Proposed Data Model Research Articles

A data model for enabling deep learning practices on discovery services of cyber‐physical systems

AbstractThe W3C Web of Things (WoT) is a leading technology that facilitates dynamic information management in the Internet of Things (IoT). In most IoT scenarios, devices and their associated information change continuously, generating a large amount of data. Hence, to correctly use the information and the data generated by different devices, a new perspective of managing and ensuring data quality is recommended. Applying Data Science techniques to create the data model can help to manage and ensure data quality by creating a common schema that can be reused in future projects, as well as producing recommendations to facilitate Service Discovery. In addition, due to the dynamic devices that change over time or under specific circumstances, the data model created must be sufficiently abstract to add new instances and to support new requirements that devices should incorporate. The use of models helps to raise the abstraction level, adapting it to the continuous changes of devices by defining instances associated with the data model. This paper proposes two data models: one for Cyber‐Physical Systems (CPS) to define device information fetched by a Discovery Service, and another for applying Deep Learning in natural language problems through a Transformer approach. The latter matches user queries in natural language sentences with WoT devices or services. These data models expand the Thing Description model to help find similar CPSs by giving a confidence level to each CPS based on features such as security and the number of times the device was accessed. The results show how the proposed models support the search process of CPSs in syntactic and natural language searches. Furthermore, the four levels of the FAIR principles are validated for the proposed data models, thus ensuring the data's transparency, reproducibility, and reusability.

A Post-Marketing Drug Evaluation Framework Based on Real-World Electronic Health Records Data.

Real-world data (RWD) could be a new way to evaluate the safety and efficacy of post-marketing drugs, while there is no common method for how to use RWD for drug evaluation. In this paper, we present a framework for real-world drug evaluation based on electronic medical record (EHR) data. We designed a data model customized for post-marketing drug evaluation and a unified post-marketing drug evaluation pipeline. The proposed framework can be applied to drug evaluations with different study paradigms for different purposes by flexible use of the proposed data model and pipeline. A prototype system has been developed according to the framework. Real-world EHRs in a tertiary hospital in China between 2010 and 2020 were converted to the proposed data model, and as a test case, we conducted a research on the risk of allergic reactions to cefodizime and ceftriaxone using the prototype system.

An effective correlation-based data modeling framework for automatic diabetes prediction using machine and deep learning techniques

The rising risk of diabetes, particularly in emerging countries, highlights the importance of early detection. Manual prediction can be a challenging task, leading to the need for automatic approaches. The major challenge with biomedical datasets is data scarcity. Biomedical data is often difficult to obtain in large quantities, which can limit the ability to train deep learning models effectively. Biomedical data can be noisy and inconsistent, which can make it difficult to train accurate models. To overcome the above-mentioned challenges, this work presents a new framework for data modeling that is based on correlation measures between features and can be used to process data effectively for predicting diabetes. The standard, publicly available Pima Indians Medical Diabetes (PIMA) dataset is utilized to verify the effectiveness of the proposed techniques. Experiments using the PIMA dataset showed that the proposed data modeling method improved the accuracy of machine learning models by an average of 9%, with deep convolutional neural network models achieving an accuracy of 96.13%. Overall, this study demonstrates the effectiveness of the proposed strategy in the early and reliable prediction of diabetes.

The InsightsNet Climate Change Corpus (ICCC)

The discourse on climate change has become a centerpiece of public debate, thereby creating a pressing need to analyze the multitude of messages created by the participants in this communication process. In addition to text, information on this topic is conveyed multimodally, through images, videos, tables and other data objects that are embedded within documents and accompany the text. This paper presents the process of building a multimodal pilot corpus to the InsightsNet Climate Change Corpus (ICCC) and using natural language processing (NLP) tools to enrich corpus (meta)data, thus creating a dataset that lends itself to the exploration of the interplay between the various modalities that constitute the discourse on climate change. We demonstrate how the pilot corpus can be queried for relevant information in two types of databases, and how the proposed data model promotes a more comprehensive sentiment analysis approach.

