Amount Of Data Research Articles

The Digital Witness: Exploring Gestural Misinformation in Tele-Forensic Interviews with 5-8-Year-Old Children

AbstractChild abuse is a major concern worldwide. While live-link interviews have been successful in legal and medical contexts, its potential for eyewitness interviews remains insufficiently studied, particularly in terms of non-verbal misinformation. This study explored tele-forensic interviewing (tele-FI), where video-conferencing software such as Zoom or Skype are used to conduct forensic interviews, as an alternative to face-to-face interviews. Focus was given to the susceptibility of eyewitness memory to the gestural misinformation effect (GME) where post-event information in the form of gesture can distort recall of a witnessed incident. Forty-seven children were recruited ranging in age from 5- to 8-years-old (M = 6 years 11 months). Comparisons were made to face-to-face conditions from prior published work by the authors (N = 63, M = 7 years 2 months) using the same methodology, video, and question sets. Results find support for the GME during tele-FI, with 1.23 misinformation details recorded on average and tele-FI showing a similar response pattern as face-to-face interviews. Accuracy was shown to be comparable in tele-FI (M = 16.21) compared to face-to-face interviews (M = 14.02), with a notable increase in the amount of relevant information provided in the tele-FI condition. Developmental age showed significant increases in the quality and quantity of data. This study provides evidence for tele-FI as a viable alternative to face-to-face interviews, and represents the first exploration of the GME in tele-FI, to the best of our knowledge. Discussion focuses on the benefits of tele-FI and the implications for police interview guidelines.

Artificial intelligence applications in the diagnosis and treatment of bacterial infections

The diagnosis and treatment of bacterial infections in the medical and public health field in the 21st century remain significantly challenging. Artificial Intelligence (AI) has emerged as a powerful new tool in diagnosing and treating bacterial infections. AI is rapidly revolutionizing epidemiological studies of infectious diseases, providing effective early warning, prevention, and control of outbreaks. Machine learning models provide a highly flexible way to simulate and predict the complex mechanisms of pathogen-host interactions, which is crucial for a comprehensive understanding of the nature of diseases. Machine learning-based pathogen identification technology and antimicrobial drug susceptibility testing break through the limitations of traditional methods, significantly shorten the time from sample collection to the determination of result, and greatly improve the speed and accuracy of laboratory testing. In addition, AI technology application in treating bacterial infections, particularly in the research and development of drugs and vaccines, and the application of innovative therapies such as bacteriophage, provides new strategies for improving therapy and curbing bacterial resistance. Although AI has a broad application prospect in diagnosing and treating bacterial infections, significant challenges remain in data quality and quantity, model interpretability, clinical integration, and patient privacy protection. To overcome these challenges and, realize widespread application in clinical practice, interdisciplinary cooperation, technology innovation, and policy support are essential components of the joint efforts required. In summary, with continuous advancements and in-depth application of AI technology, AI will enable doctors to more effectivelyaddress the challenge of bacterial infection, promoting the development of medical practice toward precision, efficiency, and personalization; optimizing the best nursing and treatment plans for patients; and providing strong support for public health safety.

Stronger together: different community science platforms all contribute to wildlife research

Context Engaging the general public can increase spatio-temporal coverage of wildlife monitoring. Given the potentially substantial costs, we need to evaluate the contribution of known and planned initiatives and confirm whether multiple platforms increase the efficiency of data collection. As observer behaviour affects data quantity and quality, users of specialised and generalist platforms are expected to behave differently, resulting in more connected networks for specialised and higher nestedness for generalist platforms. Specialist observers are expected to contribute a balanced ratio of rare and common species, whereas non-specialist contribution will depend more on species detectability. Aims We aim to evaluate whether the combined contribution of observers from different platforms can improve the quality of occurrence and distribution data of 218 endemic Atlantic Forest bird species in Brazil. We also describe and compare observer-bird species interaction networks to illustrate observer behaviour on different platforms. Methods On the basis of data from five community science platforms in Brazil, namely, eBird, WikiAves, Biofaces, iNaturalist and Táxeus, we compared the spatial distribution of bird observations, the number of observers, the presence of the same observers on various platforms, bird species coverage, and the proportion of duplicate observations within and among platforms. Key results Although species coverage of the joint dataset increased by up to 100%, spatial completeness among the five platforms was low. The network of individual platforms had low values of clustering, and the network of the joint dataset had low connectance and high nestedness. Conclusions Each platform had a somewhat unique contribution. Pooling these datasets and integrating them with standardised data can inform our knowledge on bird distributions and trends in this fragile biome. Nevertheless, we encourage observers to provide precise coordinates, dates and other data (and platforms to accommodate such data) and recommend submitting data from all platforms into the Global Biodiversity Information Facility to support wildlife research and conservation. Implications If new platforms engage more and different people, new initiatives can cover poorly represented areas and successfully expand monitoring effort for Atlantic Forest endemic bird species.

