Traditional Data Models Research Articles

Research on Distributed Storage Technology of Database Big Data Based on Cloud Computing

With China entering the era of big data, the storage and management of big data has become a hot issue. The proposal and use of distributed storage technology has achieved good results. The continuous development of Internet communication technology and the popularization of computer information technology promote the evolution of traditional data model. The distributed storage system for big data has strong expansibility. It can optimize the existing storage space, realize the optimal allocation of space resources, and reduce the cost of data storage. Cloud computing as a resource sharing business computing model, it receives a huge amount of data every day, and the amount of data is still expanding. This has caused great pressure on distributed storage in cloud computing environment. This paper studies the distributed storage technology of database big data based on cloud computing, in order to strengthen the data management in the era of big data and improve the security and utilization of data.

Predicting the 9-year course of mood and anxiety disorders with automated machine learning: A comparison between auto-sklearn, naïve Bayes classifier, and traditional logistic regression

BackgroundPredicting the onset and course of mood and anxiety disorders is of clinical importance but remains difficult. We compared the predictive performances of traditional logistic regression, basic probabilistic machine learning (ML) methods, and automated ML (Auto-sklearn). MethodsData were derived from the Netherlands Study of Depression and Anxiety. We compared how well multinomial logistic regression, a naïve Bayes classifier, and Auto-sklearn predicted depression and anxiety diagnoses at a 2-, 4-, 6-, and 9-year follow up, operationalized as binary or categorical variables. Predictor sets included demographic and self-report data, which can be easily collected in clinical practice at two initial time points (baseline and 1-year follow up). ResultsAt baseline, participants were 42.2 years old, 66.5% were women, and 53.6% had a current mood or anxiety disorder. The three methods were similarly successful in predicting (mental) health status, with correct predictions for up to 79% (95% CI 75–81%). However, Auto-sklearn was superior when assessing a more complex dataset with individual item scores. ConclusionsAutomated ML methods added only limited value, compared to traditional data modelling when predicting the onset and course of depression and anxiety. However, they hold potential for automatization and may be better suited for complex datasets.

Prediction research for surface topography of internal grinding based on mechanism and data model

Grinding surface topography is an important evaluation index of grinding surface integrity. In order to realize the prediction of the grinding surface topography, a three-dimensional theoretical model of the surface topography of internal grinding was established in terms of the mutual movement between the grinding wheel and the workpiece and a data model for the surface roughness of internal grinding was set up based on the grey wolf algorithm-support vector machine (GWO-SVM). Taking the inner ring of rolling bearing as an example, the grinding experiment was carried out to analyze the influence of technological parameters on the surface topography and surface roughness. Three traditional data models were compared with the GWO-SVM data model, the comparison results showed that the GWO-SVM data model had a small change in the relative error of the predicted value of the test sample, the maximum relative error was 5.42%, and the average relative error was 3.95%. Experimental results were compared with the results of theoretical model and data model, the results show that the error of both the theoretical model and the data model is within 10%. This work should be helpful in reflecting the formation mechanism of the surface topography of the internal grinding and provide theoretical and technical support for high-precision and high-efficiency manufacturing of high-end rolling bearing.

Evaluation Model of Soccer Training Technology Based on Artificial Intelligence

Modern football competition has the characteristics of fierce confrontation, long duration, intensity of the game and large amount of exercise, and has high technical and tactical requirements. Therefore, the scientific and technical ability evaluation system plays a decisive role in football. One of the key factors for the real development of Chinese football is how to train and select young football talents scientifically. In order to train football talents better, this paper combines with artificial intelligence technology to study the evaluation model of football training technology. According to the characteristics and laws of football, this paper analyzes the index composition of football competitive ability. Combined with the traditional clustering data model, a support vector machine classification algorithm is proposed to construct the evaluation model of football training technology. Finally, this paper takes a professional team and a semi-professional team as an example and adds soccer evaluation model into their daily training. The results show that the evaluation efficiency is 24.12% higher than that of traditional artificial team, which proves the feasibility of this model.

A Conceptual Data Model and Its Automatic Implementation for IoT-Based Business Intelligence Applications

In the context of Industry 4.0, the analysis of Internet-of-Things (IoT) data with business intelligence (BI) technologies has acquired high relevance. However, designing and implementing IoT-based BI applications is hard for several reasons. Therefore, we propose a novel conceptual data model based on UML profiles and model-driven architecture (MDA) for modeling and implementing IoT-based BI applications. Our approach provides highly readable data models of IoT, which are also compatible with traditional BI data models. Furthermore, it could help in the implementation process of the IoT subsystem through automatic code generation.

