Relevant bibliographies by topics / Data-Driven Processes

Academic literature on the topic 'Data-Driven Processes'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Data-Driven Processes"

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Bonné, Dennis, and Sten Bay Jørgensen. "Data-Driven Modeling of Batch Processes." IFAC Proceedings Volumes 37, no. 1 (January 2004): 589–94. http://dx.doi.org/10.1016/s1474-6670(17)38796-7.

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Van Ameijde, Jeroen. "Data-driven Urban Design." SPOOL 9, no. 1 (May 27, 2022): 35–48. http://dx.doi.org/10.47982/spool.2022.1.03.

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Nicholas Negroponte and MIT’s Architecture Machine Group speculated in the 1970s about computational processes that were open to participation, incorporating end-user preferences and democratizing urban design. Today’s ‘smart city’ technologies, using the monitoring of people’s movement and activity patterns to offer more effective and responsive services, might seem like contemporary interpretations of Negroponte’s vision, yet many of the collectors of user information are disconnected from urban policy making. This article presents a series of theoretical and procedural experiments conducted through academic research and teaching, developing user-driven generative design processes in the spirit of ‘The Architecture Machine’. It explores how new computational tools for site analysis and monitoring can enable data-driven urban place studies, and how these can be connected to generative strategies for public spaces and environments at various scales. By breaking down these processes into separate components of gathering, analysing, translating and implementing data, and conceptualizing them in relation to urban theory, it is shown how data-driven urban design processes can be conceived as an open-ended toolkit to achieve various types of user-driven outcomes. It is argued that architects and urban designers are uniquely situated to reflect on the benefits and value systems that control data-driven processes, and should deploy these to deliver more resilient, liveable and participatory urban spaces.
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Lickert, Benjamin, Steffen Wolf, and Gerhard Stock. "Data-Driven Langevin Modeling of Nonequilibrium Processes." Journal of Physical Chemistry B 125, no. 29 (July 16, 2021): 8125–36. http://dx.doi.org/10.1021/acs.jpcb.1c03828.

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Kamgaing, Joseph Tadjuidje, Hernando Ombao, and Richard A. Davis. "Autoregressive processes with data-driven regime switching." Journal of Time Series Analysis 30, no. 5 (September 2009): 505–33. http://dx.doi.org/10.1111/j.1467-9892.2009.00622.x.

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Boukouvala, F., F. J. Muzzio, and Marianthi G. Ierapetritou. "Dynamic Data-Driven Modeling of Pharmaceutical Processes." Industrial & Engineering Chemistry Research 50, no. 11 (June 2011): 6743–54. http://dx.doi.org/10.1021/ie102305a.

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Liang, Y. C., S. Wang, W. D. Li, and X. Lu. "Data-Driven Anomaly Diagnosis for Machining Processes." Engineering 5, no. 4 (August 2019): 646–52. http://dx.doi.org/10.1016/j.eng.2019.03.012.

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Jiang, Qingchao, Huaikuan Yi, Xuefeng Yan, Xinmin Zhang, and Jian Huang. "Data-Driven Model Predictive Monitoring for Dynamic Processes." IFAC-PapersOnLine 53, no. 2 (2020): 98–103. http://dx.doi.org/10.1016/j.ifacol.2020.12.101.

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Wiebe, Johannes, Inês Cecílio, and Ruth Misener. "Data-Driven Optimization of Processes with Degrading Equipment." Industrial & Engineering Chemistry Research 57, no. 50 (November 16, 2018): 17177–91. http://dx.doi.org/10.1021/acs.iecr.8b03292.

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Hasenauer, Jan, Nick Jagiella, Sabrina Hross, and Fabian J. Theis. "Data-Driven Modelling of Biological Multi-Scale Processes." Journal of Coupled Systems and Multiscale Dynamics 3, no. 2 (June 1, 2015): 101–21. http://dx.doi.org/10.1166/jcsmd.2015.1069.

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Hashtroudi, Shahin, Susan A. Ferguson, Virginia A. Rappold, and Linda D. Chrosniak. "Data-driven and conceptually driven processes in partial-word identification and recognition." Journal of Experimental Psychology: Learning, Memory, and Cognition 14, no. 4 (1988): 749–57. http://dx.doi.org/10.1037/0278-7393.14.4.749.

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Dissertations / Theses on the topic "Data-Driven Processes"

