This study investigates data-driven business model innovation (DDBMI) for incumbent manufacturers, underscoring its importance in various strategic and managerial contexts. Employing topic modeling, the study identifies nine key topics of DDBMI. Thro...
Designing a database is a crucial step in providing businesses with high-quality data for decision making. The quality of a data model is the key to the quality of its data. Evaluating the quality of a data model is a complex and time-consuming task....
High-resolution data with nearly global coverage from Sentinel-2 mission open a new era for crop growth monitoring and yield estimation from remote sensing. The objective of this study is to demonstrate the potential of using Sentinel-2 biophysical d...
Companies face uncertainties evaluating their own capabilities when implementing data analytics or data mining. Data mining is a valuable process used to analyze data and support decisions based on the knowledge generated, creating a pipeline from th...
Data analysis is widely used in research and industry where there is a need to extract information from data. A significant amount of time within a data analysis project is required to prepare the data for subsequent analysis. This paper presents a c...
As newborn screening programs transition from paper-based data exchange toward automated, electronic methods, significant data exchange challenges must be overcome. This article outlines a data model that maps newborn screening data elements associat...
Organizations must quickly adapt their processes to understand the dynamic nature of modern business environments. As highlighted in the literature, centralized governance supports decision-making and performance measurement processes in technology c...
Many big data projects are technology-driven and thus, expensive and inefficient. It is often unclear how to exploit existing data resources and map data, systems and analytics results to actual use cases. Existing big data reference models are mostl...
The application of hydrological models in data-scarce catchments is usually limited by the amount of available data. It is of great significance to investigate the impacts of data quantity and quality on model calibration—as well as to further...
This article discusses the process of creating a digital forest model based on remote sensing data, three-dimensional modeling, and forest inventory data. Remote sensing data of the Earth provide a fundamental tool for integrating subsequent objects...
Creating, sharing, and using data are expected to lead to the development of new innovative business models. This study investigates the interplay between business model change, ethical data practices, and participation in data ecosystems in fosterin...
Data integration is one of the core responsibilities of EDM (enterprise data management) and interoperability. It is essential for almost every digitalization project, e.g., during the migration from a legacy ERP (enterprise resource planning) softwa...
An infrastructure for earth science data is emerging across the globe based on common data models and web services. As we evolve from custom file formats and web sites to standards-based web services and tools, data is becoming easier to distribute,...
Recently, datasets with various factors and indicators of cognitive diseases have been available for clinical research. Although the transformation of information to a particular data model is straightforward, many challenges arise if data from diffe...
Truncation data arise when the interested event time can be observed only if it satisfies a certain condition. Most of the existing approaches analyze this kind of data by assuming the truncated variable is quasi-independent of the interested event t...
Microarray technology allows monitoring of gene expression profiling at the genome level. This is useful in order to search for genes involved in a disease. The performances of the methods used to select interesting genes are most often judged after...
Many models assumed to be able to predict the response of structural systems fail to efficiently accomplish that purpose because of two main reasons. First, some structures in operation undergo localized damage that degrades their mechanical performa...
Bottom-up integrated assessment models, like MESSAGEix, depend on the description of the capabilities and limitations of technological, economical and ecological parameters, and their development over long-time horizons. Even small models of a few no...
Based on the Beijing Climate Center’s land surface model BCC_AVIM2.0, an ensemble Kalman filter (EnKF) algorithm is developed to assimilate the land surface temperature (LST) product of the first satellite of Fengyun-4 series meteorological sat...
Predicting building energy consumption needs to be anticipated to save building energy and effectively control the predictions. This study depicted the target building as a physical model to improve the learning performance in a data-scarce environme...
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The identification of unit hydrographs and component flows from rainfall, evapotranspiration and streamflow data (IHACRES) model has been proven to be an efficient yet basic model to simulate rainfall–runoff processes due to the difficulty in o...
This paper proposes a methodology that utilises model performance as a metric to assess the representativeness of external or pooled data when it is used by banks in regulatory model development and calibration. There is currently no formal methodolo...
The main aim of this work is to study the spatial–temporal variability of the model’s physical and spectral characteristics in the process of assimilation of observed ocean surface height data from the AVISO (Archiving, Validating and Int...
Decision management is the systems engineering life cycle process for making program/system decisions. The purpose of the decision management process is: “…to provide a structured, analytical framework for objectively identifying, charac...
The standardization of data from medical studies and hospital information systems to a common data model such as the Observational Medical Outcomes Partnership (OMOP) model can help make large datasets available for analysis using artificial intellig...
