Real Database Research Articles

Odgovornost Republike Hrvatske za štetu prouzrokovanu kompromitacijom informacionog sistema zemljišnih knjiga i katastra

Before their digitation, land registries in the Republic of Croatia had been kept manually (paper-based registration). The Land Registry Act stipulates that land registries in the Republic of Croatia are kept electronically by means of a Real Property Registration and Cadastre Joint Information System (hereinafter: JIS), containing harmonized real property and cadastral data. A real property and land database is created through the electronic entry of paper-based data, i.e. integration of real property and cadastral data into a single database. Hence, the protection of real property transfers is enhanced since the legal status of the land and all accompanying restrictions (concessions, public property status etc.) can be obtained in one place. The real property land database enables registration of every change to the real property without the need to inform both the land registration court and cadastre thereabout. The Real Property Registration and Cadastre Joint Information System is jointly coordinated and managed by the Ministry of Justice and Administration of the Republic of Croatia and the State Geodetic Administration. The Land Registry Act sets forth that the Republic of Croatia is strictly liable for any damage arising from flaws in keeping land registries. Such damage can occur in case of errors or malfunctions in the respective joint information system. This paper investigates the appropriateness of the measures for protection of the Real Property Registration and Cadastre Joint Information System and the general rules for security and control of the access to the said system, and analyses the risk of compromising it. Regarding the liability of the Republic of Croatia for any damage pertaining to flaws in the land records management, the paper depicts the model of defining the liability for any damage sustained by the holders of ownership rights due to unauthorized access to the said joint information system. The paper also sheds light on the advantages and disadvantages of the model for defining fault and strict liability in the above case, and examines some pending issues relating to the applicable model for defining such damage liability de lege lata as well as possible legal solutions and the potential need for the legislator's intervention de lege ferenda.

Traditional versus AI-Based Fraud Detection: Cost Efficiency in the Field of Automobile Insurance

Business practice and various industry reports all show that automobile insurance fraud is very common, which is why effective fraud detection is so important. In our study, we investigate whether today’s widespread AI-based fraud detection methods are more effective from a financial (cost-effectiveness) point of view than methods based on traditional statistical-econometric tools. Based on our results, we came to the unexpected conclusion that the current AI-based automobile insurance fraud detection methods tested on a real database found in the literature are less cost-effective than traditional statistical-econometric methods.

Predicting Violence-Induced Stress in an Arabic Social Media Forum

Social Media such as Facebook plays a substantial role in virtual communities by sharing ideas and ideologies among different populations over time. Social interaction analysis aids in defining people’s emotions and aids in assessing public attitudes, towards different issues such as violence against women and children. In this paper, we proposed an Arabic language prediction model to identify the issue of Violence-Induced Stress in social media. We searched for Arabic posts of many countries through Facebook application programming interface (API). We discovered that the stress state of a battered woman is usually related to her friend’s stress states on Facebook. We applied a large real database from Facebook platforms to analytically investigate the correlation of violence-induced stress states and the victim interactions on social media. We extracted a set of textual, spatial, and interaction attributes from various features. Therefore, we are proposing a hybrid model–an interaction graph model incorporated in a deep learning neural model to leverage post content and interaction data for violence-induced stress detection. Experiments depict that our proposed hybrid model can enhance the prediction performance by 10% in F1-measure. Also, considering the user interaction information can learn an interesting phenomenon, where, the sparse social interactions of violence-induced stress stressed victims is higher by around 15% percent non-battered users, signifying that the structure of the friends of such victims is less connected than non-stressed users.

Improving Inventory Management Decisions by Outliers Detection and Elimination

The presence of outliers in the real databases of many applications is often unavoidable, and one of the most important of these applications is the data in the inventory management sector and its role in the continuation of production. In this paper, a Z-Score Modified method is proposed to detect outliers in the demand data for raw materials used in the cement industry in one of Iraq's factories, and using the method of deletion and reusing the custom model for inventory management. The results showed that deleting outliers contributed to reducing the deficit rates for raw materials (oil, limestone, dust and iron) by 39.84%, 68.62%, 89.02% and 41.71%, respectively, it also contributed to an increase in profit by 614062600 ID during two years as a result of avoiding the deficit that caused the suspension of cement production.

