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  • Najafi M, Qadah T, Sadoghi M and Jacobsen H. (2024). DIBA: A Re-Configurable Stream Processor. IEEE Transactions on Knowledge and Data Engineering. 36:9. (4550-4566). Online publication date: 1-Sep-2024.

    https://doi.org/10.1109/TKDE.2024.3381192

  • Zhang Y, Zhang F, Li H, Zhang S, Guo X, Chen Y, Pan A and Du X. Data-Aware Adaptive Compression for Stream Processing. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2024.3377710. 36:9. (4531-4549).

    https://ieeexplore.ieee.org/document/10473167/

  • Meldrum M and Carbone P. μWheel: Aggregate Management for Streams and Queries. Proceedings of the 18th ACM International Conference on Distributed and Event-based Systems. (54-65).

    https://doi.org/10.1145/3629104.3666031

  • Kroviakov A, Kurapov P, Anneser C and Giceva J. Heterogeneous Intra-Pipeline Device-Parallel Aggregations. Proceedings of the 20th International Workshop on Data Management on New Hardware. (1-10).

    https://doi.org/10.1145/3662010.3663441

  • Clarkson J, Theodorakis G and Webber J. (2024). BIFROST: A Future Graph Database Runtime 2024 IEEE 40th International Conference on Data Engineering (ICDE). 10.1109/ICDE60146.2024.00448. 979-8-3503-1715-2. (5605-5613).

    https://ieeexplore.ieee.org/document/10597713/

  • Papadias S, Kaoudi Z, Pandey V, Quiané-Ruiz J and Markl V. (2024). Counting Butterflies in Fully Dynamic Bipartite Graph Streams 2024 IEEE 40th International Conference on Data Engineering (ICDE). 10.1109/ICDE60146.2024.00226. 979-8-3503-1715-2. (2917-2930).

    https://ieeexplore.ieee.org/document/10597939/

  • Heinrich R, Binnig C, Kornmayer H and Luthra M. (2024). Costream: Learned Cost Models for Operator Placement in Edge-Cloud Environments 2024 IEEE 40th International Conference on Data Engineering (ICDE). 10.1109/ICDE60146.2024.00015. 979-8-3503-1715-2. (96-109).

    https://ieeexplore.ieee.org/document/10598052/

  • Fang J, Lychagin D, Carey M and Tsotras V. (2023). A new window Clause for SQL++. The VLDB Journal. 10.1007/s00778-023-00830-z. 33:3. (595-623). Online publication date: 1-May-2024.

    https://link.springer.com/10.1007/s00778-023-00830-z

  • Miano S, Lettieri G, Antichi G and Procissi G. (2024). Accelerating network analytics with an on-NIC streaming engine. Computer Networks: The International Journal of Computer and Telecommunications Networking. 241:C. Online publication date: 1-Mar-2024.

    https://doi.org/10.1016/j.comnet.2024.110231

  • Pawar S, Pawar V and Abimannan S. (2024). Handling Uncertainty in Spatiotemporal Data. Spatiotemporal Data Analytics and Modeling. 10.1007/978-981-99-9651-3_4. (69-87).

    https://link.springer.com/10.1007/978-981-99-9651-3_4

  • Nasiri H, Darjani A, Kavand N and Goudarzi M. (2023). H-Storm: A Hybrid CPU-FPGA Architecture to Accelerate Apache Storm. Journal of Grid Computing. 21:4. Online publication date: 1-Dec-2023.

    https://doi.org/10.1007/s10723-023-09692-9

  • Jamil H, Chung J, Bicer T, Kosar T and Kettimuthu R. Throughput Optimization with a NUMA-Aware Runtime System for Efficient Scientific Data Streaming. Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis. (795-805).

    https://doi.org/10.1145/3624062.3624593

  • Lettieri G, Fais A, Antichi G and Procissi G. (2023). SmartNIC-Accelerated Stream Processing Analytics 2023 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). 10.1109/NFV-SDN59219.2023.10329593. 979-8-3503-0254-7. (135-140).

    https://ieeexplore.ieee.org/document/10329593/

  • Hu Y, Zhang F, Xia Y, Yao Z, Zeng L, Ding H, Wei Z, Zhang X, Zhai J, Du X and Ma S. Enabling Efficient Random Access to Hierarchically Compressed Text Data on Diverse GPU Platforms. IEEE Transactions on Parallel and Distributed Systems. 10.1109/TPDS.2023.3294341. 34:10. (2699-2717).