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Sham, Gregory C. (Gregory Chi-Keung). "Developing a data-driven approach for improving operating room scheduling processes." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/73397.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 52).
In the current healthcare environment, the cost of delivering patient care is an important concern for hospitals. As a result, healthcare organizations are being driven to maximize their existing resources, both in terms of infrastructure and human capital. Using a data-driven approach with analytical techniques from operations management can contribute towards this goal. More specifically, this thesis shows, drawing from a recent project at Beth Israel Deaconess Medical Center (BIDMC), that predictive modeling can be applied to operating room (OR) scheduling in order to effectively increase capacity. By examining the current usage of the existing block schedule system at BIDMC and developing a linear regression model, OR time that is expected to go unused can be instead identified in advance and freed for use. Sample model results show that it is expected to be operationally effective by capturing a large enough portion of OR time for a pooled set of blocks to be useful for advanced scheduling purposes. This analytically determined free time represents an improvement in how the current block system is employed, especially in terms of the nominal block release time. This thesis makes the argument that such a model can integrate into a scheduling system with more efficient and flexible processes, ultimately resulting in more effective usage of existing resources.
by Gregory C. Sham.
S.M.
M.B.A.
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Le, Tallec Yann. "Robust, risk-sensitive, and data-driven control of Markov Decision Processes." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/38598.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2007.
Includes bibliographical references (p. 201-211).
Markov Decision Processes (MDPs) model problems of sequential decision-making under uncertainty. They have been studied and applied extensively. Nonetheless, there are two major barriers that still hinder the applicability of MDPs to many more practical decision making problems: * The decision maker is often lacking a reliable MDP model. Since the results obtained by dynamic programming are sensitive to the assumed MDP model, their relevance is challenged by model uncertainty. * The structural and computational results of dynamic programming (which deals with expected performance) have been extended with only limited success to accommodate risk-sensitive decision makers. In this thesis, we investigate two ways of dealing with uncertain MDPs and we develop a new connection between robust control of uncertain MDPs and risk-sensitive control of dynamical systems. The first approach assumes a model of model uncertainty and formulates the control of uncertain MDPs as a problem of decision-making under (model) uncertainty. We establish that most formulations are at least NP-hard and thus suffer from the "'curse of uncertainty." The worst-case control of MDPs with rectangular uncertainty sets is equivalent to a zero-sum game between the controller and nature.
(cont.) The structural and computational results for such games make this formulation appealing. By adding a penalty for unlikely parameters, we extend the formulation of worst-case control of uncertain MDPs and mitigate its conservativeness. We show a duality between the penalized worst-case control of uncertain MDPs with rectangular uncertainty and the minimization of a Markovian dynamically consistent convex risk measure of the sample cost. This notion of risk has desirable properties for multi-period decision making, including a new Markovian property that we introduce and motivate. This Markovian property is critical in establishing the equivalence between minimizing some risk measure of the sample cost and solving a certain zero-sum Markov game between the decision maker and nature, and to tackling infinite-horizon problems. An alternative approach to dealing with uncertain MDPs, which avoids the curse of uncertainty, is to exploit directly observational data. Specifically, we estimate the expected performance of any given policy (and its gradient with respect to certain policy parameters) from a training set comprising observed trajectories sampled under a known policy.
(cont.) We propose new value (and value gradient) estimators that are unbiased and have low training set to training set variance. We expect our approach to outperform competing approaches when there are few system observations compared to the underlying MDP size, as indicated by numerical experiments.
by Yann Le Tallec.
Ph.D.
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Jiang, Tianyu. "Data-Driven Cyber Vulnerability Maintenance of Network Vulnerabilities with Markov Decision Processes." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1494203777781845.

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Ardakani, Mohammad Hamed. "Data driven methods for updating fault detection and diagnosis system in chemical processes." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/650845.