A matrix-based flexible project-planning library and indicators

Flexible approaches, such as agile, hybrid, and extreme project management methods in the software project environment, are increasingly being used in nonsoftware environments. Nevertheless, only a few methods and no topological, time-related or resource-related indicators or project databases can address projects of a flexible nature. This study proposes a unified matrix-based model (UMP) to consider single-mode, multimode, individual and multiple projects. Based on the UMP, a CMPD is specified to collect 11 existing project databases. In addition, a flexible structure generator (FSG) is proposed to generate flexible tasks and dependencies for analyzing the effects of flexibility on topology and project demand-related indicators. By correlation graphs, the relations between structure-related and demand-related indicators are analyzed. The comparison of the simulated and real-life databases shows that the interpretation range of the indicators is different; however, considering the flexibility, this problem can be resolved.

Observational UK cohort study to describe intermittent oral corticosteroid prescribing patterns and their association with adverse outcomes in asthma

IntroductionOral corticosteroids (OCS) for asthma are associated with increased risks of developing adverse outcomes (adverse outcomes); no previous study has focused exclusively on intermittent OCS use.MethodsThis historical (2008–2019) UK cohort...

Insurance fraud detection: A statistically validated network approach

AbstractFraud is a social phenomenon, and fraudsters often collaborate with other fraudsters, taking on different roles. The challenge for insurance companies is to implement claim assessment and improve fraud detection accuracy. We developed an investigative system based on bipartite networks, highlighting the relationships between subjects and accidents or vehicles and accidents. We formalize filtering rules through probability models and test specific methods to assess the existence of communities in extensive networks and propose new alert metrics for suspicious structures. We apply the methodology to a real database—the Italian Antifraud Integrated Archive—and compare the results to out‐of‐sample fraud scams under investigation by the judicial authorities.

A comparison between computer vision- and deep learning-based models for automated concrete crack detection

Systems subjected to continuous operation are exposed to different failure mechanisms such as fatigue, corrosion, and temperature-related defects, which makes inspection and monitoring their health paramount to prevent a system suffering from severe damage. However, visual inspection strongly depends on a human being’s experience, and so its accuracy is influenced by the physical and cognitive state of the inspector. Particularly, civil infrastructures need to be periodically inspected. This is costly, time-consuming, labor-intensive, hazardous, and biased. Advances in Computer Vision (CV) techniques provide the means to develop automated, accurate, non-contact, and non-destructive inspection methods. Hence, this paper compares two different approaches to detecting cracks in images automatically. The first is based on a traditional CV technique, using texture analysis and machine learning methods (TA + ML-based), and the second is based on deep learning (DL), using Convolutional Neural Networks (CNN) models. We analyze both approaches, comparing several ML models and CNN architectures in a real crack database considering six distinct dataset sizes. The results showed that for small-sized datasets, for example, up to 100 images, the DL-based approach achieved a balanced accuracy (BA) of ∼74%, while the TA + ML-based approach obtained a BA > 95%. For larger datasets, the performances of both approaches present comparable results. For images classified as having crack(s), we also evaluate three metrics to measure the severity of a crack based on a segmented version of the original image, as an additional metric to trigger the appropriate maintenance response.

Eat: An R Package for fitting Efficiency Analysis Trees

eat is a new package for R that includes functions to estimate production frontiers and technical efficiency measures through non-parametric techniques based upon regression trees. The package specifically implements the main algorithms associated with a recently introduced methodology for estimating the efficiency of a set of decision-making units in Economics and Engineering through Machine Learning techniques, called Efficiency Analysis Trees [@esteve2020]. The package includes code for estimating input- and output-oriented radial measures, input- and output-oriented Russell measures, the directional distance function and the weighted additive model, plotting graphical representations of the production frontier by tree structures, and determining rankings of importance of input variables in the analysis. Additionally, it includes the code to perform an adaptation of Random Forest in estimating technical efficiency. This paper describes the methodology and implementation of the functions, and reports numerical results using a real data base application.

