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Journal articles on the topic 'Data-driven applications'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2023

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1

Rao, K. Venkateswara. "Spatiotemporal Database Schema for Data Driven Applications in Smart Agriculture." Indian Journal Of Science And Technology 16, no. 1 (2023): 12–22. http://dx.doi.org/10.17485/ijst/v16i1.1687.

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2

Brzostowski, Krzysztof, Jaroslaw Drapala, and Jerzy Swiatek. "Data-Driven Models for eHealth Applications." International Journal of Computer Science and Artificial Intelligence 3, no. 1 (2013): 1–9. http://dx.doi.org/10.5963/ijcsai0301001.

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3

Kamath, Chandrika. "Application-Driven Data Analysis." Statistical Analysis and Data Mining 1, no. 5 (2009): 285. http://dx.doi.org/10.1002/sam.10023.

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4

Poonia, Ramesh Chandra, and Santosh R. Durugkar. "Sampling Techniques Used in Big-Data Driven Applications." Journal of Intelligent Systems and Computing 2, no. 1 (2021): 17–20. http://dx.doi.org/10.51682/jiscom.00201004.2021.

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Data-driven systems process the data from various sources in multiple applications. Data retrieved from heterogeneous sources need to be available in an aggregate and unique format. This requirement gives rise to the process of the Big-data and proposed next-generation big-data processing systems. There are many applications based on contextual data useful for identifying the traffic intensity, changing users per application, weather conditions etc., and serve as next- generation business-specific systems. In such systems data abstraction and representation are the important tasks & granul

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5

Dannana, Suresh, Govinda Rao Tamminaina, and A. R. V. Satheesh Kumar. "Data Driven Data Encoding for Low Power LDPC Applications." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 10 (2015): 375–88. http://dx.doi.org/10.14257/ijsip.2015.8.10.37.

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6

Schadd, Maarten, Nico de Reus, Sander Uilkema, and Jeroen Voogd. "Data-driven behavioural modelling for military applications." Journal of Defence & Security Technologies 4, no. 1 (2022): 12–36. http://dx.doi.org/10.46713/jdst.004.02.

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This article investigates the possibilities for creating behavioural models of military decision making in a data-driven manner. As not much data from actual operations is available, and data cannot easily be created in the military context, most approaches use simulators to learn behaviour. A simulator is however not always available or is difficult to create. This study focusses on the creation of behavioural models from data that was collected during a field exercise. As data in general is limited, noisy and erroneous, this makes the creation of realistic models challenging. Besides using t

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7

Dabbara, Rekha, M. Chandrakumar, V. Anandhi, and D. Muruganandhi. "Data Driven Marketing Applications for Agriculture Services." International Journal of Current Microbiology and Applied Sciences 9, no. 2 (2020): 2914–20. http://dx.doi.org/10.20546/ijcmas.2020.902.331.

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8

Jr., Josip Vrbanek, and Wilson Wang. "Data-Driven Forecasting Schemes: Evaluation and Applications." Journal of Computer Science 3, no. 9 (2007): 747–53. http://dx.doi.org/10.3844/jcssp.2007.747.753.

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Yang, Fan, Nitin Gupta, Chavdar Botev, Elizabeth F. Churchill, George Levchenko, and Jayavel Shanmugasundaram. "WYSIWYG development of data driven web applications." Proceedings of the VLDB Endowment 1, no. 1 (2008): 163–75. http://dx.doi.org/10.14778/1453856.1453879.

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10

Kolios, Panayiotis, Christos Panayiotou, Georgios Ellinas, and Marios Polycarpou. "Data-Driven Event Triggering for IoT Applications." IEEE Internet of Things Journal 3, no. 6 (2016): 1146–58. http://dx.doi.org/10.1109/jiot.2016.2564428.

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11

Su, Ruidan, George Panoutsos, and Xiaodong Yue. "Data-Driven Granular Computing Systems and Applications." Granular Computing 6, no. 1 (2020): 1–2. http://dx.doi.org/10.1007/s41066-020-00222-6.

