Publications

Distributed Tiered Storage for Cluster Computing

• G. Savva and H. Herodotou. Adaptive Data Prefetching for File Storage Systems Using Online Machine Learning. Big Data and Cognitive Computing (BDCC), Vol. 10, No. 1, Article 28, 26 pages, January 2026.
• E. R. Lucas Filho, A. Efstathiou, L. Yang, K. Fu, J. Shen, and H. Herodotou. DITIS: An End-to-End System-Level Simulator and Optimizer for Distributed Tiered Storage. Springer Nature Computer Science (SNCS), Vol. 6, Article 746, 28 pages, August 2025.
• E. R. Lucas Filho, G. Savva, L. Yang, K. Fu, J. Shen, and H. Herodotou. Employing Streaming Machine Learning for Modeling Workload Patterns in Multi-Tiered Data Storage Systems. Future Internet, Vol. 17, No. 4, Article 170, 37 pages, April 2025.
• C. Costa, P. Chrysanthis, H. Herodotou, M. Costa, E. Stavrakis, and N. Nicolaou. A Multiple Compression Approach using Attribute-based Signatures. Open Research Europe (ORE), Vol. 5, No. 49, pp. 1-9, February 2025.
• S. Vasileiadis, M. Paraskeva, G. Savva, A. Efstathiou, E. R. Lucas Filho, J. Shen, L. Yang, K. Fu, and H. Herodotou. Optimizing Distributed Tiered Data Storage Systems with DITIS. Proc. of VLDB Endowment (PVLDB), Vol. 17, No. 12, pp. 4393-4396, August 2024. 
• H. Herodotou and E. Kakoulli. Cost-based Data Prefetching and Scheduling in Big Data Platforms over Tiered Storage Systems. ACM Transactions on Database Systems (TODS), Vol. 48, No. 4, Article 11, pp. 1-40, November 2023.
• E. R. Lucas Filho, L. Yang, K. Fu, and H. Herodotou. Streaming Machine Learning for Supporting Data Prefetching in Modern Data Storage Systems. In Proc. of the First Workshop on AI for Systems (AI4Sys '23), 6 pages, June 2023.
• E. R. Lucas Filho, L. Odysseos, L. Yang, K. Fu, and H. Herodotou. DITIS: A Distributed Tiered Storage Simulator. Infocommunications Journal, Vol. XIV, No 4, Article 3, pp. 18-25, December 2022.
• H. Herodotou and E. Kakoulli. Trident: Task Scheduling over Tiered Storage Systems in Big Data Platforms. Proc. of VLDB Endowment (PVLDB), Vol. 14, No. 9, pp. 1570-1582, May 2021. 
• H. Herodotou and E. Kakoulli. Automating Distributed Tiered Storage Management in Cluster Computing. Proc. of VLDB Endowment (PVLDB), Vol. 13, No. 1, pp. 43-56, September 2019.
• H. Herodotou. AutoCache: Employing Machine Learning to Automate Caching in Distributed File Systems. In Proc. of the 35th IEEE Intl. Conf. on Data Engineering Workshops (ICDEW '19), pp. 133-139, April 2019.
• E. Kakoulli, N. D. Karmiris, and H. Herodotou. OctopusFS in Action: Tiered Storage Management for Data Intensive Computing. Demo, Proc. of VLDB Endowment (PVLDB), Vol. 11, No. 12, pp. 1914-1917, August 2018.
• E. Kakoulli and H. Herodotou. OctopusFS: A Distributed File System with Tiered Storage Management. In Proc. of the ACM Intl. Conf. on Management of Data (SIGMOD '17), May 2017.
• H. Herodotou. Towards a Distributed Multi-tier File System for Cluster Computing. In Proc. of the Intl. Workshop on Big Data Management on Emerging Hardware (HardBD '16), May 2016.
• H. Herodotou. A Distributed File System with Storage-Media Awareness. Poster in Proc. of the 2015 IEEE/ACM 8th Intl. Conf. on Utility and Cloud Computing (UCC '15), December 2015.