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Modern industrial processes are becoming more complex, and consequently monitoring them has become a challenging task. Fault Detection and Diagnosis (FDD) as a key element of process monitoring, needs to be investigated because of its essential role in decision making processes. Among available FDD methods, data driven approaches are currently receiving increasing attention because of their relative simplicity in implementation. Regardless of FDD types, one of the main traits of reliable FDD systems is their ability of being updated while new conditions that were not considered at their initial training appear in the process. These new conditions would emerge either gradually or abruptly, but they have the same level of importance as in both cases they lead to FDD poor performance. For addressing updating tasks, some methods have been proposed, but mainly not in research area of chemical engineering. They could be categorized to those that are dedicated to managing Concept Drift (CD) (that appear gradually), and those that deal with novel classes (that appear abruptly). The available methods, mainly, in addition to the lack of clear strategies for updating, suffer from performance weaknesses and inefficient required time of training, as reported. Accordingly, this thesis is mainly dedicated to data driven FDD updating in chemical processes. The proposed schemes for handling novel classes of faults are based on unsupervised methods, while for coping with CD both supervised and unsupervised updating frameworks have been investigated. Furthermore, for enhancing the functionality of FDD systems, some major methods of data processing, including imputation of missing values, feature selection, and feature extension have been investigated. The suggested algorithms and frameworks for FDD updating have been evaluated through different benchmarks and scenarios. As a part of the results, the suggested algorithms for supervised handling CD surpass the performance of the traditional incremental learning in regard to MGM score (defined dimensionless score based on weighted F1 score and training time) even up to 50% improvement. This improvement is achieved by proposed algorithms that detect and forget redundant information as well as properly adjusting the data window for timely updating and retraining the fault detection system. Moreover, the proposed unsupervised FDD updating framework for dealing with novel faults in static and dynamic process conditions achieves up to 90% in terms of the NPP score (defined dimensionless score based on number of the correct predicted class of samples). This result relies on an innovative framework that is able to assign samples either to new classes or to available classes by exploiting one class classification techniques and clustering approaches.
Los procesos industriales modernos son cada vez más complejos y, en consecuencia, su control se ha convertido en una tarea desafiante. La detección y el diagnóstico de fallos (FDD), como un elemento clave de la supervisión del proceso, deben ser investigados debido a su papel esencial en los procesos de toma de decisiones. Entre los métodos disponibles de FDD, los enfoques basados en datos están recibiendo una atención creciente debido a su relativa simplicidad en la implementación. Independientemente de los tipos de FDD, una de las principales características de los sistemas FDD confiables es su capacidad de actualización, mientras que las nuevas condiciones que no fueron consideradas en su entrenamiento inicial, ahora aparecen en el proceso. Estas nuevas condiciones pueden surgir de forma gradual o abrupta, pero tienen el mismo nivel de importancia ya que en ambos casos conducen al bajo rendimiento de FDD. Para abordar las tareas de actualización, se han propuesto algunos métodos, pero no mayoritariamente en el área de investigación de la ingeniería química. Podrían ser categorizados en los que están dedicados a manejar Concept Drift (CD) (que aparecen gradualmente), y a los que tratan con clases nuevas (que aparecen abruptamente). Los métodos disponibles, además de la falta de estrategias claras para la actualización, sufren debilidades en su funcionamiento y de un tiempo de capacitación ineficiente, como se ha referenciado. En consecuencia, esta tesis está dedicada principalmente a la actualización de FDD impulsada por datos en procesos químicos. Los esquemas propuestos para manejar nuevas clases de fallos se basan en métodos no supervisados, mientras que para hacer frente a la CD se han investigado los marcos de actualización supervisados y no supervisados. Además, para mejorar la funcionalidad de los sistemas FDD, se han investigado algunos de los principales métodos de procesamiento de datos, incluida la imputación de valores perdidos, la selección de características y la extensión de características. Los algoritmos y marcos sugeridos para la actualización de FDD han sido evaluados a través de diferentes puntos de referencia y escenarios. Como parte de los resultados, los algoritmos sugeridos para el CD de manejo supervisado superan el rendimiento del aprendizaje incremental tradicional con respecto al puntaje MGM (puntuación adimensional definida basada en el puntaje F1 ponderado y el tiempo de entrenamiento) hasta en un 50% de mejora. Esta mejora se logra mediante los algoritmos propuestos que detectan y olvidan la información redundante, así como ajustan correctamente la ventana de datos para la actualización oportuna y el reciclaje del sistema de detección de fallas. Además, el marco de actualización FDD no supervisado propuesto para tratar fallas nuevas en condiciones de proceso estáticas y dinámicas logra hasta 90% en términos de la puntuación de NPP (puntuación adimensional definida basada en el número de la clase de muestras correcta predicha). Este resultado se basa en un marco innovador que puede asignar muestras a clases nuevas o a clases disponibles explotando una clase de técnicas de clasificación y enfoques de agrupamiento
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Cleve, Jochen. "Data-driven theoretical modelling of the turbulent energy cascade." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2004. http://nbn-resolving.de/urn:nbn:de:swb:14-1103125565484-63361.