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12

Van Daele, Koen, Leen Meganck, and Sophie Mortier. "On data-driven systems and system-driven data." Journal of Cultural Heritage Management and Sustainable Development 6, no. 2 (2016): 153–65. http://dx.doi.org/10.1108/jchmsd-01-2016-0004.

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Purpose – Over the past 20 years, heritage inventories in Flanders (Belgium) have evolved from printed books to digital inventories. The purpose of this paper is to look at this evolution and highlight the interaction between the system and its users. Design/methodology/approach – After a short introduction about the history of inventories in Flanders, this paper mainly concerns itself with the last decade. Discrete topics will be highlighted to show the effects of the interaction that has taken place. Findings – It is obvious that a system that publishes a digital inventory needs to adapt to

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13

Li, Xi, Zehua Wang, Victor C. M. Leung, Hong Ji, Yiming Liu, and Heli Zhang. "Blockchain-empowered Data-driven Networks." ACM Computing Surveys 54, no. 3 (2021): 1–38. http://dx.doi.org/10.1145/3446373.

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The paths leading to future networks are pointing towards a data-driven paradigm to better cater to the explosive growth of mobile services as well as the increasing heterogeneity of mobile devices, many of which generate and consume large volumes and variety of data. These paths are also hampered by significant challenges in terms of security, privacy, services provisioning, and network management. Blockchain, which is a technology for building distributed ledgers that provide an immutable log of transactions recorded in a distributed network, has become prominent recently as the underlying t

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14

Sarker, Iqbal. "Mobile Data Science: Towards Understanding Data-Driven Intelligent Mobile Applications." ICST Transactions on Scalable Information Systems 5, no. 19 (2018): 155866. http://dx.doi.org/10.4108/eai.13-7-2018.155866.

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15

James, P. "Data driven drilling." Computing and Control Engineering 17, no. 4 (2006): 36–39. http://dx.doi.org/10.1049/cce:20060407.

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16

Ahmad, Aakash, Mahdi Fahmideh, Ahmed B. Altamimi, et al. "Software Engineering for IoT-Driven Data Analytics Applications." IEEE Access 9 (2021): 48197–217. http://dx.doi.org/10.1109/access.2021.3065528.

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17

van de Berg, Damien, Thomas Savage, Panagiotis Petsagkourakis, Dongda Zhang, Nilay Shah, and Ehecatl Antonio del Rio-Chanona. "Data-driven optimization for process systems engineering applications." Chemical Engineering Science 248 (February 2022): 117135. http://dx.doi.org/10.1016/j.ces.2021.117135.

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18

Ding, S. X., S. Yin, Y. Wang, Y. Wang, Y. Yang, and B. Ni. "Data-Driven Design of Observers and Its Applications." IFAC Proceedings Volumes 44, no. 1 (2011): 11441–46. http://dx.doi.org/10.3182/20110828-6-it-1002.00891.

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19

Li, Manqi (Maggie), Yan Huang, and Amitabh Sinha. "Data-Driven Promotion Planning for Paid Mobile Applications." Information Systems Research 31, no. 3 (2020): 1007–29. http://dx.doi.org/10.1287/isre.2020.0928.

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In this paper, we propose a two-step data-analytic approach to the promotion planning for mobile applications (apps). In the first step, we use historical sales data to estimate the app demand model and quantify the effect of price promotions on download volume. The estimation results reveal two interesting characteristics of the relationship between price promotion and download volume of mobile apps: (1) the magnitude of the direct immediate promotion effect is declining within a multiday promotion; and (2) due to the visibility effect (i.e., apps ranked high on the download chart are more vi

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20

Markovsky, Ivan, Jan C. Willems, Paolo Rapisarda, and Bart L. M. De Moor. "DATA DRIVEN SIMULATION WITH APPLICATIONS TO SYSTEM IDENTIFICATION." IFAC Proceedings Volumes 38, no. 1 (2005): 970–75. http://dx.doi.org/10.3182/20050703-6-cz-1902.00163.