    https://ieeexplore.ieee.org/document/10178044/

  • Lee S, Jeong Y, Park K, Jung G and Park S. (2022). zStream: towards a low latency micro-batch streaming system. Cluster Computing. 10.1007/s10586-022-03758-1. 26:5. (2773-2787). Online publication date: 1-Oct-2023.

    https://link.springer.com/10.1007/s10586-022-03758-1

  • Wang J, Pang W, Weng C and Zhou A. (2022). D-Cubicle: boosting data transfer dynamically for large-scale analytical queries in single-GPU systems. Frontiers of Computer Science. 10.1007/s11704-022-2160-z. 17:4. Online publication date: 1-Aug-2023.

    https://link.springer.com/10.1007/s11704-022-2160-z

  • Agnihotri P, Koldehofe B, Binnig C and Luthra M. Zero-Shot Cost Models for Parallel Stream Processing. Proceedings of the Sixth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management. (1-5).

    https://doi.org/10.1145/3593078.3593934

  • Gurumurthy B, Broneske D, Durand G, Pionteck T and Saake G. (2023). ADAMANT: A Query Executor with Plug-In Interfaces for Easy Co-processor Integration 2023 IEEE 39th International Conference on Data Engineering (ICDE). 10.1109/ICDE55515.2023.00093. 979-8-3503-2227-9. (1153-1166).

    https://ieeexplore.ieee.org/document/10184601/

  • Zhang Y, Zhang F, Li H, Zhang S and Du X. (2023). CompressStreamDB: Fine-Grained Adaptive Stream Processing without Decompression 2023 IEEE 39th International Conference on Data Engineering (ICDE). 10.1109/ICDE55515.2023.00038. 979-8-3503-2227-9. (408-422).

    https://ieeexplore.ieee.org/document/10184565/

  • Windisch D, Kelling J, Juckeland G and Bieberle A. (2023). Real-time Data Processing for Ultrafast X-Ray Computed Tomography using Modular CUDA based Pipelines. Computer Physics Communications. 10.1016/j.cpc.2023.108719. (108719). Online publication date: 1-Mar-2023.

    https://linkinghub.elsevier.com/retrieve/pii/S0010465523000644

  • Verwiebe J, Grulich P, Traub J and Markl V. (2023). Survey of window types for aggregation in stream processing systems. The VLDB Journal. 10.1007/s00778-022-00778-6.

    https://link.springer.com/10.1007/s00778-022-00778-6

  • Rosenfeld V, Breß S and Markl V. (2022). Query Processing on Heterogeneous CPU/GPU Systems. ACM Computing Surveys. 55:1. (1-38). Online publication date: 31-Jan-2023.

    https://doi.org/10.1145/3485126

  • Jayarajan A, Zhao W, Sun Y and Pekhimenko G. TiLT: A Time-Centric Approach for Stream Query Optimization and Parallelization. Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2. (818-832).

    https://doi.org/10.1145/3575693.3575704

  • Hadian H, Farrokh M, Sharifi M and Jafari A. (2022). An elastic and traffic-aware scheduler for distributed data stream processing in heterogeneous clusters. The Journal of Supercomputing. 10.1007/s11227-022-04669-z. 79:1. (461-498). Online publication date: 1-Jan-2023.

    https://link.springer.com/10.1007/s11227-022-04669-z

  • Liu J, Zhang F, Li H, Wang D, Wan W, Fang X, Zhai J and Du X. Exploring Query Processing on CPU-GPU Integrated Edge Device. IEEE Transactions on Parallel and Distributed Systems. 10.1109/TPDS.2022.3177811. 33:12. (4057-4070).

    https://ieeexplore.ieee.org/document/9782546/

  • Zapridou E, Mytilinis I and Ailamaki A. (2022). Dalton. Proceedings of the VLDB Endowment. 16:3. (491-504). Online publication date: 1-Nov-2022.

