A CEP-driven framework for real-time news impact prediction on financial markets

Real-time news impact prediction on financial markets is a challenging task for finance experts with limited IT expertise. Many practitioners build machine learning models trained with many low-level features extracted from multiple event-based streams (news and financial market data), which often l...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Service oriented computing and applications Ročník 17; číslo 2; s. 129 - 144
Hlavní autori:	Chen, Weisi, El Majzoub, Ahmad, Al-Qudah, Islam, Rabhi, Fethi A.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.06.2023 Springer Nature B.V
Predmet:	Algorithms Classification Computer Appl. in Administrative Data Processing Computer Science Computer Systems Organization and Communication Networks Data collection Data transmission Datasets Digital currencies e-Commerce/e-business Finance Impact prediction IT in Business Machine learning Management of Computing and Information Systems Manufacturing News News media Pharmaceutical industry Real time Securities markets Sentiment analysis Social networks Software Software Engineering/Programming and Operating Systems Special Issue Paper Subject specialists Windows (intervals) Data quality Real-time analytics Service-oriented architecture Finance Complex event processing Machine learning
ISSN:	1863-2386, 1863-2394
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Real-time news impact prediction on financial markets is a challenging task for finance experts with limited IT expertise. Many practitioners build machine learning models trained with many low-level features extracted from multiple event-based streams (news and financial market data), which often leads to poor outcomes. State-of-the-art solutions either ignore domain-specific contexts or are customised to merely one type of static datasets rather than real-time data streams. In most cases, the domain expert would have to manually conduct data collection, data cleaning and aggregation, and machine learning step by step with the assistance of IT experts, which is time-consuming and complicated. To address these limitations, we propose a technique that uses real-time data pre-processing in accordance with domain-specific event patterns to generate better-quality datasets. This technique is supported by a systematic framework featuring a data model capturing domain-specific event patterns, an SOA-based architecture and processes that integrate the capabilities of sentiment analysis, complex event processing with automated machine learning (AutoML), facilitating event pattern detection and continual learning with sliding time windows. The benefit of adopting an SOA architecture is to ensure the flexibility of the selection and seamless integration of components. This solution allows domain experts to define domain-specific event patterns via a user-friendly interface and prepare better-quality datasets by pre-processing real-time data streams accordingly via the complex event processing component, aiming to generate meaningful prediction results by the downstream AutoML component. The AutoML component allows for minimal machine learning skills by the domain expert to conduct the prediction tasks. A prototype was implemented to evaluate its feasibility and functionality on a real-life price movement prediction scenario involving 3 years of news and financial market data. The results demonstrate that finance experts are able to complete news impact prediction tasks in real time without the intervention of IT experts, which saves a large amount of time compared with traditional machine learning processes.
AbstractList	Real-time news impact prediction on financial markets is a challenging task for finance experts with limited IT expertise. Many practitioners build machine learning models trained with many low-level features extracted from multiple event-based streams (news and financial market data), which often leads to poor outcomes. State-of-the-art solutions either ignore domain-specific contexts or are customised to merely one type of static datasets rather than real-time data streams. In most cases, the domain expert would have to manually conduct data collection, data cleaning and aggregation, and machine learning step by step with the assistance of IT experts, which is time-consuming and complicated. To address these limitations, we propose a technique that uses real-time data pre-processing in accordance with domain-specific event patterns to generate better-quality datasets. This technique is supported by a systematic framework featuring a data model capturing domain-specific event patterns, an SOA-based architecture and processes that integrate the capabilities of sentiment analysis, complex event processing with automated machine learning (AutoML), facilitating event pattern detection and continual learning with sliding time windows. The benefit of adopting an SOA architecture is to ensure the flexibility of the selection and seamless integration of components. This solution allows domain experts to define domain-specific event patterns via a user-friendly interface and prepare better-quality datasets by pre-processing real-time data streams accordingly via the complex event processing component, aiming to generate meaningful prediction results by the downstream AutoML component. The AutoML component allows for minimal machine learning skills by the domain expert to conduct the prediction tasks. A prototype was implemented to evaluate its feasibility and functionality on a real-life price movement prediction scenario involving 3 years of news and financial market data. The results demonstrate that finance experts are able to complete news impact prediction tasks in real time without the intervention of IT experts, which saves a large amount of time compared with traditional machine learning processes. Real-time news impact prediction on financial markets is a challenging task for finance experts with limited IT expertise. Many practitioners build machine learning models trained with many low-level features extracted from multiple event-based streams (news and financial market data), which often leads to poor outcomes. State-of-the-art solutions either ignore domain-specific contexts or are customised to merely one type of static datasets rather than real-time data streams. In most cases, the domain expert would have to manually conduct data collection, data cleaning and aggregation, and machine learning step by step with the assistance of IT experts, which is time-consuming and complicated. To address these limitations, we propose a technique that uses real-time data pre-processing in accordance with domain-specific event patterns to generate better-quality datasets. This technique is supported by a systematic framework featuring a data model capturing domain-specific event patterns, an SOA-based architecture and processes that integrate the capabilities of sentiment analysis, complex event processing with automated machine learning (AutoML), facilitating event pattern detection and continual learning with sliding time windows. The benefit of adopting an SOA architecture is to ensure the flexibility of the selection and seamless integration of components. This solution allows domain experts to define domain-specific event patterns via a user-friendly interface and prepare better-quality datasets by pre-processing real-time data streams accordingly via the complex event processing component, aiming to generate meaningful prediction results by the downstream AutoML component. The AutoML component allows for minimal machine learning skills by the domain expert to conduct the prediction tasks. A prototype was implemented to evaluate its feasibility and functionality on a real-life price movement prediction scenario involving 3 years of news and financial market data. The results demonstrate that finance experts are able to complete news impact prediction tasks in real time without the intervention of IT experts, which saves a large amount of time compared with traditional machine learning processes.
