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About 10,000 papers for “data-driven applications”

Privacy and Data-Driven Applications

Joiverdia Arifiyanto, Ningrum Natasya Sirait, Mir’atul Khusna Mufida et al. · Advances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research · 2022

The emergence of data-driven applications brings new hope to understand and explain many world phenomena. For instance, it helps to explain how the Covid-19 pandemic spread all around the world. The objective of this research is to bridge the potential disclaimer on privacy data usage. In the middle of a crisis, it is important to keep track of how our private data is kept and used to figure out how this pandemic begins and evolves. On one hand, it is important to keep track of every single citizen related to their interaction and mobility. However, once the data is collected, there are two possibilities: it can be an opportunity, or it can be a threat especially related to the privacy of user data and its security and usage. Focusing on describing how the data collection and data usage and security related to the tracking application created, the potential mitigation about the data usage will be analysed, discussed, and elaborated. Inevitably, the technical solution will process personal data (even if pseudonymised), including health data, which is sensitive data. If the evolution were to be different and allow for broader surveillance, the balance of interests would have to be reevaluated to assess this new purpose. However, the applicable framework seems to be rather largely unknown or misunderstood, and many questions concerning the tracing of the population to contain the pandemic remain unresolved. The lawfulness of such a system will therefore depend on the purpose and legal basis of the processing.

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Data driven applications in coastal geomorphology

Lundine, Mark · University of Delaware · 2023

Our ability to digitally capture coastal processes and landforms has progressed immensely in the last several decades. Satellite-based, drone-based, surface vessel-based, and underwater vehicle-based platforms carrying sensors like multispectral cameras, LiDAR, and sonar allow us to image the texture and topography of the subaerial and subaqueous coastal landscape at high resolution and accuracy. Consequently, our improvements in data collection have exceeded our ability to analyze and discern patterns from said datasets. In this dissertation, I will present several applications of using data-driven methods (e.g., convolutional neural networks) to analyze coastal processes and landforms. This includes the detection and characterization of widespread sandy depressions on the Atlantic Coastal Plain (Carolina Bays), the detection and characterization of seabed fluid-escape depressions on the continental shelf (pockmarks), and satellite-based analysis/prediction of shoreline change.

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Data-driven sensors and their applications

Pakr, Jiří · Brno University of Technology Digital Library (Brno University of Technology) · 2021

Soft sensors are a gradually expanding technique in the field of industrial measurement. These sensors are computer programs that provide additional data using previously acquired data in a similar way to conventional hardware sensors. The additional data is obtained using predictive models based on machine learning methods such as neural networks or support vector machines. This work mainly includes a research on the function, structure and creation of soft sensors. It also describes machine learning, the distribution of its algorithms and introduces the methods commonly used in the field of virtual sensors. Towards the end, the author describes possible future development of soft sensors and the direction of further applications.

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Data‐Driven Control: Theory and Applications

Hao Liu, Yan Wan, Ci Chen et al. · International Journal of Robust and Nonlinear Control · 2025