Automatic Tuning of Distributed Data Stream Processing Systems

• L. Odysseos and H. Herodotou. On Combining System and Machine Learning Performance Tuning for Distributed Data Stream Applications. Distributed and Parallel Databases (DAPD), 28 pages, May 2023.
• H. Herodotou, L. Odysseos, Y. Chen, and J. Lu. Automatic Performance Tuning for Distributed Data Stream Processing Systems. In Proc. of the 38th IEEE Intl. Conf. on Data Engineering (ICDE '22), 4 pages, May 2022. 
• L. Odysseos and H. Herodotou. Exploring System and Machine Learning Performance Interactions when Tuning Distributed Data Stream Applications. In Proc. of the 38th IEEE Intl. Conf. on Data Engineering Workshops (ICDEW '22), 6 pages, May 2022.

Scalable Database Management Systems

• M. A. Georgiou, M. Panayiotou, L. Odysseos, A. Paphitis, M. Sirivianos, and H. Herodotou. Attaining Workload Scalability and Strong Consistency for Replicated Databases with Hihooi. Demo, In Proc. of the ACM Intl. Conf. on Management of Data (SIGMOD '21), 5 pages, June 2021. 
• H. Herodotou, P.K. Chrysanthis, S. Chen, M. Hsu, K. Daudjee, Y. Wu, and C. Costa. Introduction to the Special Issue on Self managing and Hardware optimized Database Systems 2020. Distributed and Parallel Databases (DAPD), pp. 1-3, April 2021.
• M. A. Georgiou, A. Paphitis, M. Sirivianos, and H. Herodotou. Hihooi: A Database Replication Middleware for Scaling Transactional Databases Consistently. IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol. 34, No. 2, pp. 691-707, April 2020.
• M. A. Georgiou, A. Paphitis, M. Sirivianos, and H. Herodotou. Towards Auto-Scaling Existing Transactional Databases with Strong Consistency. In Proc. of the 35th IEEE Intl. Conf. on Data Engineering Workshops (ICDEW ’19), pp. 107-112, April 2019.

Automatic Tuning of Large-scale Data Processing Systems

• E.R. Lucas Filho, E. Cunha de Almeida, S. Scherzinger, and H. Herodotou. Investigating Automatic Parameter Tuning for SQL-on-Hadoop Systems. Big Data Research, Vol. 15, Article 100204, 15 pages, Elsevier, March 2021.
• H. Herodotou, Y. Chen, and J. Lu. A Survey on Automatic Parameter Tuning for Big Data Processing Systems. ACM Computing Surveys (CSur), Vol. 53, No. 2, Article 43, 37 pages, April 2020.
• J. Lu, Y. Chen, H. Herodotou, and S. Babu. Speedup Your Analytics: Automatic Parameter Tuning for Databases and Big Data Systems. Proc. of VLDB Endowment (PVLDB), Vol. 12, No. 12, August 2019. 
• H. Herodotou. Automatic Tuning of Data-Intensive Analytical Workloads. LAP LAMBERT Academic Publishing, Saarbrucken, Germany, November 2016.
• M. Ead, H. Herodotou, A. Aboulnaga, and S. Babu. PStorM: Profile Storage and Matching for Feedback-Based Tuning of MapReduce Jobs. In Proc. of the 17th Intl. Conf. on Extending Database Technology (EDBT '14), pp. 1-12, March 2014.
• H. Herodotou and S. Babu. A What-if Engine for Cost-based MapReduce Optimization. IEEE Data Engineering Bulletin, Vol. 36, No. 1, pp. 5-14, March 2013.
• H. Lim, H. Herodotou, and S. Babu. Stubby: A Transformation-based Optimizer for MapReduce Workflows. Proc. of VLDB Endowment (PVLDB), Vol. 5, No. 1, pp. 1196-1207, August 2012.
• H. Herodotou. Automatic Tuning of Data-Intensive Analytical Workloads. Ph.D. Dissertation, Duke University, April 2012.
• H. Herodotou, F. Dong, and S. Babu. No One (Cluster) Size Fits All: Automatic Cluster Sizing for Data-intensive Analytics. In Proc. of the 2nd ACM Symposium on Cloud Computing (SOCC '11), pp. 18:1-14, October 2011.
• H. Herodotou and S. Babu. Profiling, What-if Analysis, and Cost-based Optimization of MapReduce Programs. Proc. of VLDB Endowment (PVLDB), Vol. 4, No. 11, pp. 1111-1122, August 2011.
• H. Herodotou, F. Dong, and S. Babu. MapReduce Programming and Cost-based Optimization? Crossing this Chasm with Starfish. Demo, Proc. of VLDB Endowment (PVLDB), Vol. 4, No. 12, pp. 1446-1449, August 2011.
• H. Herodotou. Hadoop Performance Models. Technical Report, CS-2011-05, Duke University, February 2011.
• H. Herodotou, H. Lim, G. Luo, N. Borisov, L. Dong, F. B. Cetin, and S. Babu. Starfish: A Self-tuning System for Big Data Analytics. In Proc. of the Fifth Biennial Conf. on Innovative Data Systems Research (CIDR '11), January 2011.

