Dr. Zhiming Zhao home page

About me

Research

Projects

Activities

Publications

Software 

Multiscale Networked Systems (MNS)

University of Amsterdam

Email: z.zhao[at]uva.nl

Tel: +31 641265121

Office:

Room L5.40, Lab 42

  Science Park 900, 1098XH Amsterdam

  the Netherlands

We are looking for

1.     Part time programmers

2.     Post doctors

3.     PhD students...

About me                     

I received my Ph.D. in computer science in 2004 from the University of Amsterdam (UvA). I am an associate professor and the chair of the Multiscale Network Systems (MNS) research group in the Informatics Institute (IvI) at UvA. I am the technical manager of the Virtual Lab and Innovation Center (VLIC) of LifeWatch ERIC, a European research infrastructure in ecology and biodiversity science.

My research focuses on quality-critical distributed computing, data-intensive workflow management, virtual research environments, and digital twins. I am the Co-PI of the Dutch LTER-LIFE project and coordinate the UvA effort in EU projects ENVRI-HUB next, EVERSE, OSCARS and BlueCloud 2026 to develop Digital Twin Virtual Research Environment, research assets search engine, and Cloud automation and optimization solutions.

I coordinated the project SWITCH (Software Workbench for interactive time-critical and highly self-adaptive cloud applications). I led the Data for Science theme in the environmental science cluster project ENVRI^PLUS, and the technical development work packages in ENVRI-FAIR, ARTICONF and CLARIFY projects.

I am an IEEE Senior member and the managing editor of the Journal of Cloud Computing.

Research: Quality Critical Applications on Programmable Infrastructures 

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In both scientific and industrial contexts, there are distributed application systems which

1.     have very high business value (e.g., on-demand business collaboration platforms), or social impact (e.g., for early warning of disasters); and

2.     have very critical requirements for Quality of Service (QoS) (e.g., tsunami emergency response time) or quality of experience (QoE) (e.g., delivery of ultra-high-definition television or collaborative business interactions); but

3.     are very difficult to develop and operate because of their distributed nature and the high requirements for the runtime environment, and in particular, the sophisticated optimization mechanisms needed for developing and integrating the system components.

Cloud environments provide virtualized, elastic, controllable, and quality on-demand services for supporting complex application systems. However, developing, deploying, and executing quality-critical applications in programmable infrastructure is still difficult and challenging due to a lack of effective programming and control mechanisms.

My research interests have revolved around modeling, developing, controlling, and optimizing such Distributed Quality-Critical Systems. I specifically focus on novel models for programming, executing, and optimizing such applications on programmable infrastructures, such as Cloud- and software-defined networking technologies. My basic approach is to use autonomous agent technologies to decompose system complexity, to develop distributed control and optimization intelligence by combining both application logic and infrastructure programmability, and to investigate self-adaptable cooperative control models for Cloud-based quality-critical systems.

Leader: Dr. Zhiming Zhao 

Postdoc researchers and developers:

·       Nafis Islam, ENVRI community knowledge base (2024-)

·       Koen Greuell, VRE developer (2024-)

·       Dr. Nafiseh Soveizi, Digital twin composition and optimization (2024-)

·       Dr. Gabriel Pelouze, NaaVRE, VL (2023-)

·       Dr. Spiros Koulouzis, DRIP, CONF, VRE and use cases (2017-)

Ph.D. students:

·       Paul Daniëlse, AI enhanced quality critical cloud native applications (2024-)

·       Stefanie Boss, legal anomalies in decentralized infrastructure (2023-)

·       Yuandou Wang, Distributed data processing (2020-)

·       Na Li, Knowledge discovery (2020-)

Former members:

·       Dr. Peide Zhu, VRE search engine (2024-2024)

·       Dr. Siamak Farshidi, Cognitive digital twins (2024-2024)

·       HongYun Liu, Cloud resource management (2019-2024)

·       Zijie Liu, Machine learning and job scheduling (2023-)

·       Yi Chen, Machine learning and job scheduling (2023-)

·       Ruyue Xin, Virtual Infrastructure control and adaptation (2019-)[thesis]

·       Dr. Yangjun Zhang, Knowledge discovery (2023-2023)

·       J.M. van der Stoep, NaaVRE developer (2022-2023)

·       Zhengqiu Zhu, Incentive models in crowd applications (2020-2022)

·       Dr. Uraz Odyurt, AI and cloud computing (2021-2022)

·       Zeshun Shi, Trustworthy Virtual infrastructure (2018-2022) [thesis]

·       Mr. Ning Chen, Sensor network and robustness (2021-2022)

·       Riccardo Bianchi (M.Sc), DevOps (2020-2022)

·       Dr. Peng Chen, Cloud infrastructure optimization (2020-2021)

·       Dr. Xiaofeng Liao, Alignment, annotation (2017-2020)

·       Dr. Paul Martin, Semantic information linking (2015-2018)

·       Dr. Arie Taal, Time critical applications (2016-2019)

·       Junchao Wang, Virtual Infrastructure planning (2015-2020)

·       Hu Yang, Time critical application deployment (2015-2019) [thesis]

·       Huan Zhou, Virtual infrastructure provisioning and DevOps (2015-2019) [thesis]

Funding and projects

Via University of Amsterdam

Via LifeWatch ERIC Virtual Lab and Innovation Center (VLIC)

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Current projects

Via UvA

1.     NWO LTER-LIFE (a research infrastructure to develop Digital Twins of ecosystems in a changing world), Large-Scale Research Infrastructure (LSRI), Duration August 2023-August 2032.

2.     EU H-Europe ENVRI-Hub Next (ENVironmental Research Infrastructures delivering an open access Hub and NEXT-level interdisciplinary research framework providing services for advancing science and society). Grant No. 101131141. HORIZON-INFRA-2023-DEV-01 call. Duration 2024-2027.

3.     EU H-Europe EVERSE (European Virtual Institute for Research Software Excellence). Grant No. 101129744. HORIZON-INFRA-2023-EOSC-01-02 call. Duration 2024-2027.

4.     EU H-Europe OSCARS (Open Science Clusters’ Action for Research and Society). Grant No. 101129751. HORIZON-INFRA-2023-EOSC-01 call. Duration 2024-2027. (Third party via LifeWatch ERIC)

5.     EU H-Europe BlueCloud-2026 (A federated European FAIR and Open Research Ecosystem for Oceans, Seas, and inland waters). Grant No. 101094227. HORIZON-INFRA-2022-EOSC-01-03 call. Duration: Jan 2023-June 2026.

Via LifeWatch

6.     EU H-Europe BioDT (Biodiversity Digital Twin for Advanced Modelling, Simulation and Prediction Capabilities) Grant No. 101057437. HORIZON-INFRA-2021-TECH-01-01 call. Duration: June 2022- May 2025. (as LifeWatch VLIC)

Finished projects

7.     EU H2020 CLARIFY (CLoud ARtificial Intelligence For pathologY). Grant No. 860627. H2020-MSCA-ITN-2019 call. Duration: Nov 2019-Oct 2023.

8.     EU H2020 ENVRI^FAIR (ENVironmental Research Infrastructures building Fair services Accessible for society, Innovation and Research). Grant No 824068. H2020-INFRAEOSC-2018-2 call. Duration: January 2019-December 2022.

9.     EU H2020 BLUECLOUD (Blue-Cloud: Piloting innovative services for Marine Research & the Blue Economy).H2020-BG-2018-2020. Grant No. 862409. Duration: Nov 2019-Oct 2022.

10.  EU H2020 ARTICONF (smART socIal media eCOsytstem in a blockchaiN Federated environment). Grant No 825134. H2020-ICT-2018-2 call. Duration: January 2019-December 2021.

11.  EU H2020 ENVRI^PLUS (ENVironmental Research Infrastructures Providing shared soLUtions for Science and society). Leader of the Data for Science Theme.  Grant No. 654182 (INFRADEV-4-2014-2015). Duration: May 2015- April 2019, Project website: www.envriplus.eu. 

12.  EU H2020 VRE4EIC (A Europe-wide interoperable Virtual Research Environment to Empower multidisciplinary research communities and accelerate Innovation and Collaboration). Grant number 676247.  H2020-EINFRA-2015-1. Duration October 2015- September 2018.

13.  EU H2020 SWITCH (Software Workbench for Interactive, Time Critical and Highly self-adaptive cloud applications). Grant No 643963. H2020-ICT 9-2014-1 call: Tools and methods for software development. Duration: February 2015- January 2018.  (SWITCH project).

Participated projects

14.  EU FP7 ENVRI. Task leader, task 3.4: linking data and infrastructure - Common operations of Environmental Research Infrastructure (ENVRI). Grant number 283465,

Community

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Editorial board

1.     Managing editor, Journal of cloud computing, advances, systems and applications

2.     International journal of: Blockchains: research and applications

Organizer/co-organizer

1. Nishant Saurabh, Dragi Kimovski, Zhiming Zhao, international workshop on intelligent and adaptive edge-cloud operations and services
1. 1^st INTEL4EC, in conjunction with UCC 2022, USA
2. 2^nd INTEL4EC, in conjunction with UCC 2023, Italy
3. 3^rd INTEL4EC, in conjunction with IPDPS 2025, Italy
2. Kaiwen Zhang, Zhiming Zhao, Vanessa Daza, program chair, IEEE Decentralized Application, and infrastructure
1. DApps 2022, USA
3. Chen L., Zhao. Z., International Workshop on Network-Aware Big Data Computing (NEAC)
1.  in conjunction with CCGrid 2019, 2020 and 2021.
4. Zhao, Z., et al., International workshop on interoperable infrastructures for interdisciplinary big data sciences (IT4RIs)
1. 1^st IT4RIs, in conjunction with IEEE e-Science 2015, Munich, Germany
2. 2^nd IT4RIs, in conjunction with IEEE RTSS (Real-time System Symposium) 2016, Porto, Portugal
3. 3^rd IT4RIs, in conjunction with Amsterdam HPC 2018, Amsterdam, the Netherlands
5. Zhao, Z., Keith, J., Magagana, B., Stocker, M., Martin, P.,

1.     Special session: Using heterogeneous environmental data for system-level science, 2018, Vienna, EGU

3. Chen, Y., et al., Zhao, Z., Session on Towards Environmental Research Infrastructure as a Service for the Open Science Cloud

1.     in Digital Infrastructure for research 2016, Krakow, Poland

2.     in Digital Infrastructure for research 2017, Brussels, Belgium

4. Zhao, Z., et al., International workshop on Workflow Systems in E-Sciences (WSES),
1. 1^st WSES, in conjunction with ICCS 2006, Reading, UK
2. 2^nd WSES, in conjunction with ICCS 2007, Beijing, China
3. 3^rd WSES, in conjunction with IEEE CCGrid 2008, Lyon, France
4. 4^th WSES, in conjunction with IEEE CCGrid 2009, Shanghai, China
5. Belloum, A., Bubak, M., Zhao, Z., International Workshop on Applications of workflows in Computational Science (AWCS) 

1. AWCS, in conjunction with ICCS, 2008
5. Belloum, A., Zhao, Z., et al, International Workshop on Scientific Workflows and Business Workflow Standards in e-Science (SWBES),

1. 1^st SWBES, in conjunction with IEEE e-Science 2006, Amsterdam, the Netherlands
2. 2^nd SWBES, in conjunction with IEEE e-Science 2007, Bangalore India
3. 3^rd SWBES, in conjunction with IEEE e-Science 2008, Indianapolis, USA

Summer school

1.    International summer school on data management in environmental and earth sciences (2018 | 2019 | 2020 | 2021 | 2022)

Workshop/Panel chair

1. Panel Co-Chair, International Conference on High-Performance Computing and Simulation (HPCS 2015, Amsterdam)
2. Workshop Chair, International Conference on Network and Distributed Computing (ICNDC 2010-2013, 2015, 2016)

Program committee

1. IEEE DAPPS (2025)
2. International conference on Blockchain Research and Application (2024)
3. IEEE Int’l Conf. on NetSoft (2023 | 2024)
4. IEEE Int’l Conf. on CloudNet (2021 | 2022)
5. Int’l workshop on Science Gateway (IWSG 2018 | 2019 | 2020 | 2021)
6. IEEE International Conference on Advanced Information Networking and Applications (AINA 2017)
7. IEEE International Conference on e-Science (2014, 2015, 2016, 2017, 2019)
8. IEEE International Conference on Ubiquitous Computing and Communication (IUCC, 2011, 2013, 2015, 2016, 2017)
9. IEEE International Conference on Internet of Things and Cloud (FiCloud, 2016)
10. International Conference on Smart Data (2016, 2017)
11. Workshop on Management of resources and services in Cloud and Sky computing (MICAS, 2016)
12. W3C – VRE4EIC Workshop on Smart Descriptions & Smarter Vocabularies (SDSVoc, 2016)
13. IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom, 2014)
14. International Workshop on Workflow Models, Systems, Services and Applications in the Cloud (2012-2014)
15. The 11th IEEE International Conference on Computer and Information Technology (2011)
16. The IET International Conference on Frontier Computing– Theory, Technologies and Applications (2010)
17. International Workshop on Scientific Workflows (2009-2011)

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Publications

Journals

1.     Li, N., Farshidi, S. & Zhao, Z. Search Multiple Types of Research Assets From Jupyter Notebook. Softw Pract Exp spe.3406 (2025) https://doi.org/10.1002/spe.3406

2.     Wang, Y., Tripathi, S., Farshidi, S., Zhao, Z.: D-VRE: From a Jupyter-enabled Private Research Environment to Decentralized Collaborative Research Ecosystem. Blockchain: Research and Applications. 100244 (2024). https://doi.org/10.1016/j.bcra.2024.100244.

