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2023

  1. van Beest, N., Groefsema, H., Cryer, A., Governatori, G., Tosatto, S. C., & Burke, H. (2023). Cross-Instance Regulatory Compliance Checking of Business Process Event Logs. IEEE Transactions on Software Engineering, 1–14. https://doi.org/10.1109/TSE.2023.3319086
  2. Haghshenas, K., Setz, B., Blosch, Y., & Aiello, M. (2023). Enough Hot Air: The Role of Immersion Cooling. Energy Informatics. https://energyinformatics.springeropen.com/articles/10.1186/s42162-023-00269-0
  3. Piontek, T., Haghshenas, K., & Aiello, M. (2023). Carbon Emission-Aware Job Scheduling for Kubernetes Deployments. The Journal of Supercomputing. https://link.springer.com/article/10.1007/s11227-023-05506-7

2022

  1. Saleh, S., & Koldehofe, B. (2022). On Memristors for Enabling Energy Efficient and Enhanced Cognitive Network Functions. IEEE Access, 10, 129279–129312. https://doi.org/10.1109/ACCESS.2022.3226447
  2. Sadhu, K., Haghshenas, K., Rouhani, M., & Aiello, M. (2022). Optimal Joint Operation of Coupled Transportation and Power Distribution Urban Networks. Energy Informatics. https://energyinformatics.springeropen.com/articles/10.1186/s42162-022-00249-w
  3. Medema, M., & Lazovik, A. (2022). Correlating the Community Structure of Constraint Satisfaction Problems with Search Time. International Journal on Artificial Intelligence Tools, 31(07). https://doi.org/10.1142/S0218213022600041
  4. Sultana, N., Hossain, S. M. Z., Almuhaini, S., & Düştegör, D. (2022). Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand. Energies 2022, Vol. 15, Page 3425, 15(9), 3425. https://doi.org/10.3390/EN15093425
  5. Kundel, R., Siegmund, F., Hark, R., Rizk, A., & Koldehofe, B. (2022). Network Testing Utilizing ProgrammableNetworking Hardware. IEEE Communications Magazine, 7 pages. https://research.rug.nl/en/publications/network-testing-utilizing-programmable-networking-hardware

2021

  1. Mezhoudi, N., Alghamdi, R., Aljunaid, R., Krichna, G., & Düştegör, D. (2021). Employability prediction: a survey of current approaches, research challenges and applications. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-03276-9
  2. Al-Saudi, K., Degeler, V., & Medema, M. (2021). Energy Consumption Patterns and Load Forecasting with Profiled CNN-LSTM Networks. Processes, 9(11). https://doi.org/10.3390/pr9111870
  3. Li, X., Zhang, Z., Wu, D., Medema, M., & Lazovik, A. (2021). A multi-robot allocation model for multi-object based on Global Optimal Evaluation of Revenue. International Journal of Advanced Robotic Systems, 18(6). https://doi.org/https://doi.org/10.1177/17298814211060650">10.1177/17298814211060650
  4. Albers, T., Lazovik, E., Yousefi, M. H. N., & Lazovik, A. (2021). Adaptive On-the-fly Changes in Distributed Processing Pipelines. Frontiers in Big Data, 79.
  5. Akbarinejad, S., Yousefi, M. H. N., & Goudarzi, M. (2021). SVNN: an efficient PacBio-specific pipeline for structural variations calling using neural networks. BMC Bioinformatics, 22(1), 1–17.
  6. Kundel, R., Nobach, L., Blendin, J., Maas, W., Zimber, A., Kolbe, H., Schyguda, G., Gurevich, V., Hark, R., Koldehofe, B., & Steinmetz, R. (2021). OpenBNG: Central office network functions on programmable data plane hardware. International Journal of Network Management, 31(e2134), 25 pages. https://doi.org/https://doi.org/10.1002/nem.2134">10.1002/nem.2134
  7. Luthra, M., Koldehofe, B., Danger, N., Weisenburger, P., Salvaneschi, G., & Stavrakakis., I. (2021). TCEP: Transitions in Operator Placement to Adapt to Dynamic Network Environments. In Journal of Computer and Systems Sciences (JCSS), Special Issue on Algorithmic Theory of Dynamic Networks and Its Applications., 122, 94–125. https://doi.org/https://doi.org/10.1016/j.jcss.2021.05.003">10.1016/j.jcss.2021.05.003