 Maritime Informatics and Data Management

• S. Vasileiadis, S. Aslam, K. Orphanides, A. Cassera, E. Garro Crevillen, A. Martınez-Romero, M. Michaelides, and H. Herodotou. Real-time Container Tracking and Damage Detection at Seaports Using Deep Learning. In Proc. of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS '25), pp. 277-285, September 2025.
• N. Evmides, M. Michaelides, and H. Herodotou. Vessel Trajectory Prediction with Deep Learning: Temporal Modeling and Operational Implications. Journal of Marine Science and Engineering (JMSE), Vol. 13, No. 8, Article 1439, 21 pages, July 2025.
• S. Aslam, A. Navarro, A. Aristotelous, E. Garro Crevillen, A. Martınez-Romero, A. Martínez-Ceballos, A. Cassera, K. Orphanides, H. Herodotou, and M. Michaelides. Machine Learning-Based Predictive Maintenance at Smart Ports Using IoT Sensor Data. Sensors, Vol. 25, No. 13, Article 3923, 21 pages, June 2025.
• R. Zheng, L. Dong, B. Wang, I. Kyriakides, H. Herodotou, and M. Michaelides. Design of a Marine Environment Buoy Monitoring Platform Based on LoRa. In Proc. of the IEEE Symposium on Maritime Informatics & Robotics (SMIR '25), 4 pages, June 2025.
• S. Aslam, M. Michaelides, and H. Herodotou. Optimizing Multi-Quay Combined Berth and Quay Crane Allocation Using Computational Intelligence. Journal of Marine Science and Engineering (JMSE), Vol. 12, No. 9, Article 1567, 21 pages, September 2024.
• N. Evmides, S. Aslam, T. Ramez, M. Michaelides, and H. Herodotou. Enhancing Prediction Accuracy of Vessel Arrival Times Using Machine Learning. Journal of Marine Science and Engineering (JMSE), Vol. 12, No. 8, Article 1362, 17 pages, August 2024.
• S. Aslam, M. Michaelides, and H. Herodotou. Multi-Quay Combined Berth and Quay Crane Allocation using the Cuckoo Search Algorithm. In Proc. of the 10th Intl. Conf. on Vehicle Technology and Intelligent Transport Systems (VEHITS '24), pp. 220-227, May 2024.
• S. Aslam, M. Michaelides, and H. Herodotou. A Survey on Computational Intelligence Approaches for Intelligent Marine Terminal Operations. IET Intelligent Transport Systems (IET-ITS), Vol. 18, No. 5, pp. 755-793, March 2024.
• S. Aslam, H. Herodotou, E. Garro, A. M. Romero, M. A. Burgos, A. Cassera, G. Papas, P. Dias, and M. Michaelides. Emerging IoT Applications and Architectures for Smart Maritime Container Terminals. In Proc. of the IEEE 9th World Forum on Internet of Things (WF-IoT '23), 6 pages, October 2023.
• S. Aslam, H. Herodotou, E. Garro, A. M. Romero, M. A. Burgos, A. Cassera, G. Papas, P. Dias, and M. Michaelides. IoT for the Maritime Industry: Challenges and Emerging Applications. In Proc. of the 18th Conference on Computer Science and Intelligence Systems (FedCSIS '23), pp. 855-858, September 2023.
• S. Aslam, M. Michaelides, and H. Herodotou. Berth Allocation Considering Multiple Quays: A Practical Approach Using Cuckoo Search Optimization. Journal of Marine Science and Engineering (JMSE), Vol. 11, No. 7, Article 1280, 23 pages, June 2023.
• S. Aslam, M. Michaelides, and H. Herodotou. Enhanced Berth Allocation Using the Cuckoo Search Algorithm. Springer Nature Computer Science (SNCS), Vol. 3, Article 325, 15 pages, June 2022.
• K. Christodoulou, H. Herodotou, and M. Michaelides. Estimation of Sea Surface Current Velocities using AIS Data. In Proc. of the 23rd IEEE International Conference on Mobile Data Management (MDM '22), pp. 407-412, June 2022. 