3.     Li, N., Qi, Y., Li, C., Zhao, Z.: Active Learning for Data Quality Control: A Survey. J. Data and Information Quality. 3663369 (2024). https://doi.org/10.1145/3663369.

4.     Petzold, A., Bundke, U., Hienola, A., Laj, P., Lund Myhre, C., Vermeulen, A., Adamaki, A., Kutsch, W., Thouret, V., Boulanger, D., Fiebig, M., Stocker, M., Zhao, Z., Asmi, A.: Opinion: New directions in atmospheric research offered by research infrastructures combined with open and data-intensive science. Atmos. Chem. Phys. 24, 5369–5388 (2024). https://doi.org/10.5194/acp-24-5369-2024.

5.     Xin, R., Chen, P., Grosso, P., Zhao, Z.: A fine-grained robust performance diagnosis framework for run-time cloud applications. Future Generation Computer Systems. 155, 300–311 (2024). https://doi.org/10.1016/j.future.2024.02.014.

6.     Song, Y., Xin, R., Chen, P., Zhang, R., Chen, J., Zhao, Z.: Autonomous selection of the fault classification models for diagnosing microservice applications. Future Generation Computer Systems. 153, 326–339 (2024). https://doi.org/10.1016/j.future.2023.12.005.

7.     Jiang, W., Luo, T., Liang, Z., Chen, K., He, J., Zhao, Z., Wen, J., Zhao, L., Song, W.: FBENet: Feature-Level Boosting Ensemble Network for Hashimoto’s Thyroiditis Ultrasound Image Classification. IEEE J. Biomed. Health Inform. 28, 5360–5369 (2024). https://doi.org/10.1109/jbhi.2024.3414389.

8.     Yuan, S., Chen, J., Jiang, W., Zhao, Z., Guo, S.: LHN etV2: A Balanced L ow-cost H ybrid Network for Single Image Dehazing. IEEE Trans. Multimedia. 1–14 (2024). https://doi.org/10.1109/TMM.2024.3377133.

9.     Cheng, L., Wang, Y., Cheng, F., Liu, C., Zhao, Z., Wang, Y.: A Deep Reinforcement Learning-Based Preemptive Approach for Cost-Aware Cloud Job Scheduling. IEEE Trans. Sustain. Comput. 1–12 (2023). https://doi.org/10.1109/TSUSC.2023.3303898.

10.  Jiang, W., Chen, K., Liang, Z., Luo, T., Yue, G., Zhao, Z., Song, W., Zhao, L., Wen, J.: HT-RCM: Hashimoto’s Thyroiditis Ultrasound Image Classification Model based on Res-FCT and Res-CAM. IEEE J. Biomed. Health Inform. 1–11 (2023). https://doi.org/10.1109/JBHI.2023.3331944.

11.  Tabatabaei, Z., Wang, Y., Colomer, A., Oliver Moll, J., Zhao, Z., Naranjo, V.: WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval. Bioengineering. 10, 1144 (2023). https://doi.org/10.3390/bioengineering10101144

12.  Rito Lima, I., Filipe, V., Marinho, C., Ulisses, A., Chakravorty, A., Hristov, A., Saurabh, N., Zhao, Z., Xin, R., Prodan, R.: ARTICONF decentralized social media platform for democratic crowd journalism. Soc. Netw. Anal. Min. 13, 116 (2023). https://doi.org/10.1007/s13278-023-01110-y.

13.  Zhang, J., Cheng, L., Liu, C., Zhao, Z., Mao, Y.: Cost-aware scheduling systems for real-time workflows in cloud: An approach based on Genetic Algorithm and Deep Reinforcement Learning. Expert Systems with Applications. 234, 120972 (2023). https://doi.org/10.1016/j.eswa.2023.120972

14.  Li, J., Li, J., Xie, C., Liang, Y., Qu, K., Cheng, L., Zhao, Z.: PipCKG-BS: A Method to Build Cybersecurity Knowledge Graph for Blockchain Systems via the Pipeline Approach. J CIRCUIT SYST COMP. 2350274 (2023). https://doi.org/10.1142/S0218126623502742

15.  Xin, R., Chen, P., Zhao, Z.: CausalRCA: Causal inference based precise fine-grained root cause localization for microservice applications. Journal of Systems and Software. 111724 (2023). https://doi.org/10.1016/j.jss.2023.111724. 

16.  Liu, H., Xin, R., Chen, P., Gao, H., Grosso, P., Zhao, Z.: Robust-PAC time-critical workflow offloading in edge-to-cloud continuum among heterogeneous resources. J Cloud Comp. 12, 58 (2023). https://doi.org/10.1186/s13677-023-00434-6.

17.   Liu, H., Chen, P., Ouyang, X., Gao, H., Yan, B., Grosso, P., Zhao, Z.: Robustness challenges in Reinforcement Learning based time-critical cloud resource scheduling: A Meta-Learning based solution. Future Generation Computer Systems. 146, 18–33 (2023). https://doi.org/10.1016/j.future.2023.03.029.

18.  Song, Y., Xin, R., Chen, P., Zhang, R., Chen, J., Zhao, Z.: Identifying performance anomalies in fluctuating cloud environments: A robust correlative-GNN-based explainable approach. Future Generation Computer Systems. 145, 77–86 (2023). https://doi.org/10.1016/j.future.2023.03.020.[OA].

19.  Xin, R., Liu, H., Chen, P., Zhao, Z.: Robust and accurate performance anomaly detection and prediction for cloud applications: a novel ensemble learning-based framework. J Cloud Comp. 12, 7 (2023). https://doi.org/10.1186/s13677-022-00383-6.

20.  Launet, L., Wang, Y., Colomer, A., Igual, J., Pulgarín-Ospina, C., Koulouzis, S., Bianchi, R., Mosquera-Zamudio, A., Monteagudo, C., Naranjo, V., Zhao, Z.: Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions. Applied Sciences. 13, 919 (2023). https://doi.org/10.3390/app13020919.

21.  Shi, Z., de Laat, C., Grosso, P., Zhao, Z.: Integration of Blockchain and Auction Models: A Survey, Some Applications, and Challenges. IEEE Commun. Surv. Tutorials. 1–1 (2022). https://doi.org/10.1109/COMST.2022.3222403.

22.  Zhu, Z., Ai, C., Chen, H., Chen, B., Duan, W., Qiu, X., Lu, X., He, M., Zhao, Z., Liu, Z.: Understanding the Necessity and Economic Benefits of Lockdown Measures to Contain COVID-19. IEEE Trans. Comput. Soc. Syst. 1–13 (2022). https://doi.org/10.1109/TCSS.2022.3194639

23.  Xiao, H., Li, P., Zeng, H., Liang, T., Jiang, W., Zhao, Z.: Metric learning-based whole health indicator model for industrial robots. Int J of Intelligent Sys. int.23008 (2022). https://doi.org/10.1002/int.23008

24.  Yuan, S., Wang, Y., Liang, T., Jiang, W., Lin, S., Zhao, Z.: Real‐time recognition and warning of mask wearing based on improved YOLOv5 R6.1. Int J of Intelligent Sys. 37, 9309–9338 (2022). https://doi.org/10.1002/int.22994.

25.  Shi, Z., Ivankovic, V., Farshidi, S., Surbiryala, J., Zhou, H., Zhao, Z.: AWESOME: an auction and witness enhanced SLA model for decentralized cloud marketplaces. J Cloud Comp. 11, 27 (2022). https://doi.org/10.1186/s13677-022-00292-8 

26.  Zhu, Z., Chen, B., Chen, H., Qiu, S., Fan, C., Zhao, Y., Guo, R., Ai, C., Liu, Z., Zhao, Z., Fang, L., Lu, X.: Strategy evaluation and optimization with an artificial society toward a Pareto optimum. The Innovation. 3, 100274 (2022). https://doi.org/10.1016/j.xinn.2022.100274

27.  Chen, P., Liu, H., Xin, R., Carval, T., Zhao, J., Xia, Y., Zhao, Z.: Effectively Detecting Operational Anomalies In Large-Scale IoT Data Infrastructures By Using A GAN-Based Predictive Model. The Computer Journal. 65, 2909–2925 (2022). https://doi.org/10.1093/comjnl/bxac085

28.  Shi, Z., Zhou, H., De Laat, C., Zhao, Z.: A Bayesian game-enhanced auction model for federated cloud services using blockchain. Future Generation Computer Systems. 136, 49–66 (2022). https://doi.org/10.1016/j.future.2022.05.017 

29.  Zhao, Z., Koulouzis, S., Bianchi, R., Farshidi, S., Shi, Z., Xin, R., Wang, Y., Li, N., Shi, Y., Timmermans, J., Kissling, W.D.: Notebook-as-a-VRE (NaaVRE): From private notebooks to a collaborative cloud virtual research environment. Softw Pract Exp. spe.3098 (2022). https://doi.org/10.1002/spe.3098.

30.  Wang, Y., Koulouzis, S., Bianchi, R., Li, N., Shi, Y., Timmermans, J., Kissling, W.D., Zhao, Z.: Scaling Notebooks as Re-configurable Cloud Workflows. Data Intelligence. 4, 409–425 (2022). https://doi.org/10.1162/dint_a_00140.

31.  Wittenburg, P., Hardisty, A., Franc, Y.L., Mozaffari, A., Peer, L., Skvortsov, N.A., Zhao, Z., Spinuso, A.: Canonical Workflows to Make Data FAIR. Data Intelligence. 1–20 (2022). https://doi.org/10.1162/dint_a_00132

32.  Jiang, W., Pan, S., Lu, C., Zhao, Z., Lin, S., Xiong, M., He, Z.: Label entropy-based cooperative particle swarm optimization algorithm for dynamic overlapping community detection in complex networks. Int J Intell Syst. int.22673 (2021). https://doi.org/10.1002/int.22673.

33.  Farshidi, S., Liao, X., Li, N., Goldfarb, D., Magagna, B., Stocker, M., Jeffery, K., Thijsse, P., Pichot, C., Petzold, A., Zhao, Z.: Knowledge sharing and discovery across heterogeneous research infrastructures. Open Res Europe. 1, 68 (2021). https://doi.org/10.12688/openreseurope.13677.1.