    https://doi.org/10.14778/3570690.3570699

  • Zhang F, Hu Y, Ding H, Yao Z, Wei Z, Zhang X and Du X. (2022). Optimizing Random Access to Hierarchically-Compressed Data on GPU SC22: International Conference for High Performance Computing, Networking, Storage and Analysis. 10.1109/SC41404.2022.00023. 978-1-6654-5444-5. (1-15).

    https://ieeexplore.ieee.org/document/10046120/

  • Kim S, Jung C and Kim Y. (2022). Comparative Analysis of GPU Stream Processing between Persistent and Non-persistent Kernels 2022 13th International Conference on Information and Communication Technology Convergence (ICTC). 10.1109/ICTC55196.2022.9952789. 978-1-6654-9939-2. (2330-2332).

    https://ieeexplore.ieee.org/document/9952789/

  • Yao Z, Chen R, Zang B and Chen H. Wukong+G: Fast and Concurrent RDF Query Processing Using RDMA-Assisted GPU Graph Exploration. IEEE Transactions on Parallel and Distributed Systems. 10.1109/TPDS.2021.3121568. 33:7. (1619-1635).

    https://ieeexplore.ieee.org/document/9582823/

  • Pan Z, Zhang F, Zhou Y, Zhai J, Shen X, Mutlu O and Du X. Exploring Data Analytics Without Decompression on Embedded GPU Systems. IEEE Transactions on Parallel and Distributed Systems. 10.1109/TPDS.2021.3119402. 33:7. (1553-1568).

    https://ieeexplore.ieee.org/document/9568774/

  • Heinrich R, Luthra M, Kornmayer H and Binnig C. (2022). Zero-shot cost models for distributed stream processing DEBS '22: The 16th ACM International Conference on Distributed and Event-based Systems. 10.1145/3524860.3539639. 9781450393089. (85-90). Online publication date: 27-Jun-2022.

    https://dl.acm.org/doi/10.1145/3524860.3539639

  • Kuhrt M, Körber M and Seeger B. iGPU-Accelerated Pattern Matching on Event Streams. Proceedings of the 18th International Workshop on Data Management on New Hardware. (1-7).

    https://doi.org/10.1145/3533737.3535099

  • Del Monte B, Zeuch S, Rabl T and Markl V. Rethinking Stateful Stream Processing with RDMA. Proceedings of the 2022 International Conference on Management of Data. (1078-1092).

    https://doi.org/10.1145/3514221.3517826

  • Mencagli G, Griebler D and Danelutto M. (2022). Towards Parallel Data Stream Processing on System-on-Chip CPU+GPU Devices 2022 30th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). 10.1109/PDP55904.2022.00014. 978-1-6654-6958-6. (34-38).

    https://ieeexplore.ieee.org/document/9756684/

  • Gulisano V, Papatriantafilou M and Papadopoulos A. (2022). Elastic Resource Management in Stream Processing. Encyclopedia of Big Data Technologies. 10.1007/978-3-319-63962-8_191-2. (1-7).

    https://link.springer.com/10.1007/978-3-319-63962-8_191-2

  • Fino A, Margara A, Cugola G, Donadoni M and Morassutto E. (2021). RStream: Simple and Efficient Batch and Stream Processing at Scale 2021 IEEE International Conference on Big Data (Big Data). 10.1109/BigData52589.2021.9671932. 978-1-6654-3902-2. (2764-2774).

    https://ieeexplore.ieee.org/document/9671932/

  • Geethakumari P and Sourdis I. (2021). StreamZip: Compressed Sliding-Windows for Stream Aggregation 2021 International Conference on Field-Programmable Technology (ICFPT). 10.1109/ICFPT52863.2021.9609952. 978-1-6654-2010-5. (1-9).

    https://ieeexplore.ieee.org/document/9609952/

  • Wladdimiro D, Arantes L, Sens P and Hidalgo N. (2021). A Multi-Metric Adaptive Stream Processing System 2021 IEEE 20th International Symposium on Network Computing and Applications (NCA). 10.1109/NCA53618.2021.9685871. 978-1-6654-9550-9. (1-8).

    https://ieeexplore.ieee.org/document/9685871/

  • Mencagli G, Torquati M, Cardaci A, Fais A, Rinaldi L and Danelutto M. WindFlow: High-Speed Continuous Stream Processing With Parallel Building Blocks. IEEE Transactions on Parallel and Distributed Systems. 10.1109/TPDS.2021.3073970. 32:11. (2748-2763).