Author	El Majzoub, Ahmad Rabhi, Fethi A. Chen, Weisi Al-Qudah, Islam
Author_xml	– sequence: 1 givenname: Weisi orcidid: 0000-0001-8131-392X surname: Chen fullname: Chen, Weisi email: chenweisi@xmut.edu.cn organization: Xiamen University of Technology – sequence: 2 givenname: Ahmad surname: El Majzoub fullname: El Majzoub, Ahmad organization: The University of New South Wales – sequence: 3 givenname: Islam surname: Al-Qudah fullname: Al-Qudah, Islam organization: University of Sharjah, University City Rd, University City, Higher Colleges of Technology – sequence: 4 givenname: Fethi A. surname: Rabhi fullname: Rabhi, Fethi A. organization: The University of New South Wales
BookMark	eNp9kMtKAzEUhoNUsNa-gKuA62guzWWWpdQLFOxC1yGdSSTtTKYmqcW3d8YRBRc9HDhn8X_n8l-CUWiDBeCa4FuCsbxLhEhBEKYMYcy4QuoMjIkSDFFWzEa_vRIXYJrSFnfBqFRCjsF6DhfLNaqi_7ABumgae2zjDro2wmhNjbJvLAz2mKBv9qbMcB9t5cvs2wC7dD6YUHpTw8bEnc3pCpw7Uyc7_akT8Hq_fFk8otXzw9NivkIlEywjbjAlTjiBqXVlxYvuAyI3igpLC8xngjMuKuqkdc5J5pQlRmBiJOHYbahiE3AzzN3H9v1gU9bb9hBDt1IzykUhOZ_1KjWoytimFK3Tpc-mPz5H42tNsO4t1IOFurNQf1uoe5T-Q_fRd09-nobYAKVOHN5s_LvqBPUFqsaEUA
CitedBy_id	crossref_primary_10_3390_electronics12122605 crossref_primary_10_32604_cmes_2023_031388 crossref_primary_10_1109_ACCESS_2023_3295694 crossref_primary_10_3390_bdcc8080086 crossref_primary_10_1007_s10791_025_09573_7 crossref_primary_10_1016_j_techfore_2025_124098 crossref_primary_10_3390_app14198749 crossref_primary_10_1007_s00779_024_01820_w crossref_primary_10_4018_JOEUC_358454
Cites_doi	10.1007/s10796-016-9633-2 10.1007/s00778-019-00557-w 10.1016/j.eswa.2020.113251 10.1109/ICOMET.2019.8673428 10.1002/int.22732 10.1007/s00521-022-07297-z 10.1145/3377325.3377501 10.1016/j.procs.2020.06.111 10.1145/3379336.3381474 10.1007/978-3-030-64466-6_1 10.1145/3448016.3457557 10.18488/journal.76.2021.81.1.7 10.1109/ICEIT54416.2022.9690729 10.1109/TFUZZ.2022.3157951 10.14778/3415478.3415542 10.1080/13504851.2020.1725230 10.1080/17517575.2018.1493145 10.1007/978-3-030-05318-5_4 10.1016/j.knosys.2020.106622 10.1007/978-3-540-31865-1_25 10.1016/B978-0-12-805394-2.00002-7 10.1016/j.dcan.2017.10.002 10.1007/s00521-020-04874-y 10.1016/j.future.2021.06.024 10.1609/aaai.v34i03.5626 10.1145/3485463 10.1016/j.ejor.2019.04.013 10.1007/s00521-014-1550-z 10.1007/s41060-016-0029-7 10.1007/978-3-030-58548-8_46 10.1016/j.ins.2021.10.054 10.1007/978-3-030-05318-5_8 10.1093/bioinformatics/bth361 10.1145/3399579.3399870 10.1007/s41060-022-00309-0 10.1145/3470918 10.1080/00036846.2018.1564115 10.1145/3448326.3448353 10.1109/HICSS.2016.137 10.1109/EDOC49727.2020.00016 10.1007/s12652-020-01839-w 10.1007/978-3-030-05318-5 10.1007/s11227-021-04013-x 10.18653/v1/S17-2088 10.1016/j.eswa.2018.08.003 10.7551/mitpress/10654.001.0001 10.1145/3361118 10.1016/j.procs.2018.05.020 10.1007/s10479-021-04464-8 10.1007/s13218-020-00637-y 10.1007/s00607-012-0193-0 10.1007/978-3-030-05318-5_6 10.1145/3290605.3300911 10.1002/9781119198697 10.1016/j.dss.2018.06.008
ContentType	Journal Article
Copyright	The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023.
Copyright_xml	– notice: The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023.
DBID	AAYXX CITATION 8FE 8FG ABJCF AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO GNUQQ HCIFZ JQ2 K7- L6V M7S P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS
DOI	10.1007/s11761-023-00358-8
DatabaseName	CrossRef ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC ProQuest Central Technology Collection ProQuest One Community College ProQuest Central ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Engineering Collection Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection
DatabaseTitle	CrossRef Computer Science Database ProQuest Central Student Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection SciTech Premium Collection ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest Central Korea ProQuest Central (New) Engineering Collection Advanced Technologies & Aerospace Collection Engineering Database ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition Materials Science & Engineering Collection ProQuest One Academic ProQuest One Academic (New)
DatabaseTitleList	Computer Science Database
Database_xml	– sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
EISSN	1863-2394
EndPage	144
ExternalDocumentID	10_1007_s11761_023_00358_8
GrantInformation_xml	– fundername: Xiamen Scientific Research Funding for Overseas Chinese Scholars – fundername: Natural Science Foundation of Fujian Province grantid: 2022J05291 funderid: http://dx.doi.org/10.13039/501100003392
GroupedDBID	-59 -5G -BR -EM -Y2 -~C .VR 06D 0R~ 123 203 2J2 2JN 2JY 2KG 2KM 2LR 2VQ 2~H 30V 4.4 406 408 409 40E 5VS 67Z 6NX 875 8TC 8UJ 95- 95. 95~ AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBXA ABDZT ABECU ABFTD ABFTV ABHQN ABJNI ABJOX ABKCH ABMNI ABMQK ABNWP ABQBU ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACZOJ ADHHG ADHIR ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEGAL AEGNC AEJHL AEJRE AEMSY AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFGCZ AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARMRJ AXYYD AYJHY B-. BA0 BDATZ BGNMA CAG COF CS3 CSCUP DDRTE DNIVK DPUIP EBLON EBS EIOEI EJD ESBYG FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HF~ HG5 HG6 HLICF HMJXF HQYDN HRMNR HZ~ IJ- IKXTQ IWAJR IXC IXD IXE IZIGR IZQ I~X J-C J0Z JBSCW JCJTX JZLTJ KDC KOV LLZTM M4Y MA- NPVJJ NQJWS NU0 O9- O93 O9J OAM P2P P9O PF0 PT4 QOS R89 R9I RIG ROL RPX RSV S16 S1Z S27 S3B SAP SCO SDH SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 TSG TSK TSV TUC U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W48 YLTOR Z45 Z7X Z83 Z88 ZMTXR ~A9 AAPKM AAYXX ABBRH ABDBE ABFSG ABJCF ABRTQ ACSTC AEZWR AFDZB AFFHD AFHIU AFKRA AFOHR AHPBZ AHWEU AIXLP ARAPS ATHPR AYFIA BENPR BGLVJ CCPQU CITATION HCIFZ K7- M7S PHGZM PHGZT PQGLB PTHSS 8FE 8FG AZQEC DWQXO GNUQQ JQ2 L6V P62 PKEHL PQEST PQQKQ PQUKI PRINS
ID	FETCH-LOGICAL-c363t-5a021f6f602efcd5976117b826e2905465356d2f7efff73f8e1a601a7150fb283
IEDL.DBID	RSV
ISICitedReferencesCount	8
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000941054500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1863-2386
