2020).
- (2020) Distributed Fine-Grained Traffic Speed Prediction for Large-Scale Transportation Networks based on Automatic LSTM Customization and Sharing. Euro-Par 2020: Euro-Par 2020: Parallel Processing.
- (2020) ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series. 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC).
- (2020) DALC: Distributed Automatic LSTM Customization for Fine-Grained Traffic Speed Prediction. Proceedings of the 34th International Conference on Advanced Information Networking and Applications (AINA 2020).
- (2020) A Configurable and Executable Model of Spark Streaming on Apache YARN. International Journal of Grid and Utility Computing (IJGUC). vol. 11 (2).
- (2020) A Framework for Flexible Program Evolution and Verification of Distributed Systems. Communications in Computer and Information Science. vol. 1161 CCIS.
- (2019) PDS: Deduce elder privacy from smart homes. nternet of Things: Engineering Cyber Physical Human Systems. vol. 7.
- (2019) A Flexible Framework for Program Evolution and Verification. Modelsward 2019.
- (2018) EasyChoose: A Continuous Feature Extraction and Review Highlighting Scheme on Hadoop YARN. Advanced Information Networking and Applications. vol. 2018-May.
- (2018) Privacy Mining from IoT-based Smart Homes (Online version). Lecture Notes on Data Engineering and Communications Technologies.
- (2018) Modeling and simulation of spark streaming. Advanced Information Networking and Applications. vol. 2018-May.
- (2017) A model-Based Scalability Optimization Methodology for Cloud Applications. Proceedings of the IEEE.
- (2016) AWS deployments using model-based predictions. the 7th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (ISoLA 2016) ; 2016-10-05 - 2016-10-14.
- (2016) Comparing AWS Deployments Using Model-Based Predictions. Lecture Notes in Computer Science (LNCS). vol. 9953.
- (2016) Hybrid Job-Driven Scheduling for Virtual MapReduce Clusters. IEEE Transactions on Parallel and Distributed Systems. vol. 27 (6).
- (2016) Performance evaluation of job schedulers on Hadoop YARN. Concurrency and Computation. vol. 28 (9).
- (2016) Impacts of Task Re-Execution Policy on MapReduce Jobs. Computer journal. vol. 59 (5).
- (2016) ABS-YARN: A Formal Framework for Modeling Hadoop YARN Clusters. 19th International Conference on Fundamental Approaches to Software Engineering ; 2016-04-02 - 2016-04-08.
- (2016) ABS-YARN: A Formal Framework for Modeling Hadoop YARN Clusters. Fundamental Approaches to Software Engineering: 19th International Conference, FASE 2016, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2016, Eindhoven, The Netherlands, April 2-8, 2016, Proceedings.
- (2015) A Formal Framework Supporting Unrestricted Software Changes in Object-Oriented Concurrent Systems. 27th Nordic Workshop on Programming Theory (NWPT 2015) . Reykjavik University; Reykjavik. 2015-10-21 - 2015-10-23.