Indirect Effect of Earnings Quality in the Linkage between Managerial Ability and Firm Performance: Evidence from an Emerging Economy

Purpose: This paper investigates the associations among managerial ability (MA), earnings quality (EQ) and financial performance of the firm (FP) in the context of Pakistani economy.
 Design/methodology/approach: For testing objectives, the research proposed panel data model based on three earnings quality attributes, such as predictability, smoothness, and conservatism, between the managerial ability and corporate financial performance. For estimation, the study analyzed an unbalanced panel of 219 non-financial listed firms on the Pakistan Stock Exchange (PSX) for 2008-2021. To address the concerns of heteroscedasticity and autocorrelation, the study applied fixed effect regression with robust standard errors clustered at the firm level, as suggested by Newey and West (1987).
 Findings: -The study provides that overall managerial ability enhances earnings quality and firm performance. In addition, it is observed that earnings quality plays a significant role in enhancing the firm financial performance. Furthermore, the findings also indicate that earnings quality partially mediates the linkage between managerial ability and firm performance. These results support the hypothesis that efficient managers enhance the earnings quality, reduce information asymmetries which translates into higher firm performance.
 Research limitations/implications: The current research is limited to the non-financial sector; however, the study findings are undeniably beneficial for corporate managers in the context of developing economies where the managerial skills, quality of earnings and regulatory control is poor.
 Originality/value: -The novelty of the study lies in the managerial ability and performance linkage considering earning quality as a mediator. Standard-setters and regulators can use the study's findings to understand how managerial skills affect corporate practices, accounting standards and behaviour.

Multi-agent collaborative conceptual design method for robotic manufacturing systems in small- and mid-sized enterprises

Robotic manufacturing systems are essential in the post-pandemic world owing to their high level of flexibility and automation during a labor crisis. However, designing a suitable robotic manufacturing system for small- and mid-sized enterprises (SMEs) is challenging, considering both their financial burdens and the current supply chain disruptions of component suppliers. To address these challenges, this study proposes a distributed multi-agent collaborative conceptual design method involving designers and suppliers to assist SMEs in implementing robotic manufacturing systems. First, we propose a common data model that enables knowledge interaction between different agents during collaborative conceptual design. Subsequently, based on the proposed data model, the agent-based collaborative conceptual design process is developed, which enables different agents to communicate, interact, and negotiate with each other according to their experiences and knowledge. Third, an integrative algorithm based on 2-additive fuzzy measures, Choquet integral, and stochastic multi-criteria acceptability analysis is implemented to support the multi-agent decision-making process to robustly select architecture alternatives. As a case study, a real industrial design project of a robotic manufacturing system required by our industrial partner, is adopted to demonstrate the effectiveness of the proposed method.

A Modified Model-Free Adaptive Control Method for Large-Scale Morphing Unmanned Vehicles

This paper investigates the attitude control problem for large-scale morphing unmanned vehicles. Considering the rapid time-varying and strong aerodynamic interference caused by large-scale morphing, a modified model-free control method utilizing only the system input and output is proposed. Firstly, a two-loop equivalent data model for the morphing unmanned vehicle is developed, which can better reflect the practical dynamics of morphing unmanned vehicles compared to the traditional compact form dynamic linearization data model. Based on the proposed data model, a modified model-free adaptive control (MMFAC) scheme is proposed, consisting of an external-loop and an inner-loop controller, so as to generate the required combined control torques. Additionally, in light of the aerodynamic uncertainties of the large-scale morphing unmanned vehicle, a rudder deflection actuator control scheme is designed by employing the model-free adaptive control approach. Finally, the boundedness of the closed-loop system and the convergence of tracking errors are guaranteed, based on the stability analysis. Additionally, numerical examples are presented to demonstrate the effectiveness and robustness of the proposed control scheme in the case of the effect of large-scale morphing.

Nonlinear generalized predictive control with virtual unmodeled dynamics decomposition compensation and data driven

In this study, a novel nonlinear generalized predictive control (NGPC) method is proposed to tackle the tracking control problem which considers a mathematical model in conjunction with the virtual unmodeled dynamics decomposition compensation technology. First, data-modeling technology is adopted to take advantage of the input and output data information from the process to decompose the virtual unmodeled dynamics into the form of posterior measurement unmodeled dynamics and an unknown increment. Then, feedforward compensator is designed. The essential difference between the proposed data modeling algorithm and existing methods is that the effect of the measurement unmodeled dynamics is eliminated by the feedforward compensator. For the increment of the virtual unmodeled dynamics, a novel estimation algorithm is established through a data driven approach; A nonlinear compensator for the unknown increment of the virtual unmodeled dynamics is designed to suppress the effect of the increment of the virtual unmodeled dynamics on the closed-loop system. Second, the developed compensator is combined with the GPC algorithm to construct a nonlinear generalized predictive controller. Theoretical analysis proves that the proposed modeling and control algorithm have bounded-input bounded-output (BIBO) stability. Finally, an experimental study is conducted, which indicates the effectiveness of the proposed algorithm.