• N. Evmides, L. Odysseos, M. Michaelides, and H. Herodotou. An Intelligent Framework for Vessel Traffic Monitoring using AIS Data. In Proc. of the 23rd IEEE International Conference on Mobile Data Management (MDM '22), pp. 413-418, June 2022. 
• A. Worth, A. Televantos, N. Evmides, M. Michaelides, and H. Herodotou. Online Analytical Processing of Port Calls for Decision Support. Demo. In Proc. of the 23rd IEEE International Conference on Mobile Data Management (MDM '22), pp. 437-439, June 2022. 
• S. Aslam, M. Michaelides, and H. Herodotou. Optimizing Multi-Quay Berth Allocation using the Cuckoo Search Algorithm. In Proc. of the 8th Intl. Conf. on Vehicle Technology and Intelligent Transport Systems (VEHITS '22), 10 pages, April 2022.
• S. Aslam, M. Michaelides, and H. Herodotou. Dynamic and Continuous Berth Allocation Using Cuckoo Search Optimization. In Proc. of the 7th Intl. Conf. on Vehicle Technology and Intelligent Transport Systems (VEHITS '21), pp. 72-81, April 2021.
• N. Evmides, S. Aslam, A. Televantos, A. Karagiannis, A. Paraskeva, M. Michaelides, and H. Herodotou. Employing Fuzzy Matching for Cleaning Manual AIS Entries. In Proc. of the 2021 World of Shipping Portugal - An International Research Conference on Maritime Affairs (WofSP '21), January 2021.
• M. Michaelides, H. Herodotou, P. Schmitt, M. Karlson, I. Kyriakides, D. Hayes, and X. Fu. Maritime Informatics Technology. In Proc. of the 2021 World of Shipping Portugal - An International Research Conference on Maritime Affairs (WofSP '21), January 2021.
• H. Herodotou, S. Aslam, H. Holm, and S. Theodossiou Big Maritime Data Management. In M. Lind, M. Michaelides, R. Ward, and R.Th. Watson (editors), Maritime Informatics. pp. 313-334, Springer International Publishing, Switzerland, November 2020.
• S. Aslam, M. Michaelides, and H. Herodotou. Internet of Ships: A Survey on Architectures, Emerging Applications, and Challenges. IEEE Internet of Things (IoT) Journal, Vol. 7, No. 10, pp. 9714-9727, October 2020.
• M. Lind, M. Michaelides, R. Ward, H. Herodotou, and R. T. Watson. Boosting data-sharing to improve Short Sea Shipping Performance: Evidence from Limassol port calls analysis. UNCTAD Transport and Trade Facilitation Newsletter N°82 - Second Quarter 2019, Article No. 35, May 2019.
• M. Michaelides, H. Herodotou, M. Lind, and R. T. Watson. Port-2-Port Communication Enhancing Short Sea Shipping Performance: The Case Study of Cyprus and the Eastern Mediterranean. Sustainability Journal, Vol. 11, No. 7, pp. 1912-34, March 2019.
• M. Lind, M. Bergmann, N. Bjørn-Andersen, W. Robert, S. Haraldson, R. Watson, T. Andersen, M. Michaelides, N. Evmides, N. Gerosavva, M. Karlsson, H. Holm, E. Olsson, A. Zerem, H. Herodotou, G. Ferrus, J. Gimenez, J. Arjona, M. Marquez, T. Rygh, S. Voskarides, and A. Gonzales. Substantial Value for Shipping found in PortCDM Testbeds. Technical Report, Concept Note #22, STM Validation Project, pp. 1-12, March 2019.
• M. Lind, M. Bergmann, S. Haraldson, R. Watson, M. Michaelides, H. Herodotou, and S. Voskarides. Port-2-Port Communication Enabling Short Sea Shipping: Cyprus and the Eastern Mediterranean. Technical Report, Concept Note #5, STM Validation Project, pp. 1-8, March 2018.