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Durch eine Modellierung der Energiekaskade gewinnt man wertvolle Einsichten in die Dynamik turbulenter Strömungen. In dieser Arbeit werden multiplikative Kaskadenprozesse untersucht und mit verschiedenen experimentellen Zeitreihen der Energiedissipation verglichen. Zur Berechnung der Energiedissipation ist es unvermeidlich auf eine Hilfskonstruktion zurückzugreifen, die die nicht gemessenen Komponenten des Geschwindigkeitsfeldes ersetzt. Der Schwerpunkt des Vergleichs zwischen Modell und Experiment liegt auf Zweipunktkorrelationen, weil andere Observablen, wie z. B. integrale Momente, durch diese Hilfskonstruktion der Dissipation verfälscht werden. Es werden explizite Ausdrücke für die Zweipunktkorrelationen abgeleitet, die auch Korrekturen, die von einem endlichen Skalierungsbereich stammen,berücksichtigen. Mit diesen Ausdrücken ist es möglich, auch Datensätze mit niedrigen oder moderaten Reynoldszahlen zu fitten und genaue Werte für die Skalierungsexponenten zu bestimmen. Mit einer umfassenden Datenanalyse wird versucht, die freien Parameter des Kaskadengenerators zu bestimmen. Die verfügbare Statistik der Daten ist zu gering, um genauere Aussagen zu treffen, als dass die Verteilung des Kaskadengenerators ähnlich einer log-normal Verteilung sein wird. Mit dem Intermittenzexponenten, der der fundamentalste Skalierungsexponent des Dissipationsfeldes ist, lassen sich die Daten charakterisieren. Die untersuchten Daten teilen sich in zwei Gruppen auf: Die Daten, die aus Luftströmungen gewonnen wurden, weisen einen mit der Reynoldszahl steigenden Intermittenzexponenten auf, der für hohe Reynoldszahlen gegen den konstanten Wert 0.2 konvergiert. Die Daten aus einem Helium-Freistrahl andererseits können am besten mit einem konstanten Intermittenzexponenten 0.1 charakterisiert werden. Diese Unterschiede können nicht vollständig erklärt werden.Um diesen Sachverhalt genauer zu untersuchen wird ein neues Modell vorgeschlagen, das die Kramers-Moyal-Koeffizienten des Geschwindigkeitsfeldes in ein Dissipationsfeld übersetzt, um den Intermittenzexponenten aus einer anderen Perspektive zu berechnen.Schließlich wird eine dynamische Verallgemeinerung des Kaskadenprozesses,die kürzlich vorgestellt wurde, getestet. Das dynamische Modell macht Vorhersagen für allgemeine n-Punktkorrelationen. Die analytischen Ausdrücke für Dreipunktkorrelationen werden mit experimentellen Daten verglichen. Die Übereinstimmung zwischen Modellvorhersage und Experiment ist überzeugend
Modelling the turbulent energy cascade gives valuable insight into the dynamics of a turbulent flow. In this work, random multiplicative cascade processes are studied and compared with dissipation time series obtained from various experiments. The emphasis of this comparison is laid on the two-point correlation function because the unavoidable surrogacy of the dissipation field, i.e.the substitution of the multi-component expression by a single component of the velocity signal, corrupts the scaling behaviour of other observables such as integral moments. Finite-size expressions for the two-point correlation function are derived, which make it possible to fit data obtained at moderate or low Reynolds numbers and extract accurate values of scaling exponents. A comprehensive data analysis attempts to determine the free parameters of the cascade generator. The statistics are too limited to claim more than that the cascade generator will be close to having a log-normal distribution. The most basic scaling exponent of the dissipation field is called intermittency exponent and can be used to characterise the data. The investigated data fall into two groups. One set of data obtained from measurements with air show an increasing intermittency exponent with an increasing Reynolds number and saturate for high Reynolds numbers to a value of 0.2. The other set, obtained in a helium jet is best characterised with a constant intermittency exponent of 0.1. The differences are not fully understood. To investigate this issue further, a new construction is suggested, that translates the Kramers-Moyal coefficients of the velocity field into a dissipation field in order to calculate the intermittency exponent from different perspective. Finally, a dynamical generalisation of the cascade process, introduced recently, is tested. The dynamical model makes predictions for point correlation functions. The analytical expressions for three-point correlation functions are compared with their counterparts obtained from experimental data and show remarkable agreement
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Tu, Zhuowen. "Image Parsing by Data-Driven Markov Chain Monte Carlo." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1038347031.

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Stubbs, Shallon Monique. "Data-driven, mechanistic and hybrid modelling for statistical fault detection and diagnosis in chemical processes." Thesis, University of Newcastle Upon Tyne, 2012. http://hdl.handle.net/10443/1519.

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Research and applications of multivariate statistical process monitoring and fault diagnostic techniques for performance monitoring of continuous and batch processes continue to be a very active area of research. Investigations into new statistical and mathematical methods and there applicability to chemical process modelling and performance monitoring is ongoing. Successive researchers have proposed new techniques and models to address the identified limitations and shortcomings of previously applied linear statistical methods such as principal component analysis and partial least squares. This thesis contributes to this volume of research and investigation into alternative approaches and their suitability for continuous and batch process applications. In particular, the thesis proposes a modified canonical variate analysis state space model based monitoring scheme and compares the proposed scheme with several existing statistical process monitoring approaches using a common benchmark simulator – Tennessee Eastman benchmark process. A hybrid data driven and mechanistic model based process monitoring approach is also investigated. The proposed hybrid scheme gives more specific considerations to the implementation and application of the technique for dynamic systems with existing control structures. A nonmechanistic hybrid approach involving the combination of nonlinear and linear data based statistical models to create a pseudo time-variant model for monitoring of large complex plants is also proposed. The hybrid schemes are shown to provide distinct advantages in terms of improved fault detection and reliability. The demonstration of the hybrid schemes were carried out on two separate simulated processes: a CSTR with recycle through a heat exchanger and a CHEMCAD simulated distillation column. Finally, a batch process monitoring schemed based on a proposed implementation of interval partial least squares (IPLS) technique is demonstrated using a benchmark simulated fed-batch penicillin production process. The IPLS strategy employs data unfolding methods and a proposed algorithm for segmentation of the batch duration into optimal intervals to give a unique implementation of a Multiway-IPLS model. Application results show that the proposed method gives better model prediction and monitoring performance than the conventional IPLS approach.
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Höcker, Filip, and Finn Brand. "‘Data over intuition’ – How big data analytics revolutionises the strategic decision-making processes in enterprises." Thesis, Internationella Handelshögskolan, Jönköping University, IHH, Företagsekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48560.