34.  Karandikar, N., Abhishek, R., Saurabh, N., Zhao, Z., Lercher, A., Marina, N., Prodan, R., Rong, C., Chakravorty, A.: Blockchain-based prosumer incentivization for peak mitigation through temporal aggregation and contextual clustering. Blockchain: Research and Applications. 2, 100016 (2021). https://doi.org/10.1016/j.bcra.2021.100016 

35.  Saurabh, N., Rubia, C., Palanisamy, A., Koulouzis, S., Sefidanoski, M., Chakravorty, A., Zhao, Z., Karadimce, A., Prodan, R.: The ARTICONF Approach to Decentralized Car-Sharing. Blockchain: Research and Applications. 100013 (2021). https://doi.org/10.1016/j.bcra.2021.100013.

36.  Zhou, H., Shi, Z., Ouyang, X. Zhao, Z., Building a blockchain-based decentralized ecosystem for cloud and edge computing: an ALLSTAR approach and empirical study. Peer-to-Peer Network Application. (2021). https://doi.org/10.1007/s12083-021-01198-z [OA]

37.  Calyam, P., Wilkins-Diehr, N., Miller, M., Brookes, E.H., Arora, R., Chourasia, A., Jennewein, D.M., Nandigam, V., Drew LaMar, M., Cleveland, S.B., Newman, G., Wang, S., Zaslavsky, I., Cianfrocco, M.A., Ellett, K., Tarboton, D., Jeffery, K.G., Zhao, Z., González - Aranda, J., Perri, M.J., Tucker, G., Candela, L., Kiss, T., Gesing, S.: Measuring success for a future vision: Defining impact in science gateways/virtual research environments. Concurrency Computat Pract Exper. cpe.6099 (2020). https://doi.org/10.1002/cpe.6099.

38.  Zhu, Z., Chen, B., Liu, W., Zhao, Y., Liu, Z., and Zhao Z., A Cost-Quality Beneficial Cell Selection Approach for Sparse Mobile Crowdsensing With Diverse Sensing Costs, in IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3831-3850, 1 March1, 2021, https://doi.org/10.1109/JIOT.2020.3024833.[OA].

39.  Zhang, L., Jiang, W., Zhao, Z.: Short -text feature expansion and classification based on nonnegative matrix factorization. Int Journal of Intelligent Systems. int.22290 (2020)https://doi.org/10.1002/int.22290[OA].

40.  Uriarte, R.B., Zhou, H., Kritikos, K., Shi, Z., Zhao, Z., De Nicola, R.: Distributed service- level agreement management with smart contracts and blockchain. Concurrency Computat Pract Exper. (2020). https://doi.org/10.1002/cpe.5800.

41.  Hu, Y., de Laat, C., Zhao, Z.: Optimizing Service Placement for Microservice Architecture in Clouds. Applied Sciences. 9, 4663 (2019). https://doi.org/10.3390/app9214663.

42.  Hu, Y., Zhou, H., de Laat, C., Zhao, Z.: Concurrent container scheduling on heterogeneous clusters with multi-resource constraints. Future Generation Computer Systems. 102, 562–573 (2020). https://doi.org/10.1016/j.future.2019.08.025.

43.  Remy, L., Ivanovic, D., Theodoridou, M., Kritsotaki, A., Martin, P., Bailo, D., Sbarra, M., Zhao, Z., Jeffery, K.: Building an Integrated Enhanced Virtual Research Environment Metadata Catalogue. The Electronic Library. (2019) https://doi.org/10.1108/EL-09-2018-0183 [OA].

44.  Zhou, H., Hu, Y., Ouyang, X., Su, J., Koulouzis, S., Laat, C., Zhao, Z.: CloudsStorm: A framework for seamlessly programming and controlling virtual infrastructure functions during the DevOps lifecycle of cloud applications. Softw: Pract Exper. 49, 1421–1447 (2019). https://doi.org/10.1002/spe.2741.

45.  Tanhua, T., Pouliquen, S., Hausman, J., O’Brien, K., Bricher, P., de Bruin, T., Buck, J.J.H., Burger, E.F., Carval, T., Casey, K.S., Diggs, S., Giorgetti, A., Glaves, H., Harscoat, V., Kinkade, D., Muelbert, J.H., Novellino, A., Pfeil, B., Pulsifer, P.L., Van de Putte, A., Robinson, E., Schaap, D., Smirnov, A., Smith, N., Snowden, D., Spears, T., Stall, S., Tacoma, M., Thijsse, P., Tronstad, S., Vandenberghe, T., Wengren, M., Wyborn, L., Zhao, Z.: Ocean FAIR Data Services. Front. Mar. Sci. 6, 440 (2019). https://doi.org/10.3389/fmars.2019.00440.

46.  Martin, P., Remy, L., Theodoridou, M., Jeffery, K., Zhao, Z.: Mapping heterogeneous research infrastructure metadata into a unified catalogue for use in a generic virtual research environment. Future Generation Computer Systems. 101, 1–13 (2019). https://doi.org/10.1016/j.future.2019.05.076. [OA]

47.  Zhou, H., Ouyang, X., Su, J., Laat, C., Zhao, Z.: Enforcing trustworthy cloud SLA with witnesses: A game theory–based model using smart contracts. Concurrency Computation: Practice Experience (2019). https://doi.org/10.1002/cpe.5511.

48.  Taal, A., Wang, J., de Laat, C., Zhao, Z.: Profiling the scheduling decisions for handling critical paths in deadline-constrained cloud workflows. Future Generation Computer Systems. 100, 237–249 (2019). https://doi.org/10.1016/j.future.2019.05.002.

49.  Štefanič, P., Cigale, M., Jones, A.C., Knight, L., Taylor, I., Istrate, C., Suciu, G., Ulisses, A., Stankovski, V., Taherizadeh, S., Salado, G.F., Koulouzis, S., Martin, P., Zhao, Z.: SWITCH workbench: A novel approach for the development and deployment of time-critical microservice-based cloud-native applications. Future Generation Computer Systems. 99, 197–212 (2019). https://doi.org/10.1016/j.future.2019.04.008.

50.  Liao, X., Zhao, Z.: Unsupervised Approaches for Textual Semantic Annotation, A Survey. ACM Comput. Surv. 52, 1–45 (2019). https://doi.org/10.1145/3324473.

51.  Liao, X., Bottelier, J., Zhao, Z.: A Column Styled Composable Schema Matcher for Semantic Data-Types. Data Science Journal. 18-25 (2019). https://doi.org/10.5334/dsj-2019-025.

52.  Koulouzis, S., Martin, P., Zhou, H., Hu, Y., Wang, J., Carval, T., Grenier, B., Heikkinen, J., Laat, C., Zhao, Z.: Time-critical data management in clouds: Challenges and a Dynamic Real-Time Infrastructure Planner (DRIP) solution. Concurrency Computat Pract Exper. e5269 (2019). https://doi.org/10.1002/cpe.5269.

53.  Taherizadeh, S., Jones, A.C., Taylor, I., Zhao, Z., Stankovski, V.: Monitoring self-adaptive applications within edge computing frameworks: A state-of-the-art review. Journal of Systems and Software. 136, 19–38 (2018). https://doi.org/10.1016/j.jss.2017.10.033.

54.  Li, J., Yang, Y., Wang, X., Zhao, Z., Li, T.: A novel parallel distance metric-based approach for diversified ranking on large graphs. Future Generation Computer Systems. 88, 79–91 (2018). https://doi.org/10.1016/j.future.2018.05.031.

55.  Jiang, W., Zhai, Y., Zhuang, Z., Martin, P., Zhao, Z., Liu, J.-B.: Vertex Labelling and Routing for Farey-Type Symmetrically-Structured Graphs. Symmetry. 10, 407 (2018). https://doi.org/10.3390/sym10090407.

56.  Jiang, W., Zhai, Y., Zhuang, Z., Martin, P., Zhao, Z., Liu, J.-B.: An Efficient Method of Generating Deterministic Small-World and Scale-Free Graphs for Simulating Real-World Networks. IEEE Access. 6, 59833–59842 (2018). https://doi.org/10.1109/ACCESS.2018.2875928.

57.  Jiang, W., Zhai, Y., Martin, P., Zhao, Z.: Structure Properties of Generalized Farey graphs based on Dynamical Systems for Networks. Sci Rep. 8, 12194 (2018). https://doi.org/10.1038/s41598-018-30712-2.

58.  Wang, J., Taal, A., Martin, P., Hu, Y., Zhou, H., Pang, J., de Laat, C., Zhao, Z.: Planning virtual infrastructures for time critical applications with multiple deadline constraints. Future Generation Computer Systems. 75, 365–375 (2017). https://doi.org/10.1016/j.future.2017.02.001, [Zenodo].

59.  Koulouzis, S., Belloum, A.S.Z., Bubak, M.T., Zhao, Z., Živković, M., de Laat, C.T.A.M.: SDN-aware federation of distributed data. Future Generation Computer Systems. 56, 64–76 (2016). https://doi.org/10.1016/j.future.2015.09.032.

60.  Zhu, H., van der Veldt, K., Zhao, Z., Grosso, P., Pavlov, D., Soeurt, J., Liao, X., de Laat, C.: A semantic enhanced Power Budget Calculator for distributed computing using IEEE 802.3az. Cluster Comput. 18, 61–77 (2015). https://doi.org/10.1007/s10586-014-0395-7.

61.  Ghijsen, M., van der Ham, J., Grosso, P., Dumitru, C., Zhu, H., Zhao, Z., de Laat, C.: A semantic-web approach for modelling computing infrastructures. Computers & Electrical Engineering. 39, 2553–2565 (2013). https://doi.org/10.1016/j.compeleceng.2013.08.011.

62.  Zhao, Z., Grosso, P., van der Ham, J., Koning, R., de Laat, C.: An agent-based network resource planner for workflow applications. MGS. 7, 187–202 (2011). https://doi.org/10.3233/MGS-2011-0180.

63.  Belloum, A., Inda, M.A., Vasunin, D., Korkhov, V., Zhao, Z., Rauwerda, H., Breit, T.M., Bubak, M., Hertzberger, L.O.: Collaborative e-Science Experiments and Scientific Workflows. IEEE Internet Comput. 15, 39–47 (2011). https://doi.org/10.1109/MIC.2011.87.

64.  Zhao, Z., van Albada, D., Sloot, P.: Agent-Based Flow Control for HLA Components. SIMULATION. 81, 487–501 (2005). https://doi.org/10.1177/0037549705058060.

65.  Kommers, P. and Zhao, Z.: Conceptual Support with Virtual Reality in Web-based Learning, International Journal of Continuing Engineering Education and Life-Long Learning, vol. 8, nr 1 1998. ISSN 0957-4344 (1998). https://www.inderscienceonline.com/doi/abs/10.1504/IJCEELL.1998.030134.

Editorial (special issues and proceedings)

66.  Cheng, L., Chen, X., Zhao, Z.: Preface of special issue on Artificial Intelligence for time-critical computing systems. Future Generation Computer Systems. 159, 102–104 (2024). https://doi.org/10.1016/j.future.2024.05.011.

67.  Wittenburg, P., Hardisty, A., Mozzafari, A., Peer, L., Skvortsov, N., Spinuso, A., Zhao, Z.: Editors’ Note: Special Issue on Canonical Workflow Frameworks for Research. Data Intelligence. 4, 149–154 (2022). https://doi.org/10.1162/dint_e_00122.

68.  Rong, C., Zhao, Z.: Welcome to the new Journal of Cloud Computing by Springer. J Cloud Comp. 10, 49, s13677-021-00263–5 (2021). https://doi.org/10.1186/s13677-021-00263-5.

69.  Zhao, Z., Taylor, I., Prodan, R.: Editorial for FGCS Special issue on “Time-critical Applications on Software-defined Infrastructures.” Future Generation Computer Systems. 112, 1170–1171 (2020). https://doi.org/10.1016/j.future.2020.07.056.[OA] [Content]

70.  Zhao, Z., Altmeyer, S., Keith J., Atkinson, M. and Ulisses A.: Nearly real time data processing and time critical cloud applications. Proceedings of the 2^nd International workshop on Interoperable infrastructures for interdisciplinary big data sciences (IT4RIs 16), in the context of IEEE Real-time System Symposium (RTSS), Porto, Portugal, November 29-December 2, (2016). [doi:10.5281/zenodo.204685]

71.  Lu, S., Deelman, E. & Zhao, Z.: Scientific workflows special issue. International journal of business process integration and management (pp. 1-2). Inder science Enterprises Ltd. (2010). [Full text]

72. Zhao, Z., Belloum, A., Bubak, M.: Special section on workflow systems and applications in e-Science. Future Generation Computer Systems. 25, 525–527 (2009). https://doi.org/10.1016/j.future.2008.10.011.

