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Journal articles on the topic 'Event-based'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Ciancia, Vincenzo, Gianluigi Ferrari, Roberto Guanciale, and Daniele Strollo. "Event based choreography." Science of Computer Programming 75, no. 10 (2010): 848–78. http://dx.doi.org/10.1016/j.scico.2010.02.009.

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2

Fisher, Greg, Matthew A. Josefy, and Emily Neubert. "Event-based entrepreneurship." Journal of Business Venturing 39, no. 1 (2024): 106366. http://dx.doi.org/10.1016/j.jbusvent.2023.106366.

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3

Cao, Jiuxin, Ziqing Zhu, Liang Shi, Bo Liu, and Zhuo Ma. "Multi-feature based event recommendation in Event-Based Social Network." International Journal of Computational Intelligence Systems 11, no. 1 (2018): 618. http://dx.doi.org/10.2991/ijcis.11.1.48.

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4

Mahir, Kaya1 and Yasemin Çetin-Kaya2. "COMPLEX EVENT PROCESSING USING IOT DEVICES BASED ON ARDUINO." International Journal on Cloud Computing: Services and Architecture (IJCCSA) 7, December (2018): 01–12. https://doi.org/10.5281/zenodo.1452025.

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Complex event processing systems have gained importance since recent developments in communication and integrated circuits technologies. Developers can easily develop many smart space systems by connecting various sensors to an Arduino as an internet of thing device. These systems are useful for many places such as factories, greenhouses (plant house) and smart-homes. Especially in plant houses when the desired humidity, temperature, light and soil moisture drops the certain level, the users should be notified through their smartphones. The sensor information is sent to a central server over t

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5

Kim, Jeongmin, Dipon Kumar Ghosh, and Yong Ju Jung. "Event-based video deblurring based on image and event feature fusion." Expert Systems with Applications 223 (August 2023): 119917. http://dx.doi.org/10.1016/j.eswa.2023.119917.

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6

Xiangsheng, Kong. "RFID Event Analysis Based on Complex Event Processing." International Journal of Online Engineering (iJOE) 10, no. 1 (2014): 5. http://dx.doi.org/10.3991/ijoe.v10i1.3049.

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7

Mao, Qianren, Xi Li, Hao Peng, et al. "Event prediction based on evolutionary event ontology knowledge." Future Generation Computer Systems 115 (February 2021): 76–89. http://dx.doi.org/10.1016/j.future.2020.07.041.

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8

Patiniotakis, Ioannis, Nikos Papageorgiou, Yiannis Verginadis, Dimitris Apostolou, and Gregoris Mentzas. "Dynamic event subscriptions in distributed event based architectures." Expert Systems with Applications 40, no. 6 (2013): 1935–46. http://dx.doi.org/10.1016/j.eswa.2012.10.005.

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9

Nan Guo and Shuang Lu. "Event Recommendation in Impromptu Event-based Social Networks." Research Briefs on Information and Communication Technology Evolution 3 (November 15, 2017): 204–9. http://dx.doi.org/10.56801/rebicte.v3i.60.

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In recent years, event-based Social Network (EBSN), which incorporates online and offline socialinteractions, has experienced rapid growth and attracted a lot of research. In this paper, we proposeImpromptu Event-Based Social Network (IEBSN) as an EBSN variant, where users setup impromptuevents, get involved in event-related topics, and participate in nearby events. Impromptu events arenot supposed to be well-planned and well-organized; creation and participation of events are morecasual. Online interactions in IEBSN are not dependent on group relationship but event-based topicrecommendation,

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10

Li, Yanhao, and Wei Liu. "Sudden Event Prediction Based on Event Knowledge Graph." Applied Sciences 12, no. 21 (2022): 11195. http://dx.doi.org/10.3390/app122111195.

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Event prediction is a knowledge inference problem that predicts the consequences or effects of an event based on existing information. Early work on event prediction typically modeled the event context to predict what would happen next. Moreover, the predicted outcome was often singular. These studies had difficulty coping with both the problems of predicting sudden events in unknown contexts and predicting outcomes consisting of multiple events. To address these two problems better, we present the heterogeneous graph event prediction model (HGEP), which is based on an event knowledge graph. T

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11

Sun, Xiao, Kenji Hashimoto, Ayanori Koizumi, et al. "Event-based climbing motion planning for a quadruped robot." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2015.6 (2015): 78–79. http://dx.doi.org/10.1299/jsmeicam.2015.6.78.

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12

Bækgaard, Lars. "Event‐based conceptual modeling." Business Process Management Journal 15, no. 4 (2009): 469–86. http://dx.doi.org/10.1108/14637150910975499.

