scholarly journals Comparação de Metodologias de Migração de Bancos de Dados Relacionais para Bancos Orientados a Documentos

2020 ◽  
Author(s):  
Vanessa Souza∗ ◽  
Melise Paula ◽  
Tiago Barros
Keyword(s):  
Systems Engineering ◽  
Data Model ◽  
Relational Databases ◽  
Operating Cost ◽  
Relational Model ◽  
Data Migration ◽  
Migration Process ◽  
Relational Models ◽  
Model Migration ◽  
Active Services

Companies have migrated data from relational databases to NoSQLdatabases to improve their business through more active services ata lower operating cost, especially by the adoption of cloud services.This process is called Data Migration and is considered by someauthors one of the biggest challenges in systems engineering today.Although it is advantageous, the process of migrating data fromthe relational model to NoSQL models is not trivial and has led tothe development of different methodologies for this purpose. Theobjective of this work was to analyze and compare three differentmigration methodologies between Relational and NoSQL DocumentOriented databases under the following aspects: algorithminput, method documentation, migration process and generateddocuments. For that, different relational models were empiricallymigrated using such methodologies, allowing the analysis of theevaluated aspects. The results show that there is no consolidatedway to perform the migration and that the method to be chosendepends on the context of the application. So, scenarios that showwhen to use each method are presented. Although not performingcomputational tests, this work provides suggestions and insightsthrough the evaluation of the migration processes under the theoreticalmodels. It expected that the results presented here will helpIT managers decide on the best data model migration methodologyto follow in their actual projects.

Multivalued Dependencies in Fuzzy Multivalued Relational Databases Using Fuzzy Functions

2015 ◽  
Vol 23 (04) ◽  
pp. 589-626 ◽  
Author(s):  
Devendra K. Tayal ◽  
P. C. Saxena
Keyword(s):  
Real World ◽  
Data Model ◽  
Relational Databases ◽  
Relational Model ◽  
Relational Data ◽  
Inference Rules ◽  
Functional Dependencies ◽  
Technical Institute ◽  
Relational Schema ◽  
Relational Data Model

In this paper we discuss an important integrity constraint called multivalued dependency (mvd), which occurs as a result of the first normal form, in the framework of a newly proposed model called fuzzy multivalued relational data model. The fuzzy multivalued relational data model proposed in this paper accommodates a wider class of ambiguities by representing the domain of attributes as a “set of fuzzy subsets”. We show that our model is able to represent multiple types of impreciseness occurring in the real world. To compute the equality of two fuzzy sets/values (which occur as tuple-values), we use the concept of fuzzy functions. So the main objective of this paper is to extend the mvds in context of fuzzy multivalued relational model so that a wider class of impreciseness can be captured. Since the mvds may not exist in isolation, a complete axiomatization for a set of fuzzy functional dependencies (ffds) and mvds in fuzzy multivalued relational schema is provided and the role of fmvds in obtaining the lossless join decomposition is discussed. We also provide a set of sound Inference Rules for the fmvds and derive the conditions for these Inference Rules to be complete. We also derive the conditions for obtaining the lossless join decomposition of a fuzzy multivalued relational schema in the presence of the fmvds. Finally we extend the ABU's Algorithm to find the lossless join decomposition in context of fuzzy multivalued relational databases. We apply all of the concepts of fmvds developed by us to a real world application of “Technical Institute” and demonstrate that how the concepts fit well to capture the multiple types of impreciseness.

Re-grouping information in a domain-theoretic data model

1998 ◽  
Vol 8 (1) ◽  
pp. 67-92
Author(s):  
HERMANN PUHLMANN
Keyword(s):  
Data Structure ◽  
Data Model ◽  
Relational Databases ◽  
Relational Model ◽  
Theoretic Approach ◽  
Database Model ◽  
Nested Data ◽  
Building Type ◽  
Type Constructor ◽  
First Time

In recent years, the problem of incorporating a set-building type-constructor into a domain theoretic data model has been addressed by different authors. In Jung and Puhlmann (1995) and Puhlmann (1995) we have shown why the so-called snack powerdomain is particularly suitable for modelling a set constructor. We obtain a generalized database model that covers the nested relational model.While, with the snack powerconstruction, the data structure of domain theoretic databases seems clear, suitable operations for the data model are still to be defined.In this paper we start this task by defining the operations nest and unnest for the passage between different nesting-levels of the snack powerconstruction. These functions are shown to form an embedding-projection pair, a property that the corresponding functions of nested relational databases do not have. This demonstrates the usefulness of the domain-theoretic approach for modelling databases: for the first time we have operators for re-grouping nested data that respect the idea of an information ordering.The use of the snack powerdomain leads to fairly complex formulas. To help the reader, illustrations and pictorial interpretations of formulas are given throughout the paper.

scholarly journals Generalizing Possibility-Based Fuzzy Relational Models

2006 ◽  
Vol 10 (5) ◽  
pp. 633-646 ◽  
Author(s):  
Michinori Nakata ◽  
Keyword(s):  
Data Model ◽  
Relational Algebra ◽  
Relational Model ◽  
Imprecise Data ◽  
Semantic Ambiguity ◽  
Possibility Distribution ◽  
Relational Models ◽  
Data Value ◽  
Attribute Value

