{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:53:55Z","timestamp":1774313635012,"version":"3.50.1"},"reference-count":76,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2021,3,27]],"date-time":"2021-03-27T00:00:00Z","timestamp":1616803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Database Syst."],"published-print":{"date-parts":[[2021,3,31]]},"abstract":"<jats:p>\n            Window aggregation is a core operation in data stream processing. Existing aggregation techniques focus on reducing latency, eliminating redundant computations, or minimizing memory usage. However, each technique operates under different assumptions with respect to workload characteristics, such as properties of aggregation functions (e.g., invertible, associative), window types (e.g., sliding, sessions), windowing measures (e.g., time- or count-based), and stream (dis)order. In this article, we present\n            <jats:italic>Scotty<\/jats:italic>\n            , an efficient and general open-source operator for sliding-window aggregation in stream processing systems, such as Apache Flink, Apache Beam, Apache Samza, Apache Kafka, Apache Spark, and Apache Storm. One can easily extend Scotty with user-defined aggregation functions and window types. Scotty implements the concept of general stream slicing and derives workload characteristics from aggregation queries to improve performance without sacrificing its general applicability. We provide an in-depth view on the algorithms of the general stream slicing approach. Our experiments show that Scotty outperforms alternative solutions.\n          <\/jats:p>","DOI":"10.1145\/3433675","type":"journal-article","created":{"date-parts":[[2021,3,27]],"date-time":"2021-03-27T16:06:37Z","timestamp":1616861197000},"page":"1-46","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Scotty"],"prefix":"10.1145","volume":"46","author":[{"given":"Jonas","family":"Traub","sequence":"first","affiliation":[{"name":"Technische Universit\u00e4t Berlin, Germany"}]},{"given":"Philipp Marian","family":"Grulich","sequence":"additional","affiliation":[{"name":"Technische Universit\u00e4t Berlin, Germany"}]},{"given":"Alejandro Rodr\u00edguez","family":"Cu\u00e9llar","sequence":"additional","affiliation":[{"name":"Gal\u00e1pago Agroconsultores S.A.S., Bicaramanga, Colombia"}]},{"given":"Sebastian","family":"Bre\u00df","sequence":"additional","affiliation":[{"name":"Technische Universit\u00e4t Berlin, Germany"}]},{"given":"Asterios","family":"Katsifodimos","sequence":"additional","affiliation":[{"name":"Delft University of Technology, Netherlands"}]},{"given":"Tilmann","family":"Rabl","sequence":"additional","affiliation":[{"name":"HPI, University of Potsdam, Germany"}]},{"given":"Volker","family":"Markl","sequence":"additional","affiliation":[{"name":"Technische Universit\u00e4t Berlin &amp; DFKI, Berlin, Germany"}]}],"member":"320","published-online":{"date-parts":[[2021,3,27]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.14778\/2824032.2824076"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-014-0357-y"},{"key":"e_1_2_1_3_1","unstructured":"Apache Apex. 2018. Enterprise-grade unified stream and batch processing engine. Retrieved from https:\/\/apex.apache.org\/.  Apache Apex. 2018. Enterprise-grade unified stream and batch processing engine. Retrieved from https:\/\/apex.apache.org\/."},{"key":"e_1_2_1_4_1","unstructured":"Apache Beam. 2018. An advanced unified programming model. Retrieved from https:\/\/beam.apache.org\/.  Apache Beam. 2018. An advanced unified programming model. Retrieved from https:\/\/beam.apache.org\/."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-012088469-8.50032-2"},{"key":"e_1_2_1_6_1","volume-title":"et\u00a0al","author":"Armbrust Michael","year":"2018","unstructured":"Michael Armbrust , Tathagata Das , Joseph Torres , Burak Yavuz , Shixiong Zhu , Reynold Xin , et\u00a0al . 