Robustness optimization of aviation-high-speed rail coupling network

Recently, interactions between the real networks have received extensive attention from scholars in different fields. Considering the edge coupling relationship between aviation networks (AN) and high-speed rail networks (HRN), we established the aviation-high-speed-rail coupling network (AHSRCN) model of China according to the real database of flights and trains. We defined the initial load of each edge based on the importance index of edges obtained by importance evaluation method, and then established a nonlinear load capacity model. Furthermore, according to the actual situation, we applied two dynamic load distribution strategies for each network with different load capacity exponential parameters and load capacity coefficient parameters, and use network congestion factor defined to reflect the robustness of the networks to study optimization of robustness of AHSRCN. The results show that as the proportion of remained edges increases, the aviation network and the high-speed rail network’s congestion reach the peak when the proportion of remained edges is 6% and 2%, respectively. The increase of load capacity parameters can effectively reduce the congestion factor of networks in nonlinearity way. For the case that the load capacity exponential parameter is less than 0, the Load Balancing Distribution (LBD) strategy can reduce the network congestion to the maximum extent. However, for the case that the load capacity exponential parameter is greater than 0, the Load Extreme Distribution (LED) strategy can suppress the network congestion more effectively, while the change of load capacity coefficient parameter will not cause this difference.

Multi-Class Plant Leaf Disease Detection Using a Deep Convolutional Neural Network

Traditional machine learning methods of plant leaf disease detection lack successful performances due to poor feature representation and correlation. This paper presents a novel methodology for automatic plant leaf disease detection using cascaded deep convolutional neural network (CDCNN) which focusses on increasing the feature representation and correlation factors. It provides distinctive features that gives low intra-class variability and higher inter-class variability. CDCNN were performed on a plant-village leaf disease database which consists of 13 classes of tomato, potato, and pepper bell plant diseases; DCNN model performs better with an overall accuracy, recall, and precision of 98.50%, 0.98, and 0.97 respectively. Additionally, performance of the proposed algorithm is evaluated on real time cotton leaf database for bacterial blight, leaf miner, and spider mite diseases detection and provides 99.00% accuracy. The proposed DCNN outperforms well compared to traditional machine learning and deep learning models and is able to detect the diseases present in the leaves of the plant.

A Niching Augmented Evolutionary Algorithm for the Identification of Functional Dependencies in Complex Technical Infrastructures From Alarm Data

Functional dependencies in complex technical infrastructures can cause unexpected cascades of failures. Given the complexity and continuous evolution in time of the infrastructure architecture, the identification of functional dependencies using the classical methods of system decomposition and logic analysis is not feasible. Then, we consider the availability of databases of alarm messages and frame the identification of functional dependencies in complex technical infrastructures as an optimization problem whose objective is the maximization of a metric measuring the level of dependence among alarms. A niching-based evolutionary algorithm has been developed to sequentially evolve a population of candidate solutions (group of alarms), maintaining diversity among them thanks to the use of a mechanism of population augmentation. The proposed algorithm is applied to a synthetic database of alarms generated by a complex technical infrastructure simulation model and to a real large-scale database of alarms collected from the particle accelerator of European Organization for Nuclear Research. The proposed algorithm is shown able to identify functional dependencies and to overperform other approaches based on the use of association rule mining algorithms, in terms of capability of extracting rare rules and computational efficiency.

Materials role in pavement design and its impacts in LCA of road construction and use phase

Connecting real material databases at a very early stage enables to measure, predict and support informed decision making based on energy and carbon performance in the infrastructure sector. While, in the past twenty years, a significant increase of Life Cycle Assessment (LCA) research for road construction projects was seen, data collection and management still remains one of the main challenges. Having a capability to combine the planning, execution, maintenance and recycling on one platform greatly contributes to reducing the effort and assessment execution time and early stage decision makings. ORIS is the first web-based material platform enabling more sustainable road construction with optimised solutions, bringing an assessment of different pavement design options within a geolocalised material sourcing environment. Based on the embedding local design catalogues and its automatized data-driven approach, an exhaustive LCA analysis is performed on road for its whole life cycle conformed to LCA standards. A case study in the United Kingdom demonstrated the life cycle carbon and cost assessment with a significant contribution of use phase modules (30-40% of the total) and the potential of 56% reduction in the global warming indicator from the product and construction phase and 24% in total life cycle saving 720 k£.