Comparative analysis of vision transformers and convolutional neural networks in osteoporosis detection from X-ray images

Within the scope of this investigation, we carried out experiments to investigate the potential of the Vision Transformer (ViT) in the field of medical image analysis. The diagnosis of osteoporosis through inspection of X-ray radio-images is a substantial classification problem that we were able to address with the assistance of Vision Transformer models. In order to provide a basis for comparison, we conducted a parallel analysis in which we sought to solve the same problem by employing traditional convolutional neural networks (CNNs), which are well-known and commonly used techniques for the solution of image categorization issues. The findings of our research led us to conclude that ViT is capable of achieving superior outcomes compared to CNN. Furthermore, provided that methods have access to a sufficient quantity of training data, the probability increases that both methods arrive at more appropriate solutions to critical issues.

Examining the lateralization of electrophysiological correlates of auditory awareness.

The neurological basis for perceptual awareness remains unclear, and theories disagree as to whether sensory cortices per se generate awareness. Critically, neural activity in the sensory cortices is only a neural correlate of consciousness (NCC) if it closely matches the contents of perceptual awareness. Research in vision and touch suggest that contralateral activity in sensory cortices is an NCC. Similarly, research in hearing with two sound sources (left and right) presented over headphones also suggests that a candidate NCC called the auditory awareness negativity (AAN) matches perceived location of sound. The current study used 13 different sound sources presented over loudspeakers for natural localization cues and measured event-related potentials to a threshold stimulus in a sound localization task. Preregistered Bayesian mixed models provided moderate evidence against an overall AAN and very strong evidence against its lateralization. Because of issues regarding data quantity and quality, exploratory analyses with aggregated data from multiple loudspeakers were conducted. Results provided moderate evidence for an overall AAN and strong evidence against its lateralization. Nonetheless, the interpretations of these results remain inconclusive. Therefore, future research should reduce the number of conditions and/or test over several sessions to procure a sufficient amount of data. Taken at face value, the results may suggest issues with AAN as an NCC of auditory awareness, as it does not laterally map onto experiences in a free-field auditory environment, in contrast to the NCCs of vision and touch.

DDANF: Deep denoising autoencoder normalizing flow for unsupervised multivariate time series anomaly detection

In recent years, the proliferation of IoT technologies and the widespread adoption of wireless sensors across various critical infrastructures such as power plants, service monitoring systems, space and earth exploration missions, and water treatment facilities have resulted in the generation of vast quantities of multivariate time series data. Within this context, unsupervised anomaly detection has emerged as a pivotal yet challenging problem in time series research, necessitating machine learning models capable of identifying rare anomalies amidst massive datasets. Traditionally, unsupervised methods have approached this issue by learning representations of primary patterns within sequences and detecting deviations through reconstruction errors. However, the effectiveness of this approach is often limited due to the intricate dynamics and diverse patterns inherent in these dynamic systems. Moreover, many existing unsupervised anomaly detection techniques fail to fully exploit inter-feature relationships within multivariate time series data, thereby overlooking a crucial criterion for accurate detection. To address these shortcomings, this paper introduces a novel unsupervised method for multivariate time series anomaly detection based on normalized flows and autoencoders. Central to our approach is the incorporation of a channel shuffling mechanism during training, enhancing the model’s capacity to discern inter-channel patterns and anomalies. Concurrently, the application of normalized flows within the autoencoder framework serves to constrain the latent space, effectively isolating anomalies and improving detection accuracy. Experimental validation conducted on two large-scale public datasets demonstrates the efficacy of the proposed method compared to established benchmarks, highlighting its superior performance.