IngestDate	Tue Oct 07 06:21:17 EDT 2025 Sat Nov 29 01:50:26 EST 2025 Tue Nov 18 20:51:50 EST 2025 Fri Feb 21 02:44:29 EST 2025
IsDoiOpenAccess	false
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	2
Keywords	Data quality Real-time analytics Service-oriented architecture Finance Complex event processing Machine learning
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c363t-5a021f6f602efcd5976117b826e2905465356d2f7efff73f8e1a601a7150fb283
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-8131-392X
OpenAccessLink	https://link.springer.com/content/pdf/10.1007/s11761-023-00358-8.pdf
PQID	3256975548
PQPubID	2044172
PageCount	16
ParticipantIDs	proquest_journals_3256975548 crossref_citationtrail_10_1007_s11761_023_00358_8 crossref_primary_10_1007_s11761_023_00358_8 springer_journals_10_1007_s11761_023_00358_8
PublicationCentury	2000
PublicationDate	20230600 2023-06-00 20230601
PublicationDateYYYYMMDD	2023-06-01
PublicationDate_xml	– month: 6 year: 2023 text: 20230600
PublicationDecade	2020
PublicationPlace	London
PublicationPlace_xml	– name: London – name: Heidelberg
PublicationTitle	Service oriented computing and applications
PublicationTitleAbbrev	SOCA
PublicationYear	2023
Publisher	Springer London Springer Nature B.V
Publisher_xml	– name: Springer London – name: Springer Nature B.V
References	AkyildirimEBarivieraAFNguyenDKSensoyAForecasting high-frequency stock returns: a comparison of alternative methodsAnn Oper Res2022444076310.1007/s10479-021-04464-807553129 AllenDEMcAleerMSinghAKDaily market news sentiment and stock pricesAppl Econ201951303212323510.1080/00036846.2018.1564115 AbdallahZSDuLWebbGISammutCWebbGIData preparationEncyclopedia of machine learning and data mining2016BostonSpringer111 MilosevicZChenWBerryARabhiFABuyyaRCalheirosRNDastjerdiAVChapter 2—real-time analyticsBig Data2016BurlingtonMorgan Kaufmann396110.1016/B978-0-12-805394-2.00002-7 TruongQNguyenMDangHMeiBHousing price prediction via improved machine learning techniquesProcedia Comput Sci202017443344210.1016/j.procs.2020.06.111 Hanussek M, Blohm M, Kintz M (2020) Can AutoML outperform humans? An evaluation on popular OpenML datasets using AutoML benchmark. In: 2020 2nd International conference on artificial intelligence, robotics and control Wang Q et al (2019) ATMSeer: increasing transparency and controllability in automated machine learning. In: Proceedings of the 2019 CHI conference on human factors in computing systems Rabhi FA, Mehandjiev N, Baghdadi A (2020) State-of-the-art in applying machine learning to electronic trading. In: Enterprise applications, markets and services in the finance industry. Springer, Cham, pp 3–20 HeXZhaoKChuXAutoML: a survey of the state-of-the-artKnowl Based Syst202121210662210.1016/j.knosys.2020.106622 MaoYHow data scientistswork together with domain experts in scientific collaborations: To find the right answer or to ask the right question?Proc ACM Hum Comput Interact2019323710.1145/3361118 RazaMOPathanNUmarABuxRActivity recognition and creation of web service for activity recognition using mobile sensor data using azure machine learning studioRev Comput Eng Res2021811710.18488/journal.76.2021.81.1.7 MilosevicZChenWBerryARabhiFAAn open architecture for event-based analyticsInt J Data Sci Anal201621132710.1007/s41060-016-0029-7 Apache. Flume. https://flume.apache.org SoftwareAG. Apama. https://www.softwareag.com/en_corporate/platform/iot/apama.html HussainWMerigóJMRazaMRGaoHA new QoS prediction model using hybrid IOWA-ANFIS with fuzzy C-means, subtractive clustering and grid partitioningInf Sci202258428030010.1016/j.ins.2021.10.054 AdiEAnwarABaigZZeadallySMachine learning and data analytics for the IoTNeural Comput Appl20203220162051623310.1007/s00521-020-04874-y ChenWRabhiFAEnabling user-driven rule management in event data analysisInf Syst Front20161851152810.1007/s10796-016-9633-2 KaurPSharmaMMittalMBig data and machine learning based secure healthcare frameworkProcedia Comput Sci20181321049105910.1016/j.procs.2018.05.020 Luckham D (2012) Event processing for business: organizing the real-time enterprise OlsonRSMooreJHHutterFKotthoffLVanschorenJTPOT: a tree-based pipeline optimization tool for automating machine learningAutomated machine learning: methods, systems, challenges2019ChamSpringer15116010.1007/978-3-030-05318-5_8 BhardwajAYangJCudré-MaurouxPA human-AI loop approach for joint keyword discovery and expectation estimation in micropost event detectionProc AAAI Conf Artif Intell202034032451245810.1609/aaai.v34i03.5626 Apache. Storm. http://storm.apache.org YakovlevAOracle AutoML: a fast and predictive AutoML pipelineProc VLDB Endow202013123166318010.14778/3415478.3415542 HuangBHuanYXuLDZhengLZouZAutomated trading systems statistical and machine learning methods and hardware implementation: a surveyEnterp Inf Syst201913113214410.1080/17517575.2018.1493145 LuJ-YStructural break-aware pairs trading strategy using deep reinforcement learningJ Supercomput202278338433882439399310.1007/s11227-021-04013-x KotthoffLThorntonCHoosHHHutterFLeyton-BrownKHutterFKotthoffLVanschorenJAuto-WEKA: automatic model selection and hyperparameter optimization in WEKAAutomated machine learning: methods, systems, challenges2019ChamSpringer819510.1007/978-3-030-05318-5_4 Luong NNT, Milosevic Z, Berry A, Rabhi F (2020) An open architecture for complex event processing with machine learning. In: 2020 IEEE 24th international enterprise distributed object computing conference (EDOC), 5–8 Oct. 2020, pp 51–56. https://doi.org/10.1109/EDOC49727.2020.00016 Cloudera. Streaming analytics. https://docs.cloudera.com/csa Wang D et al (2020) AutoAI: automating the end-to-end AI lifecycle with humans-in-the-loop. In; Presented at the proceedings of the 25th international conference on intelligent user interfaces companion, Cagliari, Italy, 2020. [Online]. Available: https://doi.org/10.1145/3379336.3381474 ZhuXComplex event detection for commodity distribution Internet of Things model incorporating radio frequency identification and wireless sensor networkFuture Gener Comput