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.

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.

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.

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.

Database and Informatic Challenges in Representing Both Diploid and Tetraploid Xenopus Species in Xenbase

2015 ◽  
Vol 145 (3-4) ◽  
pp. 278-282 ◽  
Author(s):  
Peter D. Vize ◽  
Yu Liu ◽  
Kamran Karimi
Keyword(s):  
Data Model ◽  
Relational Databases ◽  
Biological Data ◽  
Data Types ◽  
Database Model ◽  
Data Schema ◽  
Table Data

At the heart of databases is a data model referred to as a schema. Relational databases store information in tables, and the schema defines the tables and provides a map of relationships that show how the different table/data types relate to one another. In Xenbase, we were tasked to represent genomic, molecular, and biological data of both a diploid and tetraploid Xenopus species. When the database model was built over a decade ago, we had very little information on the nature of the X.laevis tetraploidization, but a Chado-based data model was proposed that could deal with the various forms of data in both species. Once the X.laevis genome was sequenced and annotated, it became clear that the data schema is very like the evolutionary schema that resulted in the X.laevis genome.

Designing an Engineering Database for Telephone Networks

1991 ◽  
Author(s):  
Scott G. Danielson
Keyword(s):  
Data Structure ◽  
Data Model ◽  
Database Modeling ◽  
Telephone Networks ◽  
Semantic Data ◽  
Engineering Data ◽  
Engineering Database ◽  
Engineering Work ◽  
Entity Relationship ◽  
Semantic Data Model

Abstract An engineering database modeling telephone outside plant networks is developed. Semantic and relational database design methodologies are used with the semantic data model developed based on an extended entity-relationship approach. This logical model is used to generate a normalized relational data structure. This database holds engineering data supporting engineering analyses, engineering work order generation procedures, and network planning activities. The database has been linked to separate network analysis programs and CAD-based network maps by a database application.

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.

Computer Data Structure for Geological Entities Modelling Based on OO-Solid Model

2011 ◽  
Vol 383-390 ◽  
pp. 2484-2491
Author(s):  
Jun Fan
Keyword(s):  
Data Structure ◽  
Data Model ◽  
Object Oriented ◽  
Geological Structure ◽  
Solid Model ◽  
Topological Relations ◽  
Seamless Integration ◽  
Computer Data ◽  
Model Based ◽  
Geometric Primitives

In the long evolution of the earth formation often form a complex geological structure, modeling for these complex geological entities (such as thinning-out, bifurcation, reverse, etc.) still require in-depth 3D modeling study. Because of discontinuity, complexity and uncertainty of distribution of 3D geo-objects, some models only are suitable for regular, continuous and relatively simple spatial objects, and some are suitable for discontinue, complex and uncertain geo-objects, but some improvements on these models, such as, updating of model, maintenance of topological and seamless integration between models, are still to be made. OO-Solid model, put forward by writer in 2002, is an object- oriented topological model based on sections. The OO-Solid Model is an object-oriented 3D topologic data model based on component for geology modeling with fully considering the topological relations between geological objects and its geometric primitives, Comparatively, it accords with the actual requirements of three-dimensional geological modeling . The key issue of 3D geology modeling is the 3D data model. Some data models are suitable for discontinue, complex and uncertain geo-objects, but the OO-Solid model is an object-oriented 3D topologic data model based on component for geology modeling with fully considering the topological relations between geological objects and its geometric primitives. OO-Solid model and data structure are designed. At last, 3D complex geological entities modeling based on OO-Solid are studied in this paper. These study is important and one of the core techniques for the 3DGM.

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.

Humanitites Data Warehousing

2008 ◽  
pp. 2364-2370
Author(s):  
Janet Delve
Keyword(s):  
Data Warehouse ◽  
Relational Databases ◽  
Data Warehousing ◽  
Numerical Data ◽  
Complex Nature ◽  
Data Warehouses ◽  
Textual Data ◽  
Numeric Data ◽  
First Time ◽  
And Linguistics

Data Warehousing is now a well-established part of the business and scientific worlds. However, up until recently, data warehouses were restricted to modeling essentially numerical data – examples being sales figures in the business arena (e.g. Wal-Mart’s data warehouse) and astronomical data (e.g. SKICAT) in scientific research, with textual data providing a descriptive rather than a central role. The lack of ability of data warehouses to cope with mainly non-numeric data is particularly problematic for humanities1 research utilizing material such as memoirs and trade directories. Recent innovations have opened up possibilities for non-numeric data warehouses, making them widely accessible to humanities research for the first time. Due to its irregular and complex nature, humanities research data is often difficult to model and manipulating time shifts in a relational database is problematic as is fitting such data into a normalized data model. History and linguistics are exemplars of areas where relational databases are cumbersome and which would benefit from the greater freedom afforded by data warehouse dimensional modeling.

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