Data Management for IoT and Wearable Devices

• A. Pamboris, C. Kozis, and H. Herodotou. Cuttlefish: A Flexible and Lightweight Middleware for Combining Heterogeneous IoT Devices. In Proc. of the 17th IEEE Annual Consumer Communications & Networking Conference (CCNC '20), pp. 1-6, January 2020.
• A. Pamboris, P. Andreou, H. Herodotou, and G. Samaras. MULTI-WEAR: A Multi-Wearable Platform For Enhancing Mobile Experiences. In Proc. of the 15th IEEE Annual Consumer Communications & Networking Conference (CCNC '18), January 2018.
• S. E. Alshaal, S. Michael, A. Pamboris, H. Herodotou, G. Samaras and P. Andreou. Enhancing Virtual Reality Systems with Smart Wearable Devices. Demo in Proc. of the 17th IEEE Intl. Conf. on Mobile Data Management (MDM '16), June 2016.

Cloud Computing Systems and Networks

• N. Kourtellis, H. Herodotou, M. Grzenda, P. Wawrzyniak, and A. Bifet. S2CE: A Hybrid Cloud and Edge Orchestrator for Mining Exascale Distributed Streams. In Proc. of the 15th ACM Intl. Conf. on Distributed and Event-based Systems (DEBS '21), pp. 103-111, June 2021.
• A. Chatzigeorgiou, R. Dondi, H. Herodotou, C. Kapoutsis, Y. Manolopoulos, G.A. Papadopoulos, and F. Sikora. SOFSEM 2020: Theory and Practice of Computer Science. Springer Nature, Switzerland, ISBN 978-3-030-38918-5, 723 pages, January 2020.
• S. Elnikety, M. Syamala, V. Narasayya, H. Herodotou, P. Tomita, A. Chen, J. Zhang, and J. Wang. PerfIso: Performance Isolation for Commercial Latency-Sensitive Services. In Proc. of the USENIX Annual Technical Conference (USENIX ATC '18), pp. 519-531, July 2018.
• H. Herodotou. Business Intelligence and Analytics: Big Systems for Big Data. In E. G. Carayannis and S. Sindakis (editors), Analytics, Innovation, and Excellence-Driven Enterprise Sustainability. Palgrave Macmillan, New York, USA, January 2017.
• H. Herodotou, B. Ding, S. Balakrishnan, G. Outhred, and P. Fitter. Scalable Near Real-time Failure Localization of Data Center Networks. In Proc. of the 20th ACM Intl. Conf. on Knowledge Discovery and Data Mining (SIGKDD '14), pp. 1689-1698, August 2014.
• S. Babu and H. Herodotou. Massively Parallel Databases and MapReduce Systems. Foundations and Trends in Databases, Vol. 5, No. 1, pp. 1-104, now publishers Inc., November 2013.