2020

  1. Degeler, V., Heydenrijk-Ottens, L., Luo, D., van N. Oort, & van Lint, H. (2020). Unsupervised approach towards analysing the public transport bunching swings formation phenomenon. Public Transport. https://doi.org/https://doi.org/10.1007/s12469-020-00251-z">10.1007/s12469-020-00251-z
  2. Haghshenas, K., & Mohammadi, S. (2020). Prediction-Based Underutilized and Destination Host Selection Approaches for Energy-Efficient Dynamic VM Consolidation in Data Centers. The Journal of Supercomputing. https://link.springer.com/article/10.1007/s11227-020-03248-4
  3. Groefsema, H., van Beest, N. R. T. P., & Armas-Cervantes, A. (2020). Efficient conditional compliance checking of business process models. Computers in Industry, 115. https://doi.org/10.1016/j.compind.2019.103181
  4. Haghshenas, K., Taheri, S., Goudarzi, M., & Mohammadi, S. (2020). Infrastructure Aware Heterogeneous-Workloads Scheduling for Data Center Energy Cost Minimization. IEEE Transactions on Cloud Computing, 10, 972–983. https://doi.org/10.1109/TCC.2020.2977040
  5. Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Education, 17(1). https://doi.org/10.1186/s41239-020-0177-7
  6. Tatar, A. E., & Düştegör, D. (2020). Prediction of academic performance at undergraduate graduation: Course grades or grade point average? Applied Sciences (Switzerland), 10(14). https://doi.org/10.3390/app10144967
  7. Kundel, R., Nobach, L., Blendin, J., Maas, W., Zimber, A., Kolbe, H., Schyguda, G., Gurevich, V., Hark, R., Koldehofe, B., & Steinmetz, R. (2020). OpenBNG: Central office network functions on programmable data plane hardware. International Journal of Network Management, 25 pages. https://doi.org/10.1002/nem.2134
  8. Schulte, S., Koldehofe, B., Reinhardt, A., & Braun, T. (2020). Grußwort der Gastherausgeber zum Thema Fog Computing. Informatik Spektrum, 42(6), 455—456. https://doi.org/10.1007/s00287-019-01230-w
  9. Langley, D. J., van Doorn, J., Ng, I. C. L., Stieglitz, S., Lazovik, A., & Boonstra, A. (2020). The Internet of Everything: Smart things and their impact on business models. Journal of Business Research. https://doi.org/https://doi.org/10.1016/j.jbusres.2019.12.035
  10. Yousefi, M. H. N., Ghiassi, A., Hashemi, B. S., & Goudarzi, M. (2020). Workload Scheduling on heterogeneous Mobile Edge Cloud in 5G networks to Minimize SLA Violation. ArXiv Preprint ArXiv:2003.02820.

2019

  1. Haghshenas, K., Pahlevan, A., Zapater, M., & Atienza, D. (2019). MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers. IEEE Transactions on Services Computing, 15, 30–44. https://doi.org/10.1109/TSC.2019.2919555
  2. van Beest, N. R. T. P., Groefsema, H., Garcia-Banuelos, L., & Aiello, M. (2019). Variability in business processes: Automatically obtaining a generic specification. Information Systems, 80, 36–55. https://doi.org/10.1016/j.is.2018.09.005
  3. Düştegör, D., Farooqui, M., & Khan, M. A. A. (2019). Fostering higher cognitive skills through design thinking in digital hardware course: A case study. ICIC Express Letters, 13(10). https://doi.org/10.24507/icicel.13.10.891
  4. Düştegör, D. (2019). Analytical tool for the modelling and simulation of curriculum: Towards automated design, assessment, and improvement. International Journal of Engineering Education, 35(2).
  5. Blaauw, F. J., van der Gaag, M. A. E., Snell, N. R., Emerencia, A. C., Kunnen, E. S., & de Jonge, P. (2019). The u-can-act Platform: A Tool to Study Intra-individual Processes of Early School Leaving and Its Prevention Using Multiple Informants. Frontiers in Psychology, 10, 1808. https://doi.org/10.3389/fpsyg.2019.01808
  6. Fiorini, L., & Aiello, M. (2019). Energy management for user’s thermal and power needs: A survey. Energy Reports, 5, 1048–1076. https://doi.org/https://doi.org/10.1016/j.egyr.2019.08.003
  7. Yousefi, M. H. N., Goudarzi, M., & Motahari, S. A. (2019). IMOS: improved meta-aligner and Minimap2 on spark. BMC Bioinformatics, 20(1), 1–14.