73.  Belloum, A., Deelman, E. & Zhao, Z.: Scientific workflows. Scientific Programming, 14(3-4), 171-171 (2006). [Full text]

Book (Eds)

74.  Zhao, Z., Hellström, M. eds: Towards Interoperable Research Infrastructures for Environmental and Earth Sciences: A Reference Model Guided Approach for Common Challenges. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4. [ENVRI archive][Book interview] 

Book chapters

75.  Jeffery, K., Pursula, A., Zhao, Z.: ICT Infrastructures for Environmental and Earth Sciences. In: Zhao, Z. and Hellström, M. (eds.) Towards Interoperable Research Infrastructures for Environmental and Earth Sciences. pp. 17–29. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4_2.

76.  Magagna, B., Martin, P., de la Hidalga, A.N., Atkinson, M., Zhao, Z.: Common Challenges and Requirements. In: Zhao, Z. and Hellström, M. (eds.) Towards Interoperable Research Infrastructures for Environmental and Earth Sciences. pp. 30–57. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4_3.

77.  de la Hidalga, A.N., Hardisty, A., Martin, P., Magagna, B., Zhao, Z.: The ENVRI Reference Model. In: Zhao, Z. and Hellström, M. (eds.) Towards Interoperable Research Infrastructures for Environmental and Earth Sciences. pp. 61–81. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4_4.

78.   Zhao, Z., Jeffery, K.: Reference Model Guided Engineering. In: Zhao, Z. and Hellström, M. (eds.) Towards Interoperable Research Infrastructures for Environmental and Earth Sciences. pp. 82–99. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4_5.

79.  Martin, P., Liao, X., Magagna, B., Stocker, M., Zhao, Z.: Semantic and Knowledge Engineering Using ENVRI RM. In: Zhao, Z. and Hellström, M. (eds.) Towards Interoperable Research Infrastructures for Environmental and Earth Sciences. pp. 100–119. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4_6.

80.  Quimbert, E., Jeffery, K., Martens, C., Martin, P., Zhao, Z.: Data Cataloguing. In: Zhao, Z. and Hellström, M. (eds.) Towards Interoperable Research Infrastructures for Environmental and Earth Sciences. pp. 140–161. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4_8.

81.  Koulouzis, S., Martin, P., Zhao, Z.: Virtual Infrastructure Optimisation. In: Zhao, Z. and Hellström, M. (eds.) Towards Interoperable Research Infrastructures for Environmental and Earth Sciences. pp. 192–207. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4_11.

82.  Magagna, B., Goldfarb, D., Martin, P., Atkinson, M., Koulouzis, S., Zhao, Z.: Data Provenance. In: Zhao, Z. and Hellström, M. (eds.) Towards Interoperable Research Infrastructures for Environmental and Earth Sciences. pp. 208–225. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4_12.

83.  Martin, P., Magagna, B., Liao, X., Zhao, Z.: Semantic Linking of Research Infrastructure Metadata. In: Zhao, Z. and Hellström, M. (eds.) Towards Interoperable Research Infrastructures for Environmental and Earth Sciences. pp. 226–246. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4_13.

84.  Koulouzis, S., Carval, T., Heikkinen, J., Pursula, A., Zhao, Z.: Case Study: Data Subscriptions Using Elastic Cloud Services. In: Zhao, Z. and Hellström, M. (eds.) Towards Interoperable Research Infrastructures for Environmental and Earth Sciences. pp. 293–306. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4_16.

85.  Zhao, Z., Jeffery, K., Stocker, M., Atkinson, M., Petzold, A.: Towards Operational Research Infrastructures with FAIR Data and Services. In: Zhao, Z. and Hellström, M. (eds.) Towards Interoperable Research Infrastructures for Environmental and Earth Sciences. pp. 360–372. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4_20.

86.  Martin, P., Chen, Y. Hardisty, A., Jeffery, K., and Zhao, Z.: Computational Challenges in Global Environmental Research Infrastructures. in the book Terrestrial Ecosystem Research Infrastructures: Challenges, New Developments and Perspectives (2017). [ISBN 9781498751315] [OA].

87.  Zhao, Z., Grosso, P., Ham, J. van der, Koning, R. & de Laat, C.: Quality guaranteed media delivery over advanced network. Chapter in book Next Generation Content Delivery Infrastructure: Emerging Paradigms and Technologies, IGI, (2012). ISBN 978-1-4666-1794-0 [doi: 10.4018/978-1-4666-1794-0.ch006]. 

Conferences

88.  Cheng, L., He, H., Gu, Y., Liu, Q., Zhao, Z., Fang, F.: MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection. In: 2024 IEEE 30th International Conference on Parallel and Distributed Systems (ICPADS). pp. 657–666. IEEE, Belgrade, Serbia (2024). https://doi.org/10.1109/ICPADS63350.2024.00091. (Best paper)

89.  Hou, S., Wang, Y., Zhao, Z.: CrowdAL: Towards a Blockchain-empowered Active Learning System in Crowd Data Labeling. In: 2024 IEEE 20th International Conference on e-Science (e-Science). pp. 1–2. IEEE, Osaka, Japan (2024). https://doi.org/10.1109/e-Science62913.2024.10678683. 

90.  Krishnasamy, A., Wang, Y., Zhao, Z.: A Collaborative Framework for Facilitating Federated Learning among Jupyter Users. In: 2024 IEEE 20th International Conference on e-Science (e-Science). pp. 1–2. IEEE, Osaka, Japan (2024). https://doi.org/10.1109/e-Science62913.2024.10678679.

91.  Wang, Y., Kanwal, N., Engan, K., Rong, C., Grosso, P., Zhao, Z.: PriCE: Privacy-Preserving and Cost-Effective Scheduling for Parallelizing the Large Medical Image Processing Workflow over Hybrid Clouds. In: Carretero, J., Shende, S., Garcia-Blas, J., Brandic, I., Olcoz, K., and Schreiber, M. (eds.) Euro-Par 2024: Parallel Processing. pp. 210–224. Springer Nature Switzerland, Cham (2024). https://doi.org/10.1007/978-3-031-69577-3_15[OA].

92.  Pan, R., Shi, Z., Belloum, A., Zhao, Z.: Operating ZKPs on Blockchain: A Performance Analysis Based on Hyperledger Fabric. In: 2024 IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS). pp. 69–78. IEEE, Shanghai, China (2024). https://doi.org/10.1109/DAPPS61106.2024.00018 [OA](Best paper).

93.  Zhu, P., Li, N., Zhao, Z.: Retrieval-augmented Query Reformulation for Heterogeneous Research Asset Retrieval in Virtual Research Environment. In: Companion Proceedings of the ACM on Web Conference 2024. pp. 907–910. ACM, Singapore Singapore (2024). https://doi.org/10.1145/3589335.3651553.

94.  Van De Kamp, R., Bakker, K., Zhao, Z.: Paving the Path Towards Platform Engineering Using a Comprehensive Reference Model. In: Sales, T.P., De Kinderen, S., Proper, H.A., Pufahl, L., Karastoyanova, D., and Van Sinderen, M. (eds.) Enterprise Design, Operations, and Computing. EDOC 2023 Workshops. pp. 177–193. Springer Nature Switzerland, Cham (2024). https://doi.org/10.1007/978-3-031-54712-6_11 [OA]

95.  Ashraf, A., Belleman, R.G., Zhao, Z.: Visualization Techniques and Tools for Developing Digital Twins of Ecosystems: State-of-the-Art and Selection. In: 2023 IEEE Smart World Congress (SWC). pp. 1–8. IEEE, Portsmouth, United Kingdom (2023). https://doi.org/10.1109/SWC57546.2023.10448753 [OA]

96.  Li, N., Qi, Y., Xin, R., Zhao, Z.: Ocean Data Quality Assessment through Outlier Detection-enhanced Active Learning. In: 2023 IEEE International Conference on Big Data (BigData). pp. 102–107. IEEE, Sorrento, Italy (2023). https://doi.org/10.1109/BigData59044.2023.10386969 [OA].

97.  Li, N., Zhang, Y., Zhao, Z.: A Dense Retrieval System and Evaluation Dataset for Scientific Computational Notebooks. In: 2023 IEEE 19th International Conference on e-Science (e-Science). pp. 1–10. IEEE, Limassol, Cyprus (2023). https://doi.org/10.1109/e-Science58273.2023.10254859[OA].

98.  Christou, V., Wang, Y., Zhao, Z.: Towards a Knowledge Graph Enhanced Automation and Collaboration Framework for Digital Twins. In: 2023 IEEE 19th International Conference on e-Science (e-Science). pp. 1–2. IEEE, Limassol, Cyprus (2023). https://doi.org/10.1109/e-Science58273.2023.10254845 [OA].

99.  Kontomaris, C., Wang, Y., Zhao, Z.: CWL-FLOps: A Novel Method for Federated Learning Operations at Scale. In: 2023 IEEE 19th International Conference on e-Science (e-Science). pp. 1–2. IEEE, Limassol, Cyprus (2023). https://doi.org/10.1109/e-Science58273.2023.10254788[OA].

100.   Marra, M.L., Henkemans, D.B., Titocci, J., Koulouzis, S., Rosati, I., Zhao, Z.: Integrating R in a Distributed Scientific Workflow via a Jupyter-Based Environment. In: 2023 IEEE 19th International Conference on e-Science (e-Science). pp. 1–2. IEEE, Limassol, Cyprus (2023). https://doi.org/10.1109/e-Science58273.2023.10254945[OA].

101.   Blanco, A.F., Shi, Z., Roy, D., Zhao, Z.: Improving the Resiliency of Decentralized Crowdsourced Blockchain Oracles. In: Mikyška, J., De Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., and Sloot, P.M.A. (eds.) Computational Science – ICCS 2023. pp. 3–17. Springer Nature Switzerland, Cham (2023). https://doi.org/10.1007/978-3-031-35995-8_1

102.   Wang, Y., Kanwal, N., Engan, K., Rong, C., Zhao, Z.: Towards a Privacy-Preserving Distributed Cloud Service for Preprocessing Very Large Medical Images. In: 2023 IEEE International Conference on Digital Health (ICDH). pp. 325–327. IEEE, Chicago, IL, USA (2023). https://doi.org/10.1109/ICDH60066.2023.00055 [OA]

103.   Wang, Y., Janse, N., Bianchi, R., Koulouzis, S., Zhao, Z.: Towards a Service-based Adaptable Data Layer for Cloud Workflows. In: 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC). pp. 904–911. IEEE, Torino, Italy (2023). https://doi.org/10.1109/COMPSAC57700.2023.00121 [OA]

104.   Li, N., Zhang, Y., Zhao, Z.: CNSVRE: A Query Reformulated Search System with Explainable Summarization for Virtual Research Environment. In: Companion Proceedings of the ACM Web Conference 2023. pp. 254–257. ACM, Austin TX USA (2023). https://doi.org/10.1145/3543873.3587360 [OA].

105.   Song, Y., Xin, R., Zhang, R., Chen, J., Zhao, Z.: A Robust and Accurate Multivariate Time Series Anomaly Detection in Fluctuating Cloud-Edge Computing Systems. In: 2022 IEEE 24th Int Conf on High-Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys). pp. 357–365. IEEE, Hainan, China (2022). https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00077

106.   Lima, I.R., Marinho, C., Filipe, V., Ulisses, A., Saurabh, N., Chakravorty, A., Zhao, Z., Hristov, A., Prodan, R.: MOGPlay: A Decentralized Crowd Journalism Application for Democratic News Production. In: 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). pp. 462–469. IEEE, Istanbul, Turkey (2022). https://doi.org/10.1109/ASONAM55673.2022.10068697.