    https://ieeexplore.ieee.org/document/9408386/

  • Theodorakis G, Kounelis F, Pietzuch P and Pirk H. (2022). Scabbard. Proceedings of the VLDB Endowment. 15:2. (361-374). Online publication date: 1-Oct-2021.

    https://doi.org/10.14778/3489496.3489515

  • Zhang F, Zhang C, Yang L, Zhang S, He B, Lu W and Du X. (2021). Fine-Grained Multi-Query Stream Processing on Integrated Architectures. IEEE Transactions on Parallel and Distributed Systems. 32:9. (2303-2320). Online publication date: 1-Sep-2021.

    https://doi.org/10.1109/TPDS.2021.3066407

  • Wang D, Zhang F, Wan W, Li H and Du X. (2021). FineQuery: Fine-Grained Query Processing on CPU-GPU Integrated Architectures 2021 IEEE International Conference on Cluster Computing (CLUSTER). 10.1109/Cluster48925.2021.00020. 978-1-7281-9666-4. (355-365).

    https://ieeexplore.ieee.org/document/9556045/

  • Lee S, Jeong Y, Kim M and Park S. (2021). Q-Spark: QoS Aware Micro-batch Stream Processing System Using Spark 2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C). 10.1109/ACSOS-C52956.2021.00027. 978-1-6654-4393-7. (38-43).

    https://ieeexplore.ieee.org/document/9599219/

  • Geethakumari P and Sourdis I. (2021). A Specialized Memory Hierarchy for Stream Aggregation 2021 31st International Conference on Field-Programmable Logic and Applications (FPL). 10.1109/FPL53798.2021.00041. 978-1-6654-3759-2. (204-210).

    https://ieeexplore.ieee.org/document/9556485/

  • Michas G, Chrysogelos P, Mytilinis I and Ailamaki A. Hardware-Conscious Sliding Window Aggregation on GPUs. Proceedings of the 17th International Workshop on Data Management on New Hardware. (1-5).

    https://doi.org/10.1145/3465998.3466014

  • Stein C, Rockenbach D, Griebler D, Torquati M, Mencagli G, Danelutto M and Fernandes L. (2020). Latency‐aware adaptive micro‐batching techniques for streamed data compression on graphics processing units. Concurrency and Computation: Practice and Experience. 10.1002/cpe.5786. 33:11. Online publication date: 10-Jun-2021.

    https://onlinelibrary.wiley.com/doi/10.1002/cpe.5786

  • Farhat O, Daudjee K and Querzoni L. Klink: Progress-Aware Scheduling for Streaming Data Systems. Proceedings of the 2021 International Conference on Management of Data. (485-498).

    https://doi.org/10.1145/3448016.3452794

  • Zhang S, Mao Y, He J, Grulich P, Zeuch S, He B, Ma R and Markl V. Parallelizing Intra-Window Join on Multicores. Proceedings of the 2021 International Conference on Management of Data. (2089-2101).

    https://doi.org/10.1145/3448016.3452793

  • Katsipoulakis N, Labrinidis A and Chrysanthis P. (2021). Improving Stream Load Balance through Shedding 2021 IEEE 37th International Conference on Data Engineering Workshops (ICDEW). 10.1109/ICDEW53142.2021.00028. 978-1-6654-4890-1. (120-126).

    https://ieeexplore.ieee.org/document/9438748/

  • Lee S and Park S. (2021). Performance Analysis of Big Data ETL Process over CPU-GPU Heterogeneous Architectures 2021 IEEE 37th International Conference on Data Engineering Workshops (ICDEW). 10.1109/ICDEW53142.2021.00015. 978-1-6654-4890-1. (42-47).

    https://ieeexplore.ieee.org/document/9438792/

  • Zhang F, Pan Z, Zhou Y, Zhai J, Shen X, Mutlu O and Du X. (2021). G-TADOC: Enabling Efficient GPU-Based Text Analytics without Decompression 2021 IEEE 37th International Conference on Data Engineering (ICDE). 10.1109/ICDE51399.2021.00148. 978-1-7281-9184-3. (1679-1690).