2018 . Structured streaming: A declarative API for real-time applications in Apache Spark. In Proceedings of the ACM Special Interest Group on Management of Data (SIGMOD\u201918). 601--613. Michael Armbrust, Tathagata Das, Joseph Torres, Burak Yavuz, Shixiong Zhu, Reynold Xin, et\u00a0al. 2018. Structured streaming: A declarative API for real-time applications in Apache Spark. In Proceedings of the ACM Special Interest Group on Management of Data (SIGMOD\u201918). 601--613."},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the International Conference on Extending Database Technology (EDBT\u201919)","author":"Awad Ahmed","year":"2019","unstructured":"Ahmed Awad , Jonas Traub , and Sherif Sakr . 2019 . Adaptive watermarks: A concept drift-based approach for predicting event-time progress in data streams . In Proceedings of the International Conference on Extending Database Technology (EDBT\u201919) . Ahmed Awad, Jonas Traub, and Sherif Sakr. 2019. Adaptive watermarks: A concept drift-based approach for predicting event-time progress in data streams. In Proceedings of the International Conference on Extending Database Technology (EDBT\u201919)."},{"key":"e_1_2_1_8_1","volume-title":"Proceedings of the International Workshop on Data Management for Sensor Networks (DMSN\u201911)","author":"Balkesen Cagri","year":"2011","unstructured":"Cagri Balkesen and Nesime Tatbul . 2011 . Scalable data partitioning techniques for parallel sliding window processing over data streams . In Proceedings of the International Workshop on Data Management for Sensor Networks (DMSN\u201911) . Cagri Balkesen and Nesime Tatbul. 2011. Scalable data partitioning techniques for parallel sliding window processing over data streams. In Proceedings of the International Workshop on Data Management for Sensor Networks (DMSN\u201911)."},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the International Conference on Extending Database Technology (EDBT\u201920)","author":"Benson Lawrence","year":"2020","unstructured":"Lawrence Benson , Philipp M. Grulich , Steffen Zeuch , Volker Markl , and Tilmann Rabl . 2020 . Disco: Efficient distributed window aggregation . In Proceedings of the International Conference on Extending Database Technology (EDBT\u201920) . Lawrence Benson, Philipp M. Grulich, Steffen Zeuch, Volker Markl, and Tilmann Rabl. 2020. Disco: Efficient distributed window aggregation. In Proceedings of the International Conference on Extending Database Technology (EDBT\u201920)."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2663165.2663334"},{"key":"e_1_2_1_11_1","unstructured":"Brice Bingman. 2018. Poor performance with sliding time windows. In Flink Jira Issues. Retrieved from issues.apache.org\/jira\/browse\/FLINK-6990.  Brice Bingman. 2018. Poor performance with sliding time windows. In Flink Jira Issues. Retrieved from issues.apache.org\/jira\/browse\/FLINK-6990."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920874"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.14778\/3137765.3137777"},{"key":"e_1_2_1_15_1","unstructured":"Paris Carbone Gyula F\u00f3ra Stephan Ewen Seif Haridi and Kostas Tzoumas. 2015. Lightweight asynchronous snapshots for distributed dataflows. Retrieved from https:\/\/arxiv.org\/abs\/1506.08603.  Paris Carbone Gyula F\u00f3ra Stephan Ewen Seif Haridi and Kostas Tzoumas. 2015. Lightweight asynchronous snapshots for distributed dataflows. Retrieved from https:\/\/arxiv.org\/abs\/1506.08603."},{"key":"e_1_2_1_16_1","first-page":"28","article-title":"Apache Flink: Stream and batch processing in a single engine","volume":"38","author":"Carbone Paris","year":"2015","unstructured":"Paris Carbone , Asterios Katsifodimos , Stephan Ewen , Volker Markl , Seif Haridi , and Kostas Tzoumas . 