{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:43:39Z","timestamp":1774539819914,"version":"3.50.1"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T00:00:00Z","timestamp":1654732800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T00:00:00Z","timestamp":1654732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100006764","name":"Technische Universit\u00e4t Berlin","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100006764","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Datenbank Spektrum"],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Window aggregations and windowed joins are central operators of modern real-time analytic workloads and significantly impact the performance of stream processing systems.<\/jats:p><jats:p>This paper gives an overview of state-of-the-art research in this area conducted by the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and the Technische Universit\u00e4t Berlin. To this end, we present different algorithms for efficiently processing windowed operators and discuss techniques for distributed stream processing. Recently, several approaches have leveraged modern hardware for windowed stream processing, which we will also include in this overview. Additionally, we describe the integration of windowed operators into various stream processing systems and diverse applications that use specialized window operations.<\/jats:p>","DOI":"10.1007\/s13222-022-00417-y","type":"journal-article","created":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T13:03:50Z","timestamp":1654779830000},"page":"99-107","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Algorithms for Windowed Aggregations and Joins on Distributed Stream Processing Systems"],"prefix":"10.1007","volume":"22","author":[{"given":"Juliane","family":"Verwiebe","sequence":"first","affiliation":[]},{"given":"Philipp M.","family":"Grulich","sequence":"additional","affiliation":[]},{"given":"Jonas","family":"Traub","sequence":"additional","affiliation":[]},{"given":"Volker","family":"Markl","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,9]]},"reference":[{"issue":"2","key":"417_CR1","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/s00778-003-0095-z","volume":"12","author":"DJ Abadi","year":"2003","unstructured":"Abadi\u00a0DJ, Carney\u00a0D, Cetintemel\u00a0U, Cherniack\u00a0M, Convey\u00a0C, Lee\u00a0S, Stonebraker\u00a0M, Tatbul\u00a0N, Zdonik\u00a0S (2003) Aurora: a\u00a0new model and architecture for data stream management. VLDB\u00a0J 12(2):120\u2013139","journal-title":"VLDB\u00a0J"},{"issue":"2005","key":"417_CR2","first-page":"277","volume":"5","author":"DJ Abadi","year":"2005","unstructured":"Abadi\u00a0DJ, Ahmad\u00a0Y, Balazinska\u00a0M, Cetintemel\u00a0U, Cherniack\u00a0M, Hwang\u00a0J-H, Lindner\u00a0W, Maskey\u00a0A, Rasin\u00a0A, Ryvkina\u00a0E et\u00a0al (2005) The design of the borealis stream processing engine. CIDR 5(2005):277\u2013289","journal-title":"CIDR"},{"issue":"12","key":"417_CR3","first-page":"1792","volume":"8","author":"T Akidau","year":"2015","unstructured":"Akidau\u00a0T, Bradshaw\u00a0R, Chambers\u00a0C et\u00a0al (2015) The dataflow model: A\u00a0practical approach to balancing correctness, latency, and cost in massive-scale, unbounded, out-of-order data processing. PVLDB 8(12):1792\u20131803","journal-title":"PVLDB"},{"key":"417_CR4","volume-title":"ICDE","author":"M Ali","year":"2011","unstructured":"Ali M, Chandramouli B, Goldstein J et al (2011) The extensibility framework in microsoft streaminsight. In: ICDE (IEEE)"},{"key":"417_CR5","first-page":"336","volume":"4","author":"A Arasu","year":"2004","unstructured":"Arasu\u00a0A, Widom\u00a0J (2004) Resource sharing in continuous sliding-window aggregates. VLDB 4:336\u2013347","journal-title":"VLDB"},{"key":"417_CR6","volume-title":"BTW 2019","author":"A Bartnik","year":"2019","unstructured":"Bartnik