2018

  1. Kollenstart, M., Harmsma, E., Langius, E., Andrikopoulos, V., & Lazovik, A. (2018). Adaptive Provisioning of Heterogeneous Cloud Resources for Big Data Processing. Big Data and Cognitive Computing, 2, 15. https://doi.org/10.3390/bdcc2030015
  2. Saleh, S., Qadir, J., & Ilyas, M. U. (2018). Shedding Light on the Dark Corners of the Internet: A Survey of Tor Research. Journal of Network and Computer Applications, 114, 1–28. https://doi.org/10.1016/j.jnca.2018.04.002
  3. Groefsema, H., van Beest, N. R. T. P., & Aiello, M. (2018). A Formal Model for Compliance Verification of Service Compositions. Ieee Transactions on Services Computing, 11(3), 466–479. https://doi.org/10.1109/TSC.2016.2579621
  4. Haghshenas, K., Hashemi, M., & Nikoubin, T. (2018). Fast and Energy-Efficient CNFET Adders with CDM and Sensitivity-Based Device-Circuit Co-Optimization. IEEE Transactions on Nanotechnology, 17, 783–794. https://doi.org/10.1109/TNANO.2018.2834511
  5. Bahnasy, M., Elbiaze, H., & Boughzala, B. (2018). Zero-queue ethernet congestion control protocol based on available bandwidth estimation. Journal of Network and Computer Applications, 105, 1–20.
  6. Santiago da Silva, J., Stimpfling, T., Luinaud, T., Fradj, B., & Boughzala, B. (2018). One for All, All for One: A Heterogeneous Data Plane for Flexible P4 Processing. ArXiv e-Prints, arXiv–1807.
  7. Düştegör, D., Sultana, N., Felemban, N., & Qahtani, D. A. (2018). A smarter electricity grid for the Eastern Province of Saudi Arabia: Perceptions and policy implications. Utilities Policy, 50. https://doi.org/10.1016/j.jup.2017.09.009
  8. Düştegör, D., Elhussein, M. A., Alghamdi, A., & Nagy, N. (2018). Learning behind glass walls: learning style and partition-room, is there a correlation? International Journal of Innovation Science. https://doi.org/10.1108/IJIS-09-2017-0100
  9. Elhussein, M. A., Düştegör, D., Nagy, N., & Alghamdi, A. K. H. (2018). The impact of digital technology on female students’ learning experience in partition-rooms: Conditioned by social context. IEEE Transactions on Education, 61(4). https://doi.org/10.1109/TE.2018.2840501
  10. Bos, F. M., Blaauw, F. J., Snippe, E., van der Krieke, L., de Jonge, P., & Wichers, M. (2018). Exploring the emotional dynamics of subclinically depressed individuals with and without anhedonia: An experience sampling study. Journal of Affective Disorders, 228, 186–193. https://doi.org/https://doi.org/10.1016/j.jad.2017.12.017
  11. Fiorini, L., Aiello, M., Poli, D., & Pelacchi, P. (2018). Topological Considerations on the Use of Batteries to Enhance the Reliability of HV-Grids . Journal of Energy Storage , 18, 316–326. https://doi.org/https://doi.org/10.1016/j.est.2018.04.025
  12. Pratama, A. R., Widyawan, W., Lazovik, A., & Aiello, M. (2018). Multi-User Low Intrusive Occupancy Detection. Sensors, 18(3). https://doi.org/10.3390/s18030796
  13. Dijkhuis, T. B., Blaauw, F. J., van Ittersum, M. W., Velthuijsen, H., & Aiello, M. (2018). Personalized Physical Activity Coaching: A Machine Learning Approach. Sensors, 18(2). https://doi.org/10.3390/s18020623
  14. Sha, A., & Aiello, M. (2018). Topological Considerations on Decentralised Energy Exchange in the Smart Grid. Procedia Computer Science, 130, 720–727. https://doi.org/https://doi.org/10.1016/j.procs.2018.04.126
  15. Yousefi, M. H. N., & Goudarzi, M. (2018). A task-based greedy scheduling algorithm for minimizing energy of mapreduce jobs. Journal of Grid Computing, 16(4), 535–551.