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13

Sailer, A., M. Frank, F. Gaede, et al. "DD4Hep Based Event Reconstruction." Journal of Physics: Conference Series 898 (October 2017): 042017. http://dx.doi.org/10.1088/1742-6596/898/4/042017.

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14

Malony, Allen D. "Event-based performance perturbation." ACM SIGPLAN Notices 26, no. 7 (1991): 201–12. http://dx.doi.org/10.1145/109626.109646.

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15

FIEGE, LUDGER, GERO MÜHL, and FELIX C. GÄRTNER. "Modular event-based systems." Knowledge Engineering Review 17, no. 4 (2002): 359–88. http://dx.doi.org/10.1017/s0269888903000559.

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Event-based systems are developed and used to integrate components in loosely coupled systems. Research and product development have focused so far on efficiency issues but neglected methodological support to build such systems. In this article, the modular design and implementation of an event system is presented which supports scopes and event mappings, two new and powerful structuring methods that facilitate engineering and coordination of components in event-based systems. We give a formal specification of scopes and event mappings within a trace-based formalism adapted from temporal logic

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16

Artikis, Alexander, Anastasios Skarlatidis, François Portet, and Georgios Paliouras. "Logic-based event recognition." Knowledge Engineering Review 27, no. 4 (2012): 469–506. http://dx.doi.org/10.1017/s0269888912000264.

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AbstractToday's organizations require techniques for automated transformation of their large data volumes into operational knowledge. This requirement may be addressed by using event recognition systems that detect events/activities of special significance within an organization, given streams of ‘low-level’ information that is very difficult to be utilized by humans. Consider, for example, the recognition of attacks on nodes of a computer network given the Transmission Control Protocol/Internet Protocol messages, the recognition of suspicious trader behaviour given the transactions in a finan

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17

Jiang, Jian-Min, Huibiao Zhu, Qin Li, et al. "Event-based functional decomposition." Information and Computation 271 (April 2020): 104484. http://dx.doi.org/10.1016/j.ic.2019.104484.

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18

Benosman, Ryad, Charles Clercq, Xavier Lagorce, Sio-Hoi Ieng, and Chiara Bartolozzi. "Event-Based Visual Flow." IEEE Transactions on Neural Networks and Learning Systems 25, no. 2 (2014): 407–17. http://dx.doi.org/10.1109/tnnls.2013.2273537.

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19

Bovey, J. D. "Event-based personal retrieval." Journal of Information Science 22, no. 5 (1996): 357–66. http://dx.doi.org/10.1177/016555159602200504.

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20

Sarrate, R., J. Aguilar, and F. Nejjari. "Event-based process monitoring." Engineering Applications of Artificial Intelligence 20, no. 8 (2007): 1152–62. http://dx.doi.org/10.1016/j.engappai.2007.02.008.

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21

Kim, Joocheol. "Event tree based sampling." Computers & Operations Research 33, no. 5 (2006): 1184–99. http://dx.doi.org/10.1016/j.cor.2004.09.008.

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22

Tyler, Neil. "Event-Based Vision Sensor." New Electronics 51, no. 18 (2019): 9. http://dx.doi.org/10.12968/s0047-9624(22)61429-9.

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23

Zhang, Shuo, and Qin Lv. "Hybrid EGU-based group event participation prediction in event-based social networks." Knowledge-Based Systems 143 (March 2018): 19–29. http://dx.doi.org/10.1016/j.knosys.2017.12.002.

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24

Fan, Wen Hao, Lei Ding, Bi Hua Tang, Fan Wu, and Hong Guang Zhang. "Event Probability Based Priority Filter for Efficient Event Matching." Applied Mechanics and Materials 687-691 (November 2014): 1672–76. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1672.

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Event matching plays a critical role in content-based publish/subscribe system. Most traditional methods focus on existing subscriptions separation and combination. However, an event usually comes with certain probability distribution in each dimension. Thus taking both existing subscriptions and probable coming event into consideration can improve event matching time efficiency. Based on that, we put forward PF (Priority Filter), a highly efficient event matching algorithm. By building up a unified model with historical subscriptions for continuous and discrete attributes, we derive formulas

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25

Ren, Kexin, Zhipeng Zhang, and Chengyi Xia. "Event-Based Fault Diagnosis of Networked Discrete Event Systems." IEEE Transactions on Circuits and Systems II: Express Briefs 69, no. 3 (2022): 1787–91. http://dx.doi.org/10.1109/tcsii.2021.3120486.

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26

Cai, Yi, Haoran Xie, Raymond Y. K. Lau, Qing Li, Tak-Lam Wong, and Fu Lee Wang. "Temporal event searches based on event maps and relationships." Applied Soft Computing 85 (December 2019): 105750. http://dx.doi.org/10.1016/j.asoc.2019.105750.