A, Del Monte B, Rabl T et al (2019) On-the-fly reconfiguration of query plans for stateful stream processing engines. In: BTW 2019"},{"key":"417_CR7","volume-title":"CIDR","author":"L Benson","year":"2022","unstructured":"Benson L, Rabl T (2022) Darwin: Scale-in stream processing. In: CIDR"},{"key":"417_CR8","volume-title":"EDBT","author":"L Benson","year":"2020","unstructured":"Benson L, Grulich PM, Zeuch S et al (2020) Disco: Efficient distributed window aggregation. In: EDBT"},{"key":"417_CR9","volume-title":"SIAM SDM","author":"A Bifet","year":"2007","unstructured":"Bifet A, Gavald\u00e0 R (2007) Learning from time-changing data with adaptive windowing. In: SIAM SDM"},{"key":"417_CR10","volume-title":"Cbix: Incremental sliding-window aggregation for real-time analytics over out-of-order data streams","author":"S Bou","year":"2018","unstructured":"Bou S, Kitagawa H, Amagasa T (2018) Cbix: Incremental sliding-window aggregation for real-time analytics over out-of-order data streams"},{"issue":"8","key":"417_CR11","doi-asserted-by":"publisher","first-page":"3107","DOI":"10.1007\/s10115-020-01444-5","volume":"62","author":"S Bou","year":"2020","unstructured":"Bou\u00a0S, Kitagawa\u00a0H, Amagasa\u00a0T (2020) L\u2011bix: incremental sliding-window aggregation over data streams using linear bidirectional aggregating indexes. Knowl Inf Syst 62(8):3107\u20133131","journal-title":"Knowl Inf Syst"},{"key":"417_CR12","volume-title":"IEEE TKDE","author":"S Bou","year":"2021","unstructured":"Bou S, Kitagawa H, Amagasa T (2021) Cpix: Real-time analytics over out-of-order data streams by incremental sliding-window aggregation. In: IEEE TKDE"},{"key":"417_CR13","volume-title":"IEEE CS","author":"P Carbone","year":"2015","unstructured":"Carbone P, Katsifodimos A, Ewen S et al (2015) Apache flink\u2122: Stream and batch processing in a\u00a0single engine. In: IEEE CS"},{"key":"417_CR14","volume-title":"CIKM","author":"P Carbone","year":"2016","unstructured":"Carbone P, Traub J, Katsifodimos A et al (2016) Cutty: Aggregate sharing for user-defined windows. In: CIKM"},{"issue":"4","key":"417_CR15","first-page":"401","volume":"8","author":"B Chandramouli","year":"2014","unstructured":"Chandramouli\u00a0B, Goldstein\u00a0J, Barnett\u00a0M, DeLine\u00a0R, Fisher\u00a0D, Platt\u00a0JC, Terwilliger\u00a0JF, Wernsing\u00a0J (2014) Trill: A\u00a0high-performance incremental query processor for diverse analytics. PVLDB 8(4):401\u2013412","journal-title":"PVLDB"},{"key":"417_CR16","series-title":"Tech. rep., Microsoft Research","volume-title":"The trill incremental analytics engine","author":"B Chandramouli","year":"2014","unstructured":"Chandramouli B, Goldstein J, Barnett M et al (2014) The trill incremental analytics engine. Tech. rep., Microsoft Research"},{"key":"417_CR17","volume-title":"SIGMOD","author":"S Chandrasekaran","year":"2003","unstructured":"Chandrasekaran S, Cooper O, Deshpande A et al (2003) Telegraphcq: Continuous dataflow processing. In: SIGMOD"},{"key":"417_CR18","volume-title":"SIGMOD","author":"J Chen","year":"2000","unstructured":"Chen J, DeWitt DJ, Tian F et al (2000) Niagaracq: A\u00a0scalable continuous query system for internet databases. In: SIGMOD"},{"key":"417_CR19","volume-title":"IPDPSW","author":"S Chintapalli","year":"2016","unstructured":"Chintapalli S, Dagit D, Evans B et al (2016) Benchmarking streaming computation engines: Storm, flink and spark streaming. In: IPDPSW (IEEE)"},{"key":"417_CR20","volume-title":"SIGMOD","author":"B Del Monte","year":"2020","unstructured":"Del Monte B, Zeuch S, Rabl T et al (2020) Rhino: Efficient management of very large distributed state for stream processing engines. In: SIGMOD"},{"key":"417_CR21","volume-title":"SIGMOD","author":"B Del Monte","year":"2022","unstructured":"Del Monte B, Zeuch S, Rabl T et al (2022) Rethinking stateful stream processing with rdma. In: SIGMOD (to