The generalized possibility-based fuzzy relational model we propose frees possibility-based fuzzy relational models from the semantic ambiguity and the indistinguishability of membership attribute values. We demonstrate extended relational algebra in this data model. To prevent the semantic ambiguity, a membership attribute is attached to every attribute. This clarifies where each membership attribute value comes from. What each membership attribute value means depends on the property of that attribute. To prevent the indistinguishability of membership attribute values, the value is expressed in a possibility distribution in interval [0,1]. This clarifies what effects the imprecise data value allowed for an attribute has on the membership attribute value. No semantic ambiguity and no indistinguishability of membership attribute values therefore exists in the generalized possibility-based fuzzy relational model.

Set Valued Attributes

2005 ◽  
pp. 632-637 ◽  
Author(s):  
Karthikeyan Ramasamy ◽  
Prasad M. Deshpande
Keyword(s):  
Relational Database ◽  
Data Model ◽  
Relational Databases ◽  
Database Systems ◽  
Sql Server ◽  
Relational Model ◽  
Database Model ◽  
Competitive Edge ◽  
The Past ◽  
Relational Database Systems

About three decades ago, when Codd (1970) invented the relational database model, it took the database world by storm. The enterprises that adapted it early won a large competitive edge. The past two decades have witnessed tremendous growth of relational database systems, and today the relational model is by far the dominant data model and is the foundation for leading DBMS products, including IBM DB2, Informix, Oracle, Sybase, and Microsoft SQL server. Relational databases have become a multibillion-dollar industry.

scholarly journals Diseño de un sitio web dinámico para institución educativa

2019 ◽  
pp. 20-25
Author(s):  
Laura Cristina Vázquez-De Los Santos ◽  
Griselda Cortes-Morales ◽  
Alicia Guadalupe Valdez-Menchaca ◽  
Diego Arnulfo Martínez-Perales
Keyword(s):  
Systems Engineering ◽  
Data Model ◽  
System Development ◽  
Educational Institution ◽  
Database Management System ◽  
Relational Model ◽  
Database Models ◽  
Relationship Model ◽  
System Part ◽  
Entity Relationship

The objective of this article is to design a website for an educational institution with a dynamic data model that allows you to easily add, edit and update information. In the methodology, systems engineering concepts will be used during the system development process, documenting each stage. Carrying out the stages of requirements analysis and data model design, considering the parties involved. The Entity Relationship model was designed with the purpose of confirming the logical needs of the information. In addition, the relational model was created, where the attributes of each entity are detailed. MySQL was used as the database management system. Part of the design of the data model includes the way in which it interacts with it, for this the CRUD system is used. With the design of the data models: logical and database models, the script for the creation of the dynamic database was created, which will be used to store all the information relevant to the educational institution. As a result, the correct functionality of the database was guaranteed on the website.

scholarly journals Potential Energy Implications of Connected and Automated Vehicles: Exploring Key Leverage Points through Scenario Screening and Analysis

2019 ◽  
Vol 2673 (5) ◽  
pp. 84-94 ◽  
Author(s):  
Brian Bush ◽  
Laura Vimmerstedt ◽  
Jeff Gonder
Keyword(s):  
Systems Engineering ◽  
Large Scale ◽  
Operating Cost ◽  
Dynamics Model ◽  
Transportation Service ◽  
Service Outcomes ◽  
Behavioral Parameters ◽  
Computationally Intensive ◽  
Different Levels ◽  
Selection Of

Connected and automated vehicle (CAV) technologies could transform the transportation system over the coming decades, but face vehicle and systems engineering challenges, as well as technological, economic, demographic, and regulatory issues. The authors have developed a system dynamics model for generating, analyzing, and screening self-consistent CAV adoption scenarios. Results can support selection of scenarios for subsequent computationally intensive study using higher-resolution models. The potential for and barriers to large-scale adoption of CAVs have been analyzed using preliminary quantitative data and qualitative understandings of system relationships among stakeholders across the breadth of these issues. Although they are based on preliminary data, the results map possibilities for achieving different levels of CAV adoption and system-wide fuel use and demonstrate the interplay of behavioral parameters such as how consumers value their time versus financial parameters such as operating cost. By identifying the range of possibilities, estimating the associated energy and transportation service outcomes, and facilitating screening of scenarios for more detailed analysis, this work could inform transportation planners, researchers, and regulators.

