Comparison of Some Data Models for Engineering Objects

Many proposals using object-oriented data models for engineering objects have appeared in the literature. These data models try to represent the data in engineering systems more naturally by organizing it logically and/or physically into objects relevant to the engineering applications using the database. In this article we review and examine several of these proposed data models to identif important properties of the models. We show that none of the data models excels in all areas, but each has desirable properties.     

数据模型及其发展历程

数据库是数据管理的技术,是计算机学科的重要分支.经过近半个世纪的发展,数据库技术形成了坚实的理论基础、成熟的商业产品和广泛的应用领域.数据模型描述了数据库中数据的存储方式和操作方式.从数据组织形式,可以将数据模型分为结构化模型、半结构化模型、OLAP分析模型和大数据模型.20世纪60年代中后期到90年代初,结构化模型最早被提出,其主要包括层次模型、网状模型、关系模型和面向对象模型等.20世纪90年代末期,随着互联网应用和科学计算等复杂应用的快速发展,开始出现半结构化模型,包括XML模型、JSON模型和图模型等.21世纪,随着电子商务、商业智能等应用的不断发展,数据分析模型成为研究热点,主要包括关系型ROLAP和多维型MOLAP.2010年以来,随着大数据工业应用的快速发展,以NoSQL和NewSQL数据库系统为代表的大数据模型成为新的研究热点.对上述数据模型进行了综述,并选取每个模型的典型数据库系统进行了性能的分析.     

Traditional, semantic, and hypersemantic approaches to datamodeling

An overview is given of past present data-modeling trends, and future directions are identified. The three traditional and commonly used data models that gained wide acceptance in the late 1960s and early 1970s and are used extensively today, namely the relational, hierarchical, and network models, are reviewed. Semantic data models that attempt to enhance the representation of operational information by capturing more of the meaning about data values and relationships are described. Enhancements to semantic data models that characterize hypersemantic data models and emphasize capturing inferential relationships are discussed     

Power series generalized nonlinear models

We introduce in this paper a new class of discrete generalized nonlinear models to extend the binomial, Poisson and negative binomial models to cope with count data. This class of models includes some important models such as log-nonlinear models, logit, probit and negative binomial nonlinear models, generalized Poisson and generalized negative binomial regression models, among other models, which enables the fitting of a wide range of models to count data. We derive an iterative process for fitting these models by maximum likelihood and discuss inference on the parameters. The usefulness of the new class of models is illustrated with an application to a real data set.     

基于弱监督预训练深度模型的微博情感分析

微博情感分析对于商业事务和政治选举等应用非常重要。传统的做法主要基于浅层机器学习模型,对人工提取的特征有较大的依赖,而微博情感特征往往难以提取。深度学习可以自动学习层次化的特征,并被用于解决情感分析问题。随着新的深度学习技术的提出,人们发现只要提供足够多的监督数据,就能训练出好的深度模型。然而,在微博情感分析中,通常监督数据都非常少。微博中广泛存在着弱监督数据。该文提出基于弱监督数据的“预训练—微调整”训练框架(distant pretrain-finetune),使用弱监督数据对深度模型进行预训练,然后使用监督数据进行微调整。这种做法的好处是可以利用弱监督数据学习到一个初始的模型,然后利用监督数据来进一步改善模型并克服弱监督数据存在的一些问题。我们在新浪微博数据上进行的实验表明,这种做法可以在监督数据较少的情况下使用深度学习,并取得比浅层模型更好的效果。     

移动服务中的数据模型研究

移动服务是GIS的全新发展领域，本文从应用的角度评述了移动服务中的数据特点，探讨了各种数据模型的原理及应用实例。不同的数据模型造成部门间数据共享的困难，为此本文提出了一种集成数据模型，能使不同模型相互转化，提高数据共享程度。     

Learning the semantics of structured data sources

Information sources such as relational databases, spreadsheets, XML, JSON, and Web APIs contain a tremendous amount of structured data that can be leveraged to build and augment knowledge graphs. However, they rarely provide a semantic model to describe their contents. Semantic models of data sources represent the implicit meaning of the data by specifying the concepts and the relationships within the data. Such models are the key ingredients to automatically publish the data into knowledge graphs. Manually modeling the semantics of data sources requires significant effort and expertise, and although desirable, building these models automatically is a challenging problem. Most of the related work focuses on semantic annotation of the data fields (source attributes). However, constructing a semantic model that explicitly describes the relationships between the attributes in addition to their semantic types is critical.We present a novel approach that exploits the knowledge from a domain ontology and the semantic models of previously modeled sources to automatically learn a rich semantic model for a new source. This model represents the semantics of the new source in terms of the concepts and relationships defined by the domain ontology. Given some sample data from the new source, we leverage the knowledge in the domain ontology and the known semantic models to construct a weighted graph that represents the space of plausible semantic models for the new source. Then, we compute the top k candidate semantic models and suggest to the user a ranked list of the semantic models for the new source. The approach takes into account user corrections to learn more accurate semantic models on future data sources. Our evaluation shows that our method generates expressive semantic models for data sources and services with minimal user input. These precise models make it possible to automatically integrate the data across sources and provide rich support for source discovery and service composition. They also make it possible to automatically publish semantic data into knowledge graphs.     