appear)"},{"issue":"6","key":"417_CR22","first-page":"441","volume":"7","author":"M Elseidy","year":"2014","unstructured":"Elseidy\u00a0M, Elguindy\u00a0A, Vitorovic\u00a0A et\u00a0al (2014) Scalable and adaptive online joins. VLDB 7(6):441\u2013452","journal-title":"VLDB"},{"issue":"9","key":"417_CR23","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1002\/spe.2194","volume":"44","author":"B Gedik","year":"2014","unstructured":"Gedik\u00a0B (2014) Generic windowing support for extensible stream processing systems. Softw Pract Exp 44(9):1105\u20131128","journal-title":"Softw Pract Exp"},{"key":"417_CR24","volume-title":"Introducing microsoft streaminsight","author":"T Grabs","year":"2009","unstructured":"Grabs T, Schindlauer R, Krishnan R et al (2009) Introducing microsoft streaminsight (Tech. rep.)"},{"key":"417_CR25","volume-title":"DEBS","author":"M Grossniklaus","year":"2016","unstructured":"Grossniklaus M, Maier D, Miller J et al (2016) Frames: Data-driven windows. In: DEBS (ACM)"},{"key":"417_CR26","volume-title":"EDBT","author":"PM Grulich","year":"2018","unstructured":"Grulich PM, Saitenmacher R, Traub J et al (2018) Scalable detection of concept drifts on data streams with parallel adaptive windowing. In: EDBT"},{"key":"417_CR27","volume-title":"DEBS","author":"PM Grulich","year":"2019","unstructured":"Grulich PM, Traub J, Bre\u00df S et al (2019) Generating reproducible out-of-order data streams. In: DEBS"},{"key":"417_CR28","volume-title":"SIGMOD","author":"PM Grulich","year":"2020","unstructured":"Grulich PM, Sebastian B, Zeuch S et al (2020) Grizzly: Efficient stream processing through adaptive query compilation. In: SIGMOD"},{"issue":"2","key":"417_CR29","first-page":"196","volume":"15","author":"PM Grulich","year":"2021","unstructured":"Grulich\u00a0PM, Zeuch\u00a0S, Markl\u00a0V (2021) Babelfish: Efficient execution of polyglot queries. PVLDB 15(2):196\u2013210","journal-title":"PVLDB"},{"key":"417_CR30","volume-title":"DEBS","author":"M Hirzel","year":"2017","unstructured":"Hirzel M, Schneider S, Tangwongsan K (2017) Tutorial: Sliding-window aggregation algorithms. In: DEBS"},{"issue":"9","key":"417_CR31","first-page":"1002","volume":"12","author":"M Hoffmann","year":"2019","unstructured":"Hoffmann\u00a0M, Lattuada\u00a0A, McSherry\u00a0F, Kalavri\u00a0V, Liagouris\u00a0J, Roscoe\u00a0T (2019) Megaphone: Latency-conscious state migration for distributed streaming dataflows. PVLDB 12(9):1002\u20131015","journal-title":"PVLDB"},{"issue":"10","key":"417_CR32","first-page":"797","volume":"7","author":"U Jugel","year":"2014","unstructured":"Jugel\u00a0U, Jerzak\u00a0Z, Hackenbroich\u00a0G, Markl\u00a0V (2014) M4: a\u00a0visualization-oriented time series data aggregation. PVLDB 7(10):797\u2013808","journal-title":"PVLDB"},{"key":"417_CR33","volume-title":"ICDE","author":"J Kang","year":"2003","unstructured":"Kang J, Naughton JF, Viglas SD (2003) Evaluating window joins over unbounded streams. In: ICDE (IEEE)"},{"issue":"4","key":"417_CR34","first-page":"435","volume":"13","author":"J Karimov","year":"2019","unstructured":"Karimov\u00a0J, Rabl\u00a0T, Markl\u00a0V (2019) Ajoin: ad-hoc stream joins at scale. PVLDB 13(4):435\u2013448","journal-title":"PVLDB"},{"key":"417_CR35","volume-title":"SIGMOD","author":"J Karimov","year":"2019","unstructured":"Karimov J, Rabl T, Markl V (2019) Astream: Ad-hoc shared stream processing. In: SIGMOD"},{"key":"417_CR36","volume-title":"DaMoN","author":"T Karnagel","year":"2013","unstructured":"Karnagel T, Habich D, Schlegel B et al (2013) The hells-join: a\u00a0heterogeneous stream join for extremely large windows. In: DaMoN"},{"key":"417_CR37","first-page":"555","volume-title":"SIGMOD","author":"A Koliousis","year":"2016","unstructured":"Koliousis\u00a0A, Weidlich\u00a0M, Castro Fernandez\u00a0R, Wolf\u00a0AL, Costa\u00a0P, Pietzuch\u00a0P (2016) Saber: Window-based hybrid stream