Integrating cellular automata and discrete global grid systems: a case study into wildfire modelling

Abstract. With new forms of digital spatial data driving new applications for monitoring and understanding environmental change, there are growing demands on traditional GIS tools for spatial data storage, management and processing. Discrete Global Grid System (DGGS) are methods to tessellate globe into multiresolution grids, which represent a global spatial fabric capable of storing heterogeneous spatial data, and improved performance in data access, retrieval, and analysis. While DGGS-based GIS may hold potential for next-generation big data GIS platforms, few of studies have tried to implement them as a framework for operational spatial analysis. Cellular Automata (CA) is a classic dynamic modeling framework which has been used with traditional raster data model for various environmental modeling such as wildfire modeling, urban expansion modeling and so on. The main objectives of this paper are to (i) investigate the possibility of using DGGS for running dynamic spatial analysis, (ii) evaluate CA as a generic data model for dynamic phenomena modeling within a DGGS data model and (iii) evaluate an in-database approach for CA modelling. To do so, a case study into wildfire spread modelling is developed. Results demonstrate that using a DGGS data model not only provides the ability to integrate different data sources, but also provides a framework to do spatial analysis without using geometry-based analysis. This results in a simplified architecture and common spatial fabric to support development of a wide array of spatial algorithms. While considerable work remains to be done, CA modelling within a DGGS-based GIS is a robust and flexible modelling framework for big-data GIS analysis in an environmental monitoring context.

Human migration: the big data perspective

How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants.

Research on Modeling and Forecasting of Network Traffic Data with Alpha Distribution Noise Based on FARIMA Model

Network business traffic data has obvious self-similar characteristics, and its modeling and predictive analysis should also adopt self-similar models. Short correlation models, which cannot accurately describe their self-similarity, is mainly adopted in traditional network traffic data modeling. Therefore, It is necessary to research long correlation models. In addition, network traffic data also has certain pulse characteristics, which leads to inaccurate data modeling. In this research, alpha stable noise is added in FGN time series to simulate network traffic data, and the simulated data is analyzed by improved FARIMA model. Finally, the improved FARIMA model is applied to the BC-p Aug89. TL data modeling and predictive analysis. The analysis shows that the improved FARIMA model has a better prediction effect on network traffic and is of great significance for the application of network traffic prediction.

Comparing Deep Neural Network and Econometric Approaches to Predicting the Impact of Climate Change on Agricultural Yield

Predicting the impact of climate change on crop yield is difficult, in part because the production function mapping weather to yield is high dimensional and nonlinear. We compare three approaches to predicting yields: (i) deep neural networks (DNNs), (ii) traditional panel-data models, and (iii) a new panel-data model that allows for unit and time fixed-effects in both intercepts and slopes in the agricultural production function - made feasible by a new estimator developed by Keane and Neal (2020) called MO-OLS. Using U.S. county-level corn yield data from 1950-2015, we show that both DNNs and MO-OLS models outperform traditional panel data models for predicting yield, both in-sample and in a Monte Carlo cross-validation exercise. However, the MO-OLS model substantially outperforms both DNNs and traditional panel-data models in forecasting yield in a 2006-15 holdout sample. We compare predictions of all these models for climate change impacts on yields from 2016 to 2100.

Construction of multi-scale grid for massive land survey data

In the face of ever-growing and complex massive multi-source spatiotemporal data, the traditional vector data model is increasingly difficult to meet the needs of efficient data organization, management, calculation and analysis. Based on the simple and widely used geographic grid data organization model, this paper designs a technical method to convert vector data into multi-scale grid data, establishes a unified, standardized and seamless land spatial grid data model, and analyses the area accuracy of multi-scale grid data. Practice shows that the model can better meet the needs of multi-scale geospatial information integration and analysis, and it is easy to carry out distributed data processing, which provides technical support for the efficient organization, fusion and analysis of spatiotemporal data.

Migration from an SQL to a hybrid SQL/NoSQL data model

The paper presents a redesign of a database management system for a retail business company. Initially, based on a traditional data model, it is migrated to a hybrid model which combines both SQL and NoSQL databases. This approach adds flexibility, mobility, and efficiency to the data management system. The NoSQL database uses an ontology as a data schema, which we describe in this study. The NoSQL database is consulted using the SPARQL query language, and some examples of the queries are detailed in the paper. The architecture of the system and its functionality are discussed.