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Background: Digital technologies are increasingly transforming traditional businesses, and their pervasive impact is leading to a radical restructuring of entire industries. While the significance of generating competitive advantages for businesses utilizing big data analytics is recognized, there is still a lack of consensus of big data analytics influencing strategic decision-making in organisations. As big data and big data analytics become increasingly common, understanding the factors influencing decision-making quality becomes of paramount importance for businesses. Purpose: This thesis investigates how big data and big data analytics affect the operational strategic decision-making processes in enterprises through the theoretical lens of the strategy-as-practice framework. Method: The study follows an abductive research approach by testing a theory (i.e., strategy-aspractice) through the use of a qualitative research design. A single case study of IKEA was conducted to generate the primary data for this thesis. Sampling is carried out internally at IKEA by first identifying the heads of the different departments within the data analysis and from there applying the snowball sampling technique, to increase the number of interviewees and to ensure the collection of enough data for coding. Findings: The findings show that big data analytics has a decisive influence on practitioners. At IKEA, data analysts have become an integral part of the operational strategic decision-making processes and discussions are driven by data and rigor rather than by gut and intuition. In terms of practices, it became apparent that big data analytics has led to a more performance-oriented use of strategic tools and enabling IKEA to make strategic decisions in real-time, which not only increases agility but also mitigates the risk of wrong decisions.
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Lee, Michael D. "Incidental text priming without reinstatement of context, the role of data-driven processes in implicit memory." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0006/MQ45081.pdf.

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Jung, Christian [Verfasser], Günter [Akademischer Betreuer] Rudolph, and Thomas [Gutachter] Bartz-Beielstein. "Data-driven optimization of hot rolling processes / Christian Jung ; Gutachter: Thomas Bartz-Beielstein ; Betreuer: Günter Rudolph." Dortmund : Universitätsbibliothek Dortmund, 2019. http://d-nb.info/120260577X/34.

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Books on the topic "Data-Driven Processes"

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Chen, Zhiwen. Data-Driven Fault Detection for Industrial Processes. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-16756-1.

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Zhao, Jun, Wei Wang, and Chunyang Sheng. Data-Driven Prediction for Industrial Processes and Their Applications. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94051-9.

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1975-, Chiang Leo H., and Braatz Richard D. 1966-, eds. Data-driven methods for fault detection and diagnosis in chemical processes. London: Springer, 2000.

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Russell, Evan L., Leo H. Chiang, and Richard D. Braatz. Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0409-4.

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Russell, Evan L. Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes. London: Springer London, 2000.

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Shang, Chao. Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-6677-1.

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Kalita, Kanak, Xiao-Zhi Gao, and Ranjan Kumar Ghadai. Data-Driven Optimization of Manufacturing Processes. IGI Global, 2020.

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Kalita, Kanak, Xiao-Zhi Gao, and Ranjan Kumar Ghadai. Data-Driven Optimization of Manufacturing Processes. IGI Global, 2020.

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Kalita, Kanak, Xiao-Zhi Gao, and Ranjan Kumar Ghadai. Data-Driven Optimization of Manufacturing Processes. IGI Global, 2020.

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Kalita, Kanak, Ranjan Ghadai, and Xiao-Zhi Gao. Data-Driven Optimization of Manufacturing Processes. IGI Global, 2021.

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Book chapters on the topic "Data-Driven Processes"

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Länsipuro, Heidi, and Heikki Karjaluoto. "Data-driven marketing processes." In Contemporary Issues in Digital Marketing, 22–31. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003093909-4.

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Reichert, Manfred, and Barbara Weber. "User- and Data-Driven Processes." In Enabling Flexibility in Process-Aware Information Systems, 377–403. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30409-5_13.

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Ramkumar, T., M. Selvakumar, S. K. Ashok, and M. Mohanraj. "Data-driven optimization of manufacturing processes." In Materials for Lightweight Constructions, 209–21. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003252108-10.

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Haghani Abandan Sari, Adel. "Fault diagnosis in multimode nonlinear processes." In Data-Driven Design of Fault Diagnosis Systems, 65–73. Wiesbaden: Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-05807-4_5.

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Senderovich, Arik, Andreas Rogge-Solti, Avigdor Gal, Jan Mendling, Avishai Mandelbaum, Sarah Kadish, and Craig A. Bunnell. "Data-Driven Performance Analysis of Scheduled Processes." In Lecture Notes in Computer Science, 35–52. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23063-4_3.

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Kurvinen, Emil, Iines Suninen, Grzegorz Orzechowski, Jin H. Choi, Jin-Gyun Kim, and Aki Mikkola. "Accelerating design processes using data-driven models." In Real-time Simulation for Sustainable Production, 65–76. Abingdon, Oxon ; New York, NY : Routledge, 2021. | Series: Routledge advances in production and operations management: Routledge, 2021. http://dx.doi.org/10.4324/9781003054214-8.

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Chen, Zhiwen. "Introduction." In Data-Driven Fault Detection for Industrial Processes, 1–11. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-16756-1_1.

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Chen, Zhiwen. "The Basics of Fault Detection." In Data-Driven Fault Detection for Industrial Processes, 13–30. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-16756-1_2.

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Chen, Zhiwen. "Evaluation and Comparison of T 2 and Q Statistics for Fault Detection." In Data-Driven Fault Detection for Industrial Processes, 31–42. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-16756-1_3.