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27

Kou, Xinyan, and Jill Hohenstein. "An Event Conflation Model based on the Realization Event." Cognitive Semantics 6, no. 2 (2020): 188–213. http://dx.doi.org/10.1163/23526416-bja10008.

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Abstract In this theoretical analysis, we first identify four event components essential to the conceptualization of Realization – manner salience, agentivity, the intended result and the real-world result. We move on to establish an event conflation model which reflects their interplay in an attempt to outline the speech generation mechanisms behind different lexicalization patterns. By offering alternative interpretations for some well-established findings in the Motion domain from the Realization perspective, we also explore the possibility of applying unified analysis to different macro-ev

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28

Zhong, Zhaoman, Zongtian Liu, Cunhua Li, and Yan Guan. "Event ontology reasoning based on event class influence factors." International Journal of Machine Learning and Cybernetics 3, no. 2 (2011): 133–39. http://dx.doi.org/10.1007/s13042-011-0046-8.

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29

Wang, Xiaofeng, Gang Li, Guang Jiang, and Zhongzhi Shi. "Semantic trajectory-based event detection and event pattern mining." Knowledge and Information Systems 37, no. 2 (2011): 305–29. http://dx.doi.org/10.1007/s10115-011-0471-8.

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30

Meyer, Chistopher, and Rebecca Snyder. "Adapting Museum Workflows to Accommodate Omic-based, Multi-species Occurrences from Environmental Samples." Biodiversity Information Science and Standards 6 (September 19, 2022): e95054. https://doi.org/10.3897/biss.6.95054.

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Natural history museums play a critical role in curating global anthropological, biological and geological diversity. Museums host more than voucher samples for comparative identification purposes. They host a suite of derived products and associated information, which cascade from these records of diversity in time and space. Increasingly, museums capture whole communities through curation of environmental samples and their derived data. Environmental samples are defined as material samples that yield multi-species occurrence records that cannot be sorted into traditional independent lots and

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31

Shukla, Abhishek. "Bridging the Gap between Event-Based Programming and Functional Programming." International Journal of Science and Research (IJSR) 11, no. 1 (2022): 1595–98. http://dx.doi.org/10.21275/sr231116134821.

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32

Annamalai, Lakshmi, Vignesh Ramanathan, and Chetan Singh Thakur. "Event-LSTM: An Unsupervised and Asynchronous Learning-Based Representation for Event-Based Data." IEEE Robotics and Automation Letters 7, no. 2 (2022): 4678–85. http://dx.doi.org/10.1109/lra.2022.3151426.

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33

Liu, Shenghao, Bang Wang, and Minghua Xu. "SERGE: Successive Event Recommendation Based on Graph Entropy for Event-Based Social Networks." IEEE Access 6 (2018): 3020–30. http://dx.doi.org/10.1109/access.2017.2786679.

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34

Ruiz, Ángel, Manuel Beschi, Antonio Visioli, Sebastián Dormido, and Hornero Jorge Jimenez. "A unified event-based control approach for FOPTD and IPTD processes based on the filtered Smith predictor." Journal of The Franklin Institute 354, no. 2 (2017): 1239–64. https://doi.org/10.1016/j.jfranklin.2016.11.017.

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A new unified design of an event-based PID control architecture for self-regulating and integral processes is investigated in this work. The design is based on the Symmetrical-Send-On-Delta sampling technique (SSOD) and on the filtered Smith predictor (FSP). In particular, the conditions to achieve robust stability to limit cycles are studied from the point of view of the filter parameters through the describing functions theory. In this context, a simple frequency domain-based tuning methodology to address the cases of first-order-plus-dead-time and integrator-plus-dead-time processes is prop

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35

Li, Ying Na, Yang Tao, Jia Ni Wang, and Yun Hui Fu. "A New Online New Event Detection Algorithm Based on Event Merging and Event Splitting." Applied Mechanics and Materials 513-517 (February 2014): 2024–30. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2024.

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The current Online New Event Detection (ONED) algorithms based on the elements of news can effectively detect new events; however, these methods have two limitations: First, events are tend to be split into several small similar events at the beginning of events; Second, those events about the same describing object but different subject are opt to be merged into one large event. This paper proposes a new improved ONED algorithm that could effectively solve the above limitations; the new algorithm makes two improvements at the basis of the current ONED algorithms: First, at the beginning of ev

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36

Li, Wei, Dezhi Cheng, Lei He, Yuanzhuo Wang, and Xiaolong Jin. "Joint Event Extraction Based on Hierarchical Event Schemas From FrameNet." IEEE Access 7 (2019): 25001–15. http://dx.doi.org/10.1109/access.2019.2900124.