Aggregation of Linked Data in the Cultural Heritage Domain: A Case Study in the Europeana Network

Online cultural heritage resources are widely available through digital libraries maintained by numerous organizations. In order to improve discoverability in cultural heritage, the typical approach is metadata aggregation, a method where centralized efforts such as Europeana improve the discoverability by collecting resource metadata. The redefinition of the traditional data models for cultural heritage resources into data models based on semantic technology has been a major activity of the cultural heritage community. Yet, linked data may bring new innovation opportunities for cultural heritage metadata aggregation. We present the outcomes of a case study that we conducted within the Europeana cultural heritage network. In this study, the National Library of The Netherlands contributed by providing the role of data provider, while the Dutch Digital Heritage Network contributed as an intermediary aggregator that aggregates datasets and provides them to Europeana, the central aggregator. We identified and analyzed the requirements for an aggregation solution for the linked data, guided by current aggregation practices of the Europeana network. These requirements guided the definition of a workflow that fulfils the same functional requirements as the existing one. The workflow was put into practice within this study and has led to the development of software applications for administrating datasets, crawling the web of data, harvesting linked data, data analysis and data integration. We present our analysis of the study outcomes and analyze the effort necessary, in terms of technology adoption, to establish a linked data approach, from the point of view of both data providers and aggregators. We also present the expertise requirements we identified for cultural heritage data analysts, as well as determining which supporting tools were required to be designed specifically for semantic data.

An Improved Dynamic E-Commerce Recommendation System

Recommender system is a strategy in e-commerce, which recommends items based on the user’s interest. It has the ability to anticipate whether a specific user would incline toward a thing or not based on the user's profile. Recommender systems are useful for both e-commerce service provider and users. So, it should be required for a recommendation system to provide most preferable items to the user’s interest. In this presented work the recommendation system provide recommendations on the user’s interest. In this context the web usage mining and the content mining with additional factors are proposed. The proposed recommendation system computes the user product purchasing habits, product popularity, and user’s web navigational behavior. The product purchasing habit is calculated using the previously purchased product price and the current product price range. Similarly, the product popularity is calculated by the number of users provide the positive reviews about the target product. Additionally, web navigational behavior is computed using the web usage data for the user self and the other users who are watching the same product. In order to combine the calculated factors, the weight is computed and higher weights are used to recommend the suitable product. The experiments are performed with the implemented system and compared with the traditional system. The comparative performance study of the system in terms of precision, recall, and memory consumption demonstrate the proposed technique is efficient and accurate in comparison with the available traditional data model.

Fusion Modeling Method of Car-Following Characteristics

Car-following model is indispensable to evaluate the characteristics of car-following behaviors. Through an analysis and comparison of data-driven and theoretically driven car-following models, it shows that the data-driven model has poor interpretability and high quality of training data set is required, while for the theoretical-driven model, it is unable to describe the individualized features and models of the driver so as to a low model accuracy. To solve the problem, a novel modelling method is proposed using adaptive Kalman filter algorithm to integrate the long-short-time memory neural network (LSTM) data-driven model with the IDM theoretical-driven model to build the car-following model. Test results of real driving data from a single driver prove that the fusion car-following model has higher accuracy than a single model, while at the same time highlighting the driver's personality compared to the IDM model. Besides, it improves the generalization ability of the traditional data model, which is reflected by better fitting in the extreme case (for example, the stable state when the acceleration, velocity is zero). Finally, the trajectory simulation results show that the proposed integrated data-driven car-following model can better simulate the micro-traffic behavior of car following.

Structural Model of Spatial Data of Vegetation Landscape

Aiming at the characteristics of vegetation landscape such as hierarchical, structural and temporal, the landscape visualization is hard to be realized with the traditional data model. Therefore, the structured organization model of spatial data is proposed in this paper. Based on vector data and object-relationship model, the single plant vegetation, border tree landscape, and flake landscape in spatial data of vegetation landscape are abstracted into the basic elements such as points, lines, and planes by this model. Moreover, according to the structuring principles of vegetation landscape, the vegetation data are organized structurally. In the end, it is applied into the vegetation landscape smart push phone APP, providing a high-efficient organization and expression mode for the complex spatial data.