강화학습 상태 텐서 차원에 따른 학습 효과도

In reinforcement learning, an agent takes actions in an environment, which is interpreted into a reward and a “representation of the state”. It is well known that the performance of the reinforcement learning is dependent on the “data model representing the state” of a given environment. This paper proposes a data model representing the state which is suitable for a FPS (First Person Shooting) game, a military tactics simulator that changes state extremely and needs decision making quickly. The proposed data model consists of matrix (multi-dimensional tensors) for spatial features and vectors for non-spatial features. To prove the usefulness of the proposed data model, this paper shows experimental results for a FPS game.

A Relational Data Model for Uncertain Data

There are many traditional data models developed for the purpose of storing, managing, and manipulating certain data. However, data comes from different sources which may also include data which is imprecise or inexact. For an application which generates uncertain data, its storage, management, and manipulation becomes equally important as for the certain data. This study presents a relational data model for storing, managing, and manipulating uncertain data with certain data. The data model is analyzed using SQL queries to ensure the completeness of the model. Furthermore, a comparative analysis of proposed data model with existing data models is also presented.

Enabling industrial internet of things-based digital servitization in smart production logistics

ABSTRACT Digital servitization (DS) enabled by the Industrial Internet of Things (IIoT) is essential for securing long-term competitiveness in manufacturing. The literature identifies the need for developing data models for multichannel communications across IIoT devices that fulfil the DS vision. This is crucial for avoiding isolated systems based on proprietary solutions and for promoting data sharing and interoperability across existing and future DS applications. Accordingly, this study proposes a data model for multichannel communication that facilitates IIoT-enabled DS for smart production logistics (SPL). We present three findings from a case study focussing on material handling in a manufacturing company. First, this study provides a model with four modelling profiles, including IIoT devices, databases, and services for multichannel communication. Second, it shows how the proposed modelling profiles transfer information across monitoring, control, optimisation, and autonomous decision services. Third, it presents the operational benefits of applying the proposed data models, including improvements in the delivery, makespan, and energy of material handling. These findings are essential for capturing, processing, and transferring information across products, services, and software databases in IIoT-enabled DS for SPL. They are relevant for manufacturing managers and academics and improve our understanding of IIoT-enabled DS's deployment for SPL in manufacturing.

Event-Based Clustering with Energy Data

This paper describes a stochastic clustering architecture that can be used for making predictions over energy data. The machine learning approach uses some new algorithms of hyper and frequency grids. The design is discrete, localised optimisations based on similarity, followed by a global aggregating layer. The global layer is entropy-based, allowing for a comparison with the recent distributed random neural network designs, for example. The topic relates to the IDEAS Smart Home Energy Project, where a client-side Artificial Intelligence component can predict energy consumption for appliances. The proposed data model is essentially a look-up table of the key energy bands that each appliance would use. Each band represents a level of consumption by the appliance, or the amount used in a time unit and the table can replace more complicated methods, usually constructed from probability theory, for example. Results show that the table can accurately disaggregate a single source to a set of appliances, because each appliance has quite a unique energy footprint. As part of predicting energy consumption, the model could possibly reduce costs by 50%, and more than that if proposed appliance schedules are also included. A second case study considers wind power patterns, where the grid optimises over the dataset columns in a self-similar way to the rows, allowing for some level of feature analysis.

Data standardization of plant-pollinator interactions.

BackgroundAnimal pollination is an important ecosystem function and service, ensuring both the integrity of natural systems and human well-being. Although many knowledge shortfalls remain, some high-quality data sets on biological interactions are now available. The development and adoption of standards for biodiversity data and metadata has promoted great advances in biological data sharing and aggregation, supporting large-scale studies and science-based public policies. However, these standards are currently not suitable to fully support interaction data sharing.ResultsHere we present a vocabulary of terms and a data model for sharing plant–pollinator interactions data based on the Darwin Core standard. The vocabulary introduces 48 new terms targeting several aspects of plant–pollinator interactions and can be used to capture information from different approaches and scales. Additionally, we provide solutions for data serialization using RDF, XML, and DwC-Archives and recommendations of existing controlled vocabularies for some of the terms. Our contribution supports open access to standardized data on plant–pollinator interactions.ConclusionsThe adoption of the vocabulary would facilitate data sharing to support studies ranging from the spatial and temporal distribution of interactions to the taxonomic, phenological, functional, and phylogenetic aspects of plant–pollinator interactions. We expect to fill data and knowledge gaps, thus further enabling scientific research on the ecology and evolution of plant–pollinator communities, biodiversity conservation, ecosystem services, and the development of public policies. The proposed data model is flexible and can be adapted for sharing other types of interactions data by developing discipline-specific vocabularies of terms.