    https://ieeexplore.ieee.org/document/9458752/

  • Sahal R, Alsamhi S, Breslin J and Ali M. (2021). Industry 4.0 towards Forestry 4.0: Fire Detection Use Case. Sensors. 10.3390/s21030694. 21:3. (694).

    https://www.mdpi.com/1424-8220/21/3/694

  • E. Venugopal V, Theobald M, Chaychi S and Tawakuli A. (2020). AIR: A Light-Weight Yet High-Performance Dataflow Engine based on Asynchronous Iterative Routing 2020 IEEE 32nd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD). 10.1109/SBAC-PAD49847.2020.00018. 978-1-7281-9924-5. (51-58).

    https://ieeexplore.ieee.org/document/9235069/

  • Winter C, Schmidt T, Neumann T and Kemper A. (2020). Meet me halfway. Proceedings of the VLDB Endowment. 13:12. (2620-2633). Online publication date: 1-Aug-2020.

    https://doi.org/10.14778/3407790.3407849

  • Theodorakis G, Koliousis A, Pietzuch P and Pirk H. LightSaber: Efficient Window Aggregation on Multi-core Processors. Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. (2505-2521).

    https://doi.org/10.1145/3318464.3389753

  • Carbone P, Fragkoulis M, Kalavri V and Katsifodimos A. Beyond Analytics: The Evolution of Stream Processing Systems. Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. (2651-2658).

    https://doi.org/10.1145/3318464.3383131

  • Li B, Zhang Z, Zheng T, Zhong Q, Huang Q and Cheng X. (2020). Marabunta: Continuous Distributed Processing of Skewed Streams 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID). 10.1109/CCGrid49817.2020.00-68. 978-1-7281-6095-5. (252-261).

    https://ieeexplore.ieee.org/document/9139648/

  • Zhang S, Wu Y, Zhang F and He B. (2020). Towards Concurrent Stateful Stream Processing on Multicore Processors 2020 IEEE 36th International Conference on Data Engineering (ICDE). 10.1109/ICDE48307.2020.00136. 978-1-7281-2903-7. (1537-1548).

    https://ieeexplore.ieee.org/document/9101749/

  • Katsipoulakis N, Labrinidis A and Chrysanthis P. (2020). SPEAr: Expediting Stream Processing with Accuracy Guarantees 2020 IEEE 36th International Conference on Data Engineering (ICDE). 10.1109/ICDE48307.2020.00100. 978-1-7281-2903-7. (1105-1116).

    https://ieeexplore.ieee.org/document/9101830/

  • Röger H and Mayer R. (2019). A Comprehensive Survey on Parallelization and Elasticity in Stream Processing. ACM Computing Surveys. 52:2. (1-37). Online publication date: 31-Mar-2020.

    https://doi.org/10.1145/3303849

  • Kotselidis C, Diamantopoulos S, Akrivopoulos O, Rosenfeld V, Doka K, Mohammed H, Mylonas G, Spitadakis V and Morgan W. Efficient compilation and execution of JVM-based data processing frameworks on heterogeneous co-processors. Proceedings of the 23rd Conference on Design, Automation and Test in Europe. (175-179).

    /doi/10.5555/3408352.3408392

  • Kotselidis C, Diamantopoulos S, Akrivopoulos O, Rosenfeld V, Doka K, Mohammed H, Mylonas G, Spitadakis V and Morgan W. (2020). Efficient Compilation and Execution of JVM-Based Data Processing Frameworks on Heterogeneous Co-Processors 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE). 10.23919/DATE48585.2020.9116246. 978-3-9819263-4-7. (175-179).

    https://ieeexplore.ieee.org/document/9116246/

  • Zhang S, Zhang F, Wu Y, He B and Johns P. (2020). Hardware-Conscious Stream Processing. ACM SIGMOD Record. 48:4. (18-29). Online publication date: 25-Feb-2020.

    https://doi.org/10.1145/3385658.3385662

  • HoseinyFarahabady M, Jannesari A, Bao W, Tari Z and Zomaya A. (2019). Real-Time Stream Data Processing at Scale 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). 10.1109/PDCAT46702.2019.00020. 978-1-7281-2616-6. (46-51).

    https://ieeexplore.ieee.org/document/9029137/

  • Ramakrishnan Geethakumari P, Gulisano V, Trancoso P and Sourdis I. (2019). Time-SWAD: A Dataflow Engine for Time-Based Single Window Stream Aggregation 2019 International Conference on Field-Programmable Technology (ICFPT). 10.1109/ICFPT47387.2019.00017. 978-1-7281-2943-3. (72-80).

    https://ieeexplore.ieee.org/document/8977927/

  • Fu X, Ghaffar T, Davis J and Lee D. Edgewise. Proceedings of the 2019 USENIX Conference on Usenix Annual Technical Conference. (929-945).