Pyridazine (1, 2-diazine): A Versatile Pharmacophore Known for its Utility in Biological Systems

: The present review describes the biological essence of pyridazine scaffold. Around 142 biologically potential pyridazine entities are gathered in a pile from documented literature. Some of them are commercially available drugs, few are naturally occurring pyridazine compounds, and a wide variety of compounds containing pyridazine moiety are biologically tested, and some are under clinical trials. Rather than collecting large quantities of data, an attempt is made to compile valuable entities. However, efforts have been made to compile the maximum literature in brief. The main motto of this review is to provide a combination of therapeutically active pyridazine containing compounds for further drug design, discovery, and development to contribute to future medicinal chemistry. Our approach is to bring the most biologically potent pyridazine derivatives to medicinal chemists, biologists, pharmacists, and organic chemists. The present work encompasses the literature from 2000-2022 from different and authentic sources. The work is divided according to the bioactive nature of pyridazine nucleus.

The potential of synthetic minority oversampling technique to enhance the precision of gender prediction: an investigation of artificial neural networks with cephalometry

BACKGROUND: When creating models utilizing artificial neural networks, the quantity of training data and the distribution of data need to be considered, particularly when making gender predictions. AIM: This study seeks to determine the potential impact of using the synthetic minority oversampling technique (SMOTE) on gender prediction using the artificial neural networks model. MATERIALS AND METHODS: The current study utilized a dataset consisting of 297 cephalometric measurements from Indonesian patients, comprising 229 samples from females and 68 samples from males. WebCeph is used to measure certain parameters, such as Sella-Nation-Point A (SNA) angle, mandibular length, mandibular angle, Sella-Glabella-Point A (SGA) angle, and diagnosis. Data processing and artificial neural networks model creation were conducted using Python. RESULTS: The gender identification accuracy of the artificial neural networks model is 87% for females and 0% for males, resulting in an overall average accuracy of 78%. When using SMOTE, the accuracy is 22%, with 0% for females and 37% for males. However, when using SMOTE and normalization, the accuracy increases to 71%, with 82% for females and 30% for males. The accuracy of normalization without SMOTE is 76%, with 86% for females and 14% for males. CONCLUSIONS: This research has proven the efficacy of SMOTE in improving the classification of male matrices. Nevertheless, this study reveals that the overall accuracy results of SMOTE are suboptimal in comparison to the absence of SMOTE and normalization. The application of data balancing strategies is necessary to achieve optimal accuracy in gender prediction when artificial neural networks, and other parameters must be applied.

Automated debt recovery systems: Harnessing AI for enhanced performance

Amidst an upsurge in the quantity of delinquent loans, the financial industry is experiencing a fundamental transformation in the approaches utilised for debt recovery. The debt collection process is presently undergoing automation and improvement through the utilisation of Artificial Intelligence (AI), an emergent technology that holds the potential to revolutionise this sector. By leveraging machine learning, natural language processing, and predictive analytics, automated debt recovery systems analyse vast quantities of data, generate forecasts regarding the likelihood of recovery, and streamline operational processes. Debt collection systems powered by AI are anticipated to be compliant, precise, and effective. On the other hand, conventional approaches are linked to increasing expenditures and inefficiencies in operations. These solutions facilitate efficient resource allocation, customised communication, and rapid data analysis, all while minimising the need for human intervention. Significant progress has been made in data analytics, predictive modelling, and decision-making through the application of artificial intelligence (AI) in debt recovery; this has the potential to revolutionize the financial sector’s approach to debt management. The findings of the research underscore the criticality of artificial intelligence (AI) in attaining efficacy and precision, in addition to the imperative of a data-centric framework to fundamentally reshape approaches to debt collection. In conclusion, artificial intelligence possesses the capacity to profoundly transform the existing approaches utilized in debt management, thereby guaranteeing financial institutions’ sustained profitability and efficacy. The application of machine learning methodologies, including predictive modelling and logistic regression, signifies the potential of the system.

Application of big data analytics to forecast future waste trends and inform sustainable planning

Urbanization and industrialization have caused a substantial rise in garbage production, creating substantial environmental, economic, and social difficulties. Precise prediction of future waste patterns is essential for sustainable waste management and strategic planning. Big Data Analytics (BDA) presents a potential method for examining large quantities of waste-related data, revealing trends, and offering practical insights. This review article examines the utilization of Big Data Analytics (BDA) in predicting future waste patterns, emphasizing its capacity to provide valuable insights for sustainable planning and decision-making. The study examines several techniques, tools, and case studies, and explores the obstacles and future prospects in this new discipline.