2015 . Apache Flink: Stream and batch processing in a single engine . IEEE Data Eng. Bull. 38 , 4 (2015), 28 -- 38 . Paris Carbone, Asterios Katsifodimos, Stephan Ewen, Volker Markl, Seif Haridi, and Kostas Tzoumas. 2015. Apache Flink: Stream and batch processing in a single engine. IEEE Data Eng. Bull. 38, 4 (2015), 28--38.","journal-title":"IEEE Data Eng. Bull."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2983323.2983807"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.14778\/2735496.2735503"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPSW.2016.138"},{"key":"e_1_2_1_20_1","volume-title":"Passive and Active Network Measurement","author":"Dimitropoulos Xenofontas","unstructured":"Xenofontas Dimitropoulos , Paul Hurley , Andreas Kind , and Marc Ph Stoecklin . 2009. On the 95-percentile billing method . In Passive and Active Network Measurement . Springer , 207--216. Xenofontas Dimitropoulos, Paul Hurley, Andreas Kind, and Marc Ph Stoecklin. 2009. On the 95-percentile billing method. In Passive and Active Network Measurement. Springer, 207--216."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/spe.2194"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/1191547.1191744"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009726021843"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2933267.2933304"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389739"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3328905.3332511"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2063576.2063793"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2012.112"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2019.00138"},{"key":"e_1_2_1_30_1","volume-title":"SPL stream processing language specification. IBM Res. Report","author":"Hirzel Martin","year":"2009","unstructured":"Martin Hirzel , Henrique Andrade , Bu\u011fra Gedik , Vibhore Kumar , Giuliano Losa , Howard Nasgaard , Robert Soul\u00e9 , and Kun-Lung Wu. 2009. SPL stream processing language specification. IBM Res. Report ( 2009 ). http:\/\/cs.yale.edu\/homes\/soule\/pubs\/rc24897.pdf. Martin Hirzel, Henrique Andrade, Bu\u011fra Gedik, Vibhore Kumar, Giuliano Losa, Howard Nasgaard, Robert Soul\u00e9, and Kun-Lung Wu. 2009. SPL stream processing language specification. IBM Res. Report (2009). http:\/\/cs.yale.edu\/homes\/soule\/pubs\/rc24897.pdf."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3093742.3095107"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/2528412"},{"key":"e_1_2_1_33_1","first-page":"1579","article-title":"Service adoption and pricing of content delivery network (CDN) services. Manage","volume":"54","author":"Hosanagar Kartik","year":"2008","unstructured":"Kartik Hosanagar , John Chuang , Ramayya Krishnan , and Michael D. Smith . 2008 . Service adoption and pricing of content delivery network (CDN) services. Manage . Sci. 54 , 9 (2008), 1579 -- 1593 . Kartik Hosanagar, John Chuang, Ramayya Krishnan, and Michael D. Smith. 2008. Service adoption and pricing of content delivery network (CDN) services. Manage. Sci. 54, 9 (2008), 1579--1593.","journal-title":"Sci."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/1247480.1247535"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415479"},{"key":"e_1_2_1_36_1","volume-title":"The DEBS 2012 grand challenge. In Proceedings of the ACM International Conference on Distributed and Event-based Systems (DEBS\u201912)","author":"Jerzak Zbigniew","year":"2012","unstructured":"Zbigniew Jerzak , Thomas Heinze , Matthias Fehr , Daniel Gr\u00f6ber , Raik Hartung , and Nenad Stojanovic . 2012 . The DEBS 2012 grand challenge. In Proceedings of the ACM International Conference on Distributed and Event-based Systems (DEBS\u201912) . 393--398. Zbigniew Jerzak, Thomas Heinze, Matthias Fehr, Daniel Gr\u00f6ber, Raik Hartung, and Nenad Stojanovic. 