A Fully Precise Null Extended Nested Relational Algebra

1993 ◽  
Vol 19 (3-4) ◽  
pp. 303-342
Author(s):  
Mark Levene ◽  
George Loizou

The nested relational model extends the fiat relational model by relaxing the first normal form assumption in order to allow the modelling of complex objects. Recently many extended algebras have been suggested for the nested relational model, but only few have incorporated null values into the attribute domains. Furthermore, some of the previously defined extended algebras are defined only over a subclass of nested relations, and all of them are difficult to use, since the user must know the detailed structure of the nested relations being queried. Herein, we define an extended algebra for nested relations, which may contain null values, called the null extended algebra. The null extended algebra is defined over the general class of nested relations with null values and, in addition, allows queries to be formulated without the user having to know the detailed structure of the nested relations being queried. In this sense, our null extended join operator of the null extended algebra is unique in the literature, since it joins two nested relations by taking into account all their common attributes at all levels of their structure, whilst operating directly on the two nested relations. All the operators of the null extended algebra are proved to be faithful and precise. The null extended algebra is a complete extended algebra in the context of nested relations, and, in addition, it includes the null extended powerset operator, which provides recursion and iteration facilities.

1984 ◽  
Vol 7 (1) ◽  
pp. 129-150
Author(s):  
Joachim Biskup

We study operations on generalized database relations which possibly contain maybe tuples and two types of null values. The existential null value has the meaning “value at present unknown” whereas the universal null value has the meaning “value arbitrary”. For extending a usual relational operation to generalized relations we develop three requirements: adequacy, restrictedness, and feasibility. As demonstrated for the natural join as an example, we can essetially meet these requirements although we are faced with a minor tradeoff between restrictedness and feasibility.


2008 ◽  
pp. 187-207 ◽  
Author(s):  
Z.. M. Ma

Fuzzy set theory has been extensively applied to extend various data models and resulted in numerous contributions, mainly with respect to the popular relational model or to some related form of it. To satisfy the need of modeling complex objects with imprecision and uncertainty, recently many researches have been concentrated on fuzzy semantic (conceptual) and object-oriented data models. This chapter reviews fuzzy database modeling technologies, including fuzzy conceptual data models and database models. Concerning fuzzy database models, fuzzy relational databases, fuzzy nested relational databases, and fuzzy object-oriented databases are discussed, respectively.


2016 ◽  
Vol 27 (2) ◽  
pp. 27-48
Author(s):  
András Benczúr ◽  
Gyula I. Szabó

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.


2009 ◽  
pp. 105-125 ◽  
Author(s):  
Z.M. Ma

Fuzzy set theory has been extensively applied to extend various data models and resulted in numerous contributions, mainly with respect to the popular relational model or to some related form of it. To satisfy the need of modeling complex objects with imprecision and uncertainty, recently many researches have been concentrated on fuzzy semantic (conceptual) and object-oriented data models. This chapter reviews fuzzy database modeling technologies, including fuzzy conceptual data models and database models. Concerning fuzzy database models, fuzzy relational databases, fuzzy nested relational databases, and fuzzy object-oriented databases are discussed, respectively.


2002 ◽  
Vol 40 (1) ◽  
pp. 55-64
Author(s):  
Saran Akram Abd Al-Majeed

There has been a great deal of discussion about null values in relational databases. The relational model was defined in 1969, and Nulls Was died in 1979. Unfortunately, there is not a generally agreeable solution for rull values problem. Null is a special marker which stands for a value undefined or unknown, which means thut ne entry has been made, a missing valuc mark is not a value and not of a date type and cannot be treated as a value by Database Management System (DBMS). As we know, distributed database users are more than a single database and data will be distributed among several data sources or sites, it must be precise data, the replication is allowed there, so complex problems will appear, then there will be need for perfect practical general approaches for treatment of Nulls. A distributed database system is designed, that is "Hotel reservation control system, based on different data sources at four site, each site is represented as a Hotel, for more heterogeneity different application programming languages there are five practical approaches, designed with their rules and algorithms for Null values treatment through the distributed database sites. (1), (2), (3). 14). 15), (9).


2011 ◽  
pp. 167-196
Author(s):  
Z. M. Ma

Fuzzy set theory has been extensively applied to extend various data models and resulted in numerous contributions, mainly with respect to the popular relational model or to some related form of it. To satisfy the need of modeling complex objects with imprecision and uncertainty, recently many researches have been concentrated on fuzzy semantic (conceptual) and object-oriented data models. This chapter reviews fuzzy database modeling technologies, including fuzzy conceptual data models and database models. Concerning fuzzy database models, fuzzy relational databases, fuzzy nested relational databases, and fuzzy object-oriented databases are discussed, respectively.


2003 ◽  
pp. 116-165 ◽  
Author(s):  
Maurizio Rafanelli

In this chapter the author proposes the different approaches for defining operators able to manipulate this multidimensional structure. In particular, he initially considers operators for multidimensional aggregate data which extend relational algebra and relational calculus (the so-called enlarged relational model). Then he discusses operators for multidimensional aggregate data defined in a tabular environment. In both the cases the author defines such data as statistical (aggregate) data. Subsequently he introduces the operators for OLAP applications, giving a terminology correspondence between the multidimensional aggregate (statistical) databases and OLAP areas. Then he defines the fundamental operators deduced from the previous ones, which form the basic algebra for the manipulation of multidimensional aggregate data, giving their formal definitions and some explanatory examples.


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