processing for heterogeneous architectures. In: SIGMOD. p\u00a0555\u2013569"},{"key":"417_CR38","volume-title":"Introducing kafka streams: Stream processing made simple","author":"J Kreps","year":"2016","unstructured":"Kreps J (2016) Introducing kafka streams: Stream processing made simple (Confluent Blog, March)"},{"key":"417_CR39","volume-title":"SIGMOD","author":"S Krishnamurthy","year":"2006","unstructured":"Krishnamurthy S, Wu C, Franklin MJ (2006) On-the-fly sharing for streamed aggregation. In: SIGMOD"},{"issue":"1","key":"417_CR40","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/1058150.1058158","volume":"34","author":"J Li","year":"2005","unstructured":"Li\u00a0J, Maier\u00a0D, Tufte\u00a0K, Papadimos\u00a0V, Tucker\u00a0PA (2005) No pane, no gain: efficient evaluation of sliding-window aggregates over data streams. SIGMOD Rec 34(1):39\u201344","journal-title":"SIGMOD Rec"},{"key":"417_CR41","volume-title":"SIGMOD","author":"J Li","year":"2005","unstructured":"Li J, Maier D, Tufte K et al (2005) Semantics and evaluation techniques for window aggregates in data streams. In: SIGMOD"},{"issue":"1","key":"417_CR42","first-page":"274","volume":"1","author":"J Li","year":"2008","unstructured":"Li\u00a0J, Tufte\u00a0K, Shkapenyuk\u00a0V, Papadimos\u00a0V, Johnson\u00a0T, Maier\u00a0D (2008) Out-of-order processing: a\u00a0new architecture for high-performance stream systems. PVLDB 1(1):274\u2013288","journal-title":"PVLDB"},{"key":"417_CR43","volume-title":"USENIX","author":"H Miao","year":"2017","unstructured":"Miao H, Park H, Jeon M et al (2017) StreamBox: Modern stream processing on a\u00a0multicore machine. In: USENIX"},{"key":"417_CR44","volume-title":"Proceedings of the 17th International Workshop on Data Management on New Hardware (DaMoN 2021)","author":"A Michalke","year":"2021","unstructured":"Michalke A, Grulich PM, Lutz C et al (2021) An energy-efficient stream join for the internet of things. In: Proceedings of the 17th International Workshop on Data Management on New Hardware (DaMoN 2021)"},{"key":"417_CR45","volume-title":"Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles","author":"DG Murray","year":"2013","unstructured":"Murray DG, McSherry F, Isaacs R et al (2013) Naiad: a\u00a0timely dataflow system. In: Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles"},{"key":"417_CR46","volume-title":"ATC","author":"M Najafi","year":"2016","unstructured":"Najafi M, Sadoghi M, Jacobsen HA (2016) Splitjoin: A\u00a0scalable, low-latency stream join architecture with adjustable ordering precision. In: ATC"},{"issue":"12","key":"417_CR47","first-page":"1634","volume":"10","author":"SA Noghabi","year":"2017","unstructured":"Noghabi\u00a0SA, Paramasivam\u00a0K, Pan\u00a0Y, Ramesh\u00a0N, Bringhurst\u00a0J, Gupta\u00a0I, Campbell\u00a0RH (2017) Samza: stateful scalable stream processing at linkedin. PVLDB 10(12):1634\u20131645","journal-title":"PVLDB"},{"issue":"10","key":"417_CR48","first-page":"1818","volume":"14","author":"R Poepsel-Lemaitre","year":"2021","unstructured":"Poepsel-Lemaitre\u00a0R, Kiefer\u00a0M, von Hein\u00a0J, Quian\u00e9-Ruiz\u00a0J-A, Markl\u00a0V (2021) In the land of data streams where synopses are missing, one framework to bring them all. PVLDB 14(10):1818\u20131831","journal-title":"PVLDB"},{"issue":"9","key":"417_CR49","first-page":"709","volume":"7","author":"P Roy","year":"2014","unstructured":"Roy P, Teubner J, Gemulla R (2014) Low-latency handshake join. PVLDB 7(9):709\u2013720","journal-title":"PVLDB"},{"key":"417_CR50","volume-title":"SIGMOD","author":"A Shahvarani","year":"2020","unstructured":"Shahvarani A, Jacobsen HA (2020) Parallel index-based stream join on a\u00a0multicore cpu. In: SIGMOD"},{"key":"417_CR51","volume-title":"SSDBM","author":"AU Shein","year":"2017","unstructured":"Shein AU, Chrysanthis PK, Labrinidis A (2017) Flatfit: Accelerated incremental sliding-window