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21

Wu, Xiao, Chong-Wah Ngo, and Wan-Lei Zhao. "Data-Driven Approaches to Community-Contributed Video Applications." IEEE Multimedia 17, no. 4 (2010): 58–69. http://dx.doi.org/10.1109/mmul.2010.46.

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22

Zhang, Huaguang, Bin Jiang, and Wen Yu. "Data-Driven Fault Supervisory Control Theory and Applications." Mathematical Problems in Engineering 2013 (2013): 1–2. http://dx.doi.org/10.1155/2013/387341.

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23

Khokhar, Ashfaq A., Susanne E. Hambrusch, and Erturk Kocalar. "Termination detection in data-driven parallel computations/applications." Journal of Parallel and Distributed Computing 63, no. 3 (2003): 312–26. http://dx.doi.org/10.1016/s0743-7315(03)00014-5.

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24

Deutsch, Alin, Liying Sui, and Victor Vianu. "Specification and verification of data-driven Web applications." Journal of Computer and System Sciences 73, no. 3 (2007): 442–74. http://dx.doi.org/10.1016/j.jcss.2006.10.006.

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25

Michopoulos, J., P. Tsompanopoulou, E. Houstis, et al. "On a data-driven environment for multiphysics applications." Future Generation Computer Systems 21, no. 6 (2005): 953–68. http://dx.doi.org/10.1016/j.future.2003.12.023.

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26

Samea, Fatima, Farooque Azam, Muhammad Rashid, Muhammad Waseem Anwar, Wasi Haider Butt, and Abdul Wahab Muzaffar. "A model-driven framework for data-driven applications in serverless cloud computing." PLOS ONE 15, no. 8 (2020): e0237317. http://dx.doi.org/10.1371/journal.pone.0237317.

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27

Niebur, Glen L., and Thomas R. Chase. "A Grammar Driven Data Translation System for Computer Integrated Manufacturing." Journal of Mechanical Design 124, no. 1 (1999): 136–42. http://dx.doi.org/10.1115/1.1434268.

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The ability to share data among the numerous software applications used to design and analyze products is an important aspect of computer integrated manufacturing. In the past, data sharing has been implemented by direct translation of data files, through neutral data formats, and through central shared databases. This paper describes a data sharing architecture that addresses some of the limitations of these systems. It employs a database management system as a central repository for part data in an application independent format. A configurable translator, called Datatrans, is used to transf

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28

Sahar, Gul, Kamalrulnizam Abu Bakar, Sabit Rahim, Naveed Ali Khan Kaim Khani, and Tehmina Bibi. "Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey." Technologies 9, no. 4 (2021): 76. http://dx.doi.org/10.3390/technologies9040076.

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Wireless sensor networks (WSNs) are considered producers of large amounts of rich data. Four types of data-driven models that correspond with various applications are identified as WSNs: query-driven, event-driven, time-driven, and hybrid-driven. The aim of the classification of data-driven models is to get real-time applications of specific data. Many challenges occur during data collection. Therefore, the main objective of these data-driven models is to save the WSN’s energy for processing and functioning during the data collection of any application. In this survey article, the recent advan

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29

Park, Daehee, and Jeannie Kang. "Constructing Data-Driven Personas through an Analysis of Mobile Application Store Data." Applied Sciences 12, no. 6 (2022): 2869. http://dx.doi.org/10.3390/app12062869.

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As smartphone segments have become more complex in recent times, the importance of personas for designing and marketing has increased. Earlier, designers focused on traditional qualitative personas but have been criticised for the lack of evidence and outdated results. However, although several methods of quantitative persona creation have been developed over the last few years, the use of mobile application store data has not yet been studied. In this research, we propose a framework using work domain analysis to help designers and marketers to build personas easily from mobile phone applicat

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30

OUYANG, Ying. "Prospect of Dynamic Data Driven Application System in agricultural and environmental applications." CHINESE JOURNAL OF ECO-AGRICULTURE 16, no. 6 (2009): 1597–602. http://dx.doi.org/10.3724/sp.j.1011.2008.01597.