Data-driven Approaches for Environmental Modeling

• E. Hadjisolomou, A. E. Hassoun, M. Fakhri, A. Ghanem, R. Mina, M. Michaelides, and H. Herodotou. Modelling the Coastal Fecal Bacteria Contamination in the Eastern Mediterranean Region: Lebanon as a Case Study In Proc. of the 6th Intl. Conf. on Environmental Design and Health (ICED '25), 6 pages, November 2025.
• E. Hadjisolomou, K. Antoniadis, M. Rousou, L. Vasiliades, E. Datsi, H. Herodotou, M. Michaelides, and I. Kyriakides. Employing a Probabilistic Neural Network for Classifying Cyprus Coastal Water Quality Status based on Chlorophyll-a In Proc. of the 5th Intl. Conf. on Environmental Design and Health (ICED '24), 5 pages, October 2024.
• E. Hadjisolomou, M. Rousou, K. Antoniadis, L. Vasiliades, I. Kyriakides, M. Michaelides, and H. Herodotou. On the use of Artificial Neural Networks for Exploring the Synergies between Coastal Water Quality Parameters on the Trophic Status of Cyprus. In Proc. of Euro-Mediterranean Conference for Environmental Integration (EMCEI '24), 5 pages, May 2024.
• E. Hadjisolomou, M. Rousou, K. Antoniadis, L. Vasiliades, I. Kyriakides, H. Herodotou, and M. Michaelides. Data-Driven Models for Evaluating Coastal Eutrophication: A Case Study for Cyprus. Water Journal, Vol. 15, No. 23, Article 4097, pp. 1-16, November 2023
• E. Hadjisolomou, K. Antoniadis, M. Rousou, L. Vasiliades, R. Abu-Alhaija, H. Herodotou, M. Michaelides, and I. Kyriakides. Predicting Coastal Dissolved Inorganic Nitrogen Levels by Applying Data-Driven Modelling: The Case Study of Cyprus. In Proc. of the 4th Intl. Conf. on Environmental Design (ICED '23), 6 pages, October 2023.
• E. Hadjisolomou, K. Antoniadis, L. Vasiliades, M. Rousou, I. Thasitis, R. Abu-Alhaija, H. Herodotou, M. Michaelides, and I. Kyriakides. Predicting Coastal Dissolved Oxygen Values with the Use of Artificial Neural Networks: A Case Study for Cyprus. In IOP Conference Series: Earth and Environmental Science (ICED '22), Vol. 1123, No. 1, Article 012083, 9 pages, IOP Publishing, October 2022.
• E. Hadjisolomou, K. Antoniadis, I. Thasitis, R. Abu-Alhaija, H. Herodotou, and M. Michaelides. Exploring the Impact of Coastal Water Quality Parameters on Chlorophyll-a near Cyprus with the use of Artificial Neural Networks. In Proc. of the 39th IAHR World Congress, 9 pages, June 2022.
• E. Hadjisolomou, K. Antoniadis, I. Thasitis, M. Rousou, L. Vasiliades, R. Abu-Alhaija, I. Kyriakides, H. Herodotou, and M. Michaelides. Examining water quality parameters' interactions related to Cyprus coastal eutrophication based on the Kohonen Self-Organizing Map. Oral Presentation, THALASSA '22, June 2022.
• E. Hadjisolomou, K. Antoniadis, I. Thasitis, M. Rousou, L. Vasiliades, R. Abu-Alhaija, H. Herodotou, M. Michaelides, I. Kyriakides. The MARI-Sense Project: Employing Artificial Neural Networks for Sustainable Coastal Environmental Management. Poster, THALASSA '22, June 2022.
• E. Hadjisolomou, K. Stefanidis, H. Herodotou, M. Michaelides, G. Papatheodorou, and E. Papastergiadou. Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks. Water Journal, Vol. 13, No. 11, Article 1590, pp. 1-15, June 2021.

Data-driven Applications for Social Computing

• H. Herodotou, D. Chatzakou, and N. Kourtellis. Catching them red-handed: Real-time Aggression Detection on Social Media. In Proc. of the 37th IEEE Intl. Conf. on Data Engineering (ICDE '21), pp. 2123-2128, April 2021.
• H. Herodotou, D. Chatzakou, and N. Kourtellis. A Streaming Machine Learning Framework for Online Aggression Detection on Twitter. In Proc. of the IEEE Intl. Conf. on Big Data (BigData '20), pp. 5056-5067, December 2020.