Big Data Semantics

Big Data technology has discarded traditional data modeling approaches as no longer applicable to distributed data processing. It is, however, largely recognized that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be coordinated to ensure a high level of data quality and accessibility for the application layers, e.g., data analytics and reporting. In this paper, the third of its kind co-authored by members of IFIP WG 2.6 on Data Semantics, we propose a review of the literature addressing these topics and discuss relevant challenges for future research. Based on our literature review, we argue that methods, principles, and perspectives developed by the Data Semantics community can significantly contribute to address Big Data challenges.

A nonparametric time-varying coefficient model for panel count data

ABSTRACTIn this research, we describe a nonparametric time-varying coefficient model for the analysis of panel count data. We extend the traditional panel count data models by incorporating B-splines estimates of time-varying coefficients. We show that the proposed model can be implemented using a nonparametric maximum pseudo-likelihood method. We further examine the theoretical properties of the estimators of model parameters. The operational characteristics of the proposed method are evaluated through a simulation study. For illustration, we analyse data from a study of childhood wheezing, and describe the time-varying effect of an inflammatory marker on the risk of wheezing.

Design of Order Demand Forecasting System Based on Neural Network

As the market demand is seasonal and random, the traditional data model can not describe the order change rules accurately. In order to improve the prediction accuracy, neural network and gray theory are combined to construct a gray neural network order forecasting method. Gray system theory can capture the regularity of data sequence effectively. The optimal neural network parameters are obtained by training the data set. In the neural network model, enter a small amount of data to achieve accurate prediction of the order. The simulation results show that the improved gray neural network improves the prediction accuracy of the order requirement and provides the basis for the forecast of the market demand compared with the traditional forecasting method.

A general framework for big data knowledge discovery and integration

SummaryData structure description, conceptual modeling, and logic reasoning for knowledge discovery are three critical factors for the integration of information with heterogeneity. In particular, technologies of NoSQL databases and Internet of Things raise an urgent requirement for a uniform expression of heterogeneous data, and little attention has been paid to researches on the integration of NoSQL databases with traditional data models, as well as the semantic description of big data. To tackle these problems, in this paper, a concept‐and‐relation‐oriented grid data model called GODM model is first proposed based on the definitions of Monad, Compounder, Relation, etc. Then, the GODM model is utilized to uniformly describe traditional data models and NoSQL data models, which eliminates structure differences of heterogeneous data. Next, based on the GODM relation mechanism, an extendable semantic system is built up by choosing SHOIQ(D) description logic as the example to establish the correspondence with GODM grammar subset, providing a fundamental support for semantic integration and knowledge discovery of heterogeneous data. After that, comprehensive comparisons with GODM and other models are made, especially the distinctions between GODM and OWL on the aspects of relation mechanism, hybrid schema, description logic, grammatical constructors, etc. Besides, experimental evaluations and analyses on time and space efficiencies of some primary common data models are conducted after the proposal of a general evaluation model, with the results showing that the GODM model has great advantage on properties of expressiveness, flexibility, etc, particularly time and space efficiency. In summary, the GODM model describes heterogeneous data from both aspects of data structure and semantic relationship and realizes a hybrid schema reconciling the schemaful and schemaless data models, making it especially suitable for dynamic data integration and knowledge discovery from big data models.

Uncertain spatiotemporal data modeling and algebraic operations based on XML

The problem of modeling and operating spatiotemporal data has received a great deal of interest, due to its various applications in the real world such as GIS and sensor database. A wide range of work covering spatial data, temporal data and spatiotemporal data assumes that the data is known, accurate and complete. But in reality, information is often imprecise and imperfect. In addition, traditional data models which are investigating in the context of traditional database suffer from some inadequacy of necessary semantics such as inability to handle imprecise and uncertain information. Consequently, the advent of XML, which has the advantages of simplicity, readability and extensibility, seems to provide an opportunity for modeling and operating uncertain spatiotemporal data. Hence, the new problem that emerges is how to model and operate uncertain spatiotemporal data in XML. Therefore, in this paper, we establish an uncertain spatiotemporal data model based on XML. Then, on the basis of the model we provide a set of algebraic operations for capturing and handling uncertain spatiotemporal data. By employing algebraic operations, we demonstrate how to translate queries expressed in XQuery to our algebra. A translation example shows that our algebraic operations are full of expressive power and illustrates that our algebra can be applied to general data. Apart from this, we also propose a set of equivalence rules to optimize the process of query and give an example to show how the optimization approach works.