Hybrid Filtering and Probabilistic Techniques for Privacy-Preserving Community Detection in OSNs

Online Social Networks (OSNs) face the major challenge of protecting participant's privacy, due to the high dimensionality and volume of the data. In real-time social networks, where hundreds of personal details of people are shared every day, there remains a significant threat to privacy. Privacy preservation is challenging for a community detection problem due to the high computational complexity and memory requirements, especially in larger real-world OSN graphs. Although weighted nodes provide better results, as they allow capturing the frequencies of the values, the privacy preservation of sensitive attributes such as specific profiles becomes harder compared to these models. This problem can result in queries and subsequent learnings from social network profiles of specific individuals, which may be of personal, political or otherwise concern. Online social networks (OSNs) grapple with significant privacy challenges due to the extensive dimensions and vast quantities of data involved. This research fills the void in current privacy-preserving community detection methodologies, which face problems in computational complexity and memory usage in large-scale OSNs. The proposed framework seeks to bolster privacy preservation through a comprehensive multi-step process. Data filtering uses a blended data filter system to remove outliers and irrelevant data, thus enhancing the quality of the input data. Density-Oriented Clustering phase employs a density-oriented clustering model to identify communities, with each cluster representing a distinct community. Privacy Preservation component introduces a new privacy preservation technique for sensitive OSN attributes, surpassing existing k-anonymization methods. The developed density-based social network community detection model and its novel privacy-preserving scheme are evaluated using the datasets Yelp, Football, Zachary and Dolphin from the SNAP dataset. Experimental results on these datasets embed a comprehensive evaluation based on the order of each node and the graph-based networks, where each node is laden with weights as proximity values, indicating the semantic proximity between communities and individuals. The proposed framework employs the normalized mutual information (NMI), modularity (Q), Rand index and runtime measurements to demonstrate widespread advantages in the multi-dimensional functional space, including greater accuracy, cluster compatibility, and computational tractability over the existing prominent traditional models for OSNs.

Real-Time Analysis of Neuronal Cell Cultures for CNS Drug Discovery

The ability to screen for agents that can promote the development and/or maintenance of neuronal networks creates opportunities for the discovery of novel agents for the treatment of central nervous system (CNS) disorders. Over the past 10 years, advances in robotics, artificial intelligence, and machine learning have paved the way for the improved implementation of live-cell imaging systems for drug discovery. These instruments have revolutionized our ability to quickly and accurately acquire large standardized datasets when studying complex cellular phenomena in real-time. This is particularly useful in the field of neuroscience because real-time analysis can allow efficient monitoring of the development, maturation, and conservation of neuronal networks by measuring neurite length. Unfortunately, due to the relative infancy of this type of analysis, standard practices for data acquisition and processing are lacking, and there is no standardized format for reporting the vast quantities of data generated by live-cell imaging systems. This paper reviews the current state of live-cell imaging instruments, with a focus on the most commonly used equipment (IncuCyte systems). We provide an in-depth analysis of the experimental conditions reported in publications utilizing these systems, particularly with regard to studying neurite outgrowth. This analysis sheds light on trends and patterns that will enhance the use of live-cell imaging instruments in CNS drug discovery.

A method for estimating the rotation errors of BDS broadcast ephemeris using global satellite laser ranging data

Abstract The BeiDou-3 Navigation Satellite System (BDS-3) generates navigation messages from a regional ground tracking network and inter-satellite links (ISL), relying on predicted station coordinates and Earth rotation parameters (ERPs). These procedures, however, may lead to orientation errors in the broadcast ephemeris, termed constellation rotation errors.
Traditionally, these errors are monitored through Helmert transformation analysis comparing broadcast and post-processed orbits, a method that highlights discrepancies between the
GNSS Terrestrial Reference Frame (TRF) and the International Terrestrial Reference Frame
(ITRF) but suffers from a two-week evaluation delay. This study proposes a near real-time approach for estimating constellation rotation errors using high precision Satellite Laser-Ranging (SLR) data, representing the difference between GNSS TRF and ITRF through the
International Laser Ranging Service (ILRS) TRF. The impact of SLR data quantity and distribution on rotation estimation accuracy is investigated, and the effectiveness of this method on BDS-3 orbits with ERP-induced orientation deficiencies is validated. Results show that the root mean square (RMS) of broadcast orbit errors and the orbit user ranging error
(URE) reduce from 0.8/0.7/0.2 m to 0.1/0.1/0.1 m, respectively. This demonstrates the method’s capability for real-time monitoring and correcting orientation errors within BDS-3 navigation messages.

