Spatial and Topological Data Models

2011 ◽  
pp. 360-382
Author(s):  
Ying Deng ◽  
Paeter Revesz
Keyword(s):  
Traffic Control ◽  
Data Model ◽  
Query Language ◽  
Development Planning ◽  
Data Models ◽  
Environment Analysis ◽  
Business Applications ◽  
Region Data ◽  
Control Decision ◽  
Topological Data

Spatial and topological data models are increasingly important in business applications such as urban development planning, transportation and traffic control, decision support in agriculture, pollution and environment analysis, fire and flood prevention, etc. that require handling spatial and topological data more efficiently and more effectively than older models, for example the relational data model. In this survey we compare several alternative spatial and topological data models: the Spaghetti Data Model, the Vague Region Data Model, the Topological Data Model, Worboys’ Spatiotemporal Data Model and the Constraint Data Model. We first describe how spatial and/or topological data are represented and give examples for each data model. We also illustrate by examples the use of an appropriate query language for each data model.

Relational, Object-Oriented and Object-Relational Data Models

2005 ◽  
pp. 530-535
Author(s):  
Antonio Badia
Keyword(s):  
Programming Languages ◽  
Data Model ◽  
Query Language ◽  
Object Oriented ◽  
Data Models ◽  
Relational Model ◽  
Relational Data ◽  
Relational Data Model ◽  
Object Relational ◽  
Database Technology

The relational data model is the dominant paradigm in the commercial database market today, and it has been for several years. However, there have been challenges to the model over the years, and they have influenced its evolution and that of database technology. The object-oriented revolution that got started in programming languages arrived to the database area in the form of a brand new data model. The relational model managed not only to survive the newcomer but to continue becoming a dominant force, transformed into the object-relational model (also called extended relational, or universal) and relegating object-oriented databases to a niche product. Although this market has many nontechnical aspects, there are certainly important technical differences among the mentioned data models. In this article I describe the basic components of the relational, object-oriented, and object-relational data models. I do not, however, discuss query language, implementation, or system issues. A basic comparison is given and then future trends are discussed.

Normalizing Multimedia Databases

2005 ◽  
pp. 408-412
Author(s):  
Shi Kuo Chang ◽  
Vincenzo Deufemia ◽  
Giuseppe Polese
Keyword(s):  
Multimedia Information ◽  
Information Management ◽  
Data Model ◽  
Query Language ◽  
Data Models ◽  
Multimedia Databases ◽  
Complex Data ◽  
Data Types ◽  
Indexing Techniques ◽  
Application Fields

Multimedia databases have been used in many application fields. As opposed to traditional alphanumeric databases, they need enhanced data models and DBMSs to enable the modeling and management of complex data types. After an initial anarchy, multimedia DBMSs (MMDBMS) have been classified based on standard issues, such as the supported data model, the indexing techniques to support content-based retrieval, the query language, the support for distributed multimedia information management, and the flexibility of their architecture (Narasimhalu, 1996).

NONLINEAR PANEL DATA MODELS WITH DISTRIBUTION-FREE CORRELATED RANDOM EFFECTS

2021 ◽  
pp. 1-25
Author(s):  
Yu-Chin Hsu ◽  
Ji-Liang Shiu
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Conditional Distribution ◽  
Unobserved Heterogeneity ◽  
Random Effect ◽  
Data Models ◽  
Panel Data Model ◽  
Panel Data Models ◽  
Likelihood Functions ◽  
Correlated Random Effects

Under a Mundlak-type correlated random effect (CRE) specification, we first show that the average likelihood of a parametric nonlinear panel data model is the convolution of the conditional distribution of the model and the distribution of the unobserved heterogeneity. Hence, the distribution of the unobserved heterogeneity can be recovered by means of a Fourier transformation without imposing a distributional assumption on the CRE specification. We subsequently construct a semiparametric family of average likelihood functions of observables by combining the conditional distribution of the model and the recovered distribution of the unobserved heterogeneity, and show that the parameters in the nonlinear panel data model and in the CRE specification are identifiable. Based on the identification result, we propose a sieve maximum likelihood estimator. Compared with the conventional parametric CRE approaches, the advantage of our method is that it is not subject to misspecification on the distribution of the CRE. Furthermore, we show that the average partial effects are identifiable and extend our results to dynamic nonlinear panel data models.