Syst202112510011110.1016/j.future.2021.06.024 AgrapetidouACharonyktakisPGogasPPapadimitriouTTsamardinosIAn AutoML application to forecasting bank failuresAppl Econ Lett20212815910.1080/13504851.2020.1725230 HussainWMerigóJMRazaMRPredictive intelligence using ANFIS-induced OWAWA for complex stock market predictionInt J Intell Syst20223784586461110.1002/int.22732 Google. Cloud AutoML. https://cloud.google.com/automl Amazon. Amazon streaming. https://aws.amazon.com/streaming-data HussainWGaoHRazaMRRabhiFAMerigóJMAssessing cloud QoS predictions using OWA in neural network methodsNeural Comput Appl20223417148951491210.1007/s00521-022-07297-z LiXEmpirical analysis: stock market prediction via extreme learning machineNeural Comput Appl2016271677810.1007/s00521-014-1550-z Chen W, Liu B, Zhang X, Al-Qudah I (2022) An event-based framework for facilitating real-time sentiment analysis in educational contexts. In :2022 11th International conference on educational and information technology (ICEIT), 6–8 Jan. 2022, pp 57–61. https://doi.org/10.1109/ICEIT54416.2022.9690729 FeuerriegelSGordonJLong-term stock index forecasting based on text mining of regulatory disclosuresDecis Support Syst2018112889710.1016/j.dss.2018.06.008 Drozdal J et al (2020) Trust in AutoML: exploring information needs for establishing trust in automated machine learning systems. In: Presented at the proceedings of the 25th international conference on intelligent user interfaces, Cagliari, Italy, 2020. [Online]. Available: https://doi.org/10.1145/3377325.3377501 Apache. Kafka. https://kafka.apache.org GrezARiverosCUgarteMVansummerenSA formal framework for complex event recognitionACM Trans Database Syst202146416435060710.1145/348546307650993 HuckNLarge data sets and machine learning: applications to statistical arbitrageEur J Oper Res20192781330342395058610.1016/j.ejor.2019.04.0131414.91435 GiatrakosNAlevizosEArtikisADeligiannakisAGarofalakisMComplex event recognition in the Big Data era: a surveyVLDB J202029131335210.1007/s00778-019-00557-w PaivaFDCardosoRTNHanaokaGPDuarteWMDecision-making for financial trading: a fusion approach of machine learning and portfolio selectionExpert Syst Appl201911563565510.1016/j.eswa.2018.08.003 KarmakerSKHassanMMSmithMJXuLZhaiCVeeramachaneniKAutoML to date and beyond: challenges and opportunitiesACM Comput Surv202154817510.1145/3470918 EsperTech. Esper. https://www.espertech.com/esper HutterFKotthoffLVanschorenJAutomated machine learning: methods, systems, challenges2019BerlinSpringer10.1007/978-3-030-05318-5 Statista. Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2025. https://www.statista.com/statistics/871513/worldwide-data-created RoldánJBoubeta-PuigJLuis MartínezJOrtizGIntegrating complex event processing and machine learning: an intelligent architecture for detecting IoT security attacksExpert Syst Appl202014911325110.1016/j.eswa.2020.113251 Liu C, Dollár P, He K, Girshick R, Yuille A, Xie S (2020) Are labels necessary for neural architecture search?. In: Presented at the computer vision—ECCV 2020: 16th European conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IV, Glasgow, United Kingdom, 2020. [Online]. Available: https://doi.org/10.1007/978-3-030-58548-8_46 BahriMSalutariFPutinaASozioMAutoML: state of the art with a focus on anomaly detection, challenges, and research directionsInt J Data Sci Anal202210.1007/s41060-022-00309-0 Apache. Spark. https://spark.apache.org FeurerMKleinAEggenspergerKSpringenbergJTBlumMHutterFHutterFKotthoffLVanschorenJAuto-sklearn: efficient and robust automated machine learningAutomated machine learning: methods, systems, challenges2019ChamSpringer11313410.1007/978-3-030-05318-5_6 Omenics. Omenics. https://omenics.com RabhiFAYaoLGuabtniAADAGE: a framework for supporting user-driven ad-hoc data analysis processesComputing201294648951910.1007/s00607-012-0193-0 Microsoft. Azure streaming analytics. https://azure.microsoft.com/en-us/services/stream-analytics HussainWRazaMRJanMAMerigóJMGaoHCloud risk management with OWA-LSTM and fuzzy linguistic decision makingIEEE Trans Fuzzy Syst202230114657466610.1109/TFUZZ.2022.3157951 Das P et al (2020) Amazon sagemaker autopilot: a white box AutoML solution at scale. In: Presented at the proceedings of the fourth international workshop on data management for end-to-end machine learning, Portland, OR, USA, 2020. [Online]. Available: https://doi.org/10.1145/3399579.3399870 SokolKFlachPOne explanation does not fit allKI Künstliche Intell202034223525010.1007/s13218-020-00637-y Shah SY et al (2021) AutoAI-TS: AutoAI for time series forecasting. In: Proceedings of the 2021 international conference on management of data: association for computing machinery, pp 2584–2596 Rosenthal S, Farra N, Nakov P (2017) SemEval-2017 task 4: sentiment analysis in Twitter. In: Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017). Association for Computational Linguistics, Vancouver, Canada, pp 502–518. https://doi.org/10.18653/v1/S17-2088. [Online]. Available: https://aclanthology.org/S17-2088 KhanWGhazanfarMAAzamMAKaramiAAlyoubiKHAlfakeehASStock market prediction using machine learning classifiers and social media, newsJ Ambient Intell Humaniz Comput202010.1007/s12652-020-01839-w Taj S, Sha A Agrapetidou (358_CR54) 2021; 28 X He (358_CR8) 2021; 212 W Khan (358_CR47) 2020 W Hussain (358_CR48) 2022; 37 A Yakovlev (358_CR63) 2020; 13 358_CR19 358_CR29 358_CR9 358_CR28 358_CR27 358_CR26 358_CR25 358_CR69 358_CR24 358_CR23 358_CR67 358_CR22 T Oinn (358_CR16) 2004; 20 358_CR21 MS Mahdavinejad (358_CR38) 2018; 4 358_CR62 358_CR61 L Kotthoff (358_CR65) 2019 W Hussain (358_CR14) 2022; 34 Q Truong (358_CR50) 2020; 174 X Li (358_CR44) 2016; 27 W Hussain (358_CR70) 2022; 30 A Grez (358_CR31) 2021; 46 F Hutter (358_CR53) 2019 358_CR18 358_CR15 X Zhu (358_CR37) 2021; 125 358_CR59 W Chen (358_CR10) 2016; 18 Z Milosevic (358_CR34) 2016 358_CR13 358_CR12 358_CR11 358_CR55 B Huang (358_CR42) 2019; 13 E Akyildirim (358_CR51) 2022 A Bhardwaj (358_CR46) 2020; 34 SK Karmaker (358_CR56) 2021; 54 N Huck (358_CR43) 2019; 278 E Adi (358_CR35) 2020; 32 A Bifet (358_CR3) 2018 P Kaur (358_CR39) 2018; 132 FA Rabhi (358_CR7) 2012; 94 DE Allen (358_CR17) 2019; 51 J-Y Lu (358_CR49) 2022; 78 K Sokol (358_CR58) 2020; 34 358_CR41 358_CR40 S Feuerriegel (358_CR20) 2018; 112 J Roldán (358_CR52) 2020; 149 Z Milosevic (358_CR2) 2016; 2 MO Raza (358_CR60) 2021; 8 N Giatrakos (358_CR36) 2020; 29 ZS Abdallah (358_CR5) 2016 FD Paiva (358_CR45) 2019; 115 M Feurer (358_CR64) 2019 RS Olson (358_CR66) 2019 358_CR4 358_CR1 358_CR33 358_CR32 358_CR30 Y Mao (358_CR57) 2019; 3 M Bahri (358_CR68) 2022 W Hussain (358_CR6) 2022; 584