2012. The DEBS 2012 grand challenge. In Proceedings of the ACM International Conference on Distributed and Event-based Systems (DEBS\u201912). 393--398."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732951.2732953"},{"key":"e_1_2_1_38_1","volume-title":"IEEE Proceedings of the IEEE International Conference on Data Engineering (ICDE\u201918)","author":"Karimov Jeyhun","year":"2018","unstructured":"Jeyhun Karimov , Tilmann Rabl , Asterios Katsifodimos , Roman Samarev , Henri Heiskanen , and Volker Markl . 2018 . Benchmarking distributed stream processing engines . In IEEE Proceedings of the IEEE International Conference on Data Engineering (ICDE\u201918) . Jeyhun Karimov, Tilmann Rabl, Asterios Katsifodimos, Roman Samarev, Henri Heiskanen, and Volker Markl. 2018. Benchmarking distributed stream processing engines. In IEEE Proceedings of the IEEE International Conference on Data Engineering (ICDE\u201918)."},{"key":"e_1_2_1_39_1","volume-title":"Alexander L. Wolf, Paolo Costa, and Peter Pietzuch.","author":"Koliousis Alexandros","year":"2016","unstructured":"Alexandros Koliousis , Matthias Weidlich , Raul Castro Fernandez , Alexander L. Wolf, Paolo Costa, and Peter Pietzuch. 2016 . SABER : Window-based hybrid stream processing for heterogeneous architectures. In Proceedings of the ACM Special Interest Group on Management of Data (SIGMOD\u201916). 555--569. Alexandros Koliousis, Matthias Weidlich, Raul Castro Fernandez, Alexander L. Wolf, Paolo Costa, and Peter Pietzuch. 2016. SABER: Window-based hybrid stream processing for heterogeneous architectures. In Proceedings of the ACM Special Interest Group on Management of Data (SIGMOD\u201916). 555--569."},{"key":"e_1_2_1_40_1","unstructured":"Jay Kreps. 2016. Introducing Kafka streams: Stream processing made simple. Confluent Blog. Retrieved from https:\/\/www.confluent.io\/blog\/introducing-kafka-streams-stream-processing-made-simple\/.  Jay Kreps. 2016. Introducing Kafka streams: Stream processing made simple. Confluent Blog. Retrieved from https:\/\/www.confluent.io\/blog\/introducing-kafka-streams-stream-processing-made-simple\/."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/1807167.1807290"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/1142473.1142543"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/1058150.1058158"},{"key":"e_1_2_1_44_1","volume-title":"Tucker","author":"Li Jin","year":"2005","unstructured":"Jin Li , David Maier , Kristin Tufte , Vassilis Papadimos , and Peter A . Tucker . 2005 . Semantics and evaluation techniques for window aggregates in data streams. In Proceedings of the ACM Special Interest Group on Management of Data (SIGMOD\u201905). 311--322. Jin Li, David Maier, Kristin Tufte, Vassilis Papadimos, and Peter A. Tucker. 2005. Semantics and evaluation techniques for window aggregates in data streams. In Proceedings of the ACM Special Interest Group on Management of Data (SIGMOD\u201905). 311--322."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIC.2008.116"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.14778\/1453856.1453890"},{"key":"e_1_2_1_47_1","volume-title":"The DEBS 2013 grand challenge. In Proceedings of the ACM International Conference on Distributed and Event-based Systems (DEBS\u201913)","author":"Mutschler Christopher","year":"2013","unstructured":"Christopher Mutschler , Holger Ziekow , and Zbigniew Jerzak . 2013 . The DEBS 2013 grand challenge. In Proceedings of the ACM International Conference on Distributed and Event-based Systems (DEBS\u201913) . 289--294. Christopher Mutschler, Holger Ziekow, and Zbigniew Jerzak. 2013. The DEBS 2013 grand challenge. In Proceedings of the ACM International Conference on Distributed and Event-based Systems (DEBS\u201913). 289--294."