Legitimacy and anti-corruption disclosures: insights into the early effects of the European Non-Financial Reporting Directive

Purpose This study aims to investigate corporate anti-corruption disclosure (ACD) strategies during the regulative debate surrounding the European Directive 2014/95/EU which for the first time regulated ACD in Europe. By using a legitimacy framework, it assesses whether companies improved proactively their voluntary ACD during the transposition phase to address potential regulatory changes. Moreover, it investigates how organizational and institutional factors influence companies’ reaction. Design/methodology/approach This paper hand-collected ACD data (quantity, scope, quality and transparency) from non-financial reports for the years 2010–2015 for a set of 56 (28 EU and 28 non-EU) companies (336 firm-years observations). The study applies difference-in-difference analysis to assess the effects of the debate. Moreover, it tests the association of ACD with organizational and institutional attributes. Findings The study shows that EU-companies are proactively changing their disclosures. The response is positively influenced by industry exposure to corruption-risks and by lower government corruption, while self-reported negative disclosure impacts negatively. Further, lower government corruption increases the effects of industry exposure to corruption-risks. However, the impacts vary with the disclosure metric. Originality/value Besides reinforcing legitimacy as a driving-force in shaping ACD during the transition phase to a regulated context, the paper integrates traditional legitimacy arguments with insights related to the institutional context. This contributes to improving the understanding of the empirical setting where the production of regulation occurs and can support future regulative processes.

An ensemble method for investigating maritime casualties resulting in pollution occurrence: Data augmentation and feature analysis

Timely prediction of maritime casualties resulting in pollution occurrence remains unsolved in academia, as the significant data imbalance between non-polluting and polluting casualties poses a challenge to prediction efficacy. This study proposes an ensemble method for predicting polluting maritime casualties and exploring the contributing features to pollution. In the data preprocessing phase, key features related to casualties and vessels are extracted and encoded into model variables; in the data augmentation phase, Variational Autoencoder is employed to generate synthetic samples from the minor class, effectively mitigating the impact from data imbalance; and in the pollution indicator classification phase, machine learning models are trained on the balanced dataset to label a casualty as “polluting” or “non-polluting”. A dataset containing 25,414 worldwide maritime casualties from 2013 to 2023 is utilized for method validation. Several state-of-the-art data balancing techniques serve as baselines for comparison with the VAE on the quality of generated synthetic data. The model trained on the VAE dataset achieves the most satisfactory performances, demonstrating the superiority of VAE in augmenting data quantity and diversity. “Casualty cause”, “Vessel age” and “Vessel type” are revealed as the top three contributing features to pollution. Several insights are discussed for precautionary measures and policy development.

SparrKULee: A Speech-Evoked Auditory Response Repository from KU Leuven, Containing the EEG of 85 Participants

Researchers investigating the neural mechanisms underlying speech perception often employ electroencephalography (EEG) to record brain activity while participants listen to spoken language. The high temporal resolution of EEG enables the study of neural responses to fast and dynamic speech signals. Previous studies have successfully extracted speech characteristics from EEG data and, conversely, predicted EEG activity from speech features. Machine learning techniques are generally employed to construct encoding and decoding models, which necessitate a substantial quantity of data. We present SparrKULee, a Speech-evoked Auditory Repository of EEG data, measured at KU Leuven, comprising 64-channel EEG recordings from 85 young individuals with normal hearing, each of whom listened to 90–150 min of natural speech. This dataset is more extensive than any currently available dataset in terms of both the number of participants and the quantity of data per participant. It is suitable for training larger machine learning models. We evaluate the dataset using linear and state-of-the-art non-linear models in a speech encoding/decoding and match/mismatch paradigm, providing benchmark scores for future research.