Data-driven control refers to the use of artificial intelligence (AI) and advanced control techniques to accomplish the control objectives without resorting to an accurate system model. It collects information from actual data and experience and focuses on utilizing data generated during the operation of a system to learn its behavior and performance, enabling the formulation of more adaptive and intelligent control strategies. For multi-agent systems, especially for multiple unmanned systems, data-driven control algorithms exhibit significant advantages in the execution of complex tasks. This special issue is mainly dedicated to promoting the theory of data-driven control and its applications in multiple unmanned systems. Multiple unmanned systems are generally required to accomplish various complex tasks in challenging environments, such as collaborative path planning, formation-containment control, and air-ground coordination. Data-driven control has the advantages of effectively handling complex systems with multiple variables and nonlinear behavior, optimizing control strategies to achieve specific objectives, and continuously improving performance by learning from data and past experiences. Typical data-driven control includes machine learning, fuzzy logic control, reinforcement learning (RL), model predictive control, and genetic algorithms. In recent years, significant progress has been made in learning-based algorithms. RL algorithms can be combined with optimal control to realize data-driven optimal control and adaptive robust control, which play a role in the autonomous control of multiple unmanned systems. Additionally, complex characteristics, such as uncertainties, time delay, actuator faults, and constraints in multiple unmanned systems, cannot be ignored. There exist RL solutions to these problems. Nevertheless, data-driven control and its practical applications for multiple unmanned systems remain an open problem. The first theme of this special issue focuses on the theoretical foundations and methodological advances of data-driven control. At the single-system level, several papers investigate data-driven optimal control that lay the groundwork for intelligent control design. In [1], fundamental mechanisms behind indirect and direct policy iteration were systematically studied for unknown discrete-time linear time-invariant systems, offering a system-theoretic understanding of convergence and robustness under certain levels of excitation. In [2], by integrating the offline training of Gaussian process regression and the online high-accuracy tracking control, a data-driven predictive control method was proposed by exploiting probabilistic uncertainty in the predicted outputs. The controlled object was further considered as a discrete-time periodic system in [3], and a value iteration-based adaptive dynamic programming (ADP) algorithm was developed to achieve suboptimal control, without knowledge of system dynamics and without requiring prior stabilizing gains. In [4], a self-triggered actor-critic RL framework was developed for optimal regulation of nonlinear systems to reduce computation and communication loads. The critic weights were updated continuously while the actor weights were adjusted intermittently, achieving performance optimization and closed-loop stability without continuous signal measurement. Building upon this, the applications of data-driven control methods were also studied for multi-agent systems. In [5], a distributed data-driven consensus control approach was designed for unknown continuous-time multi-agent systems under both noiseless and noisy data scenarios. In the proposed framework, each agent computes feedback gains from locally sampled data, achieving leader-follower consensus by solving low-dimensional linear matrix inequalities. In [6], an event-triggered data-driven method was proposed for multi-agent systems achieving distributed optimal consensus. By incorporating the event-triggered mechanism and a goal-critic-actor neural network (NN) structure, the goal representation heuristic dynamic programming method was designed to ensure closed-loop stability while alleviating the communication and computation burden. The second theme of this special issue focuses on resilient and secure data-driven control in uncertain environments. In [7], the data-driven method was extended to systems under process and measurement noise, where semidefinite programming was used to construct a robust finite-horizon linear quadratic Gaussian controller from noisy data. In [8], the distributed optimal consensus problem was studied via proportional-integral regulation for high-order nonlinear agents with nonidentical unknown control directions, bounded disturbances, and input saturation over weight-unbalanced directed networks. A consensus algorithm was established based on adaptive proportional-integral variables, Nussbaum-type functions, and NNs. In [9], the distributed consensus problem under switching topology and external disturbances was formulated as a zero-sum game problem, and a single-critic NN-based integral RL algorithm combined with experience replay was employed to achieve optimal consensus performance while relaxing the traditional persistent excitation requirement. In [10], a model-free funnel control strategy was designed for multi-agent systems under denial-of-service (DoS) attacks to guarantee that tracking errors remained within prescribed bounds despite jamming signals. In [11], the resilient asymmetric bipartite consensus problem of multi-agent systems with both cooperative and antagonistic interactions under DoS attacks was studied. An attack compensation mechanism and an extended discrete state observer were designed to establish a resilient data-driven control method ensuring secure consensus. In [12], the communication links can be tampered with and blocked by distributed sequential link attacks, and a fully distributed adaptive secure consensus protocol was developed to counter distributed sequential link attacks on communication links under general directed topologies. The third theme of this special issue focuses on data-driven perception, optimization, and decision-making strategies in complex systems. In [13], the stateful time-series modeling problem was addressed through two state change detection methods, the first enhancing the discrepancy-based approach with temporal encoder-decoder modeling and the second integrating Bayesian