},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.14778\/3137765.3137770"},{"key":"e_1_2_1_49_1","unstructured":"OpenJDK. 2018. JMH benchmarking suite project website. Retrieved from http:\/\/openjdk.java.net\/projects\/code-tools\/jmh\/.  OpenJDK. 2018. JMH benchmarking suite project website. Retrieved from http:\/\/openjdk.java.net\/projects\/code-tools\/jmh\/."},{"key":"e_1_2_1_50_1","unstructured":"OpenJDK. 2018. Nashorn project ObjectSizeCalculator. Retrieved from http:\/\/openjdk.java.net\/projects\/nashorn\/.  OpenJDK. 2018. Nashorn project ObjectSizeCalculator. Retrieved from http:\/\/openjdk.java.net\/projects\/nashorn\/."},{"key":"e_1_2_1_51_1","volume-title":"Proceedings of the International Conference on Extending Database Technology (EDBT\u201906)","author":"\u00a0al Kostas Patroumpas","year":"2006","unstructured":"Kostas Patroumpas et \u00a0al . 2006 . Window specification over data streams . In Proceedings of the International Conference on Extending Database Technology (EDBT\u201906) . Kostas Patroumpas et\u00a0al. 2006. Window specification over data streams. In Proceedings of the International Conference on Extending Database Technology (EDBT\u201906)."},{"key":"e_1_2_1_52_1","volume-title":"Variable-length Codes for Data Compression","author":"Salomon David","unstructured":"David Salomon . 2007. Variable-length Codes for Data Compression . Springer . David Salomon. 2007. Variable-length Codes for Data Compression. Springer."},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806450"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3085504.3085509"},{"key":"e_1_2_1_55_1","volume-title":"Proceedings of the International Conference on Extending Database Technology (EDBT\u201918)","author":"Shein Anatoli U.","year":"2018","unstructured":"Anatoli U. Shein , Panos K. Chrysanthis , and Alexandros Labrinidis . 2018 . SlickDeque: High throughput and low latency incremental sliding-window aggregation . In Proceedings of the International Conference on Extending Database Technology (EDBT\u201918) . Anatoli U. Shein, Panos K. Chrysanthis, and Alexandros Labrinidis. 2018. SlickDeque: High throughput and low latency incremental sliding-window aggregation. In Proceedings of the International Conference on Extending Database Technology (EDBT\u201918)."},{"key":"e_1_2_1_56_1","unstructured":"Leo Syinchwun. 2016. Lightweight event time window. In Flink Jira Issues. Retrieved from issues.apache.org\/jira\/browse\/FLINK-5387.  Leo Syinchwun. 2016. Lightweight event time window. In Flink Jira Issues. Retrieved from issues.apache.org\/jira\/browse\/FLINK-5387."},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3093742.3093925"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.14778\/3339490.3339499"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.14778\/2752939.2752940"},{"key":"e_1_2_1_60_1","unstructured":"Joseph Torres Michael Armbrust Tathagata Das and Shixiong Zhu. 2018. Introducing low-latency continuous processing mode in structured streaming in Apache Spark 2.3. Databricks Blog. Retrieved from https:\/\/databricks.com\/blog\/2018\/03\/20\/low-latency-continuous-processing-mode-in-structured-streaming-in-apache-spark-2-3-0.html.  Joseph Torres Michael Armbrust Tathagata Das and Shixiong Zhu. 2018. Introducing low-latency continuous processing mode in structured streaming in Apache Spark 2.3. Databricks Blog. Retrieved from https:\/\/databricks.com\/blog\/2018\/03\/20\/low-latency-continuous-processing-mode-in-structured-streaming-in-apache-spark-2-3-0.html."},{"key":"e_1_2_1_61_1","volume-title":"et\u00a0al","author":"Toshniwal Ankit","year":"2014","unstructured":"Ankit Toshniwal , Siddarth Taneja , Amit Shukla , Karthik Ramasamy , Jignesh M. Patel , Sanjeev Kulkarni , Jason Jackson , Krishna Gade , Maosong Fu , Jake Donham , et\u00a0al . 