Enhancing Effectiveness and Security in Microservices Architecture

Microservices have allowed the decomposition of large monoliths and introduced the simple responsibility principle into its essence. It allowed complex systems to be more cost efficient and easily scalable. With this new architecture, new vulnerabilities were introduced, such as the large surface that started to get exposed to the internet. Instead of having one monolithic surface exposed, we started to have several small services exposed. New patterns were introduced to try to diminish these threats and among them the “API Gateway” pattern was introduced. The purpose of this article is to further explore the API Gateway pattern, and to further enhance the microservices communication security as part of that pattern. This research proposes a hybrid approach, using HTTPS and JWT only when strictly necessary, towards overcoming those recognized limitations of security and performance.Furthermore, proof of concept on top of NB-IoT communication protocol was developed. It intends to explore the transmission of a large quantity of low-frequency data from devices to the cloud, while the communication maintains effective and with low latency. The created supporting microservices API followed the proposed architecture and was tested against security attacks towards mitigating its impacts.

Optimizing Starch Content Prediction in Kudzu: Integrating Hyperspectral Imaging and Deep Learning with WGAN-GP

Rapid and non-destructive prediction of starch content in kudzu is essential for the food industry. In this work, we present an approach combining hyperspectral imaging (HSI) and deep learning (DL) techniques for predicting kudzu root starch content. Practical constraints such as equipment and experimental conditions limit the quantity of spectral data and labels obtained, which leads to diminish model performance. To address this restriction, we employ Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) to augment spectral and starch content data simultaneously. Through numerous iterations, synthetic data that closely resemble real data is generated, which is validated through comprehensive evaluations using various qualitative and quantitative analysis. Additionally, we establish and compare partial least squares regression (PLSR), support vector regression (SVR) and one-dimensional convolutional neural network (1DCNN) model before and after data augmentation. Experimental results demonstrate that the introduction of synthetic data could improve model performance significantly. Particularly, 1DCNN model exhibits the best performance, achieving correlation coefficients (R2) of 92.97% and 93.43% for starch content in the two types of kudzu roots. Overall, this study not only provides an effective method for rapidly, non-destructively, and accurately determining starch content in kudzu roots, but also addresses the challenge of requiring a large amount of data.

Uncertainty-aware deep learning for monitoring and fault diagnosis from synthetic data

Deep neural networks (DNNs) are often coupled with physics-based and data-driven models to perform fault detection and health monitoring. The system models serve as digital surrogates that generate large quantities of data for training DNNs which would otherwise be difficult to obtain from the real-life system. In such a scenario, the uncertainty in the system model and in the DNN parameters will influence the predictions of the DNN. Here, we quantify the impact of this uncertainty on the performance of DNNs. The uncertainty from the system model is captured with two methods, namely assumed density filtering and heteroskedastic modelling. In addition to quantification, these methods allow training DNNs in an uncertainty-aware manner. The uncertainty in the DNN parameters is captured with Monte Carlo dropout. The proposed approach is demonstrated for fault diagnosis of electric power lines. Data generated from a physics-based model calibrated with real-life measurements is used to train three neural network architectures for fault diagnosis. The results reveal that uncertainty-aware models can provide 1% to 19% improvement in classification accuracy than their deterministic counterparts. The uncertainty-aware models also exhibit better robustness to uncertainty and, thus, offer more reliable models for deployment. Remarkably, the article provides a system-agnostic framework for uncertainty-aware training of DNN models for fault diagnosis and monitoring that explicitly accounts for the synthetic nature of training data.

From SSDs Back to HDDs: Optimizing VDO to Support Inline Deduplication and Compression for HDDs as Primary Storage Media

Deduplication and compression are powerful techniques to reduce the ratio between the quantity of logical data stored and the physical amount of consumed storage. Deduplication can impose significant performance overheads, as duplicate detection for large systems induces random accesses to the backend storage. These random accesses have led to the concern that deduplication for primary storage and HDDs are not compatible. Most inline data reduction solutions are therefore optimized for SSDs and discourage their use for HDDs, even for sequential workloads. In this work, we show that these concerns are valid if and only if the lessons learned from deduplication research are not applied. We have therefore investigated data reduction solutions for primary storage based on the RedHat Virtual Disk Optimizer (VDO) and show that directly applying them can decrease sequential write performance for HDDs by 36-times. We then show that slight modifications to VDO plus the integration of a very small SSD area significantly improve performance even beyond the performance without data reduction enabled, making HDDs more cost-efficient for a wide range of mostly sequential Cloud workloads than SSDs. Additionally, these VDO optimizations do not require to maintain different code bases for HDDs and SSDs and we therefore provide the first data reduction solution applicable to both storage media.