inference to balance between detection accuracy and efficiency. In [14], a hierarchical interactive graph variational inference model was developed for data-driven information perception in complex industrial networks. By organizing multi-layer latent structures to represent influencing factors and applying hierarchical attention learning to capture entity interactions, the framework enables generative self-supervised perception for downstream tasks. At the decision-making level, in [15], reciprocal decision-making in multi-player pursuit-evasion games was studied by modeling altruistic behavior through a modified performance function. The Nash policy under irrationality was derived with the maximum principle and approximated via RL, and conditions for altruism and reciprocity were established. In [16], a game-theoretic optimal control design was proposed for building's heating, ventilation, and air conditioning (HVAC) systems, where a model-free Q-learning algorithm solves a two-player non-zero-sum cooperative game using mass flow rate and supply air temperature as decision variables, achieving effective temperature tracking under unknown dynamics and disturbances. In [17], a multiplayer hierarchical decision-making problem in discrete-time nonlinear networks was addressed from the perspective of Nash-Stackelberg-Nash game. A two-level value iteration ADP algorithm was designed to solve the coupled Hamilton-Jacobi-Bellman equations without requiring known dynamics. In [18], transformer-based large models were applied to trajectory decision-making in complex multiagent systems with topological structures. By integrating transformer architectures into model-free control, the approach enhanced decision-making efficiency, safety, and reliability in dynamic environments. A significant theme of this special issue concerns the application of data-driven control methods in unmanned systems. In [19], a suboptimal tracking controller was developed for hypersonic flight vehicles under input delay, amplitude, and rate constraints. The constraints were transformed into inequalities, and the NN-based integral RL algorithm was used for data-driven control of the unconstrained augmented system. In [20], an anti-saturation optimization algorithm based on dynamic self-triggered ADP framework was proposed for bounded acceleration guidance interception. A nonlinear input-constrained guidance system with dynamic self-triggered control was designed to improve communication efficiency. In [21], a path planning method for unmanned surface vessels (USVs) in complex environments was developed using a dynamic artificial potential field to uniformly represent dynamic and static obstacles. Considering USV state constraints and international collision avoidance rules, an obstacle avoidance strategy was formulated, and the optimal path was obtained via a genetic algorithm. In [22], a passive defensive strategy was proposed for a target-attacker-defenders game, where defenders form around an optimal formation point instead of actively intercepting the attacker. The optimal point was determined through an evaluation model and optimized using an improved pigeon-inspired algorithm with a backbone exchange mechanism, enabling effective defense against fast attackers. In [23], a distributed formation control protocol for multiple quadrotors was developed by decoupling the outer position-velocity and inner attitude loops. Self-feedback and formation registration terms were introduced to regulate formation errors and motion trajectories. Flight experiments with an optical motion capture system verified the effectiveness of the proposed method. The final theme highlights the cross-domain and engineering applications of data-driven control. The data-driven methods in [14, 16, 17] were applied to industrial networks and energy management, showing their versatility in addressing large-scale, multi-objective, and information-intensive applications. Several studies further demonstrate the engineering potential of data-driven control. In [24], a robust inverse control strategy based on disturbance boundary estimation was proposed for dissolved oxygen regulation in wastewater treatment processes. A nonlinear disturbance observer and inverse controller were designed to handle internal and external disturbances, and experiments on the BSM1 benchmark verified the effectiveness and robustness of the developed control scheme. In [25], a hybrid energy storage control strategy combining Q-learning and fuzzy logic was developed for mitigating wind power fluctuations in grid-connected microgrids. By adaptively tuning the time constants of a two-stage low-pass filter, the approach improved power quality while accounting for practical constraints of energy storage systems. In [26], deep reinforcement learning was applied to tracking control in real-time hybrid simulation of structural systems, where physical and numerical components are coupled through boundary conditions. Three DRL-based controllers integrating time-delay compensation, feedback control, and complex neural architectures were developed and validated on a virtual benchmark, showing improved synchronization and model-free adaptability. This special issue demonstrates the broad scope and practical maturity of data-driven control, spanning from theoretical developments to real-world validation. The work in [23] conducted flight experiments of multiple quadrotors with an optical motion capture system to validate the effectiveness of the developed distributed formation control approach. The work in [2] performed physical experiments on a four-wheel-mecanum mobile robot platform to validate the applicability of the proposed data-driven control method. This special issue demonstrates the broad scope and practical maturity of data-driven control, spanning from theoretical developments to real-world validation. The work in [23] conducted flight experiments of multiple quadrotors with an optical motion capture system, and the work in [2] performed experiments on a four-wheel-mecanum mobile robot platform, showing the effectiveness and applicability of the developed data-driven control methods. Collectively, the abovementioned studies highlight the transition of data-driven control from theoretical frameworks to practical implementations, underscoring its growing role in advancing autonomous and intelligent control across diverse engineering domains. The authors declare no conflicts of interest.