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31

Kyriazis, Dimosthenis, Argyro Mavrogiorgou, Yannis Poulakis, et al. "Diastema: Data-driven Stack for Big Data Applications Management and Deployment." International Journal of Big Data Management 1, no. 1 (2023): 1. http://dx.doi.org/10.1504/ijbdm.2023.10048598.

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32

Samosir, Jonathan, Maria Indrawan-Santiago, and Pari Delir Haghighi. "An Evaluation of Data Stream Processing Systems for Data Driven Applications." Procedia Computer Science 80 (2016): 439–49. http://dx.doi.org/10.1016/j.procs.2016.05.322.

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33

Mulvenna, Maurice, Marian Norwood, and Alex Büchner. "Data-Driven Marketing." Electronic Markets 8, no. 3 (1998): 32–35. http://dx.doi.org/10.1080/10196789800000038.

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34

Klinger, Volkhard. "Applications of IoT-Driven Multi-Sensor Fusion and Integration of Dynamic Data-Driven Symbiotic Machine Learning." International Journal of Practical Healthcare Innovation and Management Techniques 9, no. 2 (2022): 1–22. http://dx.doi.org/10.4018/ijphimt.315770.

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The internet of things is enabling new applications, especially in the field of biomedical systems. Such internet of things systems can not only use an existing infrastructure, but also build their own network for data exchange. By linking multiple distributed sensors, complex interpretations of data and identification of scenarios can be realized based on sensor fusion. This allows new relationships to be captured and interpreted. Data fusion is thus a key concept for object, motion, and scenario identification based on sensor values. Here, the authors first introduce specific applications of

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35

Knieps, Guenter. "Data-driven sector coupling in 5G-based smart networks." Competition and Regulation in Network Industries 22, no. 1 (2021): 53–68. http://dx.doi.org/10.1177/1783591721992762.

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5G attains the role of a GPT for an open set of downstream IoT applications in various network industries and within the app economy more generally. Traditionally, sector coupling has been a rather narrow concept focusing on the horizontal synergies of urban system integration in terms of transport, energy, and waste systems, or else the creation of new intermodal markets. The transition toward 5G has fundamentally changed the framing of sector coupling in network industries by underscoring the relevance of differentiating between horizontal and vertical sector coupling. Due to the fixed mobil

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36

Jetzek, Thorhildur, Michel Avital, and Niels Bjorn-Andersen. "Data-Driven Innovation through Open Government Data." Journal of theoretical and applied electronic commerce research 9, no. 2 (2014): 15–16. http://dx.doi.org/10.4067/s0718-18762014000200008.

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37

Arridge, Simon, Peter Maass, Ozan Öktem, and Carola-Bibiane Schönlieb. "Solving inverse problems using data-driven models." Acta Numerica 28 (May 1, 2019): 1–174. http://dx.doi.org/10.1017/s0962492919000059.

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Recent research in inverse problems seeks to develop a mathematically coherent foundation for combining data-driven models, and in particular those based on deep learning, with domain-specific knowledge contained in physical–analytical models. The focus is on solving ill-posed inverse problems that are at the core of many challenging applications in the natural sciences, medicine and life sciences, as well as in engineering and industrial applications. This survey paper aims to give an account of some of the main contributions in data-driven inverse problems.

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38

Chang, Ching-Ter, Chun-Che Huang, and Tzu-Liang (Bill) Tseng. "Preface: Data-driven Decision Making - Theory, Methods, and Applications." Applied Soft Computing 102 (April 2021): 107261. http://dx.doi.org/10.1016/j.asoc.2021.107261.

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39

Junker, Annika, Julia Timmermann, and Ansgar Trächtler. "Learning Data-Driven PCHD Models for Control Engineering Applications*." IFAC-PapersOnLine 55, no. 12 (2022): 389–94. http://dx.doi.org/10.1016/j.ifacol.2022.07.343.

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40

Kaluarachchi, Yamuna. "Implementing Data-Driven Smart City Applications for Future Cities." Smart Cities 5, no. 2 (2022): 455–74. http://dx.doi.org/10.3390/smartcities5020025.