时空数据模型的研究与进展*

时态地理信息系统(TGIS)以表达、管理和分析动态变化的地理现象为目的,其核心是时空数据库,因此对时空数据库的理论基础——时空数据模型的研究十分必要。目前的时空数据模型主要有简单模型、时空联合模型、时空属三域模型、基于对象/特征的模型和基于事件/过程的模型等。首先对这些模型进行了回顾,对其优缺点和侧重点进行了分析对比,然后在此基础上对时空数据模型的进一步研究方向进行了剖析。     

On the applicability of stochastic volatility models

The applicability of the stochastic volatility (SV) model and the SV model with jumps for US. Treasury Bill yields data is investigated. The transformation of the continuous time models into regression models is considered and their error terms are examined. The applicability of the continuous time models to the real data is assessed by comparing some atypical properties of such error terms with an application to the real data and the generated data from the models. The empirical results indicate that the SV model and the SV model with jumps are not applicable to modeling the daily/weekly released US T-Bill secondary market yields data. Some trends and correlation structure are detected to exist in the error terms of the transformed regression models for the daily/weekly released US T-Bill yields data, while the error terms of the continuous time models are supposed to be uncorrelated. These results suggest that alternative models are needed to model such T-Bill yields data.     

A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data

A suitable combination of linear and nonlinear models provides a more accurate prediction model than an individual linear or nonlinear model for forecasting time series data originating from various applications. The linear autoregressive integrated moving average (ARIMA) and nonlinear artificial neural network (ANN) models are explored in this paper to devise a new hybrid ARIMA–ANN model for the prediction of time series data. Many of the hybrid ARIMA–ANN models which exist in the literature apply an ARIMA model to given time series data, consider the error between the original and the ARIMA-predicted data as a nonlinear component, and model it using an ANN in different ways. Though these models give predictions with higher accuracy than the individual models, there is scope for further improvement in the accuracy if the nature of the given time series is taken into account before applying the models. In the work described in this paper, the nature of volatility was explored using a moving-average filter, and then an ARIMA and an ANN model were suitably applied. Using a simulated data set and experimental data sets such as sunspot data, electricity price data, and stock market data, the proposed hybrid ARIMA–ANN model was applied along with individual ARIMA and ANN models and some existing hybrid ARIMA–ANN models. The results obtained from all of these data sets show that for both one-step-ahead and multistep-ahead forecasts, the proposed hybrid model has higher prediction accuracy.     

Studying just-in-time defect prediction using cross-project models

Unlike traditional defect prediction models that identify defect-prone modules, Just-In-Time (JIT) defect prediction models identify defect-inducing changes. As such, JIT defect models can provide earlier feedback for developers, while design decisions are still fresh in their minds. Unfortunately, similar to traditional defect models, JIT models require a large amount of training data, which is not available when projects are in initial development phases. To address this limitation in traditional defect prediction, prior work has proposed cross-project models, i.e., models learned from other projects with sufficient history. However, cross-project models have not yet been explored in the context of JIT prediction. Therefore, in this study, we empirically evaluate the performance of JIT models in a cross-project context. Through an empirical study on 11 open source projects, we find that while JIT models rarely perform well in a cross-project context, their performance tends to improve when using approaches that: (1) select models trained using other projects that are similar to the testing project, (2) combine the data of several other projects to produce a larger pool of training data, and (3) combine the models of several other projects to produce an ensemble model. Our findings empirically confirm that JIT models learned using other projects are a viable solution for projects with limited historical data. However, JIT models tend to perform best in a cross-project context when the data used to learn them are carefully selected.     

Tabular data: Deep learning is not all you need

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.     