2017

  1. Georgievski, I., Nguyen, T. A., Nizamic, F., Setz, B., Lazovik, A., & Aiello, M. (2017). Planning meets activity recognition: Service coordination for intelligent buildings. Pervasive and Mobile Computing, 38(1), 110–139. https://doi.org/10.1016/j.pmcj.2017.02.008
  2. Fiorini, L., Pagani, G. A., Pelacchi, P., Poli, D., & Aiello, M. (2017). Sizing and Siting of Large-Scale Batteries in Transmission Grids to Optimize the Use of Renewables. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 7(2), 285–294. https://doi.org/10.1109/JETCAS.2017.2657795
  3. Schweitzer, E., Scaglione, A., Monti, A., & Pagani, G. A. (2017). Automated Generation Algorithm for Synthetic Medium Voltage Radial Distribution Systems. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 7(2), 271–284. https://doi.org/10.1109/JETCAS.2017.2682934
  4. Ayub, N., Raja, M., Saleh, S., & Ilyas, M. U. (2017). MuGKeG: Secure Multi-channel Group Key Generation Algorithm for Wireless Networks. Wireless Personal Communications, 96(3), 4799–4818. https://doi.org/10.1007/s11277-017-4423-9
  5. Javed, U., Iqbal, A., Saleh, S., Haider, S. A., & Ilyas, M. U. (2017). A Stochastic Model for Transit Latency in OpenFlow SDNs. Computer Networks, 113, 218–229. https://doi.org/10.1016/j.comnet.2016.12.015
  6. Vemula, D., Setz, B., Rao, G. S. V. R. K., Gangadharan, G. R., & Aiello, M. (2017). Metrics for Sustainable Data Centers. IEEE Transactions on Sustainable Computing.
  7. Pratama, A. R., Widyawan, Lazovik, A., & Aiello, M. (2017). Indoor self-localization via bluetooth low energy beacons. IDRBT JOURNAL OF IJBT, 1(1), 1–15.

2016

  1. Iqbal, A., Javed, U., Saleh, S., Kim, J., Alowibdi, J. S., & Ilyas, M. U. (2016). Analytical Modeling of End-to-End Delay in OpenFlow Based Networks. IEEE Access, 5, 6859–6871. https://doi.org/10.1109/ACCESS.2016.2636247
  2. Sumba, X., Sumba, F., Tello, A., Baculima, F., Espinoza, M., & Saquicela, V. (2016). Detecting similar areas of knowledge using semantic and data mining technologies. Electronic Notes in Theoretical Computer Science, 329, 149–167.
  3. Gualán, R., Freire, R., Tello, A., Espinoza, M., & Saquicela Vı́ctor. (2016). Automatic RDF-ization of big data semi-structured datasets. Maskana, 7, 117–127.
  4. AlSafwan, K., AlShaer, F., Hakami, L., Aseeri, K., AlJishi, M., & Düştegör, D. (2016). Design and implementation of a residential energy monitoring system prototype tailored to meet local needs. International Journal of Computing and Digital Systems, 5(4). https://doi.org/10.12785/IJCDS/050405
  5. Georgievski, I., & Aiello, M. (2016). Automated planning for ubiquitous computing. ACM Comput. Surv., 49(4), 63:1–63:46. http://doi.acm.org/10.1145/3004294
  6. van der Krieke, L., Blaauw, F. J., Emerencia, A. C., Schenk, M. H. M., Slaets, J. P. J., Bos, E. H., de Jonge, P., & Jeronimus, B. F. (2016). Temporal dynamics of health and well-being: A crowdsourcing approach to momentary assessments and automated generation of personalized feedback. Psychosomatic Medicine.
  7. Blaauw, F. J., Schenk, H. M., Jeronimus, B. F., van der Krieke, L., de Jonge, P., Aiello, M., & Emerencia, A. C. (2016). Let’s get Physiqual - an intuitive and generic method to combine ssensor technology with ecological momentary assessments. Journal of Biomedical Informatics, 63, 141–149. http://dx.doi.org/10.1016/j.jbi.2016.08.001
  8. Sha, A., & Aiello, M. (2016). A Novel Strategy for Optimising Decentralised Energy Exchange for Prosumers. Energies, 9(7), 554. http://dx.doi.org/10.3390/en9070554
  9. Kaldeli, E., Lazovik, A., & Aiello, M. (2016). Domain-Independent Planning for Services in Uncertain and Dynamic Environments. Artificial Intelligence, 236(7), 30–64. http://www.sciencedirect.com/science/article/pii/S000437021630025X
  10. Pagani, G. A., & Aiello, M. (2016). From the grid to the smart grid, topologically. Physica A, 449(5), 160–175. https://doi.org/10.1016/j.physa.2015.12.080
  11. Bucur, D., Iacca, G., Gaudesi, M., Squillero, G., & Tonda, A. (2016). Optimizing groups of colluding strong attackers in mobile urban communication networks with evolutionary algorithms. Applied Soft Computing, 40, 416–426. http://dx.doi.org/10.1016/j.asoc.2015.11.024