2014 . Storm@twitter. In Proceedings of the ACM Special Interest Group on Management of Data (SIGMOD\u201914). 147--156. Ankit Toshniwal, Siddarth Taneja, Amit Shukla, Karthik Ramasamy, Jignesh M. Patel, Sanjeev Kulkarni, Jason Jackson, Krishna Gade, Maosong Fu, Jake Donham, et\u00a0al. 2014. Storm@twitter. In Proceedings of the ACM Special Interest Group on Management of Data (SIGMOD\u201914). 147--156."},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3127479.3131621"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2018.00135"},{"key":"e_1_2_1_65_1","volume-title":"Proceedings of the International Conference on Extending Database Technology (EDBT\u201917)","author":"Traub Jonas","year":"2019","unstructured":"Jonas Traub , Philipp M. Grulich , Alejandro Rodriguez Cuellar , Sebastian Bre\u00df , Asterios Katsifodimos , Tilmann Rabl , and Volker Markl . 2019 . Efficient window aggregation with general stream slicing . In Proceedings of the International Conference on Extending Database Technology (EDBT\u201917) . Jonas Traub, Philipp M. Grulich, Alejandro Rodriguez Cuellar, Sebastian Bre\u00df, Asterios Katsifodimos, Tilmann Rabl, and Volker Markl. 2019. Efficient window aggregation with general stream slicing. In Proceedings of the International Conference on Extending Database Technology (EDBT\u201917)."},{"key":"e_1_2_1_66_1","volume-title":"SENSE: Scalable data acquisition from distributed sensors with guaranteed time coherence.","author":"Traub Jonas","year":"2019","unstructured":"Jonas Traub , Julius H\u00fclsmann , Sebastian Bre\u00df , Tilmann Rabl , and Volker Markl . 2019 . SENSE: Scalable data acquisition from distributed sensors with guaranteed time coherence. Retrieved from https:\/\/arxiv.org\/abs\/1912.04648. Jonas Traub, Julius H\u00fclsmann, Sebastian Bre\u00df, Tilmann Rabl, and Volker Markl. 2019. SENSE: Scalable data acquisition from distributed sensors with guaranteed time coherence. Retrieved from https:\/\/arxiv.org\/abs\/1912.04648."},{"key":"e_1_2_1_67_1","volume-title":"Proceedings of the International Conference on Extending Database Technology (EDBT\u201917)","author":"Traub Jonas","year":"2017","unstructured":"Jonas Traub , Nikolaas Steenbergen , Philipp M. Grulich , Tilmann Rabl , and Volker Markl . 2017 . I2: Interactive real-time visualization for streaming data . In Proceedings of the International Conference on Extending Database Technology (EDBT\u201917) . 526--529. Jonas Traub, Nikolaas Steenbergen, Philipp M. Grulich, Tilmann Rabl, and Volker Markl. 2017. I2: Interactive real-time visualization for streaming data. In Proceedings of the International Conference on Extending Database Technology (EDBT\u201917). 526--529."},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2003.1198390"},{"key":"e_1_2_1_69_1","unstructured":"Kostas Tzoumas et\u00a0al. 2015. High-throughput low-latency and exactly-once stream processing with Apache Flink. Retrieved from data-artisans.com\/blog\/high-throughput-low-latency-and-exactly-once-stream-processing-with-apache-flink.  Kostas Tzoumas et\u00a0al. 2015. High-throughput low-latency and exactly-once stream processing with Apache Flink. Retrieved from data-artisans.com\/blog\/high-throughput-low-latency-and-exactly-once-stream-processing-with-apache-flink."},{"key":"e_1_2_1_70_1","unstructured":"Mikhail Vorontsov. 2013. Memory consumption of popular Java data types - part 2. Java Performance Tuning Guide. Retrieved from http:\/\/java-performance.info\/memory-consumption-of-java-data-types-2\/.  Mikhail Vorontsov. 2013. Memory consumption of popular Java data types - part 2. Java Performance Tuning Guide. Retrieved from http:\/\/java-performance.info\/memory-consumption-of-java-data-types-2\/."},{"key":"e_1_2_1_71_1","unstructured":"Guozhang Wang. 2017. Enabling exactly-once in Kafka streams. Confluent Blog. Retrieved from https:\/\/www.confluent.io\/blog\/enabling-exactly-once-kafka-streams\/.  