HEDA

Recent years have witnessed the rapid development of the encrypted database, due to the increasing number of data privacy breaches and the corresponding laws and regulations that caused millions of dollars in loss. These encrypted databases may rely on different techniques, such as cryptographic primitives and trusted execution environments. In this work, we investigate the feasibility of utilizing fully homomorphic encryption (FHE) to support unbounded database aggregation queries, which typically involve comparisons as filtering predicates and a final aggregation. These operators are theoretically supported by FHE, but need careful algorithm design to maximize the efficiency and have not been explored before. We creatively use two types of FHE schemes, i.e. , one for numerical and one for binary value, to enjoy their advantages respectively. To bridge the encrypted values between these two schemes for seamless query processing without client-server interaction, we propose a novel ciphertext transformation mechanism, which is of independent research interest, to close this gap. We further implement our system and test it over three TPC-H queries and a query over a real social media e-commerce database. Evaluation results show that, to process an aggregation query over 8 k encrypted rows takes about 430 seconds. Although it is slower than plaintext processing in magnitudes and still has much room for improvement, as the very first work in this domain, our system demonstrates the feasibility of using FHE to process OLAP queries.

Parameters tuning of multi-model database based on deep reinforcement learning

As we all know, the performance of database management system is directly linked to a vast array of knobs, which control various aspects of system operation, ranging from memory and thread counts settings to I/O optimization. Improper settings of configuration parameters are shown to have detrimental effects on performance, reliability and availability of the overall database management system. This is also true for multi-model databases, which use a single platform to support multiple data models. Existing approaches for automatic DBMS knobs tuning are not directly applicable to multi-model databases due to the diversity of multi-model database instances and workloads. Firstly, in cloud environment, they have difficulty adapting to changing environments and diverse workloads. Secondly, they rely on large-scale high-quality training samples that are difficult to obtain. Finally, they focus primarily on throughput metrics, ignoring tuning requirements for resource utilization. Therefore, in this paper, we propose a multi-model database configuration parameters tuning solution named MMDTune. It selects influential parameters, recommends the optimal configurations in a high-dimensional continuous space. For different workloads, the TD3 algorithm is improved to generate reasonable parameter adjustment plans according to the internal state of the multi-model databases. We conduct extensive experiments under 5 different workloads on real cloud databases to evaluate MMDTune. Experimental results show that MMDTune adapts well to a new hardware environment or workloads, and significantly outperforms the representative tuning tools, such as OtterTune, CDBTune.

A hierarchical classification/regression algorithm for improving extreme wind speed events prediction

A novel method for prediction of the extreme wind speed events based on a Hierarchical Classification/Regression (HCR) approach is proposed. The idea is to improve the prediction skills of different Machine Learning approaches on extreme wind speed events, while preserving the prediction performance for steady events. The proposed HCR architecture rests on three distinctive levels: first, a data preprocessing level, where training data are divided into clusters and accordingly associated labels. At this point, balancing techniques are applied to increase the significance of clusters with poorly represented wind gusts data. At a second level of the architecture, the classification of each sample into the corresponding cluster is carried out. Finally, once we have determined the cluster a sample belongs to, the third level carries out the prediction of the wind speed value, by using the regression model associated with that particular cluster. The performance of the proposed HCR approach has been tested in a real database of hourly wind speed values in Spain, considering Reanalysis data as predictive variables. The results obtained have shown excellent prediction skill in the forecasting of extreme events, achieving a 96% extremes detection, while maintaining a reasonable performance in the non-extreme samples. The performance of the methods has also been assessed using forecast data (GFS) as predictors.

The Information Management of Real Estates Registration Under the Background of Big Data

On the national real estate registration work is the hot topic of land and resources management. Informatization of real estate registration data and database integration also constantly learn from experience in practice. The current management of key and the difficulty lies in the real estate registration information management platform application and the development of service function, Under the background of this request in today's era of big data, based on the "cloud of land resources", Integrate the positive analysis and data linkage, Using big data thought makes the resources allocation reasonable, so as to realize intelligent management.