scholarly journals Challenges to the validity of topic reconstruction

2021 ◽  
Author(s):  
Matthias Held ◽  
Grit Laudel ◽  
Jochen Gläser
Keyword(s):  
Data Model ◽  
Ground Truth ◽  
Data Models ◽  
Bibliographic Coupling ◽  
Parameter Setting ◽  
Meso Level ◽  
Resolution Level ◽  
Micro Level ◽  
Research Questions ◽  
Model Algorithm

AbstractIn this paper we utilize an opportunity to construct ground truths for topics in the field of atomic, molecular and optical physics. Our research questions in this paper focus on (i) how to construct a ground truth for topics and (ii) the suitability of common algorithms applied to bibliometric networks to reconstruct these topics. We use the ground truths to test two data models (direct citation and bibliographic coupling) with two algorithms (the Leiden algorithm and the Infomap algorithm). Our results are discomforting: none of the four combinations leads to a consistent reconstruction of the ground truths. No combination of data model and algorithm simultaneously reconstructs all micro-level topics at any resolution level. Meso-level topics are not reconstructed at all. This suggests (a) that we are currently unable to predict which combination of data model, algorithm and parameter setting will adequately reconstruct which (types of) topics, and (b) that a combination of several data models, algorithms and parameter settings appears to be necessary to reconstruct all or most topics in a set of papers.

scholarly journals Analysis of Road Traffic Accidents in the Punjab by Using Panel Count Data Models

2021 ◽  
Vol 3 (1) ◽  
pp. 1-13
Author(s):  
Muhammad Anus Hayat Khan ◽  
Ijaz Hussain
Keyword(s):  
Count Data ◽  
Data Model ◽  
Road Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Data Models ◽  
Panel Count Data ◽  
Road Traffic Accidents ◽  
Count Data Models ◽  
Count Data Model

Each year more than three thousand people die and get serious injuries in traffic accidents. Count data model provide more precise tools for planners and decision makers to conduct proactive road safety planning.We analyzed the exploratory research of Road Traffic Accidents (RTAs) and furthermore explores the factors affecting the RTAs frequency in 36 districts of the Punjab over a time period of three years (July 1, 2013 June 30, 2016) with monthly data using panel count data models. Among the models considered, the random parameters Poisson panel count data model is found to fit the data best. The exploratory analysis shows that highly dense populated districts with large number of registered vehicles causes more accidents as compared to low density populated districts. It is found that, most of the variables used to control the variation in the frequency of RTAs counts play vital role with higher significance levels. The application of regression analysis and modeling of RTAs at district level in Punjab will help to identification of districts with high RTAs rates and this could help more efficient road safety management in the Punjab.

scholarly journals The Unification of the Code Lists Provided within the Data Model Originating from the Inspire Technical Guidelines and the Ones Provided for Gesut Databases in the Context of Potential Exploitation in the Mining Industry

2016 ◽  
Vol 23 (3) ◽  
pp. 178-182
Author(s):  
Andrzej Zygmuniak ◽  
Violetta Sokoła-Szewioła
Keyword(s):  
Spatial Data ◽  
Data Model ◽  
Spatial Databases ◽  
Mining Industry ◽  
Data Models ◽  
Research Projects ◽  
Mining Areas ◽  
European Parties ◽  
Potential Exploitation ◽  
The Way

Abstract This study is aimed at exposing differences between two data models in case of code lists values provided there. The first of them is an obligatory one for managing Geodesic Register of Utility Networks databases in Poland [9] and the second is the model originating from the Technical Guidelines issued to the INSPIRE Directive. Since the second one mentioned is the basis for managing spatial databases among European parties, correlating these two data models has an effect in easing the way of harmonizing and, in consequence, exchanging spatial data. Therefore, the study presents the possibilities of increasing compatibility between the values of the code lists concerning attributes for objects provided in both models. In practice, it could lead to an increase of the competitiveness of entities managing or processing such databases and to greater involvement in scientific or research projects when it comes to the mining industry. Moreover, since utility networks located on mining areas are under particular protection, the ability of making them more fitted to their own needs will make it possible for mining plants to exchange spatial data in a more efficient way.

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