Data-driven Applications for Tourism

• A. Gregoriades, H. Herodotou, M Pampaka, and E. Christodoulou. Combining User and Venue Personality Proxies with Customers’ Preferences and Opinions to Enhance Restaurant Recommendation Performance. Artificial Intelligence (AI), Vol. 7, No. 1, Article 19, 30 pages, January 2026.
• A. Gregoriades, M. Pampaka, H. Herodotou, and E. Christodoulou. Explaining Tourist Revisit Intention using Natural Language Processing and Classification Techniques. Journal of Big Data, Vol. 10, Article 60, 31 pages, May 2023.
• E. Christodoulou, A. Gregoriades, H. Herodotou, and M. Pampaka. Extracting User Preferences and Personality from Text for Restaurant Recommendation. In Proc. of the 5th Workshop on Online Recommender Systems and User Modeling (ORSUM '22), co-located with the 16th ACM Conference on Recommender Systems (RecSys), 11 pages, September 2022.
• E. Christodoulou, A. Gregoriades, H. Herodotou, and M. Pampaka. Combination of User and Venue Personality with Topic Modelling in Restaurant Recommender Systems. In Proc. of the RecSys Workshop on Recommenders in Tourism (RecTour '22), co-located with the 16th ACM Conference on Recommender Systems (RecSys), pp. 21-36, September 2022.
• E. Christodoulou, A. Gregoriades, M. Pampaka, and H. Herodotou. Personality-Informed Restaurant Recommendation. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F. (eds) Information Systems and Technologies. WorldCIST 2022. LNNS Vol. 468, pp. 13-21, Springer, Cham, April 2022.
• E. Christodoulou, A. Gregoriades, M. Pampaka, and H. Herodotou. Evaluating the Effect of Weather on Tourist Revisit Intention using Natural Language Processing and Classification Techniques. In Proc. of the IEEE Intl. Conf. on Systems, Man, and Cybernetics (SMC '21), 6 pages, October 2021.
• A. Gregoriades, M. Pampaka, H. Herodotou, and E. Christodoulou. Supporting Digital Content Marketing and Messaging through Topic Modelling and Decision Trees. Expert Systems with Applications (ESWA), Vol. 184, No. 2021, Article 115546, 18 pages, July 2021.
• E. Christodoulou, A. Gregoriades, M. Pampaka, and H. Herodotou. Application of Classification and Word Embedding Techniques to Evaluate Tourists’ Hotel-revisit Intention. In Proc. of the 23rd Intl. Conf. on Enterprise Information Systems (ICEIS '21), pp. 226-223, April 2021.
• E. Christodoulou, A. Gregoriades, M. Pampaka, and H. Herodotou. Combination of Topic Modelling and Decision Tree Classification for Tourist Destination Marketing. In Proc. of the Advanced Information Systems Engineering Workshops (CAiSE '20), pp. 95-108, June 2020.