2015

  1. Tello, A., Freire, R., Espinoza, M., & Saquicela Vı́ctor. (2015). Integration and massive storage of hydro-meteorological data combining big data & semantic web technologies. Maskana, 6, 165–172.
  2. van der Krieke, L., Jeronimus, B. F., Blaauw, F. J., Wanders, R. B. K., Emerencia, A. C., Schenk, H. M., de Stijn, V., Snippe, E., Wichers, M., Wigman, J. T. W., Bos, E. H., Wardenaar, K. J., & de Jonge, P. (2015). HowNutsAreTheDutch (HoeGekIsNL): A crowdsourcing study of mental symptoms and strengths. International Journal of Methods in Psychiatric Research, n/a–n/a. http://dx.doi.org/10.1002/mpr.1495
  3. Bucur, D., Iacca, G., & de Boer, P. (2015). Characterizing topological bottlenecks for data delivery in CTP using simulation-based stress testing with natural selection. Ad Hoc Networks, 30, 22–45. http://dx.doi.org/10.1016/j.adhoc.2015.02.005
  4. Georgievski, I., & Aiello, M. (2015). HTN planning: Overview, comparison, and beyond. Artificial Intelligence, 222(0), 124–156. http://dx.doi.org/10.1016/j.artint.2015.02.002
  5. Emerencia, A., van der Krieke, L., Bos, E. H., de Jonge, P., Petkov, N., & Aiello, M. (2015). Automating vector autoregression on electronic patient diary data. IEEE Journal of Biomedical and Health Informatics, 20(2), 631–634. http://dx.doi.org/10.1109/JBHI.2015.2402280
  6. Pagani, G. A., & Aiello, M. (2015). A complex network approach for identifying vulnerabilities of the medium and low voltage grid. International Journal of Critical Infrastructures, 11(1), 36–61. http://www.cs.rug.nl/ds/uploads/pubs/pagani_IJCIS110104.pdf
  7. Sun, C., Zhang, X., Shang, Y., & Aiello, M. (2015). Integrating Transactions into BPEL Service Compositions: An Aspect-Based Approach. ACM Transactions on the Web, 9(2), 9:1–9:31. http://dx.doi.org/10.1145/2757288
  8. Saleh, S., Shah, Z., & Baig, A. (2015). Improving QoS of IPTV and VoIP over IEEE 802.11n. Computers & Electrical Engineering, 43, 92–111. https://doi.org/10.1016/j.compeleceng.2014.10.017

2014

  1. Degeler, V., & Lazovik, A. (2014). Dynamic Constraint Satisfaction with Space Reduction in Smart Environments. International Journal on Artificial Intelligence Tools, 23(6). http://dx.doi.org/10.1142/S0218213014600276
  2. Pagani, G. A., & Aiello, M. (2014). Generating Realistic Dynamic Prices and Services for the Smart Grid. IEEE Systems Journal, 9(1), 191–198.
  3. van Beest, N. R. T. P., Kaldeli, E., Bulanov, P., Wortmann, J. C., & Lazovik, A. (2014). Automated Runtime Repair of Business Processes. Inf. Syst., 39, 45–79. http://dx.doi.org/10.1016/j.is.2013.07.003
  4. Pagani, G. A., & Aiello, M. (2014). Power Grid Complex Network Evolutions for the Smart Grid. Physica A: Statistical Mechanics and Its Applications, 396(2), 248–266. http://dx.doi.org/10.1016/j.physa.2013.11.022
  5. van der Krieke, L., Wunderink, L., Emerencia, A., de Jonge, P., & Sjoerd, S. (2014). E-Mental Health Self-Management for Psychotic Disorders: State of the Art and Future Perspectives. Psychiatric Services, 65(1), 33–49. http://dx.doi.org/10.1176/appi.ps.201300050
  6. Bucur, D., Iacca, G., Squillero, G., & Tonda, A. (2014). The Impact of Topology on Energy Consumption for Collection Tree Protocols: An Experimental Assessment through Evolutionary Computation. Applied Soft Computing, 16(0), 210–222. https://doi.org/http://dx.doi.org/10.1016/j.asoc.2013.12.002
  7. Alsaif, F. (2014). Smart Education Modes and E-Learning Market: The Needs of the Next Generation. Smart Learning Environments Journal, The State of the Art in Smart Learning Issue.
  8. Blaauw, F., van der Krieke, L., de Jonge, P., & Aiello, M. (2014). Leefplezier: Personalized Well-being. IEEE Intelligent Informatics Bulletin, 15(1), 28–29. http://www.comp.hkbu.edu.hk/ iib/2014/Dec/paper4/iib_vol15no1_paper4.pdf