Qualitative data is an important source of information for system dynamics modeling. It can potentially support any stage of the modeling process, yet it is mainly used in the early steps such as problem identification and model conceptualization. Ex...
Ultra-high dimensional longitudinal data feature screening procedures are widely studied, but most require model assumptions. The screening performance of these methods may not be excellent if we specify an incorrect model. To resolve the above probl...
Generative-AI systems are increasingly trained on datasets containing AI-generated content, creating recursive feedback that may cause progressive losses of tail mass, diversity, and effective variance in learned distributions–a phenomenon comm...
Integrating cyberspace and geographic space through map visualization is a valuable approach for revealing distribution patterns and relational dynamics in cyberspace. The interdisciplinary integration of network science and geographic science has ga...
The traditional flow of coastal ocean model data is from High-Performance Computing (HPC) centers to the local desktop, or to a file server where just the needed data can be extracted via services such as OPeNDAP. Analysis and visualization are then...
Existing concepts and frameworks of soundscapes focus on the analysis and description of the sound source but do not explore geographical environment parameters and receiver characteristics in the geographical scene. Existing soundscape data models i...
The increasing availability of educational data provides the educational researcher with numerous opportunities to use analytics to extract useful knowledge to enhance teaching and learning. While learning analytics focuses on the collection and anal...
The force ripple of a permanent magnet synchronous linear motor (PMSLM) caused by multi-source disturbances in practical applications seriously restricts its high-precision motion control performance. The traditional single-mechanism model has diffic...
This paper presents our implementation of a harmonized data model for noise simulations in the European Union (EU). Different noise assessment methods are used by different EU member states (MS) for estimating noise at local, regional, and national s...
The Internet of Things (IoT) is a remarkable data producer and these data may be used to prevent or detect security vulnerabilities and increase productivity by the adoption of statistical and Artificial Intelligence (AI) techniques. However, these d...
Mutational signature analysis promises to reveal the processes that shape cancer genomes for applications in diagnosis and therapy. However, most current methods are geared toward rich mutation data that has been extracted from whole-genome or whole-...
In order to ensure the reliability and credibility of the data in wireless sensor networks (WSNs), this paper proposes a trust evaluation model and data fusion mechanism based on trust. First of all, it gives the model structure. Then, the calculatio...
At temperatures higher than 5 °C in the cooling chambers of refrigeration systems, bacteria multiply rapidly on fresh fishes, thereby leading to an increased risk of foodborne diseases. Maintaining the storage temperature within the recommended b...
In this paper, an exponential autoregressive model for complex time series data is presented. As for estimating the parameters of this nonlinear model, a three-step procedure based on quantile methods is proposed. This quantile-based estimation techn...
Allegories are enriched categories generalizing a category of sets and binary relations. In this paper, we extend a new, recently-introduced conceptual data model based on allegories by adding support for modal operators and developing a modal interp...
In this article, we present a new descriptive model for industrial flexibility with respect to power consumption. The advancing digitization in the energy sector opens up new possibilities for utilizing and automatizing the marketing of flexibility p...
Depth data and the digital bottom model created from it are very important in the inland and coastal water zones studies and research. The paper undertakes the subject of bathymetric data processing using reduction methods and examines the impact of...
An event is an important medium for recording, expressing, and understanding the real world. Additionally, a data model can provide a digital and structured description method for the real world. Therefore, studying event data models is highly import...
The application of hydrologic and hydrodynamic models in flash flood hazard assessment is mainly limited by the availability of robust monitoring systems and long-term hydro-meteorological observations. Nevertheless, several studies have demonstrated...
With the profound understanding of the world, modeling and simulation has been used to solve the problems of complex systems. Generally, mechanism-models are often used to model the engineering systems following the Newton laws, and this kind of mode...
This work presents a practical approach to improve risk quantification for heavy-tailed insurance claims through model averaging and grid map visualization, addressing the drawbacks of traditional single “best” model selection commonly us...
Electronic medical record (EMR) data vary between institutions. These data should be converted into a common data model (CDM) for multi-institutional joint research. To build the CDM, it is essential to integrate the EMR data of each hospital and loa...
Methodological experts suggest that psychological and educational researchers should employ appropriate methods for data-driven model exploration, such as Bayesian Model Averaging and regularized regression, instead of conventional hypothesis-driven...
Effective spare parts management (SPM) is imperative for equipment-intensive organizations to reduce equipment downtime through maintenance. Despite the extensive availability of data-driven SPM methodologies, decision-makers are challenged and tend...
In this paper, we study the problem of privacy-preserving data synthesis (PPDS) for tabular data in a distributed multi-party environment. In a decentralized setting, for PPDS, federated generative models with differential privacy are used by the exi...
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