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Chen, Zhiwen. "Canonical Correlation Analysis-based Fault Detection Methods." In Data-Driven Fault Detection for Industrial Processes, 43–58. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-16756-1_4.

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Conference papers on the topic "Data-Driven Processes"

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Gulisano, Vincenzo, Marina Papatriantafilou, Zhuoer Chen, Eduard Hryha, and Lars Nyborg. "Towards data-driven additive manufacturing processes." In Middleware '22: 23rd International Middleware Conference. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3564695.3564778.

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Benner-Wickner, Marian, Tobias Brückmann, Volker Gruhn, and Matthias Book. "Process mining for knowledge-intensive business processes." In i-KNOW '15: 15th International Conference on Knowledge Technologies and Data-Driven Business. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2809563.2809580.

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"AN APPROACH TO DATA-DRIVEN ADAPTABLE SERVICE PROCESSES." In 5th International Conference on Software and Data Technologies. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0003007401390145.

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Muller, Dominic, Manfred Reichert, Joachim Herbst, and Florian Poppa. "Data-Driven Design of Engineering Processes with COREPROModeler." In 16th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2007). IEEE, 2007. http://dx.doi.org/10.1109/wetice.2007.4407191.

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Jaenisch, Holger M., James W. Handley, and Jeffery P. Faucheux. "Data-driven differential equation modeling of fBm processes." In Optical Science and Technology, SPIE's 48th Annual Meeting, edited by Oliver E. Drummond. SPIE, 2003. http://dx.doi.org/10.1117/12.502479.

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Pereira, Sara, Marcia Baptista, and Elsa M. P. Henriques. "Data-driven quality prognostics for automated riveting processes." In 2018 IEEE Aerospace Conference. IEEE, 2018. http://dx.doi.org/10.1109/aero.2018.8396547.

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Pan, Yunpeng, and Evangelos A. Theodorou. "Data-driven differential dynamic programming using Gaussian processes." In 2015 American Control Conference (ACC). IEEE, 2015. http://dx.doi.org/10.1109/acc.2015.7172032.

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Briechle, Dominique Fabio, and Sebastian Lawrenz. "Data Driven Process Evaluation Simulation Model For Circular Economy Treatment Processes." In 2021 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2021. http://dx.doi.org/10.1109/csci54926.2021.00087.

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Xie, Jiamin, Yimeng Song, Xiaolong Lv, Hongbo Shi, and Bing Song. "Quality-related Process Monitoring of Industrial Processes based on Key Variable-Slow Feature Analysis." In 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2021. http://dx.doi.org/10.1109/ddcls52934.2021.9455692.

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Wang, Zhiwen, Chongcheng Chen, Xiaoling Chen, Dagang Li, and Feihu Zeng. "Adaptive PID control for time-varying fermentation processes." In 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2021. http://dx.doi.org/10.1109/ddcls52934.2021.9455651.

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Reports on the topic "Data-Driven Processes"

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Shand, Lyndsay, Benjamin Hillman, Lekha Patel, Gabriel Huerta, J. Tucker, Andrea Staid, Don Lyons, and Erin Schliep. Integrative data-driven approaches for characterization & prediction of aerosol-cloud processes. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769729.

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van der Sloot, Bart. The Quality of Life: Protecting Non-personal Interests and Non-personal Data in the Age of Big Data. Universitätsbibliothek J. C. Senckenberg, Frankfurt am Main, 2021. http://dx.doi.org/10.21248/gups.64579.

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Under the current legal paradigm, the rights to privacy and data protection provide natural persons with subjective rights to protect their private interests, such as related to human dignity, individual autonomy and personal freedom. In principle, when data processing is based on non-personal or aggregated data or when such data pro- cesses have an impact on societal, rather than individual interests, citizens cannot rely on these rights. Although this legal paradigm has worked well for decades, it is increasingly put under pressure because Big Data processes are typically based indis- criminate rather than targeted data collection, because the high volumes of data are processed on an aggregated rather than a personal level and because the policies and decisions based on the statistical correlations found through algorithmic analytics are mostly addressed at large groups or society as a whole rather than specific individuals. This means that large parts of the data-driven environment are currently left unregu- lated and that individuals are often unable to rely on their fundamental rights when addressing the more systemic effects of Big Data processes. This article will discuss how this tension might be relieved by turning to the notion ‘quality of life’, which has the potential of becoming the new standard for the European Court of Human Rights (ECtHR) when dealing with privacy related cases.
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Dodson, Giles. Advancing Local Marine Protection, Cross Cultural Collaboration and Dialogue in Northland. Unitec ePress, January 2015. http://dx.doi.org/10.34074/rsrp.12015.