References_xml	– reference: KotthoffLThorntonCHoosHHHutterFLeyton-BrownKHutterFKotthoffLVanschorenJAuto-WEKA: automatic model selection and hyperparameter optimization in WEKAAutomated machine learning: methods, systems, challenges2019ChamSpringer819510.1007/978-3-030-05318-5_4 – reference: BifetAGavaldàRHolmesGPfahringerBMachine learning for data streams: with practical examples in MOA2018CambridgeThe MIT Press10.7551/mitpress/10654.001.0001 – reference: Goutte C, Gaussier E (2005) A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In: Advances in information retrieval. Springer, Berlin, Heidelberg, pp 345–359 – reference: EsperTech. Esper. https://www.espertech.com/esper/ – reference: MilosevicZChenWBerryARabhiFABuyyaRCalheirosRNDastjerdiAVChapter 2—real-time analyticsBig Data2016BurlingtonMorgan Kaufmann396110.1016/B978-0-12-805394-2.00002-7 – reference: KaurPSharmaMMittalMBig data and machine learning based secure healthcare frameworkProcedia Comput Sci20181321049105910.1016/j.procs.2018.05.020 – reference: TruongQNguyenMDangHMeiBHousing price prediction via improved machine learning techniquesProcedia Comput Sci202017443344210.1016/j.procs.2020.06.111 – reference: PaivaFDCardosoRTNHanaokaGPDuarteWMDecision-making for financial trading: a fusion approach of machine learning and portfolio selectionExpert Syst Appl201911563565510.1016/j.eswa.2018.08.003 – reference: HussainWMerigóJMRazaMRGaoHA new QoS prediction model using hybrid IOWA-ANFIS with fuzzy C-means, subtractive clustering and grid partitioningInf Sci202258428030010.1016/j.ins.2021.10.054 – reference: Wang D et al (2020) AutoAI: automating the end-to-end AI lifecycle with humans-in-the-loop. In; Presented at the proceedings of the 25th international conference on intelligent user interfaces companion, Cagliari, Italy, 2020. [Online]. Available: https://doi.org/10.1145/3379336.3381474 – reference: HuckNLarge data sets and machine learning: applications to statistical arbitrageEur J Oper Res20192781330342395058610.1016/j.ejor.2019.04.0131414.91435 – reference: HutterFKotthoffLVanschorenJAutomated machine learning: methods, systems, challenges2019BerlinSpringer10.1007/978-3-030-05318-5 – reference: Apache. Kafka. https://kafka.apache.org – reference: MaoYHow data scientistswork together with domain experts in scientific collaborations: To find the right answer or to ask the right question?Proc ACM Hum Comput Interact2019323710.1145/3361118 – reference: ChenWRabhiFAEnabling user-driven rule management in event data analysisInf Syst Front20161851152810.1007/s10796-016-9633-2 – reference: Das P et al (2020) Amazon sagemaker autopilot: a white box AutoML solution at scale. In: Presented at the proceedings of the fourth international workshop on data management for end-to-end machine learning, Portland, OR, USA, 2020. [Online]. Available: https://doi.org/10.1145/3399579.3399870 – reference: Tensorflow. Robust machine learning on streaming data using Kafka and Tensorflow-IO. https://www.tensorflow.org/io/tutorials/kafka – reference: OlsonRSMooreJHHutterFKotthoffLVanschorenJTPOT: a tree-based pipeline optimization tool for automating machine learningAutomated machine learning: methods, systems, challenges2019ChamSpringer15116010.1007/978-3-030-05318-5_8 – reference: MahdavinejadMSRezvanMBarekatainMAdibiPBarnaghiPShethAPMachine learning for internet of things data analysis: a surveyDigit Commun Netw20184316117510.1016/j.dcan.2017.10.002 – reference: Shah SY et al (2021) AutoAI-TS: AutoAI for time series forecasting. In: Proceedings of the 2021 international conference on management of data: association for computing machinery, pp 2584–2596 – reference: ZhuXComplex event detection for commodity distribution Internet of Things model incorporating radio frequency identification and wireless sensor networkFuture Gener Comput Syst202112510011110.1016/j.future.2021.06.024 – reference: Apache. Spark. https://spark.apache.org – reference: Microsoft. Azure streaming analytics. https://azure.microsoft.com/en-us/services/stream-analytics/ – reference: MilosevicZChenWBerryARabhiFAAn open architecture for event-based analyticsInt J Data Sci Anal201621132710.1007/s41060-016-0029-7 – reference: GrezARiverosCUgarteMVansummerenSA formal framework for complex event recognitionACM Trans Database Syst202146416435060710.1145/348546307650993 – reference: HuangBHuanYXuLDZhengLZouZAutomated trading systems statistical and machine learning methods and hardware implementation: a surveyEnterp Inf Syst201913113214410.1080/17517575.2018.1493145 – reference: KhanWGhazanfarMAAzamMAKaramiAAlyoubiKHAlfakeehASStock market prediction using machine learning classifiers and social media, newsJ Ambient Intell Humaniz Comput202010.1007/s12652-020-01839-w – reference: Luong NNT, Milosevic Z, Berry A, Rabhi F (2020) An open architecture for complex event processing with machine learning. In: 2020 IEEE 24th international enterprise distributed object computing conference (EDOC), 5–8 Oct. 2020, pp 51–56. https://doi.org/10.1109/EDOC49727.2020.00016 – reference: HeXZhaoKChuXAutoML: a survey of the state-of-the-artKnowl Based Syst202121210662210.1016/j.knosys.2020.106622 – reference: Apache. Storm. http://storm.apache.org – reference: HussainWRazaMRJanMAMerigóJMGaoHCloud risk management with OWA-LSTM and fuzzy linguistic decision makingIEEE Trans Fuzzy Syst202230114657466610.1109/TFUZZ.2022.3157951 – reference: LuJ-YStructural break-aware pairs trading strategy using deep reinforcement learningJ Supercomput202278338433882439399310.1007/s11227-021-04013-x – reference: YakovlevAOracle AutoML: a fast and predictive AutoML pipelineProc VLDB Endow202013123166318010.14778/3415478.3415542 – reference: Rabhi FA, Mehandjiev N, Baghdadi A (2020) State-of-the-art in applying machine learning to electronic trading. In: Enterprise applications, markets and services in the finance industry. Springer, Cham, pp 3–20 – reference: GiatrakosNAlevizosEArtikisADeligiannakisAGarofalakisMComplex event recognition in the Big Data era: a surveyVLDB J202029131335210.1007/s00778-019-00557-w – reference: RazaMOPathanNUmarABuxRActivity recognition and creation of web service for activity recognition using mobile sensor data using azure machine learning studioRev Comput Eng Res2021811710.18488/journal.76.2021.81.1.7 – reference: OinnTTaverna: a tool for the composition and enactment of bioinformatics workflowsBioinformatics200420173045305410.1093/bioinformatics/bth361 – reference: RabhiFAYaoLGuabtniAADAGE: a framework for supporting user-driven ad-hoc data analysis processesComputing201294648951910.1007/s00607-012-0193-0 – reference: SoftwareAG. Apama. https://www.softwareag.com/en_corporate/platform/iot/apama.html – reference: Taj S, Shaikh BB, Meghji AF (2019) Sentiment analysis of news articles: a lexicon based approach. In: 2019 2nd International conference on computing, mathematics and engineering technologies (iCoMET), 30–31 Jan. 2019, pp 1–5. https://doi.org/10.1109/ICOMET.2019.8673428. – reference: FeuerriegelSGordonJLong-term stock index forecasting based on text mining of regulatory disclosuresDecis Support Syst2018112889710.1016/j.dss.2018.06.008 – reference: Apache. Flume. https://flume.apache.org – reference: Amazon. Amazon streaming. https://aws.amazon.com/streaming-data/ – reference: RoldánJBoubeta-PuigJLuis MartínezJOrtizGIntegrating complex event processing and machine learning: an intelligent architecture for detecting IoT security attacksExpert Syst Appl202014911325110.1016/j.eswa.2020.113251 – reference: BahriMSalutariFPutinaASozioMAutoML: state of the art with a focus on anomaly detection, challenges, and research directionsInt J Data Sci Anal202210.1007/s41060-022-00309-0 – reference: Apache. Flink. https://flink.apache.org/ – reference: AllenDEMcAleerMSinghAKDaily market news sentiment and stock pricesAppl Econ201951303212323510.1080/00036846.2018.1564115 – reference: AdiEAnwarABaigZZeadallySMachine learning and data analytics for the IoTNeural Comput Appl20203220162051623310.1007/s00521-020-04874-y – reference: Luckham D (2012) Event processing for business: organizing the real-time enterprise – reference: Google. Cloud AutoML. https://cloud.google.com/automl – reference: Feuerriegel S, Ratku A, Neumann D (2016) Analysis of how underlying topics in financial news affect stock prices using latent dirichlet allocation. In: 2016 49th Hawaii international conference on system sciences (HICSS), 5–8 Jan. 2016, pp 1072–1081. https://doi.org/10.1109/HICSS.2016.137 – reference: Liu C, Dollár P, He K, Girshick R, Yuille A, Xie S (2020) Are labels necessary for neural architecture search?. In: Presented at the computer vision—ECCV 2020: 16th European conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IV, Glasgow, United Kingdom, 2020. [Online]. Available: https://doi.org/10.1007/978-3-030-58548-8_46 – reference: SokolKFlachPOne explanation does not fit allKI Künstliche Intell202034223525010.1007/s13218-020-00637-y – reference: BhardwajAYangJCudré-MaurouxPA human-AI loop approach for joint keyword discovery and expectation estimation in micropost event detectionProc AAAI Conf Artif Intell202034032451245810.1609/aaai.v34i03.5626 – reference: LiXEmpirical analysis: stock market prediction via extreme learning machineNeural Comput Appl2016271677810.1007/s00521-014-1550-z – reference: Statista. Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2025. https://www.statista.com/statistics/871513/worldwide-data-created/ – reference: FeurerMKleinAEggenspergerKSpringenbergJTBlumMHutterFHutterFKotthoffLVanschorenJAuto-sklearn: efficient and robust automated machine learningAutomated machine learning: methods, systems, challenges2019ChamSpringer11313410.1007/978-3-030-05318-5_6 – reference: Chen W, Liu B, Zhang X, Al-Qudah I (2022) An event-based framework for facilitating real-time sentiment analysis in educational contexts. In :2022 11th International conference on educational and information technology (ICEIT), 6–8 Jan. 2022, pp 57–61. https://doi.org/10.1109/ICEIT54416.2022.9690729 – reference: Wang Q et al (2019) ATMSeer: increasing transparency and controllability in automated machine learning. In: Proceedings of the 2019 CHI conference on human factors in computing systems – reference: HussainWMerigóJMRazaMRPredictive intelligence using ANFIS-induced OWAWA for complex stock market predictionInt J Intell Syst20223784586461110.1002/int.22732 – reference: Drozdal J et al (2020) Trust in AutoML: exploring information needs for establishing trust in automated machine learning systems. In: Presented at the proceedings of the 25th international conference on intelligent user interfaces, Cagliari, Italy, 2020. [Online]. Available: https://doi.org/10.1145/3377325.3377501 – reference: AbdallahZSDuLWebbGISammutCWebbGIData preparationEncyclopedia of machine learning and data mining2016BostonSpringer111 – reference: HussainWGaoHRazaMRRabhiFAMerigóJMAssessing cloud QoS predictions using OWA in neural network methodsNeural Comput Appl20223417148951491210.1007/s00521-022-07297-z – reference: AkyildirimEBarivieraAFNguyenDKSensoyAForecasting high-frequency stock returns: a comparison of alternative methodsAnn Oper Res2022444076310.1007/s10479-021-04464-807553129 – reference: Omenics. Omenics. https://omenics.com/ – reference: Hanussek M, Blohm M, Kintz M (2020) Can AutoML outperform humans? An evaluation on popular OpenML datasets using AutoML benchmark. In: 2020 2nd International conference on artificial intelligence, robotics and control – reference: Rosenthal S, Farra N, Nakov P (2017) SemEval-2017 task 4: sentiment analysis in Twitter. In: Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017). Association