    /doi/10.5555/3358807.3358887

  • Park H, Zhai S, Lu L and Lin F. Streambox-TZ. Proceedings of the 2019 USENIX Conference on Usenix Annual Technical Conference. (537-554).

    /doi/10.5555/3358807.3358853

  • Körber M, Eckstein J, Glombiewski N and Seeger B. Event Stream Processing on Heterogeneous System Architecture. Proceedings of the 15th International Workshop on Data Management on New Hardware. (1-10).

    https://doi.org/10.1145/3329785.3329933

  • Singhal R, Zhang N, Nardi L, Shahbaz M and Olukotun K. (2019). Polystore++: Accelerated Polystore System for Heterogeneous Workloads 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). 10.1109/ICDCS.2019.00163. 978-1-7281-2519-0. (1641-1651).

    https://ieeexplore.ieee.org/document/8885059/

  • Zhang S, He J, Zhou A and He B. BriskStream. Proceedings of the 2019 International Conference on Management of Data. (705-722).

    https://doi.org/10.1145/3299869.3300067

  • Russo G, Cardellini V and Presti F. Reinforcement Learning Based Policies for Elastic Stream Processing on Heterogeneous Resources. Proceedings of the 13th ACM International Conference on Distributed and Event-based Systems. (31-42).

    https://doi.org/10.1145/3328905.3329506

  • Rockenbach D, Stein C, Griebler D, Mencagli G, Torquati M, Danelutto M and Fernandes L. (2019). Stream Processing on Multi-cores with GPUs: Parallel Programming Models' Challenges 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). 10.1109/IPDPSW.2019.00137. 978-1-7281-3510-6. (834-841).

    https://ieeexplore.ieee.org/document/8778359/

  • Miao H, Jeon M, Pekhimenko G, McKinley K and Lin F. StreamBox-HBM. Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems. (167-181).

    https://doi.org/10.1145/3297858.3304031

  • Zeuch S, Monte B, Karimov J, Lutz C, Renz M, Traub J, Breß S, Rabl T and Markl V. (2019). Analyzing efficient stream processing on modern hardware. Proceedings of the VLDB Endowment. 12:5. (516-530). Online publication date: 1-Jan-2019.

    https://doi.org/10.14778/3303753.3303758

  • Mencagli G, Torquati M, Griebler D, Danelutto M and Fernandes L. Raising the Parallel Abstraction Level for Streaming Analytics Applications. IEEE Access. 10.1109/ACCESS.2019.2941183. 7. (131944-131961).

    https://ieeexplore.ieee.org/document/8834783/

  • De Matteis T, Mencagli G, De Sensi D, Torquati M and Danelutto M. GASSER: An Auto-Tunable System for General Sliding-Window Streaming Operators on GPUs. IEEE Access. 10.1109/ACCESS.2019.2910312. 7. (48753-48769).

    https://ieeexplore.ieee.org/document/8688411/

  • Jeon Y, Lee K and Kim H. Distributed Join Processing Between Streaming and Stored Big Data Under the Micro-Batch Model. IEEE Access. 10.1109/ACCESS.2019.2904730. 7. (34583-34598).

    https://ieeexplore.ieee.org/document/8666990/

  • Gulisano V, Papatriantafilou M and Papadopoulos A. (2019). Elasticity. Encyclopedia of Big Data Technologies. 10.1007/978-3-319-77525-8_191. (693-699).

    http://link.springer.com/10.1007/978-3-319-77525-8_191

  • Segarra C, Delgado-Gonzalo R, Lemay M, Aublin P, Pietzuch P and Schiavoni V. (2019). Using Trusted Execution Environments for Secure Stream Processing of Medical Data. Distributed Applications and Interoperable Systems. 10.1007/978-3-030-22496-7_6. (91-107).