A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multioutput Gaussian Process Model.

A recent novel extension of multioutput Gaussian processes (GPs) handles heterogeneous outputs, assuming that each output has its own likelihood function. It uses a vector-valued GP prior to jointly model all likelihoods' parameters as latent functions drawn from a GP with a linear model of coregionalization (LMC) covariance. By means of an inducing points' framework, the model is able to obtain tractable variational bounds amenable to stochastic variational inference (SVI). Nonetheless, the strong conditioning between the variational parameters and the hyperparameters burdens the adaptive gradient optimization methods used in the original approach. To overcome this issue, we borrow ideas from variational optimization introducing an exploratory distribution over the hyperparameters, allowing inference together with the posterior's variational parameters through a fully natural gradient (NG) optimization scheme. Furthermore, in this work, we introduce an extension of the heterogeneous multioutput model, where its latent functions are drawn from convolution processes. We show that our optimization scheme can achieve better local optima solutions with higher test performance rates than adaptive gradient methods for both the LMC and the convolution process model. We also show how to make the convolutional model scalable by means of SVI and how to optimize it through a fully NG scheme. We compare the performance of the different methods over the toy and real databases.

Driving quality improvement through better data: The story of New Zealand's radiation oncology collection.

Aotearoa/New Zealand is one of the first nations in the world to develop a comprehensive, high-quality collection of radiation therapy data (the Radiation Oncology Collection, ROC) that is able to report on treatment delivery by health region, patient demographics and service provider. This has been guided by radiation therapy leaders, who have been instrumental in overseeing the establishment of clear and robust data definitions, a centralised database and outputs delivered via an online tool. In this paper, we detail the development of the ROC, provide examples of variation in practice identified from the ROC and how these changed over time, then consider the ramifications of the ROC in the wider context of cancer care quality improvement. In addition to a review of relevant literature, primary data were sourced from the ROC on radiation therapy provided nationally in New Zealand between 2017 and 2020. The total intervention rate, number of fractions and doses are reported for select cancers by way of examples of national variation in practice. Results from the ROC have highlighted areas of treatment variation and have prompted increased uptake of hypofractionation for curative prostate and breast cancer treatment and for palliation of bone metastases. Future development of the ROC will increase its use for quality improvement and ultimately link to a real time cancer services database.

How frequently are Autosomal and X-STRs multistep mutations perceived as single-step?

Short tandem repeats (STRs) incur in length mutations that involve the loss or gain of repeats. STR mutation rates are usually estimated considering the rates of observed Mendelian incompatibilities in one generation familial configurations. When considering multistep mutations, for the autosomal and X-chromosomal modes of genetic transmission, underestimations are inevitable when using this approach (MIA), due to the occurrence of mutational events deceptively perceived as involving fewer steps. The rate of this occurrence depends on the mode of genetic transmission considered, the parental origin of the mutation, the type of familial configuration considered, and the genotypic background of the progenitor(s). MIA biases were weighted and compared for the diploid and haplodiploid modes of transmission, using familial genotypic configurations (parent(s)-child duos and trios) generated resorting to Python™ and real population databases from Norway, Somalia, and Spain for 10 Aut-STRs and Argentina, Eastern Asia, and Northern Europe for 12 X-STRs. One two- or one three-step mutation was simulated in each of the 1,000,000 familial configurations. The frequency with which mutations could be interpreted as involving fewer steps, when the most parsimonious reasoning is employed, was computed. Results showed that the magnitude and type of biases depend on the type of familial data and the genetic mode of transmission considered, being higher in duos than in trios, both in autosomes and the X chromosome. Indeed, whether X- or Aut-STRs are analyzed, trios generally provide better estimates and should be favored over duos. The pooling of the two types of data is not advised. The greater the number of steps involved in the mutation, the worst the estimates obtained. In X-chromosomal analyzes, trios with a paternal mutation presented the best estimates and mother-daughter duos the worst; mother-son duos showed similar estimates to trios when a maternal mutation was considered.