An inductive database and query language in the relational model

2016 ◽

Vol 27 (2) ◽

pp. 27-48

Author(s):

András Benczúr ◽

Gyula I. Szabó

Keyword(s):

Normal Form ◽

Data Base ◽

Data Model ◽

Query Language ◽

Xml Schema ◽

Relational Model ◽

Regular Expressions ◽

Functional Dependencies ◽

Decision Algorithm ◽

Implication Problem

This paper introduces a generalized data base concept that unites relational and semi structured data models. As an important theoretical result we could find a quadratic decision algorithm for the implication problem of functional and join dependencies defined on the united data model. As practical contribution we presented a normal form for the new data model as a tool for data base design. With our novel representations of regular expressions, a more effective searching method could be developed. XML elements are described by XML schema languages such as a DTD or an XML Schema definition. The instances of these elements are semi-structured tuples. A semi-structured tuple is an ordered list of (attribute: value) pairs. We may think of a semi-structured tuple as a sentence of a formal language, where the values are the terminal symbols and the attribute names are the non-terminal symbols. In the authors' former work (Szabó and Benczúr, 2015) they introduced the notion of the extended tuple as a sentence from a regular language generated by a grammar where the non-terminal symbols of the grammar are the attribute names of the tuple. Sets of extended tuples are the extended relations. The authors then introduced the dual language, which generates the tuple types allowed to occur in extended relations. They defined functional dependencies (regular FD - RFD) over extended relations. In this paper they rephrase the RFD concept by directly using regular expressions over attribute names to define extended tuples. By the help of a special vertex labeled graph associated to regular expressions the specification of substring selection for the projection operation can be defined. The normalization for regular schemas is more complex than it is in the relational model, because the schema of an extended relation can contain an infinite number of tuple types. However, the authors can define selection, projection and join operations on extended relations too, so a lossless-join decomposition can be performed. They extended their previous model to deal with XML schema indicators too, e.g., with numerical constraints. They added line and set constructors too, in order to extend their model with more general projection and selection operators. This model establishes a query language with table join functionality for collected XML element data.

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A temporal relational model and a query language

Information Sciences ◽

10.1016/0020-0255(89)90026-1 ◽

1989 ◽

Vol 49 (1-3) ◽

pp. 147-175 ◽

Cited By ~ 106

Author(s):

S.B. Navathe ◽

Rafi Ahmed

Keyword(s):

Query Language ◽

Relational Model

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Application of graph databases for transport purposes

Bulletin of the Polish Academy of Sciences Technical Sciences ◽

10.1515/bpasts-2016-0051 ◽

2016 ◽

Vol 64 (3) ◽

pp. 457-466 ◽

Cited By ~ 1

Author(s):

A. Czerepicki

Keyword(s):

Information System ◽

Data Structures ◽

Public Transport ◽

Data Model ◽

Query Language ◽

Relational Model ◽

Graph Database ◽

Graph Databases ◽

Graph Data ◽

Innovative Concept

Abstract The article presents an innovative concept of applying graph databases in transport information systems. The model of a graph database has been presented together with implementation of data structures and search operations in a graph. The transformation concept of relational model to a graph data model has been developed. The schema of graph database has been proposed for public transport information system purposes. The realization methods have been illustrated by the use of search function based on the Cypher query language.

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SINDBAD and SiQL: An Inductive Database and Query Language in the Relational Model

Machine Learning and Knowledge Discovery in Databases - Lecture Notes in Computer Science ◽

10.1007/978-3-540-87481-2_48 ◽

2008 ◽

pp. 690-694 ◽

Cited By ~ 7

Author(s):

Jörg Wicker ◽

Lothar Richter ◽

Kristina Kessler ◽

Stefan Kramer

Keyword(s):

Query Language ◽

Relational Model

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Data Model of FRDB with Different Data Types and PFSQL

Handbook of Research on Fuzzy Information Processing in Databases ◽

10.4018/978-1-59904-853-6.ch016 ◽

2011 ◽

pp. 407-434 ◽

Cited By ~ 13

Author(s):

Aleksandar Takaci ◽

Srdan Škrbic

Keyword(s):

Constraint Satisfaction ◽

Query Language ◽

Constraint Satisfaction Problems ◽

Relational Model ◽

Data Types ◽

Theoretical Concepts ◽

Fuzzy Constraint ◽

Fuzzy Constraint Satisfaction ◽

Fuzzy Database ◽

Further Development

This chapter introduces a way to extend the relational model with mechanisms that can handle imprecise, uncertain, and inconsistent attribute values using fuzzy logic. It describes details on how the relational model is extended in order to include fuzzy capabilities. In addition, we describe a query language called PFSQL for this fuzzy database model. Besides basic fuzzy capabilities, this query language adds the possibility to specify priorities for fuzzy statements. This appears to be the first implementation that has such capabilities. Also we describe the relations on FRDB (fuzzy relational database) and PFCSP (priority fuzzy constraint satisfaction problems) and GPFCSP (generalized priority fuzzy constraint satisfaction problems), theoretical concepts vital for the implementation of PFSQL. The authors propose several points in which this research and implementation can be continued and extended, contributing to better understanding of fuzzy database concepts and techniques and giving numerous possibilities for further development in this area.

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Relational, Object-Oriented and Object-Relational Data Models

Encyclopedia of Database Technologies and Applications ◽

10.4018/978-1-59140-560-3.ch088 ◽

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.

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Schema-Based JSON Data Stores in Relational Databases

Journal of Database Management ◽

10.4018/jdm.2019070103 ◽

2019 ◽

Vol 30 (3) ◽

pp. 38-70 ◽

Cited By ~ 1

Author(s):

Lubna Irshad ◽

Li Yan ◽

Zongmin Ma

Keyword(s):

Data Exchange ◽

Relational Databases ◽

Query Language ◽

Relational Model ◽

Relational Schema ◽

Transaction Model ◽

Binary Format ◽

Fast Development ◽

Relational Database Management ◽

Transactional Models

JSON is a simple, compact and light weighted data exchange format to communicate between web services and client applications. NoSQL document stores evolve with the popularity of JSON, which can support JSON schema-less storage, reduce cost, and facilitate quick development. However, NoSQL still lacks standard query language and supports eventually consistent BASE transaction model rather than the ACID transaction model. This is very challenging and a burden on the developer. The relational database management systems (RDBMS) support JSON in binary format with SQL functions (also known as SQL/JSON). However, these functions are not standardized yet and vary across vendors along with different limitations and complexities. More importantly, complex searches, partial updates, composite queries, and analyses are cumbersome and time consuming in SQL/JSON compared to standard SQL operations. It is essential to integrate JSON into databases that use standard SQL features, support ACID transactional models, and has the capability of managing and organizing data efficiently. In this article, we empower JSON to use relational databases for analysis and complex queries. The authors reveal that the descriptive nature of the JSON schema can be utilized to create a relational schema for the storage of the JSON document. Then, the powerful SQL features can be used to gain consistency and ACID compatibility for querying JSON instances from the relational schema. This approach will open a gateway to combine the best features of both worlds: the fast development of JSON, consistency of relational model, and efficiency of SQL.

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