Fuzzy Object Relational Approach Computer Science Essay

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To find an approach that could provide the proper method for solving the problem of fuzziness of the real-estate & banking data whereas figures such as maps on the GIS systems and the archived checks and vouchers in banking business can be selected as the object of customer and sales men to research for specific criterions.

This paper considered the fuzziness of such business due to the suitability of objects attributes to fuzzy treatment, where the high level of fuzziness in their values.

APPROACH:

To use the OORDBMS technology to resolve issues arising because of fuzziness & unclearness of real-estate and banking data.

RESTRICTIONS/IMPLICATIONS:

People are not always receptive to introduction of new technologies. So care should be taken to educate and keep all the staff informed of the changes being brought about because of the Database technology and at the same time to convey the many benefits of Object-Oriented Relational Database Management System (OORDBMS).

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Implementing Object-Orient Relational Database Management System (OORDBMS) Models whereas business dealing with fuzziness, unclearness, and complex data and presenting online services and storing archived data.

PAPER TYPE:

Academic research paper on Key features of Object Database, Key benefits, and business implementation fields

KEYWORDS:

Keyword

Description

RDBMS

Relational Database Management System

OORDBMS

Object-Oriented Relational Database Management System

ODBMS

Object Database Management System

OO

Object-Oriented

UDDT

User Defined Data Types

FORDB

Fuzzy Object-Relational Database

FORDBMS

Fuzzy Object Relational Database Management System

INTRODUCTION

In the field of application development, Object Oriented Model is becoming the widespread option. This emerged due to the high open capabilities of object models, and the quick application development capabilities of OO languages, because of the high code reusability level achieved by the OO model key concepts: inheritance, encapsulation and polymorphism (the varied ways of usage) [1].

By way of this achievement, database models are changing to include OO concepts in order to take advantage of the OO model benefits.

Nowadays, the commercial database Management System (DBMS) move toward considering Object Relational Database Model due to the advantage that could be presented to the end users. Object-Oriented Relational Databases Management systems (OORDBMS) join the powerful modeling capabilities of an OO data model and the proved robustness of the relational model. ORDBMSs integrate much better with OO software, offering to OO applications direct object persistence functionality [2].

One of the significant key features of OORDBMSs is extensibility. Using OO concepts, OORDBMS functionality may be extended by means of User defined Data Types UDDTs, which allow transparent integration of user defined data structures and data processing, all of this encapsulated as a unit[3].

Over the last decade the market of software development especially these applications depend on databases taken advantages of fuzzy object-relational database (FORDB) models. The transparent integration of OO applications with ORDBMSs and their new fuzzy data management extensions, combine to create enhanced data management capabilities for commercial applications, allowing them to store imprecise information and query data using flexible conditions easily [1].

Business such as real-estate management process or the archived checks and vouchers in banking business can be selected as the object of our research, because of the suitability of objects attributes to fuzzy treatment, due to the high level of fuzziness in their values. Which attributes are suitable for fuzzy handling will be examined, and also, the way to represent them in the framework [1,3]

This paper propose to explain the key features of fuzzy data and to shows how the use of OORDBMS data management capabilities to improve the user-application interaction in offer-searching systems, allowing sellers to express their offers as imprecisely as they need, and buyers to express their queries as flexibly as they want. This way of expressing queries and offers, makes the interaction with the systems more natural, emulating the flexible process applied by sales agent to match offers and demands.

PROBLEM STATEMENT:

In this kind of business searching process a set of characteristics is specified for the real estate and banking to have, but usually these characteristics are not fully defined. A customer has a set of favorites, a general idea of what is being looked for, that idea not necessarily should fit to a crisp value, it might be most accurately represented by a value range, an approximate value or even an upper or lower bound. The imprecise representation of these characteristics may allow obtaining results that verify our preferences on different degrees.

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Generally the fuzziness is managed by sales agents who can easily process and handle fuzzy the sale agent, both of them can handle fuzzy information naturally.

The problem arises when one of these entities, the sales agent in our case, capable of handling fuzzy information, is replaced by an automatic system. It is necessary to provide the system with methods to handle fuzzy information in the same way the sales agent was doing before. So, a way to represent fuzzy information about real estates is proposed, in order to be able to design a system that can mimic the sales agent behavior, to interact fluidly with a customer.

The proposed case here is to represent fuzzy information in an object-oriented data model, and recent work point to a model and implementation of a FORDBMS using the object features of current ORDBMSs to extend them by means of UDTs, which encapsulate fuzzy information representation and processing.

FUZZY OBJECT-RELATIONAL DATABASE

An ORDBMS can be extended considering user defined data types (UDTs) to virtually maintain any kind of complex data, similar to multimedia or spatial data. Extending an ORDBMS with fuzzy data management UDTs, produces a FORDBMS which combines the power of fuzzy groups, and the object oriented and relational models.

This extension provides advantages over current FRDBMSs, such as tight level of integration with the fundamental DBMS, hiding implementation aspects of fuzzy types, which allow the user to be aware only of semantics and functionality, an extensible schema allowing future extensions, and efficient implementation, avoiding the use of software wrappers to allow fuzzy data management.