aggregation for real-time analytics. In: SSDBM"},{"key":"417_CR52","volume-title":"EDBT","author":"AU Shein","year":"2018","unstructured":"Shein AU, Chrysanthis PK, Labrinidis A (2018) Slickdeque: High throughput and low latency incremental sliding-window aggregation. In: EDBT"},{"issue":"7","key":"417_CR53","first-page":"702","volume":"8","author":"K Tangwongsan","year":"2015","unstructured":"Tangwongsan\u00a0K, Hirzel\u00a0M, Schneider\u00a0S, Wu\u00a0K-L (2015) General incremental sliding-window aggregation. PVLDB 8(7):702\u2013713","journal-title":"PVLDB"},{"key":"417_CR54","first-page":"66","volume-title":"DEBS","author":"K Tangwongsan","year":"2017","unstructured":"Tangwongsan K, Hirzel M, Schneider S (2017) Low-latency sliding-window aggregation in worst-case constant time. In: DEBS, pp 66\u201377"},{"issue":"10","key":"417_CR55","first-page":"1167","volume":"12","author":"K Tangwongsan","year":"2019","unstructured":"Tangwongsan\u00a0K, Hirzel\u00a0M, Schneider\u00a0S (2019) Optimal and general out-of-order sliding-window aggregation. PVLDB 12(10):1167\u20131180","journal-title":"PVLDB"},{"key":"417_CR56","volume-title":"SIGMOD","author":"J Teubner","year":"2011","unstructured":"Teubner J, Mueller R (2011) How soccer players would do stream joins. In: SIGMOD"},{"key":"417_CR57","volume-title":"Hammer slide: work-and cpu-efficient streaming window aggregation","author":"G Theodorakis","year":"2018","unstructured":"Theodorakis G, Koliousis A, Pietzuch P et al (2018) Hammer slide: work-and cpu-efficient streaming window aggregation"},{"key":"417_CR58","volume-title":"SIGMOD","author":"G Theodorakis","year":"2020","unstructured":"Theodorakis G, Koliousis A, Pietzuch P et al (2020) Lightsaber: Efficient window aggregation on multi-core processors. In: SIGMOD"},{"key":"417_CR59","volume-title":"EDBT","author":"G Theodorakis","year":"2020","unstructured":"Theodorakis G, Pietzuch PR, Pirk H (2020) Slideside: A\u00a0fast incremental stream processing algorithm for multiple queries. In: EDBT"},{"key":"417_CR60","series-title":"Databricks Blog","volume-title":"Introducing low-latency continuous processing mode in structured streaming in apache spark 2.3","author":"J Torres","year":"2018","unstructured":"Torres J, Armbrust M, Das T et al (2018) Introducing low-latency continuous processing mode in structured streaming in apache spark 2.3. Databricks Blog"},{"key":"417_CR61","volume-title":"SIGMOD","author":"A Toshniwal","year":"2014","unstructured":"Toshniwal A, Taneja S, Shukla A et al (2014) Storm@ twitter. In: SIGMOD"},{"key":"417_CR62","unstructured":"Traub\u00a0J (2019) Demand-based data stream gathering, processing, and transmission. PhD thesis, Technische Universit\u00e4t Berlin. https:\/\/www.depositonce.tu-berlin.de\/handle\/11303\/10519. Accessed 25.01.2022"},{"key":"417_CR63","volume-title":"Demand-based data stream gathering, processing, and transmission: efficient solutions for real-time data analytics in the Internet of things","author":"J Traub","year":"2021","unstructured":"Traub J (2021) Demand-based data stream gathering, processing, and transmission: efficient solutions for real-time data analytics in the Internet of things. Books on Demand, Norderstedt"},{"key":"417_CR64","volume-title":"EDBT","author":"J Traub","year":"2017","unstructured":"Traub J, Steenbergen N, Grulich PM et al (2017) I2: Interactive real-time visualization for streaming data. In: EDBT"},{"key":"417_CR65","volume-title":"ICDE","author":"J Traub","year":"2018","unstructured":"Traub J, Grulich P, Cu\u00e9llar AR et al (2018) Scotty: Efficient window aggregation for out-of-order stream processing. In: ICDE"},{"key":"417_CR66","volume-title":"EDBT","author":"J Traub","year":"2019","unstructured":"Traub J, Grulich P, Cu\u00e9llar AR et al (2019) Efficient window aggregation with general stream slicing. In: EDBT"},{"key":"417_CR67","series-title":"arXiv