Modelli di relazioni con i coetanei e adattamento psicosociale in adolescenza: una ricerca cross-nazionale e longitudinale

2009 ◽  
pp. 135-164
Author(s):  
Emanuela Rabaglietti ◽  
Silvia Ciairano
Keyword(s):  
Peer Relationships ◽  
Risk Behaviour ◽  
Relational Model ◽  
Perceived Support ◽  
Risk Behaviours ◽  
Risky Behaviour ◽  
Relational Models ◽  
Network Time ◽  
Depressive Feelings ◽  
Longitudinal Relationships

- The study is aimed at constructing a typology of patterns of peer relationships in Italy and the Netherlands and at investigating the longitudinal relationships with beliefs and expectations about relationships and school, psychological discomfort and antisocial and risky behaviour. 439 adolescents of both gender, aged from 15 to 20 years participated at the study. We described four patterns of 158 peer relationships: Isolated (dimension of network, time spent with friends and support perceived by friends were all low), Deep (only perceived support was high; more frequent among girls in both countries), Superficial (only quantitative aspects were high; more frequent among boys), Integrated (both quantitative and qualitative aspects). We found both stability (higher among Superficial and Integrated) and change (higher among Isolated and Deep). The Isolated showed the lowest beliefs and expectations and involvement in risk behaviours, the Deep and the Superficial showed intermediate levels of both beliefs and risk behaviour, and the Integrated showed the highest levels. We also found a great similarity in the links among relational models, beliefs, psychological discomfort and risk behavior in Italian and Dutch adolescents. However, when adopting the same relational model, the Italians perceived higher sense of alienation and depressive feelings and were more involved in lying and disobedience than the Dutch.

scholarly journals TRENDS IN DATA WAREHOUSING TECHNIQUES

2021 ◽  
Author(s):  
Naveen Kunnathuvalappil Hariharan
Keyword(s):  
Relational Databases ◽  
Financial Data ◽  
Relational Model ◽  
Time Data ◽  
Short Period ◽  
The Subject ◽  
Processing And Storage ◽  
And Storage ◽  
Long Term Trend

Financial data volumes are increasing, and this appears to be a long-term trend, implying that data managementdevelopment will be crucial over the next few decades. Because financial data is sometimes real-time data, itis constantly generated, resulting in a massive amount of financial data produced in a short period of time.The volume, diversity, and velocity of Big Financial Data are highlighting the significant limitations oftraditional Data Warehouses (DWs). Their rigid relational model, high scalability costs, and sometimesinefficient performance pave the way for new methods and technologies. The majority of the technologiesused in background processing and storage research were previously the subject of research in their earlystages. The Apache Foundation and Google are the two most important initiatives. For dealing with largefinancial data, three techniques outperform relational databases and traditional ETL processing: NoSQL andNewSQL storage, and MapReduce processing.

scholarly journals Multidimensional Space Structure for Adaptable Data Model

2019 ◽  
Vol 8 (3) ◽  
pp. 7753-7758
Keyword(s):  
Data Model ◽  
Cartesian Product ◽  
Data Representation ◽  
Space Structure ◽  
Automated System ◽  
Relational Model ◽  
Multidimensional Data ◽  
Multidimensional Space ◽  
Model Based ◽  
Five Axes

The article presents an adaptable data model based on multidimensional space. The main difference between a multidimensional data representation and a table representation used in relational Database Management Systems (DBMSs) is that it is possible to add new elements to sets defining the axes of multidimensional space at any time. This changes the data model. The tabular representation of the relational model does not allow you to change the model itself during the operation of an automated system. Three levels of multidimensional data presentation space are considered. There are axis of multidimensional space, the Cartesian product of the sets of axis values and the values of space points. The five axes of multidimensional space defined in the article (entities, attributes, identifiers, time, modifiers) are basic for the design of an adaptable automated system. It is shown that it is possible to use additional axes for greater granularity of the stored data. The multidimensional space structure defined in the article for an adaptable data model is a flexible set for storing a relational domain model. Two types of operations in multidimensional information space are defined. Relations of the relational model are formed dynamically depending on the conditions imposed on the coordinates of the points. Thus, an adaptable data representation model based on multidimensional space can be used to create flexible dynamic automated information systems.

Pre-Processing Highly Sparse and Frequently Evolving Standardized Electronic Health Records for Mining

2021 ◽  
pp. 8-21
Author(s):  
Shivani Batra ◽  
Shelly Sachdeva
Keyword(s):  
Electronic Health Records ◽  
Data Model ◽  
Semantic Interoperability ◽  
Relational Model ◽  
Health Records ◽  
Related Information ◽  
Health Related ◽  
Electronic Health ◽  
Mining Tools ◽  
Attribute Value

EHRs aid in maintaining longitudinal (lifelong) health records constituting a multitude of representations in order to make health related information accessible. However, storing EHRs data is non-trivial due to the issues of semantic interoperability, sparseness, and frequent evolution. Standard-based EHRs are recommended to attain semantic interoperability. However, standard-based EHRs possess challenges (in terms of sparseness and frequent evolution) that need to be handled through a suitable data model. The traditional RDBMS is not well-suited for standardized EHRs (due to sparseness and frequent evolution). Thus, modifications to the existing relational model is required. One such widely adopted data model for EHRs is entity attribute value (EAV) model. However, EAV representation is not compatible with mining tools available in the market. To style the representation of EAV, as per the requirement of mining tools, pivoting is required. The chapter explains the architecture to organize EAV for the purpose of preparing the dataset for use by existing mining tools.

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