107.   Xin, R., Stallinga, S., Liu, H., Chen, P., Zhao, Z.: Provenance-enhanced Root Cause Analysis for Jupyter Notebooks. In: 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC). pp. 327–333. IEEE, Vancouver, WA, USA (2022). https://doi.org/10.1109/UCC56403.2022.00058[OA].

108.   Geng, J., Chen, Z., Wang, Y., Woisetschlaeger, H., Schimmler, S., Mayer, R., Zhao, Z., Rong, C.: A Survey on Dataset Distillation: Approaches, Applications and Future Directions. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. pp. 6610–6618. International Joint Conferences on Artificial Intelligence Organization, Macau, SAR China (2023). https://doi.org/10.24963/ijcai.2023/741 [OA].

109.   Chen, S., Huang, G., Lin, S., Jiang, W., Zhao, Z.: Overlapping Community Discovery Algorithm Based on Three-Level Neighbor Node Influence. In: Xu, Y., Yan, H., Teng, H., Cai, J., and Li, J. (eds.) Machine Learning for Cyber Security. pp. 335–344. Springer Nature Switzerland, Cham (2023). https://doi.org/10.1007/978-3-031-20099-1_28.

110.   Li, N., Farshidi, S., Bianchi, R., Koulouzis, S., Zhao, Z.: Context-Aware Notebook Search in a Jupyter-Based Virtual Research Environment. In: 2022 IEEE 18th International Conference on e-Science (e-Science). pp. 393–394. IEEE, Salt Lake City, UT, USA (2022). https://doi.org/10.1109/eScience55777.2022.00054[OA].

111.   Li, M., Su, J., Liu, H., Zhao, Z., Ouyang, X., Zhou, H.: The Extreme Counts: Modeling the Performance Uncertainty of Cloud Resources with Extreme Value Theory. In: Troya, J., Medjahed, B., Piattini, M., Yao, L., Fernández, P., and Ruiz-Cortés, A. (eds.) Service-Oriented Computing. pp. 498–512. Springer Nature Switzerland, Cham (2022). https://doi.org/10.1007/978-3-031-20984-0_35.

112.   Launet, L., Amor, R. del, Colomer, A., Mosquera-Zamudio, A., Moscardó, A., Monteagudo, C., Zhao, Z., Naranjo, V.: Federating Unlabeled Samples: A Semi-supervised Collaborative Framework for Whole Slide Image Analysis. In: Yin, H., Camacho, D., and Tino, P. (eds.) Intelligent Data Engineering and Automated Learning – IDEAL 2022. pp. 64–72. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-21753-1_7

113.   Ivankovic, V., Shi, Z., Zhao, Z.: A Customizable dApp Framework for User Interactions in Decentralized Service Marketplaces. In: 2022 IEEE International Conference on Smart Internet of Things (SmartIoT). pp. 224–231. IEEE, Suzhou, China (2022). https://doi.org/10.1109/SmartIoT55134.2022.00043.[OA][Best paper].

114.   Liu, H., Xin, R., Chen, P., Zhao, Z.: Multi-Objective Robust Workflow Offloading in Edge-to-Cloud Continuum. In: 2022 IEEE 15th International Conference on Cloud Computing (CLOUD). pp. 469–478. IEEE, Barcelona, Spain (2022). https://doi.org/10.1109/CLOUD55607.2022.00070.[OA]

115.   Boyko, A., Farshidi, S., Zhao, Z.: An Adaptable Framework for Entity Matching Model Selection in Business Enterprises. In: 2022 IEEE 24th Conference on Business Informatics (CBI). pp. 90–99. IEEE, Amsterdam, Netherlands (2022). https://doi.org/10.1109/CBI54897.2022.00017  [OA]

116.   Farshidi, S., Zhao, Z.: An Adaptable Indexing Pipeline for Enriching Meta Information of Datasets from Heterogeneous Repositories. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., and Teng, F. (eds.) Advances in Knowledge Discovery and Data Mining. pp. 472–484. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-05936-0_37 [OA].

117.   Hoogenkamp, B., Farshidi, S., Xin, R., Shi, Z., Chen, P., Zhao, Z.: A Decentralized Service Control Framework for Decentralized Applications in Cloud Environments. In: Montesi, F., Papadopoulos, G.A., and Zimmermann, W. (eds.) Service-Oriented and Cloud Computing. pp. 65–73. Springer International Publishing, Cham (2022). [https://doi.org/10.1007/978-3-031-04718-3_4][OA]

118.   Bergers, J., Shi, Z., Korsmit, K., Zhao, Z.: DWH-DIM: A Blockchain Based Decentralized Integrity Verification Model for Data Warehouses. In: 2021 IEEE International Conference on Blockchain (Blockchain). pp. 221–228. IEEE, Melbourne, Australia (2021). https://doi.org/10.1109/Blockchain53845.2021.00037 [OA]

119.   Poon, L., Farshidi, S., Li, N., Zhao, Z.: Unsupervised Anomaly Detection in Data Quality Control. In: 2021 IEEE International Conference on Big Data (Big Data). pp. 2327–2336. IEEE, Orlando, FL, USA (2021). https://doi.org/10.1109/BigData52589.2021.9671672 [OA]

120.   Liu H., Chen P., Zhao, Z.,: Towards A Robust Meta-Reinforcement Learning-Based Scheduling Framework for Time Critical Tasks in Cloud Environments, IEEE Cloud (2021), Online, [10.1109/CLOUD53861.2021.00082] [OA](Best student paper)

121.   Shi Ze., Farshidi S., Zhou H., Zhao Z., An Auction and Witness Enhanced Trustworthy SLA Model for     Decentralized Cloud Marketplaces, The 2021 ACM International Conference on Information Technology for Social Good (GoodIT 2021), Rome Italy [https://doi.org/10.1145/3462203.3475876] [OA]

122.   Xin R., Mohazzab J., Shi Z., and Zhao Z.: CBProf: Customisable Blockchain-as-a-service Performance Profiler in Cloud Environments, International Conf. on Blockchain (2021), Online [https://doi.org/10.1007/978-3-030-96527-3_9][OA]

123.  Koulouzis, S., Bianchi, R., der Linde, R. van, Wang, Y., Zhao, Z.: SPIRIT: A Microservice-Based Framework for Interactive Cloud Infrastructure Planning. In: Chaves, R., B. Heras, D., Ilic, A., Unat, D., Badia, R.M., Bracciali, A., Diehl, P., Dubey, A., Sangyoon, O., L. Scott, S., and Ricci, L. (eds.) Euro-Par 2021: Parallel Processing Workshops. pp. 405–416. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-06156-1_32 [OA].

124.   Zhao, Z., Rong, C., Jaatun, M.G.: A Trustworthy Blockchain-based Decentralised Resource Management System in the Cloud. In: 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS). pp. 617–624. IEEE, Hong Kong (2020). https://doi.org/10.1109/ICPADS51040.2020.00086 [OA].

125.   Wang, Y., Zhao, Z.: Decentralized workflow management on software defined infrastructure, Workshop on The 1st Workshop On Data-Centric Workflows On Heterogeneous Infrastructures: Challenges And Directions (DAWHI), in the context of IEEE Service Congress, (2020), [https://doi.org/10.1109/SERVICES48979.2020.00059][OA].

126.  de Jong, K., Fahrenfort, C., Younis, A., Zhao, Z.: Sharing digital object across data infrastructures using Named Data Networking (NDN). 2^nd workshop on Network Aware Big Data Computing (NEAC), In: 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID). pp. 873–880. IEEE, Melbourne, Australia (2020). https://doi.org/10.1109/CCGrid49817.2020.00013. [OA].

127.  Zhou, H., Ouyang, X., Zhao, Z.: ALLSTAR: A Blockchain Based Decentralized Ecosystem for Cloud and Edge Computing. In: 2020 IEEE International Conference on Joint Cloud Computing. pp. 55–62. IEEE, Oxford, United Kingdom (2020). https://doi.org/10.1109/JCC49151.2020.00018 [OA].

128.   Shi, Z., Zhou, H., Surbiryala, J., Hu, Y., de Laat, C., Zhao, Z.: An Automated Customization and Performance Profiling Framework for Permissioned Blockchains in a Virtualized Environment. In: 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom). pp. 404–410. IEEE, Sydney, Australia (2019). https://doi.org/10.1109/CloudCom.2019.00069, [OA].

129.   Petzold, A., Asmi, A., Vermeulen, A., Pappalardo, G., Bailo, D., Schaap, D., Glaves, H.M., Bundke, U., Zhao, Z.: ENVRI-FAIR - Interoperable Environmental FAIR Data and Services for Society, Innovation and Research. In: 2019 15th International Conference on eScience (eScience). pp. 277–280. IEEE, San Diego, CA, USA (2019). https://doi.org/10.1109/eScience.2019.00038, [OA].

130.   Fahrenfort, C., Zhao, Z.: Effective Digital Object Access and Sharing Over a Networked Environment using DOIP and NDN. In: 2019 15th International Conference on eScience (eScience). pp. 632–633. IEEE, San Diego, CA, USA (2019). https://doi.org/10.1109/eScience.2019.00092, [OA].

131.   Demchenko, Y., Zhao, Z., Surbiryala, J., Koulouzis, S., Shi, Z., Liao, X., Gordiyenko, J.: Teaching DevOps and Cloud Based Software Engineering in University Curricula. In: 2019 15th International Conference on eScience (eScience). pp. 548–552. IEEE, San Diego, CA, USA (2019). https://doi.org/10.1109/eScience.2019.00075, [OA].

132.   Ahanach, E. el K., Koulouzis, S., Zhao, Z.: Contextual Linking between Workflow Provenance and System Performance Logs. In: 2019 15th International Conference on eScience (eScience). pp. 634–635. IEEE, San Diego, CA, USA (2019). https://doi.org/10.1109/eScience.2019.00093, [OA].

133.   Prodan, R., Saurabh, N., Zhao, Z., Orton-Johnson, K., Chakravorty, A., Karadimce, A., and Ulisses, A.: ARTICONF: Towards a Smart Social Media Ecosystem in a Blockchain Federated Environment, in the 7th Workshop on Large Scale Distributed Virtual Environments, in the context of Euro-Par conference 2019, Gottingen, Germany (2019) https://doi.org/10.1007/978-3-030-48340-1_32 [OA].

134.   Shi, Z., Zhou, H., Hu, Y., Koulouzis, S., Rubia, C., and Zhao, Z.: Co-located and Orchestrated Network Fabric (CONF): An Automated Cloud Virtual Infrastructure for Social Network Applications. in the 7th Workshop on Large Scale Distributed Virtual Environments, in the context of Euro-Par conference 2019, Gottingen, Germany (2019) https://doi.org/10.1007/978-3-030-48340-1_36, [OA].

135.   Zhou, H., Shi, Z., Hu, Y., Donkers, P., Afanasyev, A., Koulouzis, S., Taal, A., Ulisses, A., Zhao, Z.: Large Distributed Virtual Infrastructure Partitioning and Provisioning Across Providers. In: 2019 IEEE International Conference on Smart Internet of Things (SmartIoT). pp. 56–63. IEEE, Tianjin, China (2019). https://doi.org/10.1109/SmartIoT.2019.00018, [OA] (Best student paper candidate).

136.   Zhao, Z., Liao, X., Martin, P., Maduro, J., Thijsse, P., Schaap, D., Stocker, M., Goldfarb, D., Magagna, B.: Knowledge-as-a-Service: A Community Knowledge Base for Research Infrastructures in Environmental and Earth Sciences. In: 2019 IEEE World Congress on Services (SERVICES). pp. 127–132. IEEE, Milan, Italy (2019). https://doi.org/10.1109/SERVICES.2019.00041, [OA].

137.   Hu, Y., de Laat, C., Zhao, Z.: Learning Workflow Scheduling on Multi-Resource Clusters. In: 2019 IEEE International Conference on Networking, Architecture and Storage (NAS). pp. 1–8. IEEE, EnShi, China (2019). https://doi.org/10.1109/NAS.2019.8834720, [OA].