preprint arXiv","volume-title":"SENSE: Scalable data acquisition from distributed sensors with guaranteed time coherence","author":"J Traub","year":"2019","unstructured":"Traub J, H\u00fclsmann J, Bre\u00df S et al (2019) SENSE: Scalable data acquisition from distributed sensors with guaranteed time coherence. arXiv preprint arXiv, vol 191204648"},{"key":"417_CR68","volume-title":"TODS","author":"J Traub","year":"2021","unstructured":"Traub J, Grulich PM, Cu\u00e9llar AR et al (2021) Scotty: General and efficient open-source window aggregation for stream processing systems. In: TODS"},{"issue":"4","key":"417_CR69","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1145\/3456859.3456861","volume":"49","author":"J Traub","year":"2021","unstructured":"Traub\u00a0J, Kaoudi\u00a0Z, Quian\u00e9-Ruiz\u00a0J-A, Markl\u00a0V (2021) Agora: Bringing together datasets, algorithms, models and more in a\u00a0unified ecosystem [vision]. SIGMOD Rec 49(4):6\u201311","journal-title":"SIGMOD Rec"},{"key":"417_CR70","unstructured":"Yahoo! (2020) Sketches library from Yahoo! https:\/\/datasketches.apache.org\/. Accessed 12.04.2022"},{"issue":"11","key":"417_CR71","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1145\/2934664","volume":"59","author":"M Zaharia","year":"2016","unstructured":"Zaharia\u00a0M, Xin\u00a0RS, Wendell\u00a0P, Das\u00a0T, Armbrust\u00a0M, Dave\u00a0A, Meng\u00a0X, Rosen\u00a0J, Venkataraman\u00a0S, Franklin\u00a0MJ et\u00a0al (2016) Apache spark: a\u00a0unified engine for big data processing. Commun ACM 59(11):56\u201365","journal-title":"Commun ACM"},{"issue":"5","key":"417_CR72","first-page":"516","volume":"12","author":"S Zeuch","year":"2019","unstructured":"Zeuch\u00a0S, Monte\u00a0BD, Karimov\u00a0J, Lutz\u00a0C, Renz\u00a0M, Traub\u00a0J, Bre\u00df\u00a0S, Rabl\u00a0T, Markl\u00a0V (2019) Analyzing efficient stream processing on modern hardware. PVLDB 12(5):516\u2013530","journal-title":"PVLDB"},{"key":"417_CR73","volume-title":"CIDR","author":"S Zeuch","year":"2020","unstructured":"Zeuch S, Chaudhary A, Monte B et al (2020) The NebulaStream Platform: Data and application management for the internet of things. In: CIDR"},{"issue":"1","key":"417_CR74","first-page":"66","volume":"6","author":"S Zeuch","year":"2020","unstructured":"Zeuch\u00a0S, Zacharatou\u00a0ET, Zhang\u00a0S, Chatziliadis\u00a0X, Chaudhary\u00a0A, Del Monte\u00a0B, Giouroukis\u00a0D, Grulich\u00a0PM, Ziehn\u00a0A, Mark\u00a0V (2020) NebulaStream: Complex analytics beyond the cloud. Open J Internet Things 6(1):66\u201381","journal-title":"Open J Internet Things"},{"key":"417_CR75","volume-title":"ACM SIGKDD","author":"C Zhang","year":"2021","unstructured":"Zhang C, Akbarinia R, Toumani F (2021) Efficient incremental computation of aggregations over sliding windows. In: ACM SIGKDD"},{"key":"417_CR76","volume-title":"SIGMOD","author":"S Zhang","year":"2021","unstructured":"Zhang S, Mao Y, He J et al (2021) Parallelizing intra-window join on multicores: An experimental study. In: SIGMOD"}],"container-title":["Datenbank-Spektrum"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13222-022-00417-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13222-022-00417-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13222-022-00417-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T17:09:33Z","timestamp":1666458573000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13222-022-00417-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,9]]},"references-count":76,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["417"],"URL":"https:\/\/doi.org\/10.1007\/s13222-022-00417-y","relation":{},"ISSN":["1618-2162","1610-1995"],"issn-type":[{"value":"1618-2162","type":"print"},{"value":"1610-1995","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,9]]},"assertion":[{"value":"31 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}