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Applications in Data-Driven Additive Manufacturing

Mutahar Safdar, Guy Lamouche, Padma Polash Paul et al. · SpringerBriefs in applied sciences and technology · 2023

AM lifecycle is divided into major sources, and preparation of data generated by each source is discussed separately. These sources of features can be grouped into design, process, and post-process categories. In each of these categories, major sources of additive manufacturing (AM) data are identified and used to group the applications of feature engineering in AM. Their applications are discussed in detail in the subsequent sections of this chapter. A key feature of this chapter is its tabular summaries where detailed feature engineering pipelines are presented and linked with feature source, feature form, and feature applications.

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Data-Driven Network Analysis and Applications

Narisu Tao · 2016

Data is critical for scientific research and engineering systems. However, data collection procedures are often subject to high cost or heavy loss rate. It is challenging to accurately estimate missing or unobserved data points through the available ones. To cope with this challenge, data interpolation methods have been utilized to approximate the missing data with lower price. In this thesis, we study three specific problems on missing data interpolation in computer networking. They are 1) Autonomous System (AS) level path inference problem, 2) environment reconstruction problem in Wireless Sensor Networks (WSNs) and 3) rating of network paths inference problem. For the first problem, we bring a new angle to the AS path inference by exploiting the metrical tree-likeness or the low hyperbolicity of the Internet, part of the complex network properties of the Internet. We show that such property can generate a new constraint that narrows down the searching space of possible AS paths to a much smaller size. Based on this observation, we propose two new algorithms, namely HyperPath and Valley-free HyperPath. With intensive evaluations on AS paths from real-world BGP Routing Information Bases, we show that the proposed algorithms can achieve better performance. We demonstrate that our algorithms can significantly reduce inter-AS traffic for P2P applications with an improved AS path prediction accuracy. For the second problem, we propose a new approach, namely Probabilistic Model Enhanced Spatio-Temporal Compressive Sensing (PMEST-CS), to boost the performance of CS-based methods for environment reconstruction in WSNs. During algorithm design, we consider two new perspectives, which are exploiting the sparsity in spatio-temporal difference in environment and using probabilistic model and inference to enrich the available dataset. Experimental results utilizing the two real-world datasets show that significant performance gains, in terms of reconstruction quality, can be obtained in comparison with the state of the art CS-based methods. For the third problem, we investigate the rating of network paths, which is not only informative but also cheap to obtain. We firstly address the scalable acquisition of path ratings by statistical inference. By observing similarities to recommender systems, we examine the applicability of solutions to recommender system and show that our inference problem can be solved by a class of matrix factorization techniques. Then, we investigate the usability of rating-based network measurement and inference in applications. A case study is performed on whether locality awareness can be achieved for overlay networks of Pastry and BitTorrent using inferred ratings. We show that such coarse-grained knowledge can improve the performance of peer selection and that finer granularities do not always lead to larger improvements.

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Data‐Driven Materials Innovation and Applications

Zhuo Wang, Zhehao Sun, Hang Yin et al. · Advanced Materials · 2022

Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data-driven scientific research. This transition requires the development of authoritative and up-to-date frameworks for data-driven approaches for material innovation. A critical discussion on the current advances in the data-driven discovery of materials with a focus on frameworks, machine-learning algorithms, material-specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data-driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data-intensive strategies and machine-learning algorithms; ii) material databases and related tools and platforms for data generation and management; iii) commonly used molecular descriptors used in data-driven processes. Furthermore, an in-depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data-driven paradigms is outlined, and the opportunities and challenges in data-driven material innovation are highlighted.