2013

  1. Emerencia, A., van der Krieke, L., Sytema, S., Petkov, N., & Aiello, M. (2013). Generating personalized advice for schizophrenia patients. Artificial Intelligence in Medicine, 58(1), 23–36. http://www.sciencedirect.com/science/article/pii/S0933365713000043
  2. van der Krieke, L., Emerencia, A., Boonstra, N., Wunderink, L., de Jonge, P., & Sytema, S. (2013). A web-based tool to support shared decision-making for people with a psychotic disorder: a randomized clinical trial and process evaluation. Journal of Medical Internet Research, 15(10), e216. http://www.cs.rug.nl/ ando/pdfs/sdm_trial.pdf
  3. Alsaif, F., & Gauthaman, A. (2013). The Management of Elearning at University of KKU, ABHA. International Journal of Emerging Technologies in Learning, 8(1), 4–9.
  4. van der Krieke, L., Wunderink, L., Emerencia, A., de Jonge, P., & Sytema, S. (2013). E-health self-management in psychotic disorders: state of the art and future perspectives. Psychiatric Services, 65(1), 33–49. http://www.cs.rug.nl/ ando/pdfs/appi.ps.201300050.pdf
  5. Nguyen, T. A., Raspitzu, A., & Aiello, M. (2013). Ontology-based Office Activity Recognition with Applications for Energy Savings. Journal of Ambient Intelligence and Humanized Computing. http://link.springer.com/article/10.1007%2Fs12652-013-0206-7
  6. Nguyen, T. A., & Aiello, M. (2013). Energy Intelligent Buildings based on User Activity: A Survey. Energy and Buildings, 56(0), 244–257. http://dx.doi.org/10.1016/j.enbuild.2012.09.005
  7. Pagani, G. A., & Aiello, M. (2013). The Power Grid as a Complex Network: a Survey. Physica A: Statistical Mechanics and Its Applications, 392(1), 2688–2700. http://www.sciencedirect.com/science/article/pii/S0378437113000575
  8. Kaldeli, E., Warriach, E. U., Lazovik, A., & Aiello, M. (2013). Coordinating the Web of Services for a Smart Home. ACM Transactions on the Web, 7(2). http://dl.acm.org/citation.cfm?id=2460389
  9. Warriach, E. U., Kaldeli, E., Lazovik, A., & Aiello, M. (2013). An Interplatform Service-Oriented Middleware for the Smart Home. International Journal of Smart Home, 7(1), 115–142. http://www.sersc.org/journals/IJSH/vol7_no1_2013/11.pdf
  10. Aiello, M. (2013). Aware homes. Awareness Magazine: Self-Awareness in Autonomic Systems. http://www.awareness-mag.eu/pdf/005166/005166.pdf