Figure 1 Object-Relational Impedance Mismatch Diagram

The above diagram shows the Object-Relational Impedance Mismatch, People creating object hierarchies are doing something fundamentally different than people creating databases. A similar mismatch applies between people participating in many sites and people consuming them

DATA-TYPE HIERARCHY FOR FUZZY DATA MANAGEMENT

For giving complex fuzzy data management abilities for the fundamental ORDBMS, new UDTs have been defined using host DBMS object-oriented features, organized in a hierarchy and extending the basic DBMS data-types. These new data-types allow the DBMS user to deal with several kinds of imprecise data.

The types in the said hierarchy are the following: -

FUZZY DATA-TYPES (FDT) is concept of all sustained fuzzy data. This type declares common general methods to be applied in the subtypes, for instance the FEQ (fuzzy equal to) method, which extends the concept of classical equality to the fuzzy framework, returning a value in the interval representing the fuzzy resemblance degree between two fuzzy values.

ATOMIC FUZZY TYPES (AFT) collects all common behavior for the fuzzy extensions of scalar and numerical data.

ORDERED AFTS (OAFT) gives structure and behavior to atomic fuzzy data represented by a possibility distribution defined on an ordered domain (numerical fuzzy data). As this type has an associated ordered domain which defines an order relation between the domain elements, the type can define an extension of the classical relational operators, for instance fuzzy equal to (FEQ), fuzzy greater than (FGT), fuzzy greater than or equal to (FGEQ), etcetera.

NON ORDERED AFTS (NOAFT) offers structure and behavior to data defined on a scalar domain without an order relation. The user defines a fuzzy nearness relation between domain's members, which is used to compute the resemblance degree between two members using the FEQ operator.

Fuzzy Collections (FC) extends the classical collection concept to a fuzzy one, in which the collection elements have a membership degree between. Fuzziness affects only elements’ membership, it does not affect collection elements, and consequently gathering elements’ type can be fuzzy or hard. FC type provides the required structure and behavior to manage the collection, like methods for adding, removing or getting the membership of collection elements.

THE REAL ESTATE SEARCHING PROBLEM

Real-estate management process selected as an ideal object example that provides complexity whereas attributes to fuzzy treatment due to the high level of imprecision in their values.

In the real estate searching process a set of characteristics is specified for the real estate to have, but usually these characteristics are not fully defined. A customer has a set of preferences, a general idea of what is being looked for, that idea not necessarily should fit to a crisp value, it might be most accurately represented by a value range, an approximate value or even an upper or lower bound. The imprecise representation of these characteristics may allow obtaining results that verify our preferences on different degrees.

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Generally the imprecision is managed by sales agents who can easily process and handle fuzzy information. The real estate management process occurs between two humans, the customer and the sales agent, both of them can handle fuzzy information naturally.

Figure 2 Real-Estate Searching Process [3]

The problem arises when one of these entities, the sales agent in our case, capable of handling fuzzy information, is replaced by an automatic system. It is necessary to provide the system with methods to handle fuzzy information in the same way the sales agent was doing before. So, a way to represent fuzzy information about real estates is proposed, in order to be able to design a system that can mimic the sales agent behavior, to interact fluidly with a customer.

Which attributes are suitable for fuzzy handling will be examined, and also, the way to represent them in the framework defined in previous sections?

CONCLUSION

OORDBMS became fit for certain business types in today business whereas the stored and retrieved information is fuzzy and unclear. Implementing OORDBMS might give organization measurable competitive advantage over competitors whereas better services availability (24X7) with high performance are needed due to these key features came linked with OORDBMS. We have shown how is OORDBMS can be used for business like real-estate where systems like GIS, which could lead into reducing the vehicles delays eventually the fare cost. Decision Support Systems is a vital system for big cooperates that needs systems based on consistent and huge size of data in meanwhile issuing parallel query statement against the database that is require database able to respond to big number of queries simultaneously and concurrently. Database with high performance ratios should represent key success factor for such systems. As well at the operational management level where business-operational-people managing the daily business processes, management could utilizes ODBMS as solution for capturing their gained experience and talent into KM system.

REFERENCES

[1] carlos d. Barranco, jesðs r. Campaña, juan c. Cubero, juan m. Medina. A fuzzy (no date).object relational approach to flexible real estate trade.[Online]. Available at <http://www.springerlink.com/content/k637p4235k28/front-matter.pdf> [Accessed by 10th November 2010]

[2] Victor Daniels (2007). Object Relations Theory. [Online]. available at

< http://www.sonoma.edu/users/d/daniels/objectrelations.html>

[Accessed by 11th November 2010]

[3] Guha Priya .M (no date). Object oriented relational database management system. [Online]. Available at:

<http://www.kasc.ac.in/cspgdept/itxcels/magazine/issue13/oordbms.doc>

[Accessed by 10th November 2010]

[4] Detroit Homes For Sale (no date). [online] available at.

< http://detroithomesforsale.org/>[Accessed by 10th November 2010]