Guozhang Wang. 2017. Enabling exactly-once in Kafka streams. Confluent Blog. Retrieved from https:\/\/www.confluent.io\/blog\/enabling-exactly-once-kafka-streams\/."},{"key":"e_1_2_1_72_1","unstructured":"Jark Wu. 2017. Improve performance of sliding time window with pane optimization. In Flink Jira Issues. Retrieved from issues.apache.org\/jira\/browse\/FLINK-7001.  Jark Wu. 2017. Improve performance of sliding time window with pane optimization. In Flink Jira Issues. Retrieved from issues.apache.org\/jira\/browse\/FLINK-7001."},{"key":"e_1_2_1_73_1","volume-title":"Pradeep Kumar Gunda, and Michael Isard","author":"Yu Yuan","year":"2009","unstructured":"Yuan Yu , Pradeep Kumar Gunda, and Michael Isard . 2009 . Distributed aggregation for data-parallel computing: Interfaces and implementations. In Proceedings of the ACM Special Interest Group on Operating Systems (SIGOPS\u201909). 247--260. Yuan Yu, Pradeep Kumar Gunda, and Michael Isard. 2009. Distributed aggregation for data-parallel computing: Interfaces and implementations. In Proceedings of the ACM Special Interest Group on Operating Systems (SIGOPS\u201909). 247--260."},{"key":"e_1_2_1_74_1","volume-title":"Proceedings of the USENIX Conference on Hot Topics in Cloud Computing.","author":"Zaharia Matei","year":"2012","unstructured":"Matei Zaharia , Tathagata Das , Haoyuan Li , Scott Shenker , and Ion Stoica . 2012 . Discretized streams: An efficient and fault-tolerant model for stream processing on large clusters . In Proceedings of the USENIX Conference on Hot Topics in Cloud Computing. Matei Zaharia, Tathagata Das, Haoyuan Li, Scott Shenker, and Ion Stoica. 2012. Discretized streams: An efficient and fault-tolerant model for stream processing on large clusters. In Proceedings of the USENIX Conference on Hot Topics in Cloud Computing."},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1145\/2934664"},{"key":"e_1_2_1_76_1","volume-title":"Proceedings of the Conference on Innovative Data Systems Research (CIDR\u201920)","author":"Zeuch Steffen","year":"2020","unstructured":"Steffen Zeuch , Ankit Chaudhary , Bonaventura Del Monte , et\u00a0al. 2020 . The NebulaStream Platform: Data and application management for the Internet of Things . In Proceedings of the Conference on Innovative Data Systems Research (CIDR\u201920) . Steffen Zeuch, Ankit Chaudhary, Bonaventura Del Monte, et\u00a0al. 2020. The NebulaStream Platform: Data and application management for the Internet of Things. In Proceedings of the Conference on Innovative Data Systems Research (CIDR\u201920)."},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.14778\/3303753.3303758"},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1145\/3385658.3385662"}],"container-title":["ACM Transactions on Database Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3433675","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3433675","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:28:10Z","timestamp":1750195690000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3433675"}},"subtitle":["General and Efficient Open-source Window Aggregation for Stream Processing Systems"],"short-title":[],"issued":{"date-parts":[[2021,3,27]]},"references-count":76,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,3,31]]}},"alternative-id":["10.1145\/3433675"],"URL":"https:\/\/doi.org\/10.1145\/3433675","relation":{},"ISSN":["0362-5915","1557-4644"],"issn-type":[{"value":"0362-5915","type":"print"},{"value":"1557-4644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,27]]},"assertion":[{"value":"2020-03-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-11-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-03-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}