 Data-driven Applications in the Energy Domain

• K. Mustafa, S. Khan, S. Aslam, H. Herodotou, N. Ashraf, A. Daraz, and T. Alkhalifah. Towards Holistic Energy Management by Electricity Load and Price Forecasting: A Comprehensive Survey. IEEE Access, Vol. 11, pp. 132604-132626, November 2023.
• S. Aslam, H. Herodotou, and N. Ashraf. Smart Grid Analytics for Sustainability and Urbanization in Big Data. MDPI, Basel, Switzerland, ISBN 978-3-0365-9172-8, 280 pages, November 2023.
• T. Ali, S. A. Malik, A. Daraz, M Adeel, S. Aslam, and H. Herodotou. Load Frequency Control and Automatic Voltage Regulation in a Four-Area Interconnected Power Systems Using a Gradient-Based Optimizer. Energies Journal, Vol. 16, No. 5, Article 2086, 27 pages, February 2023.
• H. Herodotou and S. Aslam. Data-Intensive Computing in Smart Microgrids: Volume II. Energies Journal, Vol. 15, No. 16, Article 5833, 2 pages, August 2022.
• H. Herodotou. Data-Intensive Computing in Smart Microgrids. MDPI, Basel, Switzerland, ISBN 978-3-0365-1627-1, 238 pages, August 2021.
• H. Herodotou. Introduction to the Special Issue on Data-Intensive Computing in Smart Microgrids. Energies Journal, Vol. 14, No. 9, Article 2704, 3 pages, May 2021.
• K. Ullah, A. Basit, Z. Ullah, S. Aslam, and H. Herodotou. Automatic Generation Control Strategies in Conventional and Modern Power Systems: A Comprehensive Overview. Energies Journal, Vol. 14, No. 9, Article 2376, 44 pages, April 2021.
• S. Aslam, H. Herodotou, S. M. Mohsin, N. Javaid, N. Ashraf, and Sh. Aslam. A Survey on Deep Learning Methods for Power Load and Renewable Energy Forecasting in Smart Microgrids. Renewable and Sustainable Energy Reviews, Vol. 144, Article 110992, 23 pages, April 2021.
• Sh. Aslam, S. Aslam, H. Herodotou, S. M. Mohsin, and K. Aurangzeb. Towards Energy Efficiency and Power Trading Exploiting Renewable Energy in Cloud Data Centers. In Proc. of the IEEE Intl. Conf. on Advances in the Emerging Computing Technologies (AECT '19), December 2019.
• S. Aslam, H. Herodotou, N. Ayub, and S. M. Mohsin. Deep Learning based Techniques to Enhance the Performance of Microgrids: A Review. In Proc. of the 17th IEEE Intl. Conf. on Frontiers of Information Technology (FIT '19), December 2019.
• K. Aurangzeb, S. Aslam, H. Herodotou, M. Alhussein, and S.I. Haider. Towards Electricity Cost Alleviation by Integrating RERs in a Smart Community: A Case Study. In Proc. of the 23rd IEEE Intl. Conf. Electronics, pp. 1-6, June 2019.

Database Query Optimization and Tuning

• H. Herodotou, N. Borisov, and S. Babu. Query Optimization Techniques for Partitioned Tables. In Proc. of the ACM Intl. Conf. on Management of Data (SIGMOD '11), pp. 49-60, June 2011.
• H. Herodotou and S. Babu. Xplus: A SQL-Tuning-Aware Query Optimizer. Proc. of VLDB Endowment (PVLDB), Vol. 3, No. 1-2, pp. 1149-1160, September 2010.
• H. Herodotou and S. Babu. Automated SQL Tuning through Trial and (Sometimes) Error. In Proc. of the Second Intl. Workshop on Testing Database Systems (DBTest '09), pp. 1-6, June 2009.
• S. Babu, N. Borisov, S. Duan, H. Herodotou, and V. Thummala. Automated Experiment-Driven Management of (Database) Systems. In Proc. of the 12th Workshop on Hot Topics in Operating Systems (HotOS-XII), May 2009.
• H. Herodotou. zTuned: Automated SQL Tuning through Trial and (Sometimes) Error. Master's Thesis, Duke University, April 2009.

Applied Database Technologies

• F. Mitha, H. Herodotou, N. Borisov, C. Jiang, J. Yoder, and K. Owzar. SNPpy - Database Management for SNP Data from Genome Wide Association Studies. PLoS ONE, Vol. 6, No. 10, pp. e24982, October 2011.
• Y. Zhang, H. Herodotou, and J. Yang. RIOT: I/O-Efficient Numerical Computing without SQL. In Proc. of the Fourth Biennial Conf. on Innovative Data Systems Research (CIDR '09), January 2009.

Patents

• L. Yang, K. Fu, J. Shen, H. Herodotou, and E.R. Lucas Filho. Data processing method and data storage system (一种数据处理方法及数据存储系统). Patent WO 2024/174478 A1, CN 118535074A, EP 4664297 A1, US 20250390463 A1. August 2024.
• S. Babu and H. Herodotou. Cost-Based Optimization of Configuration Parameters and Cluster Sizing for Hadoop. Patent US 9,367,601 B2. June 2016.
• S. Babu and H. Herodotou. Systems and Methods for Cost-Based Optimization for MapReduce Workflows. US Provisional Patent DU4146PROV. March 2013.