for Computational Linguistics, Vancouver, Canada, pp 502–518. https://doi.org/10.18653/v1/S17-2088. [Online]. Available: https://aclanthology.org/S17-2088 – reference: AgrapetidouACharonyktakisPGogasPPapadimitriouTTsamardinosIAn AutoML application to forecasting bank failuresAppl Econ Lett20212815910.1080/13504851.2020.1725230 – reference: KarmakerSKHassanMMSmithMJXuLZhaiCVeeramachaneniKAutoML to date and beyond: challenges and opportunitiesACM Comput Surv202154817510.1145/3470918 – reference: Cloudera. Streaming analytics. https://docs.cloudera.com/csa – volume: 18 start-page: 511 year: 2016 ident: 358_CR10 publication-title: Inf Syst Front doi: 10.1007/s10796-016-9633-2 – volume: 29 start-page: 313 issue: 1 year: 2020 ident: 358_CR36 publication-title: VLDB J doi: 10.1007/s00778-019-00557-w – volume: 149 start-page: 113251 year: 2020 ident: 358_CR52 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2020.113251 – ident: 358_CR19 doi: 10.1109/ICOMET.2019.8673428 – volume: 37 start-page: 4586 issue: 8 year: 2022 ident: 358_CR48 publication-title: Int J Intell Syst doi: 10.1002/int.22732 – volume: 34 start-page: 14895 issue: 17 year: 2022 ident: 358_CR14 publication-title: Neural Comput Appl doi: 10.1007/s00521-022-07297-z – ident: 358_CR59 doi: 10.1145/3377325.3377501 – ident: 358_CR23 – volume: 174 start-page: 433 year: 2020 ident: 358_CR50 publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2020.06.111 – ident: 358_CR1 – ident: 358_CR27 – ident: 358_CR62 doi: 10.1145/3379336.3381474 – ident: 358_CR4 doi: 10.1007/978-3-030-64466-6_1 – ident: 358_CR55 doi: 10.1145/3448016.3457557 – volume: 8 start-page: 1 issue: 1 year: 2021 ident: 358_CR60 publication-title: Rev Comput Eng Res doi: 10.18488/journal.76.2021.81.1.7 – ident: 358_CR22 doi: 10.1109/ICEIT54416.2022.9690729 – volume: 30 start-page: 4657 issue: 11 year: 2022 ident: 358_CR70 publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/TFUZZ.2022.3157951 – ident: 358_CR32 – volume: 13 start-page: 3166 issue: 12 year: 2020 ident: 358_CR63 publication-title: Proc VLDB Endow doi: 10.14778/3415478.3415542 – volume: 28 start-page: 5 issue: 1 year: 2021 ident: 358_CR54 publication-title: Appl Econ Lett doi: 10.1080/13504851.2020.1725230 – volume: 13 start-page: 132 issue: 1 year: 2019 ident: 358_CR42 publication-title: Enterp Inf Syst doi: 10.1080/17517575.2018.1493145 – start-page: 81 volume-title: Automated machine learning: methods, systems, challenges year: 2019 ident: 358_CR65 doi: 10.1007/978-3-030-05318-5_4 – volume: 212 start-page: 106622 year: 2021 ident: 358_CR8 publication-title: Knowl Based Syst doi: 10.1016/j.knosys.2020.106622 – ident: 358_CR26 – ident: 358_CR13 doi: 10.1007/978-3-540-31865-1_25 – start-page: 39 volume-title: Big Data year: 2016 ident: 358_CR34 doi: 10.1016/B978-0-12-805394-2.00002-7 – volume: 4 start-page: 161 issue: 3 year: 2018 ident: 358_CR38 publication-title: Digit Commun Netw doi: 10.1016/j.dcan.2017.10.002 – volume: 32 start-page: 16205 issue: 20 year: 2020 ident: 358_CR35 publication-title: Neural Comput Appl doi: 10.1007/s00521-020-04874-y – volume: 125 start-page: 100 year: 2021 ident: 358_CR37 publication-title: Future Gener Comput Syst doi: 10.1016/j.future.2021.06.024 – volume: 34 start-page: 2451 issue: 03 year: 2020 ident: 358_CR46 publication-title: Proc AAAI Conf Artif Intell doi: 10.1609/aaai.v34i03.5626 – ident: 358_CR12 – volume: 46 start-page: 16 issue: 4 year: 2021 ident: 358_CR31 publication-title: ACM Trans Database Syst doi: 10.1145/3485463 – volume: 278 start-page: 330 issue: 1 year: 2019 ident: 358_CR43 publication-title: Eur J Oper Res doi: 10.1016/j.ejor.2019.04.013 – ident: 358_CR33 – volume: 27 start-page: 67 issue: 1 year: 2016 ident: 358_CR44 publication-title: Neural Comput Appl doi: 10.1007/s00521-014-1550-z – volume: 2 start-page: 13 issue: 1 year: 2016 ident: 358_CR2 publication-title: Int J Data Sci Anal doi: 10.1007/s41060-016-0029-7 – ident: 358_CR15 – ident: 358_CR40 – ident: 358_CR69 doi: 10.1007/978-3-030-58548-8_46 – volume: 584 start-page: 280 year: 2022 ident: 358_CR6 publication-title: Inf Sci doi: 10.1016/j.ins.2021.10.054 – start-page: 151 volume-title: Automated machine learning: methods, systems, challenges year: 2019 ident: 358_CR66 doi: 10.1007/978-3-030-05318-5_8 – volume: 20 start-page: 3045 issue: 17 year: 2004 ident: 358_CR16 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bth361 – ident: 358_CR25 – ident: 358_CR61 doi: 10.1145/3399579.3399870 – year: 2022 ident: 358_CR68 publication-title: Int J Data Sci Anal doi: 10.1007/s41060-022-00309-0 – ident: 358_CR11 – volume: 54 start-page: 175 issue: 8 year: 2021 ident: 358_CR56 publication-title: ACM Comput Surv doi: 10.1145/3470918 – volume: 51 start-page: 3212 issue: 30 year: 2019 ident: 358_CR17 publication-title: Appl Econ doi: 10.1080/00036846.2018.1564115 – ident: 358_CR9 doi: 10.1145/3448326.3448353 – ident: 358_CR21 doi: 10.1109/HICSS.2016.137 – ident: 358_CR41 doi: 10.1109/EDOC49727.2020.00016 – year: 2020 ident: 358_CR47 publication-title: J Ambient Intell Humaniz Comput doi: 10.1007/s12652-020-01839-w – start-page: 1 volume-title: Encyclopedia of machine learning and data mining year: 2016 ident: 358_CR5 – volume-title: Automated machine learning: methods, systems, challenges year: 2019 ident: 358_CR53 doi: 10.1007/978-3-030-05318-5 – volume: 78 start-page: 3843 issue: 3 year: 2022 ident: 358_CR49 publication-title: J Supercomput doi: 10.1007/s11227-021-04013-x – ident: 358_CR18 doi: 10.18653/v1/S17-2088 – volume: 115 start-page: 635 year: 2019 ident: 358_CR45 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2018.08.003 – ident: 358_CR24 – ident: 358_CR29 – volume-title: Machine learning for data streams: with practical examples in MOA year: 2018 ident: 358_CR3 doi: 10.7551/mitpress/10654.001.0001 – ident: 358_CR28 – volume: 3 start-page: 237 year: 2019 ident: 358_CR57 publication-title: Proc ACM Hum Comput Interact doi: 10.1145/3361118 – volume: 132 start-page: 1049 year: 2018 ident: 358_CR39 publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2018.05.020 – year: 2022 ident: 358_CR51 publication-title: Ann Oper Res doi: 10.1007/s10479-021-04464-8 – volume: 34 start-page: 235 issue: 2 year: 2020 ident: 358_CR58 publication-title: KI Künstliche Intell doi: 10.1007/s13218-020-00637-y – volume: 94 start-page: 489 issue: 6 year: 2012 ident: 358_CR7 publication-title: Computing doi: 10.1007/s00607-012-0193-0 – start-page: 113 volume-title: Automated machine learning: methods, systems, challenges year: 2019 ident: 358_CR64 doi: 10.1007/978-3-030-05318-5_6 – ident: 358_CR67 doi: 10.1145/3290605.3300911 – ident: 358_CR30 doi: 10.1002/9781119198697 – volume: 112 start-page: 88 year: 2018 ident: 358_CR20 publication-title: Decis Support Syst doi: 10.1016/j.dss.2018.06.008