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Cities are investing in data-driven smart technologies to improve performance and efficiency and to generate a vast amount of data. Finding the opportunities to innovatively use this data help governments and authorities to forecast, respond, and plan for future scenarios. Access to real-time data and information can provide effective services that improve productivity, resulting in environmental, social, and economic benefits. It also assists in the decision-making process and provides opportunities for community engagement and participation by improving digital literacy and culture. This pap

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41

Dösinger, Christoph, Tobias Spitaler, Alexander Reichmann, Daniel Scheiber, and Lorenz Romaner. "Applications of Data Driven Methods in Computational Materials Design." BHM Berg- und Hüttenmännische Monatshefte 167, no. 1 (2021): 29–35. http://dx.doi.org/10.1007/s00501-021-01182-3.

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AbstractIn today’s digitized world, large amounts of data are becoming available at rates never seen before. This holds true also for materials science where high-throughput simulations and experiments continuously produce new data. Data driven methods are required which can make best use of the information stored in large data repositories. In the present article, two of such data driven methods are presented. First, we apply machine learning to generalize and extend the results obtained from computationally intense density functional theory (DFT) simulations. We show how grain boundary segre

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42

Linaza, Maria Teresa, Jorge Posada, Jürgen Bund, et al. "Data-Driven Artificial Intelligence Applications for Sustainable Precision Agriculture." Agronomy 11, no. 6 (2021): 1227. http://dx.doi.org/10.3390/agronomy11061227.

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One of the main challenges for the implementation of artificial intelligence (AI) in agriculture includes the low replicability and the corresponding difficulty in systematic data gathering, as no two fields are exactly alike. Therefore, the comparison of several pilot experiments in different fields, weather conditions and farming techniques enhances the collective knowledge. Thus, this work provides a summary of the most recent research activities in the form of research projects implemented and validated by the authors in several European countries, with the objective of presenting the alre

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43

Liu, Xuanzhe, Yun Ma, Gang Huang, Junfeng Zhao, Hong Mei, and Yunxin Liu. "Data-Driven Composition for Service-Oriented Situational Web Applications." IEEE Transactions on Services Computing 8, no. 1 (2015): 2–16. http://dx.doi.org/10.1109/tsc.2014.2304729.

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44

Blasch, Erik, Guna Seetharaman, and Kitt Reinhardt. "Dynamic Data Driven Applications System Concept for Information Fusion." Procedia Computer Science 18 (2013): 1999–2007. http://dx.doi.org/10.1016/j.procs.2013.05.369.

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45

Xie, Tao, and Xiao Qin. "Security-driven scheduling for data-intensive applications on grids." Cluster Computing 10, no. 2 (2007): 145–53. http://dx.doi.org/10.1007/s10586-007-0015-x.

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46

Liang, Hong, Qi Sun, and Qiang Du. "Data-driven compressive sensing and applications in uncertainty quantification." Journal of Computational Physics 374 (December 2018): 787–802. http://dx.doi.org/10.1016/j.jcp.2018.07.056.

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47

Kulkarni, Devdatta. "Programming framework for sensor-data driven context-aware applications." ACM SIGBED Review 5, no. 1 (2008): 1–2. http://dx.doi.org/10.1145/1366283.1366302.

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48

Pohl, Martin. "Data driven parallelism in experimental high energy physics applications." Computer Physics Communications 45, no. 1-3 (1987): 311–17. http://dx.doi.org/10.1016/0010-4655(87)90167-6.

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49

Liaskos, Christos K., Sophia G. Petridou, Georgios I. Papadimitriou, Petros Nicopolitidis, Andreas S. Pomportsis, and Mohammad S. Obaidat. "Clustering-Driven Wireless Data Broadcasting." IEEE Wireless Communications 16, no. 6 (2009): 80–87. http://dx.doi.org/10.1109/mwc.2009.5361182.

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50

Markovsky, Ivan, and Paolo Rapisarda. "Data-driven simulation and control." International Journal of Control 81, no. 12 (2008): 1946–59. http://dx.doi.org/10.1080/00207170801942170.

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