Computer Science 代写 Fuzzy Object Relational Approach
中文简介：在应用开发领域，面向对象模型正成为广泛的选择。这出现了由于高的开放能力的对象模型，和快速的应用程序开发能力的面向对象的语言，因为高代码的可重用性水平，实现了面向对象的模型的关键概念：继承，封装和多态性（不同的使用方式）[ 1 ]。
通过这一成就的方式，数据库模型正在改变，包括面向对象的概念，以利用面向对象的模型的好处。
如今，商业数据库管理系统（数据库管理系统）的移动向考虑对象-关系数据库模型，由于可以提交给最终用户的优势。面向对象的关系数据库管理系统（oordbms）加入一个面向对象数据模型的强大的建模能力和鲁棒性的证明的关系模型。关系型数据库管理系统结合面向对象的软件更好，提供面向对象的应用程序直接对象持久化功能[ 2 ]。
一个重要的oordbmss主要特点是可扩展性。使用面向对象的概念，oordbms功能可以由用户定义的数据类型uddts扩展，允许用户定义的数据结构和数据处理透明整合，所有这一切都封装为一个单位【3】。
在过去的十年中市场软件开发特别是这些应用程序依赖于采取模糊对象关系型数据库的数据库（待遇确定）模型的优点。与ORDBMSs和他们的新的模糊数据管理扩展的面向对象的应用程序透明的集成，结合商业应用程序创建增强的数据管理能力，使他们能够存储和查询数据的不精确信息的使用灵活的条件很容易[ 1 ]。
如房地产管理过程或银行业务的存档支票和凭证的业务可以被选择为我们的研究对象，因为对象属性的适用性模糊处理，由于高层次的模糊性，在他们的价值观。哪些属性是适合于模糊处理将被检查，并代表他们在框架[1,3]的方式
本文提出了解释模糊数据的关键特征，说明oordbms数据管理能力的提高提供了检索系统的用户应用程序的交互使用，让卖家提供他们表达自己不需要，买家表达自己的查询，灵活地为他们想要的。这种表达方式的查询和报价，与系统进行交互更自然，仿真通过代理销售应用柔性流程匹配提供和需求。
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).
INNOVATION/ADDED VALUE:
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:
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.
Computer Science 代写 Fuzzy Object Relational Approach
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.
PROBLEM DESCRIPTION:
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.
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?
Computer Science 代写 Fuzzy Object Relational Approach
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.
oordbms成为适合特定的业务类型在今天的生意而存储和检索的信息是模糊的，不清楚。实施oordbms可能给组织的可衡量的竞争优势超过竞争对手而更好的服务可用性（24x7）高性能需要由于这些关键特征来与oordbms。我们已经说明是oordbms可用于商业房地产系统如GIS，这可能导致减少车辆延误最终票价成本。决策支持系统是一个重要的系统，大的合作，需要系统的基础上一致的和巨大的大小的数据，同时发出对数据库的并行查询语句，是需要数据库能够响应大量的查询，同时并同时。高性能比率的数据库应该是这样的系统的关键成功因素。以及在经营管理水平，经营管理日常业务流程，管理可以利用来作为捕捉他们的经验和人才到知识管理系统解决方案。
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