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This research report summarises findings and observations arising from the Advancing marine protection through cross-cultural dialogue project, which examines community-driven, collaborative marine protection campaigns currently being pursued in Northland. This project consists of a series of case studies undertaken between 2012–2014 and draws on data obtained from archival research, semistructured interviews with campaign participants, and published documents. The aims of these case studies have been to compare different approaches taken towards marine protection in Northland and to understand the composition of effective marine protection campaigns, within the context of collaborative approaches to environmental management and the communicative processes underpinning these engagements. The report provides a number of insights into how contemporary marine protection campaigns have been developed and the place of cross-cultural (Māori – non-Māori) collaboration and communication within these processes.
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Dodson, Giles. Advancing Local Marine Protection, Cross Cultural Collaboration and Dialogue in Northland. Unitec ePress, January 2015. http://dx.doi.org/10.34074/rsrp.12015.

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This research report summarises findings and observations arising from the Advancing marine protection through cross-cultural dialogue project, which examines community-driven, collaborative marine protection campaigns currently being pursued in Northland. This project consists of a series of case studies undertaken between 2012–2014 and draws on data obtained from archival research, semistructured interviews with campaign participants, and published documents. The aims of these case studies have been to compare different approaches taken towards marine protection in Northland and to understand the composition of effective marine protection campaigns, within the context of collaborative approaches to environmental management and the communicative processes underpinning these engagements. The report provides a number of insights into how contemporary marine protection campaigns have been developed and the place of cross-cultural (Māori – non-Māori) collaboration and communication within these processes.
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Dodson, Giles. Advancing Local Marine Protection, Cross Cultural Collaboration and Dialogue in Northland. Unitec ePress, January 2015. http://dx.doi.org/10.34074/rsrp.12015.

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This research report summarises findings and observations arising from the Advancing marine protection through cross-cultural dialogue project, which examines community-driven, collaborative marine protection campaigns currently being pursued in Northland. This project consists of a series of case studies undertaken between 2012–2014 and draws on data obtained from archival research, semistructured interviews with campaign participants, and published documents. The aims of these case studies have been to compare different approaches taken towards marine protection in Northland and to understand the composition of effective marine protection campaigns, within the context of collaborative approaches to environmental management and the communicative processes underpinning these engagements. The report provides a number of insights into how contemporary marine protection campaigns have been developed and the place of cross-cultural (Māori – non-Māori) collaboration and communication within these processes.
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van den Boogaard,, Vanessa, and Fabrizio Santoro. Co-Financing Community-Driven Development Through Informal Taxation: Experimental Evidence from South-Central Somalia. Institute of Development Studies (IDS), September 2021. http://dx.doi.org/10.19088/ictd.2021.016.

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Community contributions are often required as part of community-driven development (CDD) programmes, with payment encouraged through matching grants. However, little remains known about the impact of matching grants, or the implications of requiring community contributions in order for communities to receive development funding. This paper describes research where we partner with two non-governmental organisations (NGOs) – one international and one Somali – and undertake a randomised control trial of a CDD matching grant programme designed to incentivise informal contributions for local public goods in Gedo region in south-central Somalia. We rely on household survey data collected from 1,297 respondents in 31 treatment and 31 control communities, as well as surveys of village leaders and data on informal contributions from the mobile money platform used by community leaders to collect revenue. Two key findings emerge. First, our research shows that working with communities and incentivising informal revenue generation can be an effective way to deliver public goods and to support citizens and communities. Second, building on research exploring the potential for development interventions to spur virtuous or adverse cycles of governance, we show that development partners may work directly with community leaders and informal taxing institutions without necessarily undermining – and indeed perhaps strengthening – state legitimacy and related ongoing processes of statebuilding in the country. Indeed, despite playing no direct role in the matching grant programme, taxpayer perceptions of the legitimacy of the local government improved as a result of the programme. These findings deepen our understanding of how community contributions may be incentivised through matching grant programmes, and how they may contribute to CDD and public goods provision in a context of weak institutional capacity.
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Seale, Maria, R. Salter, Natàlia Garcia-Reyero,, and Alicia Ruvinsky. A fuzzy epigenetic model for representing degradation in engineered systems. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45582.

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Degradation processes are implicated in a large number of system failures, and are crucial to understanding issues related to reliability and safety. Systems typically degrade in response to stressors, such as physical or chemical environmental conditions, which can vary widely for identical units that are deployed in different places or for different uses. This situational variance makes it difficult to develop accurate physics-based or data-driven models to assess and predict the system health status of individual components. To address this issue, we propose a fuzzy set model for representing degradation in engineered systems that is based on a bioinspired concept from the field of epigenetics. Epigenetics is concerned with the regulation of gene expression resulting from environmental or other factors, such as toxicants or diet. One of the most studied epigenetic processes is methylation, which involves the attachment of methyl groups to genomic regulatory regions. Methylation of specific genes has been implicated in numerous chronic diseases, so provides an excellent analog to system degradation. We present a fuzzy set model for characterizing system degradation as a methylation process based on a set-theoretic representation for epigenetic modeling of engineered systems. This model allows us to capture the individual dynamic relationships among a system, environmental factors, and state of health.
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Klasky, Hilda, Ozgur Ozmen, Olufemi Omitaomu, Mohammed Olama, Laura Pullum, Addi Thakur Malviya, and Teja Kuruganti. Comparative Assessment of Data-driven Process Models in Health Information Technology. Office of Scientific and Technical Information (OSTI), May 2021. http://dx.doi.org/10.2172/1824947.