SSID	ssj0000327867
Score	2.3455245
Snippet	Real-time news impact prediction on financial markets is a challenging task for finance experts with limited IT expertise. Many practitioners build machine...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	129
SubjectTerms	Algorithms Classification Computer Appl. in Administrative Data Processing Computer Science Computer Systems Organization and Communication Networks Data collection Data transmission Datasets Digital currencies e-Commerce/e-business Finance Impact prediction IT in Business Machine learning Management of Computing and Information Systems Manufacturing News News media Pharmaceutical industry Real time Securities markets Sentiment analysis Social networks Software Software Engineering/Programming and Operating Systems Special Issue Paper Subject specialists Windows (intervals)
SummonAdditionalLinks	– databaseName: Computer Science Database dbid: K7- link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3PS8MwFA46PXhx_sTplBy8abBJ2iQ9yRgbgjB2UNittGkCwtxmO_37fWnTFQV3EXprGkreS9733st7H0K3NMq0tYDcYp2HJKRBTFTILVFxFGmW5TSUNdmEnEzUbBZPfcCt9NcqmzOxOqjzpXYx8gcOtjmWYPzU4-qDONYol131FBq7aI8yRp2eP0uyibEEnElVkchSJTgB6yR83UxdPUfBhydgtIjLpymiftqmFnD-ypFWpmfc_e9PH6FDDzrxoNaSY7RjFieo2xA6YL-_T9F0gIejKckLdwRi29zbwgBsMYDLOXFM9NgBcVxXV-JV4RI9TrgYHtu078DvVTF1eYZex6OX4RPxlAtEc8HXJErB5lthRcCM1Tl4GwJWKQMfxLA4cMTpPBI5s9JYayW3ytAUXLpUAq60GUCVc9RZLBfmAmFllDDKMGVB4IanMaOZgCESAAr4gLqHaLPYifb9yB0txjxpOyk7ASUgoKQSUKJ66G7zzaruxrF1dL-RSuJ3Zpm0Iumh-0au7eu_Z7vcPtsVOmCVKrkATR911sWnuUb7-mv9VhY3lV5-AwHI5Nw priority: 102 providerName: ProQuest
Title	A CEP-driven framework for real-time news impact prediction on financial markets
URI	https://link.springer.com/article/10.1007/s11761-023-00358-8 https://www.proquest.com/docview/3256975548
Volume	17
WOSCitedRecordID	wos000941054500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1863-2394 dateEnd: 20241207 omitProxy: false ssIdentifier: ssj0000327867 issn: 1863-2386 databaseCode: P5Z dateStart: 20230301 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1863-2394 dateEnd: 20241207 omitProxy: false ssIdentifier: ssj0000327867 issn: 1863-2386 databaseCode: K7- dateStart: 20230301 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 1863-2394 dateEnd: 20241207 omitProxy: false ssIdentifier: ssj0000327867 issn: 1863-2386 databaseCode: M7S dateStart: 20230301 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1863-2394 dateEnd: 20241207 omitProxy: false ssIdentifier: ssj0000327867 issn: 1863-2386 databaseCode: BENPR dateStart: 20230301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1863-2394 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000327867 issn: 1863-2386 databaseCode: RSV dateStart: 20070401 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3dS8MwED_c5oMvzk-czpIH3zTQNm2SPs6xIQijbCrDl9KPBIQ5Rzf9-730w6KooFD60jSEuyT3u1zufgAXjp-kWiNyC9LMo55jB1R6TFMZ-H7qJpnjiZJsQkwmcj4PwiopbF3fdq9DksVO3SS7OehyU7Qx1IS_JJUt6KC5k2Y5TmcPHycrNnOFLKhjHckZRZvEq2yZ77v5bJEamPklMloYnHH3f0Pdg90KYJJBOSP2YUstD6BbkzeQai0fQjggw1FIs9xsd0TXd7QIgliCQHJBDes8MaCblJmUZJWboI5RJMFH16U6yHOROL0-gvvx6G54Qyt6BZoyzjbUj9G-a6657SqdZuhZcBx3gv6GcgPbkKQzn2euFkprLZiWyonRfYsFYkidICw5hvbyZalOgEgluZLKlRqVq1gcuE7CsYlAMIL-XtoDpxZxlFa1xw0FxiJqqiYbkUUosqgQWSR7cPnxz6qsvPFr636tuahaheuIIZ4LBAIm_HxVa6r5_HNvp39rfgY7bqFsczjTh_Ymf1XnsJ2-bZ7WuQWd69EknFrQuhXUMpdLZ_gO_UermLfv923fZQ
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LS8QwEB58gV58i-szBz1pcJO0SXoQER8oq8seFLzVbZqAoOu6uyr-KX-jk7ZxUdCbB6G3piltvsx8yWTmA9hicWacQ-aWmDyiEasnVEfCUZ3EseFZziJVik2oZlPf3CStEXgPuTD-WGWwiYWhzh-N3yPfE-ibE4XOTx90n6hXjfLR1SChUcKiYd9eccnW3z8_xvHd5vz05OrojFaqAtQIKQY0bqNbc9LJOrfO5EioJWMqQ5pteVL32uAiljl3yjrnlHDasjauWtoKqZPL0Btjv6MwHgmtfK3-hqKfezp1wZUuRGuZloKiN5RVnk6ZrcfwVRSdJPXxO031V184JLjfYrKFqzud-W8_aRamK1JNDstZMAcjtjMPM0GwglT2awFah-TopEXznjfxxIVzaQSJO0HyfE8Hdw-W-IUGKbNHSbfnA1kevAQvF8qTkIciWby_CNd_8l1LMNZ57NhlINpqabXl2iGgrWgnnGUSmygkYLjGNTVgYXBTU9Vb97If9-mwUrQHRIqASAtApLoGO5_PdMtqI7-2XgsoSCvL00-HEKjBbsDR8PbPva383tsmTJ5dXV6kF-fNxipM8QLGfjNqDcYGvWe7DhPmZXDX720Uc4LA7V_j6wNtdz_9
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dS8MwEA86RXxxfuJ0ah5807ClaZP0ccwNRRkFP9hbadMEhFlHV_37vfTDqqggQt_yQbhLer_L5e6H0Cn1YmUMIDdfJS5xad8n0mWGSN_zlBMn1BUl2YSYTOR06gcfsviL1-51SLLMabBVmtK8N09Mr0l8o-B-E7A3xIbCJJHLaMW1pEHWX799eL9l6TNHyIJGlkrOCNgnXmXOfD_NZ-vUQM4vUdLC-Izb_1_2JtqogCcelDtlCy3pdBu1a1IHXJ3xHRQM8HAUkCSzv0Fs6rdbGMAtBoA5I5aNHlswjssMSzzPbLDHKhjDZ-oSHvipSKhe7KL78ehueEkq2gWiGGc58SKw-4Yb3ne0UQl4HBzWHYMfoh2_b8nTmccTxwhtjBHMSE0jcOsiAdjSxABX9lArfU71PsJSS66ldqQBpWsW-Q6NOXQRAFLAD1QdRGtxh6qqSW6pMWZhU03ZiiwEkYWFyELZQWfvY-ZlRY5fe3drLYbV6VyEDHCeLwBIQfN5rbWm-efZDv7W_QStBRfj8OZqcn2I1p1C7_b-potaefaij9Cqes0fF9lxsWnfAEiE5ss
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+CEP-driven+framework+for+real-time+news+impact+prediction+on+financial+markets&rft.jtitle=Service+oriented+computing+and+applications&rft.au=Chen%2C+Weisi&rft.au=El+Majzoub%2C+Ahmad&rft.au=Al-Qudah%2C+Islam&rft.au=Rabhi%2C+Fethi+A.&rft.date=2023-06-01&rft.pub=Springer+London&rft.issn=1863-2386&rft.eissn=1863-2394&rft.volume=17&rft.issue=2&rft.spage=129&rft.epage=144&rft_id=info:doi/10.1007%2Fs11761-023-00358-8&rft.externalDocID=10_1007_s11761_023_00358_8
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1863-2386&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1863-2386&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1863-2386&client=summon