Cited by 163Open access

Data-driven Performance Optimization for Data-intensive Applications

Liu, Jie · eScholarship (California Digital Library) · 2024

Data-intensive applications have attracted considerable attention from researchersin information sciences and enterprises, as these applications have made\nevolutionary breakthroughs in scientific fields and are extremely valuable to produce\nproductivity in businesses. Recently, as the high speed growth of the new generated\ndata, researchers have begun to leverage the useful knowledge hidden in such\nhuge volume of data to optimize the performance of the data-intensive applications.\nHowever, optimize the performance of the data-intensive applications based on the\ndata-driven approaches are still need to be explored.\nIn this thesis, we focus on data-driven performance optimization for dataintensive\napplications. We first study an application, auto-labeling data on mobile\ndevices. How to accurately and efficiently label data on a mobile device is critical\nfor the success of training machine learning models on mobile devices. Auto-labeling\ndata for data-intensive applications on mobile devices is a challenging task, because\ndata is incrementally generated and there is a possibility of having unknown labels\namong new coming data. Furthermore, the rich hardware heterogeneity on mobile\ndevices creates challenges on efficiently executing the autolabeling workload. We\nintroduce Flame, an auto-labeling system that can label dynamically generated data\nwith unknown labels. Flame includes an execution engine that efficiently schedules\nand executes auto-labeling workloads on heterogeneous mobile processors. Evaluating\nFlame with six datasets on two mobile devices, we demonstrate that the labeling\naccuracy of Flame is 11.8%, 16.1%, 18.5%, and 25.2% higher than a state-of-the-art\nlabeling method, transfer learning, semi-supervised learning, and boosting methods\nrespectively. Flame is also energy efficient, it consumes only 328.65mJ and 414.84mJ\nwhen labeling 500 data instances on Samsung S9 and Google Pixel2 respectively.\nFurthermore, running Flame on mobile devices only brings about 0.75 ms additional\nframe latency which is imperceivable by the users.\nSecond, we explore another data-intensive application, the cardinality estimation\nin database systems. Cardinality estimation is a fundamental and critical\nproblem in databases. Recently, many estimators based on deep learning have been\nproposed to solve this problem and they have achieved promising results. However,\nthese estimators struggle to provide accurate results for complex queries, due to not\ncapturing real inter-column and inter-table correlations. Furthermore, none of these\nestimators contain the uncertainty information about their estimations. In this paper,\nwe present a join cardinality estimator called . learns the correlations across\nall columns and all tables in the database. It also contains the uncertainty information\nof each estimation. Among all studied learned estimators, our results are\npromising: (1) has the smallest model size; (2) It has the fastest inference speed; (3)\nCompared with the state of the art estimator, has 10× faster inference speed, and\nprovides 1.3× ∼ 6.7× smaller estimation errors for complex queries; (4) To the best\nof our knowledge, is the first estimator that incorporates uncertainty information for\ncardinality estimation into a deep learning model.\nFurthermore, we also study the data loading problem for large-scale distributed\ntraining. The resource-hungry and time-consuming process of training Deep\nNeural Networks (DNNs) can be accelerated by optimizing and/or scaling computations\non accelerators such as GPUs. However, the loading and pre-processing of\ntraining samples then often emerges as a new bottleneck. This data loading process\nengages a complex pipeline that extends from the sampling of training data on\nexternal storage to delivery of those data to GPUs, and that comprises not only\nexpensive I/O operations but also decoding, shuffling, batching, augmentation, and\nother operations. We propose in this paper a new holistic approach to data loading\nthat addresses three challenges not sufficiently addressed by other methods: I/O load\nimbalances among the GPUs on a node; rigid resource allocations to data loading and\ndata preprocessing steps, which lead to idle resources and bottlenecks; and limited efficiency\nof caching strategies based on pre-fetching due to eviction of training samples\nneeded soon at the expense of those needed later. We first present a study of key bottlenecks observed as training samples flow through the data loading and preprocessing\npipeline. Then, we describe Lobster, a data loading runtime that uses performance\nmodeling and advanced heuristics to combine flexible thread management with optimized\neviction for distributed caching in order to mitigate I/O overheads and load\nimbalances. Experiments with a range of models and datasets show that the Lobster\napproach reduces both I/O overheads and end-to-end training times by up to 1.5×\ncompared with state-of-the-art approaches.\nFinally, we study the cardinality estimation for string predicates in database\nsystems. Cardinality estimation for string predicates is a notoriously challenging\nproblem in database systems. This paper presents ArbiLIKE, an advanced deep\nlearning-based cardinality estimator for arbitrary LIKE predicates. ArbiLIKE utilizes\na cardinality-aware embedding technique to encode LIKE predicates into feature\nvectors. It further incorporates an innovative sequence model to capture the semantic\ninformation of different substrings, enhancing the estimation accuracy. ArbiLIKE is\nalso capable of handling LIKE predicates with any combination of wildcards (“%”,\n“ ”). Empirical evaluations showcase ArbiLIKE’s promising accuracy, achieving estimation\nerrors that are up to 165.1× smaller than those of eight baselines, including\nstate-of-the-art methods. As a generic estimator, ArbiLIKE realizes error reductions\nranging from 1.4 to 93.1× for LIKE predicates with multiple wildcards in comparison\nto the existing techniques. To the best of our knowledge, ArbiLIKE is the first deep\nlearning-based estimator capable of handling arbitrary LIKE predicates.