Development of a Conceptual Data Model for 3D Geospatial Road Management Based on LandInfra Standard: A Case Study of Korea

In practice, road management data are typically managed in two-dimensional (2D) geospatial forms. However, 2D geographic information system (GIS)-based road infrastructure management data have limitations in their representation of complex roads, such as interchanges, bridges, and tunnels. As such, complex and large road network management data cannot be adequately managed in a 2D GIS-based form. This study discusses the use of the LandInfra standard for road infrastructure management in Korea, considering its focus on land and civil engineering infrastructure facilities. To facilitate the transition from 2D to 3D GIS, we analyzed existing road management models of road pavement and road register information and created Unified Modeling Language (UML) class diagrams depicting these models. Then, existing road management classes and LandInfra classes were mapped. Based on the results, we propose a road management model based on the Facility, Alignment, and Road parts of LandInfra. For its implementation, several classes of the proposed data model were encoded into InfraGML using real-world data input. Taken together, this study shows how the LandInfra standard can be extended and applied to the field of road infrastructure management in Korea, supporting the transition from a 2D to a 3D GIS-based model.

Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval

The drastic growth of coastal observation sensors results in copious data that provide weather information. The intricacies in sensor-generated big data are heterogeneity and interpretation, driving high-end Information Retrieval (IR) systems. The Semantic Web (SW) can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval. This paper focuses on exploiting the SW base system to provide interoperability through ontologies by combining the data concepts with ontology classes. This paper presents a 4-phase weather data model: data processing, ontology creation, SW processing, and query engine. The developed Oceanographic Weather Ontology helps to enhance data analysis, discovery, IR, and decision making. In addition to that, it also evaluates the developed ontology with other state-of-the-art ontologies. The proposed ontology’s quality has improved by 39.28% in terms of completeness, and structural complexity has decreased by 45.29%, 11% and 37.7% in Precision and Accuracy. Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model. The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.

BIM-based decision support for building condition assessment

Building condition assessment requires the integration of various types of data such as building characteristics, the properties of elements/systems and maintenance records. Previous research has focused on identifying these data and developing a building condition risk assessment model based on Bayesian networks (BN). However, due to interoperability issues, the process of transferring the data is performed manually, which requires considerable time and effort. To address this issue, this paper presents a data model to integrate the building condition risk assessment model into BIM. The proposed data model is implemented in existing software as a case study and tested and evaluated on three scenarios. Addressing interoperability will leverage the BIM tool as a data repository to automate the data transfer process and improve its consistency and reliability. It will also enable BIM to be a more effective tool for building condition and causality analysis visualization.

Fixed effects panel interval-valued data models and applications

Interval-valued data is a complex data type which can be got by summarizing large datasets, linear regression models for interval-valued data have been widely studied. Panel data models combining cross-section and time series real-valued data have become increasingly popular in economic research and data mining. It is very important to construct the regression models for panel data with uncertainty and range variability. This paper introduces panel data regression model for interval-valued data and constructs three kinds of panel interval-valued data regression models: the centre model of fixed effects panel interval-valued data regression, the min–max model of fixed effects panel interval-valued data regression and its special model, the centre and range model of fixed effects panel interval-valued data regression. Then combining the parameters estimation of interval-valued regression and analysis of covariance for panel data, this paper presents the parameters estimations for three kinds of panel interval-valued data regression models. Finally, our proposed panel interval-valued data regression models are applied in forecasting of Air Quality Index, the experimental evaluation of actual data sets shows the advantages and the performance of our proposed panel interval-valued data models.

Analyzing Patient Secure Messages Using a Fast Health Care Interoperability Resources (FIHR)-Based Data Model: Development and Topic Modeling Study.