138.   Ahanach, E. el K., Koulouzis, S., Zhao, Z.: Linking provenance with system logs: a context aware information integration and exploration framework for analyzing workflow execution. 10th International Workshop on Science Gateways (IWSG 2019), pp. 13-15 June 2019 (2019). [OA]

139.   Zhou, H., Ouyang, X., Ren, Z., Su, J., de Laat, C., Zhao, Z.: A Blockchain based Witness Model for Trustworthy Cloud Service Level Agreement Enforcement. In: IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. pp. 1567–1575. IEEE, Paris, France (2019). https://doi.org/10.1109/INFOCOM.2019.8737580, [OA] Best presentation in the session.

140.   Shi, Z., Zhou, H., Hu, Y., Jayachander, S., de Laat, C., Zhao, Z.: Operating Permissioned Blockchain in Clouds: A Performance Study of Hyperledger Sawtooth. In: 2019 18th International Symposium on Parallel and Distributed Computing (ISPDC). pp. 50–57. IEEE, Amsterdam, Netherlands (2019). https://doi.org/10.1109/ISPDC.2019.00010, [OA].

141.   Hu, Y., De Laat, C., Zhao, Z.: Multi-objective Container Deployment on Heterogeneous Clusters. In: 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). pp. 592–599. IEEE, Larnaca, Cyprus (2019). https://doi.org/10.1109/CCGRID.2019.00076, [OA] Best paper award.

142.   Zhou, H., Koulouzis, S., Hu, Y., Wang, J., de Laat, C., Ulisses, A., Zhao, Z.: Migrating Live Streaming Applications onto Clouds: Challenges and a CloudStorm Solution. In: 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion). pp. 321–326. IEEE, Zurich (2018). https://doi.org/10.1109/UCC-Companion.2018.00075, [OA].

143.   Zhou, H., de Laat, C., Zhao, Z.: Trustworthy Cloud Service Level Agreement Enforcement with Blockchain Based Smart Contract. In: 2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom). pp. 255–260. IEEE, Nicosia (2018). https://doi.org/10.1109/CloudCom2018.2018.00057, [OA].

144.   Hu, Y., Zhou, H., de Laat, C., Zhao, Z.: ECSched: Efficient Container Scheduling on Heterogeneous Clusters. In: Aldinucci, M., Padovani, L., and Torquati, M. (eds.) Euro-Par 2018: Parallel Processing. pp. 365–377. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-96983-1_26 [OA].

145.   Qin, Y., Chi, M., Liu, X., Zhang, Y., Zeng, Y., Zhao, Z.: Classification of High Resolution Urban Remote Sensing Images Using Deep Networks by Integration of Social Media Photos. In: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. pp. 7243–7246. IEEE, Valencia (2018). https://doi.org/10.1109/IGARSS.2018.8518538.

146.   Zhou, H., Hu, Y., Su, J., Chi, M., de Laat, C., Zhao, Z.: Empowering Dynamic Task-Based Applications with Agile Virtual Infrastructure Programmability. In: 2018 IEEE 11th International Conference on Cloud Computing (CLOUD). pp. 484–491. IEEE, San Francisco, CA, USA (2018). https://doi.org/10.1109/CLOUD.2018.00068.

147.   Zhou, H., Hu, Y., Su, J., de Laat, C., Zhao, Z.: CloudsStorm: An Application-Driven Framework to Enhance the Programmability and Controllability of Cloud Virtual Infrastructures. In: Luo, M. and Zhang, L.-J. (eds.) Cloud Computing – CLOUD 2018. pp. 265–280. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-94295-7_18.

148.   Martin, P., Remy, L., Theodoridou, M., Jeffery, K., Zhao, Z.: Mapping metadata from different research infrastructures into a unified framework for use in a virtual research environment. 10th International Workshop on Science Gateways (IWSG 2018), 13-15 June 2018, (2018). [PDF]

149.   Zhou, H., Taal, A., Koulouzis, S., Wang, J., Hu, Y., Suciu, G., Poenaru, V., de Laat, C., Zhao, Z.: Dynamic Real-Time Infrastructure Planning and Deployment for Disaster Early Warning Systems. In: Shi, Y., Fu, H., Tian, Y., Krzhizhanovskaya, V.V., Lees, M.H., Dongarra, J., and Sloot, P.M.A. (eds.) Computational Science – ICCS 2018. pp. 644–654. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-93701-4_51.

150.   Koulouzis, S., Mousa, R., Karakannas, A., de Laat, C., Zhao, Z.: Information Centric Networking for Sharing and Accessing Digital Objects with Persistent Identifiers on Data Infrastructures. In: 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). pp. 661–668. IEEE, Washington, DC, USA (2018). https://doi.org/10.1109/CCGRID.2018.00098.

151.   Zhou, H., de Laat, C., Zhao, Z.: Cloudsstorm: An Application-Driven Devops Framework For Managing Networked Infrastructures On Federated Clouds. (2018). https://doi.org/10.5281/ZENODO.1162914.

152.   Zhao, Z., Martin, P., Jones, A., Taylor, I., Stankovski, V., Salado, G.F., Suciu, G., Ulisses, A., de Laat, C.: Developing, Provisioning and Controlling Time Critical Applications in Cloud. In: Mann, Z.Á. and Stolz, V. (eds.) Advances in Service-Oriented and Cloud Computing. pp. 169–174. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-79090-9_14.

153.   Wang, J., Zhou, H., Hu, Y., De Laat, C., Zhao, Z.: Deadline-Aware Coflow Scheduling in a DAG. In: 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom). pp. 341–346. IEEE, Hong Kong (2017). https://doi.org/10.1109/CloudCom.2017.55.

154.   Wang, J., de Laat, C., Zhao, Z.: QoS-aware virtual SDN network planning. In: 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). pp. 644–647. IEEE, Lisbon, Portugal (2017). https://doi.org/10.23919/INM.2017.7987350.

155.   Taherizadeh, S., Taylor, I., Jones, A., Zhao, Z., Stankovski, V.: A Network Edge Monitoring Approach for Real-Time Data Streaming Applications. In: Bañares, J.Á., Tserpes, K., and Altmann, J. (eds.) Economics of Grids, Clouds, Systems, and Services. pp. 293–303. Springer International Publishing, Cham (2017). https://doi.org/10.1007/978-3-319-61920-0_21.

156.   Koulouzis, S., Martin, P., Carval, T., Grenier, B., Judeau, G., Heikkinen, J., Wang, J., Zhou, H., Hu, Y., De Laat, C., Zhao, Z.: Seamless Infrastructure Customisation and Performance Optimisation for Time-critical Services in Data Infrastructures. (2017). https://doi.org/10.5281/ZENODO.1570919.

157.   Hu, Y., Wang, J., Zhou, H., Martin, P., Taal, A., de Laat, C., Zhao, Z.: Deadline-Aware Deployment for Time Critical Applications in Clouds. In: Rivera, F.F., Pena, T.F., and Cabaleiro, J.C. (eds.) Euro-Par 2017: Parallel Processing. pp. 345–357. Springer International Publishing, Cham (2017). https://doi.org/10.1007/978-3-319-64203-1_25.

158.   Elzinga, O., Koulouzis, S., Taal, A., Wang, J., Hu, Y., Zhou, H., Martin, P., de Laat, C., Zhao, Z.: Automatic Collector for Dynamic Cloud Performance Information. In: 2017 International Conference on Networking, Architecture, and Storage (NAS). pp. 1–6. IEEE, Shenzhen, China (2017). https://doi.org/10.1109/NAS.2017.8026845.

159.   Zhou, H., Wang, J., Hu, Y., Su, J., Martin, P., De Laat, C., Zhao, Z.: Fast Resource Co-provisioning for Time Critical Applications Based on Networked Infrastructures. In: 2016 IEEE 9th International Conference on Cloud Computing (CLOUD). pp. 802–805. IEEE, San Francisco, CA, USA (2016). https://doi.org/10.1109/CLOUD.2016.0111.

160.   Zhou, H., Martin, P., Su, J., De Laat, C., Zhao, Z.: A Flexible Inter-Locale Virtual Cloud For Nearly Real-Time Big Data Applications. (2016). https://doi.org/10.5281/ZENODO.204774.

161.   Zhou, H., Hu, Y., Wang, J., Martin, P., De Laat, C., Zhao, Z.: Fast and Dynamic Resource Provisioning for Quality Critical Cloud Applications. In: 2016 IEEE 19th International Symposium on Real-Time Distributed Computing (ISORC). pp. 92–99. IEEE, York, United Kingdom (2016). https://doi.org/10.1109/ISORC.2016.22.

162.   Zhao, Z., Martin, P., De Laat, C., Jeffery, K., Jones, A., Taylor, I., Hardisty, A., Atkinson, M., Zuiderwijk, A., Yin, Y., Chen, Y.: Time Critical Requirements And Technical Considerations For Advanced Support Environments For Data-Intensive Research. (2016). https://doi.org/10.5281/ZENODO.204756.

163.   Taherizadeh, S., Jones, A.C., Taylor, I., Zhao, Z., Martin, P., Stankovski, V.: Runtime Network-Level Monitoring Framework. In The Adaptation Of Distributed Time-Critical Cloud Applications. Zenodo (2016). https://doi.org/10.5281/ZENODO.53869.

164.   Petcu, D., Fazio, M., Prodan, R., Zhao, Z., Rak, M.: On the Next Generations of Infrastructure-as-a-Services: In: Proceedings of the 6th International Conference on Cloud Computing and Services Science. pp. 320–326. SCITEPRESS - Science and and Technology Publications, Rome, Italy (2016). https://doi.org/10.5220/0005912503200326.

165.   Martin, P., Taal, A., Quevedo, F., Rogers, D., Evans, K., Jones, A., Stankovski, V., Taherizadeh, S., Trnkoczy, J., Suciu, G., Zhao, Z.: Information Modelling and Semantic Linking for a Software Workbench for Interactive, Time Critical and Self-Adaptive Cloud Applications. In: 2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA). pp. 127–132. IEEE, Crans-Montana, Switzerland (2016). https://doi.org/10.1109/WAINA.2016.38.

166.   Casale, G., Chesta, C., Deussen, P., Di Nitto, E., Gouvas, P., Koussouris, S., Stankovski, V., Symeonidis, A., Vlassiou, V., Zafeiropoulos, A., Zhao, Z.: Current and Future Challenges of Software Engineering for Services and Applications. Procedia Computer Science. 97, 34–42 (2016). https://doi.org/10.1016/j.procs.2016.08.278.

167.   Zhao, Z., Taal, A., Jones, A., Taylor, I., Stankovski, V., Vega, I.G., Hidalgo, F.J., Suciu, G., Ulisses, A., Ferreira, P., Laat, C. de: A Software Workbench for Interactive, Time Critical and Highly Self-Adaptive Cloud Applications (SWITCH). In: 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. pp. 1181–1184. IEEE, Shenzhen, China (2015). https://doi.org/10.1109/CCGrid.2015.73.

168.   Zhao, Z., Martin, P., Wang, J., Taal, A., Jones, A., Taylor, I., Stankovski, V., Vega, I.G., Suciu, G., Ulisses, A., de Laat, C.: Developing and Operating Time Critical Applications in Clouds: The State of the Art and the SWITCH Approach. Procedia Computer Science. 68, 17–28 (2015). https://doi.org/10.1016/j.procs.2015.09.220.

169.   Zhao, Z., Martin, P., Grosso, P., Los, W., Laat, C. de, Jeffrey, K., Hardisty, A., Vermeulen, A., Castelli, D., Legre, Y., Kutsch, W.: Reference Model Guided System Design and Implementation for Interoperable Environmental Research Infrastructures. In: 2015 IEEE 11th International Conference on e-Science. pp. 551–556. IEEE, Munich, Germany (2015). https://doi.org/10.1109/eScience.2015.41.

170.   Mork, R., Martin, P., Zhao, Z.: Contemporary challenges for data-intensive scientific workflow management systems. In: Proceedings of the 10th Workshop on Workflows in Support of Large-Scale Science - WORKS ’15. pp. 1–11. ACM Press, Austin, Texas (2015). https://doi.org/10.1145/2822332.2822336.