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37

Yin, Xiaoting, Hao Shi, Jiaan Chen, et al. "Exploring event-based human pose estimation with 3D event representations." Computer Vision and Image Understanding 249 (December 2024): 104189. http://dx.doi.org/10.1016/j.cviu.2024.104189.

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38

Court, Alecsandra, and Christoph Bruecker. "Event Based Cameras: A Comparative Study For Fluid Event Detection." Proceedings of the International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics 21 (July 8, 2024): 1–15. http://dx.doi.org/10.55037/lxlaser.21st.208.

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A feasibility study using an event-based camera for the detection of plane-wise motion of a 2D array of bio-inspired sensing pillars to detect generated flow events. EBCs show potential for future applications where PIV may have been classically used. They have the benefit of only generating data when an event happens and with comparatively cheaper setups, they hold great potential for flow monitoring. EBCs collect pixel x and y locations, as well as pixel contrast change data at each time there is an event and can be trained on regions of interest. Comparative experiments were carried out in

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39

Zhang, Jason Shuo, and Qin Lv. "Understanding Event Organization at Scale in Event-Based Social Networks." ACM Transactions on Intelligent Systems and Technology 10, no. 2 (2019): 1–23. http://dx.doi.org/10.1145/3243227.

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40

Guo, Chunhui, Chuan Lyu, Jiayu Chen, and Dong Zhou. "A multi-event combination maintenance model based on event correlation." PLOS ONE 13, no. 11 (2018): e0207390. http://dx.doi.org/10.1371/journal.pone.0207390.

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41

BN, Nithya, D. Evangelin Geetha, and Manish Kumar. "Optimization-assisted personalized event recommendation for event-based social networks." Advances in Engineering Software 176 (March 2023): 103368. http://dx.doi.org/10.1016/j.advengsoft.2022.103368.

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42

Oltean, Ecaterina. "Discrete-Event Modeling of Continuous Processes for Event-Based Control." IFAC Proceedings Volumes 28, no. 24 (1995): 293–98. http://dx.doi.org/10.1016/s1474-6670(17)46565-7.

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43

Lu, Jing, and Vincent Ng. "Span-Based Event Coreference Resolution." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 15 (2021): 13489–97. http://dx.doi.org/10.1609/aaai.v35i15.17591.

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Motivated by the recent successful application of span-based models to entity-based information extraction tasks, we investigate span-based models for event coreference resolution, focusing on determining (1) whether the successes of span-based models of entity coreference can be extended to event coreference; (2) whether exploiting the dependency between event coreference and the related subtask of trigger detection; and (3) whether automatically computed entity coreference information can benefit span-based event coreference resolution. Empirical results on the standard evaluation dataset pr

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44

Schober, Christian, Christof Pruss, Andreas Faulhaber, and Alois Herkommer. "Event based coherence scanning interferometry." Optics Letters 46, no. 17 (2021): 4332. http://dx.doi.org/10.1364/ol.437489.

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45

Xujie Zhang, Zongtian Liu, Wei Liu, and Xiaoying Su. "Concept Algebra-Based Event Model." International Journal of Digital Content Technology and its Applications 7, no. 5 (2013): 172–81. http://dx.doi.org/10.4156/jdcta.vol7.issue5.21.

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46

Piciarelli, C., C. Micheloni, and G. L. Foresti. "Trajectory-Based Anomalous Event Detection." IEEE Transactions on Circuits and Systems for Video Technology 18, no. 11 (2008): 1544–54. http://dx.doi.org/10.1109/tcsvt.2008.2005599.

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47

Singh, M., A. Basu, and M. Mandal. "Event Dynamics Based Temporal Registration." IEEE Transactions on Multimedia 9, no. 5 (2007): 1004–15. http://dx.doi.org/10.1109/tmm.2007.898937.

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48

Simonetto, Paolo, Daniel Archambault, and Stephen Kobourov. "Event-Based Dynamic Graph Visualisation." IEEE Transactions on Visualization and Computer Graphics 26, no. 7 (2020): 2373–86. http://dx.doi.org/10.1109/tvcg.2018.2886901.

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49

Sabatier, Quentin, Sio-Hoi Ieng, and Ryad Benosman. "Asynchronous Event-Based Fourier Analysis." IEEE Transactions on Image Processing 26, no. 5 (2017): 2192–202. http://dx.doi.org/10.1109/tip.2017.2661702.

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50

Bertolissi, Clara, and Maribel Fernandez. "Distributed event-based access control." International Journal of Information and Computer Security 3, no. 3/4 (2009): 306. http://dx.doi.org/10.1504/ijics.2009.031042.

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