BackgroundPatient portals tethered to electronic health records systems have become attractive web platforms since the enacting of the Medicare Access and Children’s Health Insurance Program Reauthorization Act and the introduction of the Meaningful Use program in the United States. Patients can conveniently access their health records and seek consultation from providers through secure web portals. With increasing adoption and patient engagement, the volume of patient secure messages has risen substantially, which opens up new research and development opportunities for patient-centered care.ObjectiveThis study aims to develop a data model for patient secure messages based on the Fast Healthcare Interoperability Resources (FHIR) standard to identify and extract significant information.MethodsWe initiated the first draft of the data model by analyzing FHIR and manually reviewing 100 sentences randomly sampled from more than 2 million patient-generated secure messages obtained from the online patient portal at the Mayo Clinic Rochester between February 18, 2010, and December 31, 2017. We then annotated additional sets of 100 randomly selected sentences using the Multi-purpose Annotation Environment tool and updated the data model and annotation guideline iteratively until the interannotator agreement was satisfactory. We then created a larger corpus by annotating 1200 randomly selected sentences and calculated the frequency of the identified medical concepts in these sentences. Finally, we performed topic modeling analysis to learn the hidden topics of patient secure messages related to 3 highly mentioned microconcepts, namely, fatigue, prednisone, and patient visit, and to evaluate the proposed data model independently.ResultsThe proposed data model has a 3-level hierarchical structure of health system concepts, including 3 macroconcepts, 28 mesoconcepts, and 85 microconcepts. Foundation and base macroconcepts comprise 33.99% (841/2474), clinical macroconcepts comprise 64.38% (1593/2474), and financial macroconcepts comprise 1.61% (40/2474) of the annotated corpus. The top 3 mesoconcepts among the 28 mesoconcepts are condition (505/2474, 20.41%), medication (424/2474, 17.13%), and practitioner (243/2474, 9.82%). Topic modeling identified hidden topics of patient secure messages related to fatigue, prednisone, and patient visit. A total of 89.2% (107/120) of the top-ranked topic keywords are actually the health concepts of the data model.ConclusionsOur data model and annotated corpus enable us to identify and understand important medical concepts in patient secure messages and prepare us for further natural language processing analysis of such free texts. The data model could be potentially used to automatically identify other types of patient narratives, such as those in various social media and patient forums. In the future, we plan to develop a machine learning and natural language processing solution to enable automatic triaging solutions to reduce the workload of clinicians and perform more granular content analysis to understand patients’ needs and improve patient-centered care.

A Weather Forecasting Model using the Data Mining Technique

The weather conditions are changing continuously and the entire world is suffers from the changing Clemet and their side effects. Therefore pattern on changing weather conditions are required to observe. With this aim the proposed work is intended to investigate about the weather condition pattern and their forecasting model. On the other hand data mining technique enables us to analyse the data and extract the valuable patterns from the data. Therefore in order to understand fluctuating patterns of the weather conditions the data mining based predictive model is reported in this work. The proposed data model analyse the historical weather data and identify the significant on the data. These identified patterns from the historical data enable us to approximate the upcoming weather conditions and their outcomes. To design and develop such an accurate data model a number of techniques are reviewed and most promising approaches are collected.

Notifiable diseases interoperable framework toward improving Iran public health surveillance system: Lessons learned from COVID-19 pandemic.

BACKGROUND:Direct transmission of notifiable disease information in a real-time and reliable way to public health decision-makers is imperative for early identification of epidemiological trends as well as proper response to potential pandemic like ongoing coronavirus disease 2019 crisis. Thus, this research aimed to develop of semantic-sharing and collaborative-modeling to meet the information exchange requirements of Iran's notifiable diseases surveillance system.MATERIALS AND METHODS:First, the Iran's Notifiable diseases Minimum Data Set (INMDS) was determined according to a literature review coupled with agreements of experts. Then the INMDS was mapped to international terminologies and classification systems, and the Health Level seven-Clinical Document Architecture (HL7-CDA) standard was leveraged to define the exchangeable and machine-readable data formats.RESULTS:A core dataset consisting of 15 classes and 96 data fields was defined. Data elements and response values were mapped to Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) reference terminology. Then HL7-CDA standard for interoperable data exchange were defined.CONCLUSION:The notifiable disease surveillance requires an integrative participation of multidisciplinary team. In this field, data interoperability is more essential due to the heterogeneous nature of health information systems. Developing of INMDS based on HL7-CDA along with SNOMED-CT codes offers an inclusive and interoperable dataset that can help make notifiable diseases data more comparable and reportable across studies and organizations. The proposed data model will be further modifications in the future according probable changes in Iran's notifiable diseases list.