171.   Martin, P., Grosso, P., Magagna, B., Schentz, H., Chen, Y., Hardisty, A., Los, W., Jeffery, K., Laat, C. de, Zhao, Z.: Open Information Linking for Environmental Research Infrastructures. In: 2015 IEEE 11th International Conference on e-Science. pp. 513–520. IEEE, Munich, Germany (2015). https://doi.org/10.1109/eScience.2015.66.

172.   Jeferry, K., Kousiouris, G., Kyriazis, D., Altmann, J., Ciuffoletti, A., Maglogiannis, I., Nesi, P., Suzic, B., Zhao, Z.: Challenges Emerging from Future Cloud Application Scenarios. Procedia Computer Science. 68, 227–237 (2015). https://doi.org/10.1016/j.procs.2015.09.238.

173.   Evans, K., Trnkoczy, J., Suciu, G., Suciu, V., Martin, P., Wang, J., Zhao, Z., Jones, A., Preece, A., Quevedo, F., Rogers, D., Spasić, I., Taylor, I., Stankovski, V., Taherizadeh, S.: Dynamically reconfigurable workflows for time-critical applications. In: Proceedings of the 10th Workshop on Workflows in Support of Large-Scale Science - WORKS ’15. pp. 1–10. ACM Press, Austin, Texas (2015). https://doi.org/10.1145/2822332.2822339.

174.   Zhao, Z., Wibisono, A., Grosso, P., Los, W., De Laat, C., Chen, Y., Hardisty, A., Martin, P., Atkinson, M., Schentz, H., Magagna, B.: Oeilm: A Semantic Linking Framework For Environmental Research Infrastructures. (2013). https://doi.org/10.5281/ZENODO.205781.

175.   Jiang, W., Zhao, Z., Wibisono, A., Grosso, P., Laat, C. de: Dynamic Workflow Planning on Programmable Infrastructure. In: 2013 IEEE Eighth International Conference on Networking, Architecture and Storage. pp. 326–330. IEEE, Xi’an, Shaanxi, China (2013). https://doi.org/10.1109/NAS.2013.53.

176.   Jiang, W., Zhao, Z., Laat, C. de: An Autonomous Security Storage Solution for Data-Intensive Cooperative Cloud Computing. In: 2013 IEEE 9th International Conference on e-Science. pp. 369–372. IEEE, Beijing, China (2013). https://doi.org/10.1109/eScience.2013.31.

177.   Chen, Y., Martin, P., Schentz, H., Magagna, B., Zhao, Z., Hardisty, A., Preece, A., Atkinson, M., Huber, R., and Legre. R.: A common reference model for environmental science research infrastructures. In Proceedings of EnviroInfo2013, (2013). [Full text]

178.   Chen, Y., Hardisty, A., Preece, A., Atkinson, M., Martin, P., Zhao, Z., Schentz, H., Magagna, B. and Legre. R., (2013) Analysis of Common Requirements for Environmental Science Research Infrastructures. In Proceedings of The International Symposium on Grids and Clouds (ISGC). [Full text]

179.   Dumitru, C., Zhao, Z., Grosso, P., de Laat, C.: HybridFlow: Towards intelligent video delivery and processing over hybrid infrastructures. In: 2013 International Conference on Collaboration Technologies and Systems (CTS). pp. 473–478. IEEE, San Diego, CA, USA (2013). https://doi.org/10.1109/CTS.2013.6567271.

180.   Zhu, H., van der Veldt, K., Grosso, P., Zhao, Z., Liao, X., and de Laat, C.: Energy aware Semantic modelling in large scale infrastructure, International conference on Green Com, France, (2012) [Fulltext].

181.   Belloum, A.S.Z., Cushing, R., Koulouzis, S., Korkhov, V., Vasunin, D., Guevara-Masis, V., Zhao, Z., Bubak, M.: Support for Cooperative Experiments in e-Science: From Scientific Workflows to Knowledge Sharing. In: Roterman-Konieczna, I. (ed.) Identification of Ligand Binding Site and Protein-Protein Interaction Area. pp. 135–159. Springer Netherlands, Dordrecht (2013). https://doi.org/10.1007/978-94-007-5285-6_7.

182.   Zhao, Z., Ham, J. van der, Taal, A., Koning, R., Dumitru, C., Wibisono, A., Grosso, P., Laat, C. de: Planning Data Intensive Workflows on Inter-domain Resources Using the Network Service Interface (NSI). In: 2012 SC Companion: High Performance Computing, Networking Storage and Analysis. pp. 150–156. IEEE, Salt Lake City, UT (2012). https://doi.org/10.1109/SC.Companion.2012.30.

183.   Zhao, Z., Grosso, P., de Laat, C.: OEIRM: An Open Distributed Processing Based Interoperability Reference Model for e-Science. In: Park, J.J., Zomaya, A., Yeo, S.-S., and Sahni, S. (eds.) Network and Parallel Computing. pp. 437–444. Springer Berlin Heidelberg, Berlin, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35606-3_52.

184.   Zhao, Z., Dumitru, C., Grosso, P., de Laat, C.: Network Resource Control for Data Intensive Applications in Heterogeneous Infrastructures. In: 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum. pp. 2069–2076. IEEE, Shanghai, China (2012). https://doi.org/10.1109/IPDPSW.2012.243.

185.   Pavlov, D., Soeurt, J., Grosso, P., Zhao, Z., Veldt, K. van der, Zhu, H., Laat, C. de: Towards Energy Efficient Data Intensive Computing Using IEEE 802.3az. In: 2012 SC Companion: High Performance Computing, Networking Storage and Analysis. pp. 806–810. IEEE, Salt Lake City, UT (2012). https://doi.org/10.1109/SC.Companion.2012.112.

186.   Demchenko, Y., Zhao, Z., Grosso, P., Wibisono, A., de Laat, C.: Addressing Big Data challenges for Scientific Data Infrastructure. In: 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings. pp. 614–617. IEEE, Taipei, Taiwan (2012). https://doi.org/10.1109/CloudCom.2012.6427494.

187.   Zhao, Z., Taal, A., Grosso, P., de Laat, C.: Resource Discovery in Large Scale Network Infrastructure. In: 2011 IEEE Sixth International Conference on Networking, Architecture, and Storage. pp. 186–190. IEEE, Dalian, China (2011). https://doi.org/10.1109/NAS.2011.43.

188.   Zhao, Z., Grosso, P., Koning, R., van der Ham, J., de Laat, C.: An agent based planner for including network QoS in scientific workflows. In: Proceedings of the International Multiconference on Computer Science and Information Technology. pp. 231–238. IEEE, Wisla (2010). https://doi.org/10.1109/IMCSIT.2010.5680060, Best paper award. 

189.   Zhao, Z., Grosso, P., Koning, R., van der Ham, J., de Laat, C.: Network resource selection for data transfer processes in scientific workflows. In: The 5th Workshop on Workflows in Support of Large-Scale Science. pp. 1–6. IEEE, New Orleans, LA, USA (2010). https://doi.org/10.1109/WORKS.2010.5671840.

190.   Zhao, Z., Grosso, P., Koning, R., Ham, J., de Laat, C.: An Architecture Including Network QoS in Scientific Workflows. In: 2010 Ninth International Conference on Grid and Cloud Computing. pp. 104–109. IEEE, Nanjing, China (2010). https://doi.org/10.1109/GCC.2010.32.

191.   Zhao, Z., Belloum, A., Bubak, M., Hertzberger, B.: Support for Cooperative Experiments in VL-e: From Scientific Workflows to Knowledge Sharing. In: 2008 IEEE Fourth International Conference on eScience. pp. 329–330. IEEE, Indianapolis, IN, USA (2008). https://doi.org/10.1109/eScience.2008.120.

192.   Wibisono, A., Zhao, Z., Belloum, A., Bubak, M.: A Framework for Interactive Parameter Sweep Applications. In: Bubak, M., van Albada, G.D., Dongarra, J., and Sloot, P.M.A. (eds.) Computational Science – ICCS 2008. pp. 481–490. Springer Berlin Heidelberg, Berlin, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69389-5_55.

193.   Wibisono, A., Zhao, Z., Belloum, A., Bubak, M.: A Framework for Interactive Parameter Sweep Applications. In: 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (CCGRID). pp. 703–703. IEEE, Lyon, France (2008). https://doi.org/10.1109/CCGRID.2008.111.

194.   Belloum, A., Zhao, Z., Bubak, M.: International Workshop on Applications of Workflows in Computational Science (AWCS 08). In: Bubak, M., van Albada, G.D., Dongarra, J., and Sloot, P.M.A. (eds.) Computational Science – ICCS 2008. pp. 459–462. Springer Berlin Heidelberg, Berlin, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69389-5_52.

195.   Zhao, Z., Belloum, A., De Laat, C., Adriaans, P., Hertzberger, B.: Using Jade agent framework to prototype an e-Science workflow bus. In: Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid ’07). pp. 655–660. IEEE, Rio de Janeiro, Brazil (2007). https://doi.org/10.1109/CCGRID.2007.120.

196.   Zhao, Z., Belloum, A., de Laat, C., Adriaans, P., Hertzberger, B.: Distributed execution of aggregated multi domain workflows using an agent framework. In: 2007 IEEE Congress on Services (Services 2007). pp. 183–190. IEEE, Salt Lake City, UT, USA (2007). https://doi.org/10.1109/SERVICES.2007.30.

197.   Wibisono, A., Vasyunin, D., Korkhov, V., Zhao, Z., Belloum, A., de Laat, C., Adriaans, P., Hertzberger, B.: WS-VLAM: A GT4 Based Workflow Management System. In: Shi, Y., van Albada, G.D., Dongarra, J., and Sloot, P.M.A. (eds.) Computational Science – ICCS 2007. pp. 191–198. Springer Berlin Heidelberg, Berlin, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72588-6_34.

198.   Terpstra, F., Zhao, Z., Mulder, W., Adriaans, P.: Towards a Formal Foundation for Aggregating Scientific Workflows. In: Shi, Y., van Albada, G.D., Dongarra, J., and Sloot, P.M.A. (eds.) Computational Science – ICCS 2007. pp. 216–219. Springer Berlin Heidelberg, Berlin, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72588-6_38.

199.   Zhao, Z., Booms, S., Belloum, A., Laat, C., Hertzberger, B.: VLE-WFBus: A Scientific Workflow Bus for Multi e-Science Domains. In: 2006 Second IEEE International Conference on e-Science and Grid Computing (e-Science’06). pp. 11–11. IEEE, Amsterdam, The Netherlands (2006). https://doi.org/10.1109/E-SCIENCE.2006.261095.

200.   Zhao, Z., van Albada, D., Sloot, P.: Rapid Prototyping of Complex Interactive Simulation Systems. In: 10th IEEE International Conference on Engineering of Complex Computer Systems (ICECCS’05). pp. 366–375. IEEE, Shanghai, China (2005). https://doi.org/10.1109/ICECCS.2005.69.

201.   Zhao, Z., Belloum, A., Yakali, H., Sloot, P., Hertzberger, B.: Dynamic Work.ow in a Grid Enabled Problem Solving Environment. In: The Fifth International Conference on Computer and Information Technology (CIT’05). pp. 339–345. IEEE, Shanghai, China (2005). https://doi.org/10.1109/CIT.2005.101.

202.   Zhao, Z., Belloum, A., Wibisono, A., Terpstra, F., de Boer, P.T., Sloot, P., Hertzberger, B.: Scientific workflow management: between generality and applicability. In: Fifth International Conference on Quality Software (QSIC’05). pp. 357–364. IEEE, Melbourne, Australia (2005). https://doi.org/10.1109/QSIC.2005.56.

203.   Zhao, Z., Belloum, A., Sloot, P., Hertzberger, B.: Agent technology and scientific workflow management in an e-science environment. In: 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’05). pp. 5 pp. – 23. IEEE, Hong Kong, China (2005). https://doi.org/10.1109/ICTAI.2005.29.