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Snyder, Victor A., Dani Or, Amos Hadas, and S. Assouline. Characterization of Post-Tillage Soil Fragmentation and Rejoining Affecting Soil Pore Space Evolution and Transport Properties. United States Department of Agriculture, April 2002. http://dx.doi.org/10.32747/2002.7580670.bard.

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Tillage modifies soil structure, altering conditions for plant growth and transport processes through the soil. However, the resulting loose structure is unstable and susceptible to collapse due to aggregate fragmentation during wetting and drying cycles, and coalescense of moist aggregates by internal capillary forces and external compactive stresses. Presently, limited understanding of these complex processes often leads to consideration of the soil plow layer as a static porous medium. With the purpose of filling some of this knowledge gap, the objectives of this Project were to: 1) Identify and quantify the major factors causing breakdown of primary soil fragments produced by tillage into smaller secondary fragments; 2) Identify and quantify the. physical processes involved in the coalescence of primary and secondary fragments and surfaces of weakness; 3) Measure temporal changes in pore-size distributions and hydraulic properties of reconstructed aggregate beds as a function of specified initial conditions and wetting/drying events; and 4) Construct a process-based model of post-tillage changes in soil structural and hydraulic properties of the plow layer and validate it against field experiments. A dynamic theory of capillary-driven plastic deformation of adjoining aggregates was developed, where instantaneous rate of change in geometry of aggregates and inter-aggregate pores was related to current geometry of the solid-gas-liquid system and measured soil rheological functions. The theory and supporting data showed that consolidation of aggregate beds is largely an event-driven process, restricted to a fairly narrow range of soil water contents where capillary suction is great enough to generate coalescence but where soil mechanical strength is still low enough to allow plastic deforn1ation of aggregates. The theory was also used to explain effects of transient external loading on compaction of aggregate beds. A stochastic forInalism was developed for modeling soil pore space evolution, based on the Fokker Planck equation (FPE). Analytical solutions for the FPE were developed, with parameters which can be measured empirically or related to the mechanistic aggregate deformation model. Pre-existing results from field experiments were used to illustrate how the FPE formalism can be applied to field data. Fragmentation of soil clods after tillage was observed to be an event-driven (as opposed to continuous) process that occurred only during wetting, and only as clods approached the saturation point. The major mechanism of fragmentation of large aggregates seemed to be differential soil swelling behind the wetting front. Aggregate "explosion" due to air entrapment seemed limited to small aggregates wetted simultaneously over their entire surface. Breakdown of large aggregates from 11 clay soils during successive wetting and drying cycles produced fragment size distributions which differed primarily by a scale factor l (essentially equivalent to the Van Bavel mean weight diameter), so that evolution of fragment size distributions could be modeled in terms of changes in l. For a given number of wetting and drying cycles, l decreased systematically with increasing plasticity index. When air-dry soil clods were slightly weakened by a single wetting event, and then allowed to "age" for six weeks at constant high water content, drop-shatter resistance in aged relative to non-aged clods was found to increase in proportion to plasticity index. This seemed consistent with the rheological model, which predicts faster plastic coalescence around small voids and sharp cracks (with resulting soil strengthening) in soils with low resistance to plastic yield and flow. A new theory of crack growth in "idealized" elastoplastic materials was formulated, with potential application to soil fracture phenomena. The theory was preliminarily (and successfully) tested using carbon steel, a ductile material which closely approximates ideal elastoplastic behavior, and for which the necessary fracture data existed in the literature.
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Cao, Shoufeng, Uwe Dulleck, Warwick Powell, Charles Turner-Morris, Valeri Natanelov, and Marcus Foth. BeefLedger blockchain-credentialed beef exports to China: Early consumer insights. Queensland University of Technology, May 2020. http://dx.doi.org/10.5204/rep.eprints.200267.

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The BeefLedger Export Smart Contracts project is a collaborative research study between BeefLedger Ltd and QUT co-funded by the Food Agility CRC. This project exists to deliver economic value to those involved in the production, export and consumption of Australian beef to China through: (1) reduced information asymmetry; (2) streamlined compliance processes, and; (3) developing and accessing new data-driven value drivers, through the deployment of decentralised ledger technologies and associated governance systems. This report presents early insights from a survey deployed to Chinese consumers in Nov/Dec 2019 exploring attitudes and preferences about blockchain-credentialed beef exports to China. Our results show that most local and foreign consumers were willing to pay more than the reference price for a BeefLedger branded Australian cut and packed Sirloin steak at the same weight. Although considered superior over Chinese processed Australian beef products, the Chinese market were sceptical that the beef they buy was really from Australia, expressing low trust in Australian label and traceability information. Despite lower trust, most survey respondents were willing to pay more for traceability supported Australian beef, potentially because including this information provided an additional sense of safety. Therefore, traceability information should be provided to consumers, as it can add a competitive advantage over products without traceability.
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