    https://link.springer.com/10.1007/978-3-030-22496-7_6

  • Katsipoulakis N, Labrinidis A and Chrysanthis P. (2018). Concept-Driven Load Shedding: Reducing Size and Error of Voluminous and Variable Data Streams 2018 IEEE International Conference on Big Data (Big Data). 10.1109/BigData.2018.8622265. 978-1-5386-5035-6. (418-427).

    https://ieeexplore.ieee.org/document/8622265/

  • Traub J, Grulich P, Rodriguez Cuellar A, Bress S, Katsifodimos A, Rabl T and Markl V. (2018). Scotty: Efficient Window Aggregation for Out-of-Order Stream Processing 2018 IEEE 34th International Conference on Data Engineering (ICDE). 10.1109/ICDE.2018.00135. 978-1-5386-5520-7. (1300-1303).

    https://ieeexplore.ieee.org/document/8509356/

  • Walulya I, Nikolakopoulos Y, Gulisano V, Papatriantafilou M and Tsigas P. (2018). Viper: Communication-Layer Determinism and Scaling in Low-Latency Stream Processing. Euro-Par 2017: Parallel Processing Workshops. 10.1007/978-3-319-75178-8_11. (129-140).

    https://link.springer.com/10.1007/978-3-319-75178-8_11

  • Gulisano V, Papatriantafilou M and Papadopoulos A. (2018). Elasticity. Encyclopedia of Big Data Technologies. 10.1007/978-3-319-63962-8_191-1. (1-7).

    http://link.springer.com/10.1007/978-3-319-63962-8_191-1

  • Mayer R, Slo A, Tariq M, Rothermel K, Gräber M and Ramachandran U. SPECTRE. Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference. (161-173).

    https://doi.org/10.1145/3135974.3135983

  • Dell’Aglio D, Della Valle E, van Harmelen F, Bernstein A and Kuhn T. Stream reasoning: A survey and outlook. Data Science. 10.3233/DS-170006. 1:1-2. (59-83).

    https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/DS-170006

  • Affetti L, Tommasini R, Margara A, Cugola G and Della Valle E. (2017). Defining the execution semantics of stream processing engines. Journal of Big Data. 10.1186/s40537-017-0072-9. 4:1. Online publication date: 1-Dec-2017.

    http://journalofbigdata.springeropen.com/articles/10.1186/s40537-017-0072-9

  • Geethakumari P, Gulisano V, Svensson B, Trancoso P and Sourdis I. (2017). Single window stream aggregation using reconfigurable hardware 2017 International Conference on Field Programmable Technology (ICFPT). 10.1109/FPT.2017.8280128. 978-1-5386-2656-6. (112-119).

    http://ieeexplore.ieee.org/document/8280128/

  • Katsipoulakis N, Labrinidis A and Chrysanthis P. (2017). A holistic view of stream partitioning costs. Proceedings of the VLDB Endowment. 10:11. (1286-1297). Online publication date: 1-Aug-2017.

    https://doi.org/10.14778/3137628.3137639

  • Mayer R, Tariq M and Rothermel K. Minimizing Communication Overhead in Window-Based Parallel Complex Event Processing. Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems. (54-65).

    https://doi.org/10.1145/3093742.3093914

  • Li S, Amin M, Ganti R, Srivatsa M, Hu S, Zhao Y and Abdelzaher T. (2017). Stark: Optimizing In-Memory Computing for Dynamic Dataset Collections 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). 10.1109/ICDCS.2017.143. 978-1-5386-1792-2. (103-114).

    http://ieeexplore.ieee.org/document/7979959/

  • Nikolic M, Chandramouli B and Goldstein J. Enabling Signal Processing over Data Streams. Proceedings of the 2017 ACM International Conference on Management of Data. (95-108).

    https://doi.org/10.1145/3035918.3035935

  • Ogden P, Thomas D and Pietzuch P. AT-GIS. Proceedings of the 2016 International Conference on Management of Data. (1041-1054).

    https://doi.org/10.1145/2882903.2882962

  • Koliousis A, Weidlich M, Fernandez R, Wolf A, Costa P and Pietzuch P. The SABER system for window-based hybrid stream processing with GPGPUs. Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems. (354-357).

    https://doi.org/10.1145/2933267.2933291