2012

  1. van de Krieke, L., Emerencia, A., Aiello, M., & Sytema, S. (2012). Usability Evaluation of a Web-Based Support System for People With a Schizophrenia Diagnosis. Journal of Medical Internet Research, 14(1). http://www.ncbi.nlm.nih.gov/pubmed/22311883
  2. Pernici, B., Aiello, M., vom Brocke, J., Donnellan, B., Gelenbe, E., & Kretsis, M. (2012). What IS can do for Environmental Sustainability. Communications of the Association for Information Systems, 30(18). http://eprints.nuim.ie/3021/1/CAiSE_Panel_Green_IS-Dec13.pdf
  3. Pagani, G. A., & Aiello, M. (2012). Service-Orientation and the Smart Grid: State and Trend. Service Oriented Computing and Applications, 6(3), 267–282. http://www.springerlink.com/content/44q14653u6325748/?MUD=MP
  4. Degeler, V., & Lazovik, A. (2012). Reduced Context Consistency Diagrams for Resolving Inconsistent Data. ICST Transactions on Ubiquitous Environments, 12(10-12). http://eudl.eu/pdf/10.4108/trans.ubienv.2012.10-12.e2
  5. Georgievski, I., Degeler, V., Pagani, G. A., Nguyen, T. A., Lazovik, A., & Aiello, M. (2012). Optimizing Energy Costs for Offices Connected to the Smart Grid. IEEE Transactions on Smart Grid, 3(4), 2273–2285. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6377248
  6. Nizamic, F., Degeler, V., Groenboom, R., & Lazovik, A. (2012). Policy-Based Scheduling of Cloud Services. Scalable Computing: Practice and Experience, 13(3), 187–199. http://www.cs.rug.nl/ds/uploads/pubs/scheduleClouds.pdf
  7. Aiello, M., Bezhanishvili, G., Bloch, I., & Goranko, V. (2012). Logic for physical space. Synthese, 18(3), 619–632. http://www.springerlink.com/content/w633k7l745k12p62/fulltext.pdf
  8. Aiello, M., Agarwal, N., Raman, R., & Rathod, U. (2012). Forward. Journal of System Assurance Engineering and Management, 3(2), 71–72.

2011

  1. Pagani, G. A., & Aiello, M. (2011). Towards Decentralized Trading: A Topological Investigation of the Medium and Low Voltage Grids. IEEE Transactions on Smart Grid, 2(3), 538–547. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5783965
  2. Sun, C., el Khoury, E., & Aiello, M. (2011). Transaction management in Service-Oriented Systems: requirements and a proposal. IEEE Transactions on Service Computing, 2(4), 167–180. http://www.cs.rug.nl/ aiellom/publications/TSC_Trans10.pdf
  3. Frankova, G., Seguran, M., Gilcherc, F., Trabelsic, S., Dorflingerd, J., & Aiello, M. (2011). Deriving Business Processes with Service Level Agreements from Early Requirements. Journal of Systems and Software, 84(8), 1351–1363. http://iwi.eldoc.ub.rug.nl/FILES/root/2011/JSystSoftwFrankova/2011JSystSoftwFrankova.pdf
  4. Krause, C., Maraikar, Z., Lazovik, A., & Arbab, F. (2011). Modeling Dynamic Reconfigurations in Reo using High-Level Replacement Systems. Science of Computer Programming, 76(1), 23–36. http://www.cs.rug.nl/ lazovik/papers/2011/reo-reconf.pdf
  5. van der Krieke, L., Emerencia, A., & Sytema, S. (2011). An Online Portal on Outcomes for Dutch Service Users. Psychiatric Services, 62(7), 803. http://www.cs.rug.nl/ ando/pdfs/frontline_report.pdf

2010

  1. Düştegör, D., Poroseva, S. V., Hussaini, M. Y., & Woodruff, S. (2010). Automated graph-based methodology for fault detection and location in power systems. IEEE Transactions on Power Delivery, 25(2). https://doi.org/10.1109/TPWRD.2009.2037005
  2. Sun, C., Rossing, R., Sinnema, M., Bulanov, P., & Aiello, M. (2010). Modelling and Managing the Variability of Web Service-based Systems. Journal of Systems and Software, 83(3), 502–516. http://www.cs.rug.nl/ aiellom/publications/jss09.pdf
  3. Clarke, D., Proenca, J., Lazovik, A., & Arbab, F. (2010). Channel-based Coordination via Constraint Satisfaction. Science of Computer Programming. https://lirias.kuleuven.be/bitstream/123456789/288820/1/2nd-revision-Channel-based+Coordination+via+Constraint+Satisfaction.pdf

2008

  1. Laursen, M., Blanke, M., & Düştegör, D. (2008). Fault diagnosis of a water for injection system using enhanced structural isolation. International Journal of Applied Mathematics and Computer Science, 18(4). https://doi.org/10.2478/v10006-008-0052-5
  2. Aiello, M., & Dustdar, S. (2008). Are our homes ready for services? A domotic infrastructure based on the Web service stack. Pervasive and Mobile Computing, 4(4), 506–525. http://www.sciencedirect.com/science/article/B7MF1-4RMFP60-1/2/e599fbbfb162b2e67ebac22939eb1d52