基于类型系统的元数据模型

本研究了程序设计语言的类型系统与数据模型的；认识到类型间关系是表示数据模型的一种。基地这一思想，提出了ＰＣＴ类型描述语言及其基于ＰＣＴ的元数据模型，以描述多种数据模型，ＰＣＴ将Ｃ＋＋类型系统与一阶谓词演算相结合，可以形式化描述特写数据模型所规定的多方面规范。     

Zur Identifikation von Strukturanalogien in Datenmodellen

On the one hand, data models decrease the complexity of information system development. On the other hand, data models causes additional complexity. Recently structural analogies are discussed as instruments reducing the complexity of data models. This piece of research presents a procedure to identify structural analogies in data models and demonstrates its performance by analyzing Scheer’s reference model for industrial enterprises (Y-CIM-model). The proposed procedure is based on formalizing data models within set theory and uses a quantitative similarity measure. The obtained results show both identical and very similar information structures within the Y-CIM-model. Furthermore, ways of dealing with the identified structural analogies are discussed from an analysis and software design perspective.     

Referenzmodellierung im Data-Warehousing — State-of-the-Art und konfigurative Ansätze für die Fachkonzeption

Reference models increase the efficiency of data warehouse projects by providing construction patterns. This paper presents an overview of existing applications of reference models for data warehousing which shows that there is only insufficient support of model alternatives during requirements definition. Especially configurable reference models provide an adequate solution for creating project-specific models. Therefore, we suggest an extension of data warehouse modeling techniques by configuration rules. The configuration of reference models is embedded in the data warehouse development process. Furthermore, supplementary operational instructions for reference model designers are outlined.     

A universal relation data model with semantic abstractions

Two important features of modern database models are support for complex data structures and support for high-level data retrieval and update. The first issue has been studied by the development of various semantic data models; the second issue has been studied through universal relation data models. How the advantages of these two approaches can be combined is presently examined. A new data model that incorporates standard concepts from semantic data models such as entities, aggregations, and ISA hierarchies is introduced. It is then shown how nonnavigational queries and updates can be interpreted in this model. The main contribution is to demonstrate how universal relation techniques can be extended to a more powerful data model. Moreover, the semantic constructs of the model allow one to eliminate many of the limitations of previous universal relation models     

Reconsideration of T-spline data models and their exchanges using STEP

T-spline is a new approach to define freeform surfaces with relatively less control points than NURBS and is able to represent a model using a single surface without joining errors. Whereas, the complexity of T-spline data models leads numerous difficulties in its programming, which hinders the research and development of T-spline technologies. In addition, the data exchange of T-spline models still remains on a primitive level, and no standardized data format has been published so far. This article gives a reconsideration to the existing T-spline definitions, and proposes a set of redesigned data models which have much more understanding conveniences to both human and computer. Moreover, STEP-compliant data models are designed using the proposed T-spline models to standardize their data exchange between different CAx systems. The combination of T-spline with other product models in ISO 10303 makes it convenient to exchange the versatile resource data in a hybrid neutral file. A prototype system is developed for the validation purpose, which consists of a TSM-to-STEP convertor, a STEP parser and a T-spline kernel. Using the developed prototype system, one can automatically convert a Rhino system exported TSM file to a STEP file in the P21 format, which can be then parsed using the STEP reader and processed by the T-spline kernel. Some testing examples show that the proposed data models are much more efficient in processing and exchanging the T-spline data.     

Cost efficiency analysis with ordinal data: a theoretical and computational view

Standard Data Envelopment Analysis models obtain the cost efficiency of units when the data are known exactly, but these models fail to evaluate the units in the presence of ordinal data. Therefore, this paper provides models for the treatment of ordinal data in cost efficiency analysis. The models have multiplier forms with additional weight restrictions. The main idea in constructing these models is based on the weighted enumeration of the number of inputs/outputs of each unit which are categorized on the same scale rate. Some techniques to reduce the complexity of the models are introduced.     

Constraint-based Interoperability of Spatiotemporal Databases*

We propose constraint databases as an intermediate level facilitating the interoperability of spatiotemporal data models. Constraint query languages are used to express translations between different data models. We illustrate our approach in the context of a number of temporal, spatial, and spatiotemporal data models.     

时空数据库中数据建模的研究

研究了时空数据库中的时空建模技术。早期表示时空信息的数据模型通常用基于几何学的空间对象来表示实体，重要的特性都用空间对象的属性来表示。时态信息可以与基于时间戳的独立层次相关联，也可以与独立的空间对象相关联。随着时空建模的进一步发展，出现了面向对象的数据模型和基于事件的数据模型。综合研究了这些典型的时空数据模型，讨论了它们的应用及时空分析建模的作用。此外介绍了针对移动对象的数据类型的建模方法，以及在时空分析数据库管理系统STADBS中，基于Realms的二级平衡二叉树的时空数据模型。