204.   Zhao, Z., Belloum, A., Sloot, P., Hertzberger, B.: Agent Technology and Generic Workflow Management in an e-Science Environment. In: Zhuge, H. and Fox, G.C. (eds.) Grid and Cooperative Computing - GCC 2005. pp. 480–485. Springer Berlin Heidelberg, Berlin, Heidelberg (2005). https://doi.org/10.1007/11590354_61.

205.   Zhao, Z. (2004, december 09). An agent based architecture for constructing Interactive Simulation Systems. UvA Universiteit van Amsterdam. Promoter/co-promoter: prof. dr. P.M.A. Sloot & dr. G.D. van Albada. [Link]

206.   Zhao, Z., van Albada, D., Sloot, P.: Agent-Based Flow Control for HLA Components. SIMULATION. 81, 487–501 (2005). https://doi.org/10.1177/0037549705058060.

207.   Zajac, K., Tirado-Ramos, A., Zhao, Z., Sloot, P., Bubak, M.: Grid Services for HLA-Based Distributed Simulation Frameworks. In: Fernández Rivera, F., Bubak, M., Gómez Tato, A., and Doallo, R. (eds.) Grid Computing. pp. 147–154. Springer Berlin Heidelberg, Berlin, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24689-3_19.

208.   Zhao, Z., van Albada, G.D., Tirado-Ramos, A., Zajac, K., Sloot, P.M.A.: ISS-Studio: A Prototype for a User-Friendly Tool for Designing Interactive Experiments in Problem Solving Environments. In: Sloot, P.M.A., Abramson, D., Bogdanov, A.V., Dongarra, J.J., Zomaya, A.Y., and Gorbachev, Y.E. (eds.) Computational Science — ICCS 2003. pp. 679–688. Springer Berlin Heidelberg, Berlin, Heidelberg (2003). https://doi.org/10.1007/3-540-44860-8_70.

209.   Tirado-Ramos, A., Zajac, K., Zhao, Z., Sloot, P.M.A., van Albada, D., Bubak, M.: Experimental Grid Access for Dynamic Discovery and Data Transfer in Distributed Interactive Simulation Systems. In: Sloot, P.M.A., Abramson, D., Bogdanov, A.V., Dongarra, J.J., Zomaya, A.Y., and Gorbachev, Y.E. (eds.) Computational Science — ICCS 2003. pp. 284–292. Springer Berlin Heidelberg, Berlin, Heidelberg (2003). https://doi.org/10.1007/3-540-44860-8_29.

210.   Zhao, Z., Belleman, R.G., van Albada, G.D., Sloot, P.M.A.: AG-IVE: An Agent Based Solution to Constructing Interactive Simulation Systems. In: Sloot, P.M.A., Hoekstra, A.G., Tan, C.J.K., and Dongarra, J.J. (eds.) Computational Science — ICCS 2002. pp. 693–703. Springer Berlin Heidelberg, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-46043-8_70.

211.   Zhao, Z., Belleman, R.G., Albada, G.D. van & Sloot, P.M.A. (2002). Reusability and Efficiency in Constructing Interactive Simulation Systems. In E.F. Deprettere, A.S.Z. Belloum, J.W.J. Heijnsdijk & F. van der Stappen (Eds.), ASCI 2002, Proceedings of the eighth annual conference of the Advanced School for Computing and Imaging (pp. 268-275). Delft: ASCI. [Link]

212.   Zhao, Z., Belleman, R.G., Albada, G.D. van & Sloot, P.M.A. (2002). Scenario Switches and State Updates in an Agent-based Solution to Constructing Interactive Simulation Systems. In Proceedings of the Communication Networks and Distributed Systems Modeling and Simulation Conference (CNDS 2002) (pp. 3-10). [Link]

213.   Zhao, Z., Belleman R. G., Albada G.D.and Sloot P.M.A. (2001): System integration for interactive simulation systems using intelligent agents, in R.L. Lagendijk; J.W.J. Heijnsdijk; A.D. Pimentel and M.H.F. Wilkinson, editors, Proceedings of the 7th annual conference of the Advanced School for Computing and Imaging, pp. 399-406. ASCI, May 2001. ISBN 90-803086-6-8. [Link]

214.   Belleman,R.G., Zhao,Z., Albada G.D. van and Sloot P.M.A (2000) Design considerations for the construction of immersive dynamic exploration environments, in L.J. van Vliet; J.W.J. Heijnsdijk; T. Kielmann and P.M.W. Knijnenburg, editors, ASCI 2000, Proceedings of the sixth annual conference of the Advanced School for Computing and Imaging, pp. 195-201. ASCI, Delft, June 2000. ISBN 90-803086-5-x. [Link]

Peer-reviewed abstracts and posters

215.  Li, N., Zhu, P., Gabriel Pelouze, G., Koulouzis, S., Zhao, Z., Zhao, Z.: Research Notebook Retrieval with Explainable Query Reformulation. oral (2024). https://doi.org/10.5194/egusphere-egu24-19358

216.  Bundke, U., Bailo, D., Carval, T., Cervone, L., De Nart, D., Dema, C., Ferrari, T., Petzold, A., Thijsse, P., Vermeulen, A., Zhao, Z.: ENVRI-Hub-NEXT, the open-access platform of the environmental sciences community in Europe. display (2024). https://doi.org/10.5194/egusphere-egu24-8465

217.  Pelouze, G., Koulouzis, S., Zhao, Z.: Notebook-as-a-VRE (NaaVRE): From private notebooks to a collaborative cloud virtual research environment. oral (2024). https://doi.org/10.5194/egusphere-egu24-17978

218.  Petzold, A., Bundke, U., Hienola, A., Laj, P., Lund Myhre, C., Vermeulen, A., Adamaki, A., Kutsch, W., Thouret, V., Boulanger, D., Fiebig, M., Stocker, M., Zhao, Z., and Asmi, A. (2023): Opinion: New directions in atmospheric research offered by research infrastructures combined with open and data-intensive science, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1423

219.  Shi, Y., Koulouzis, S., Bianchi, R., Timmermans, J., Kissling, W. D., and Zhao, Z. (2023): Generating geospatial data products of ecosystem structure from LiDAR using Notebook-as-a-VRE (NaaVRE), EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-11716, https://doi.org/10.5194/egusphere-egu23-11716, 2023.

220.  Petzold, A., Bundke, U., Schleiermacher, C., Gomes, A. R., Seemeyer, K., Adamaki, A., Vermeulen, A., Zhao, Z., Boulanger, D., Carval, T., and Hienola, A. (2023): The ENVRI-Hub as a service for accelerating FAIRification of the Environment Domain Research Infrastructures, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7708, https://doi.org/10.5194/egusphere-egu23-7708, 2023.

221.  Petzold, A., Asmi, A., Gomes, R., Seemeyer, K., Adamaki, A., Vermeulen, A., Bailo, D., Jeffery, K., Glaves, H., Zhao, Z., Stocker, M., Hellstrom, M(2021).: Creating ENVRI-hub, the Open-Access Platform of the Environmental Sciences Community in Europe. 2021, IN53A-04. [2021AGUFMIN53A..04P]

222.  Alex Boyko, Siamak Farshidi, Zhiming Zhao, State-of-the-art instance matching methods for knowledge graphs, The Sixteenth International Workshop on Ontology Matching (OM-2021), in the context of 20th International Semantic Web Conference ISWC-2021, online [OA]

223.  Koulouzis, S., Shi, Y., Wan, Y., Bianchi, R., Kissling, D., Zhao, Z.: Enabling LiDAR data processing as a service in a Jupyter environment (2021). EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8294, https://doi.org/10.5194/egusphere-egu21-8294 

224.  Petzold, A., Asmi, A., Seemeyer, K., Adamaki, A., Vermeulen, A., Bailo, D., Jeffery, K., Glaves, H., Zhao, Z., Stocker, M., Hellström, M.: Advancing the FAIRness and Openness of Earth system science in Europe. pico (2021). https://doi.org/10.5194/egusphere-egu21-8052 

225.  Liao, X., Goldfarb, D., Magagna, B., Stocker, M., Thijsse, P., Schaap, D., Zhao, Z.: ENVRI knowledge base: A community knowledge base for research, innovation and society. oral (2020). https://doi.org/10.5194/egusphere-egu2020-20708 

226.  Zhao, Z., Martin, P., Koulouzis, S.: (2019) Optimizing environmental data services on federated Cloud and e-Infrastructures, EGU, April 2019 [2019EGUGA..21.3683Z]

227.  Koulouzis, S., Carval, T., Martin, P., Grenier, B., Chen, Y., Heikkinen, J., Zhao, Z.: Dynamic Optimization for Time-critical Data Services: A Case Study in Euro-Argo Research Infrastructure.  20th EGU General Assembly, EGU2018, Proceedings from the conference held 4-13 April, 2018 in Vienna, Austria, p.16012. [2018EGUGA..2016012K]

228.  Nieva de la Hidalga, A., Hardisty, A.R., Magagna, B., Martin, P.W., Zhao, Z.: Use of the ENVRI Reference Model to Support the Design of Environmental Research Infrastructures. 18552 (2018). [2018EGUGA..2018552N]

229.  Kutsch, W. L.; Zhao, Z.; Hardisty, A.; Hellström, M.; Chin, Y.; Magagna, B.; Asmi, A.; Papale, D.; Pfeil, B.; Atkinson, M. (2017) Data interoperability between European Environmental Research Infrastructures and their contribution to global data networks, AGU 2018, American Geophysical Union, Fall Meeting 2017, abstract #IN44B-01. [2017AGUFMIN44B..01K]

Technical report and newsletters

230.  Zhao, Z., Martin, P. Jeffery K., (2017) VRE in the Data for Science Approach to Common Challenges in ENVRIPLUS, ERCIM, Newsletter, April 2017

231.  Ghijsen, M., Ham, J. van der, Grosso, P., Dumitru, C., Zhu, H., Zhao, Z. & Laat, C. de (2013). A semantic-web approach for modeling computing infrastructures. (SNE technical report 2013-01). Amsterdam: Universiteit van Amsterdam, System and Network Engineering. [Full text]

232.  Hertzberger, L.O., Belleman, R.G., Jansen, M.G., Zhao, Z., Hooft, P. van, Belloum, A.S.Z., Mirzadeh, N., Yakali, H.H., Liere, R. van, Nuallain, B.S., Verstoep, K., Groep, D.L. & Bouwhuis, M.C. (2005). Recommendation to VLeIT: Scientific workflow management systems for the PoC r1. (Internal report). Amsterdam: Informatics Institute.

Supervised PhD thesis

233. Hongyun Liu (2024) Robust Resource Management for Time-Critical Tasks in the Cloud-Edge Continuum, (ISBN: 979-88-9379-233-1)

234. Ruyue Xin (2023) Towards effective performance diagnosis for distributed applications, (ISBN: 978-94-6473-267-2)

235. Zeshun Shi (2022) Enhancing Service-Level Agreements using Decentralized Auctions and Witnesses, [ISBN: 978-94-6421-916-6]

236. Yang Hu (2019) Resource Scheduling for Quality-Critical Applications on Cloud Infrastructure, University of Amsterdam, [ISBN: 978-94-028-1713-3].

237. Huan Zhou (2019) Seamless Infrastructure Programming and Control for Quality-critical Cloud Applications, [ISBN: 978-94-028-1727-0]

In Chinese

238. Zhao, Z., Liao, X.,  Wang, X.,  Ruan, C., Zhu, Y., Feng, D. (2019),  An Reference Model approach for developing agriculture big data infrastructures, Journal of East China Normal University (Natural Sc   2019 Vol.2019 (2): 77-96,  [10.3969/j.issn.1000-5641.2019.02.009]

239. Demchenko, Y., Zhao, Z., Grosso, P., Wibisono, A., de Laat, C., (2013) 科研信息化基础 设施的大数据挑战 (Big Data Challenges for e-Science Infrastructure) China Science and Technology Resources Review, Vol.45 No.1 30-35,40 Jan. 2013. ISSN 1674-1544 [10.3772/j.issn.1674-1544.2013.01.006]

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