Cited by 0Open access

Advanced Data Driven Visualisation for Geo-spatial Data Dynamic Data Driven Applications Systems 383

Anthony Jones, Dan Cornford · 2014

Abstract. Most current 3D landscape visualisation systems either use bespoke hardware solutions, or offer a limited amount of interaction and detail when used in realtime mode. We are developing a modular, data driven 3D visualisation system that can be readily customised to specific requirements. By utilising the latest software engineering methods and bringing a dynamic data driven approach to geo-spatial data visualisation we will deliver an unparalleled level of customisation in near-photo realistic, realtime 3D landscape visualisation. In this paper we show the system framework and describe how this employs data driven techniques. In particular we discuss how data driven approaches are applied to the spatiotemporal management aspect of the application framework, and describe the advantages these convey. 1

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Data Driven Data Encoding for Low Power LDPC Applications

Suresh Dannana, Govinda Rao Tamminaina, Арун Кумар · International Journal of Signal Processing Image Processing and Pattern Recognition · 2015

The power consumption of network-on-chip (NoC) links start to compete with that of NoC routers. Reduce power consumption in NoCs, in both wires and logic, is to reduce the switching activity by means of coding schemes. The fundamental basic schemes of a NoC-based communicate are Network Interfaces (NIs), Links and routers. These links misuse power due to the switching activity (both self and coupling) induces by following data model traverse the links and routers. Three schemes join to reduce the dynamic power of the NoC data path through a decrease in the number of bit transitions. The transitions are of Type (I to IV) and correspondingly. In this paper, LDPC the encoder is replaced with data encoding schemes in control to reduce the dynamic power consumption in Low Density Parity Check Techniques (LDPC) and the power values are varied to the package of XC3S100E and TQ144 device is used to the comparing of NLDPC to LDPC, best results are evaluated in the proposed technique.

Cited by 1Open access

Building Extensible Data-Driven Web Applications Using Dynamic Data

Sandeep Chanda, Damien Foggon · Apress eBooks · 2013

To be honest, many front-end developers in the world of .NET hate it when it comes to writing queries against a database and figuring out if the data is consistent with the state of the application. They are more content working within the limits of Visual Studio and would line up to get their hands on a tool that would let them access relational data without having to write SQL queries. They are also pretty happy operating on data in memory using their language of choice, be it C#, VB, or LINQ, but not SQL. This is primarily due to the fact that each native relational database has its own flavor of SQL, and it is simply overwhelming to try and learn each one of them.KeywordsDynamic DataVisual StudioDynamic ViewDatabase TableTable PageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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