Generalization Data Mining in Fuzzy Object-Oriented Databases

2011 ◽  
pp. 85-112
Author(s):  
Rafal Angryk ◽  
Roy Ladner ◽  
Frederick E. Petry
Keyword(s):  
Data Mining ◽  
Relational Databases ◽  
Complex Structure ◽  
Object Oriented ◽  
Concept Hierarchy ◽  
Fuzzy Concept ◽  
Fuzzy Similarity ◽  
Specific Attribute ◽  
The Given ◽  
Object Oriented Databases

In this chapter, we consider the application of generalization-based data mining to fuzzy similarity-based object-oriented databases (OODBs). Attribute generalization algorithms have been most commonly applied to relational databases, and we extend these approaches. A key aspect of generalization data mining is the use of a concept hierarchy. The objects of the database are generalized by replacing specific attribute values by the next higher-level term in the hierarchy. This will then eventually result in generalizations that represent a summarization of the information in the database. We focus on the generalization of similarity-based simple fuzzy attributes for an OODB using approaches to the fuzzy concept hierarchy developed from the given similarity relation of the database. Then consideration is given to applying this approach to complex structure-valued data in the fuzzy OODB.

Generalization Data Mining in Fuzzy Object-Oriented Databases

2008 ◽  
pp. 2121-2140
Author(s):  
Rafal Angryk ◽  
Roy Ladner ◽  
Frederick E. Petry
Keyword(s):  
Data Mining ◽  
Relational Databases ◽  
Complex Structure ◽  
Object Oriented ◽  
Concept Hierarchy ◽  
Fuzzy Concept ◽  
Fuzzy Similarity ◽  
Specific Attribute ◽  
The Given ◽  
Object Oriented Databases

In this chapter, we consider the application of generalization-based data mining to fuzzy similarity-based object-oriented databases (OODBs). Attribute generalization algorithms have been most commonly applied to relational databases, and we extend these approaches. A key aspect of generalization data mining is the use of a concept hierarchy. The objects of the database are generalized by replacing specific attribute values by the next higher-level term in the hierarchy. This will then eventually result in generalizations that represent a summarization of the information in the database. We focus on the generalization of similarity-based simple fuzzy attributes for an OODB using approaches to the fuzzy concept hierarchy developed from the given similarity relation of the database. Then consideration is given to applying this approach to complex structure-valued data in the fuzzy OODB.

A Literature Overview of Fuzzy Database Modeling

2008 ◽  
pp. 187-207 ◽  
Author(s):  
Z.. M. Ma
Keyword(s):  
Relational Databases ◽  
Object Oriented ◽  
Data Models ◽  
Relational Model ◽  
Complex Objects ◽  
Database Modeling ◽  
Database Models ◽  
Fuzzy Database ◽  
Conceptual Data ◽  
Object Oriented Databases

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.

A Literature Overview of Fuzzy Database Modeling

2009 ◽  
pp. 105-125 ◽  
Author(s):  
Z.M. Ma
Keyword(s):  
Relational Databases ◽  
Object Oriented ◽  
Data Models ◽  
Relational Model ◽  
Complex Objects ◽  
Database Modeling ◽  
Database Models ◽  
Fuzzy Database ◽  
Conceptual Data ◽  
Object Oriented Databases

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.

A Literature Overview of Fuzzy Database Modeling

2011 ◽  
pp. 167-196
Author(s):  
Z. M. Ma
Keyword(s):  
Relational Databases ◽  
Object Oriented ◽  
Data Models ◽  
Relational Model ◽  
Complex Objects ◽  
Database Modeling ◽  
Database Models ◽  
Fuzzy Database ◽  
Conceptual Data ◽  
Object Oriented Databases

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.

Converting a Legacy Database to Object-Oriented Database

2005 ◽  
pp. 99-104
Author(s):  
Reda Alhajj ◽  
Faruk Polat
Keyword(s):  
Relational Database ◽  
Relational Databases ◽  
Object Oriented ◽  
Professional Staff ◽  
Relational Model ◽  
Object Oriented Technology ◽  
The People ◽  
Two Generations ◽  
Object Oriented Databases ◽  
Object Oriented Database

We present an approach to transfer content of an existing conventional relational database to a corresponding existing object-oriented database. The major motivation is having organizations with two generations of information systems; the first is based on the relational model, and the second is based on the object-oriented model. This has several drawbacks. First, it is impossible to get unified global reports that involve information from the two databases without providing a wrapper that facilitates accessing one of the databases within the realm of the other. Second, organizations should keep professional staff familiar with the system. Finally, most of the people familiar with the conventional relational technology are willing to learn and move to the emerging object-oriented technology. Therefore, one appropriate solution is to transfer content of conventional relational databases into object-oriented databases; the latter are extensible by nature, hence, are more flexible to maintain. However, it is very difficult to extend and maintain a conventional relational database.

Generalization-based data mining in object-oriented databases using an object cube model

1998 ◽  
Vol 25 (1-2) ◽  
pp. 55-97 ◽  
Author(s):  
Jiawei Han ◽  
Shojiro Nishio ◽  
Hiroyuki Kawano ◽  
Wei Wang
Keyword(s):  
Data Mining ◽  
Object Oriented ◽  
Object Oriented Databases

scholarly journals Ext-LOUDS: A Space Efficient Extended LOUDS Index for Superset Query

2020 ◽  
Vol 10 (23) ◽  
pp. 8530
Author(s):  
Lianyin Jia ◽  
Yuna Zhang ◽  
Jiaman Ding ◽  
Jinguo You ◽  
Yinong Chen ◽  
...  
Keyword(s):  
Data Mining ◽  
Degree Sequence ◽  
Object Oriented ◽  
Experimental Results ◽  
Integer Vector ◽  
Query Performance ◽  
Synthetic Datasets ◽  
Query Algorithm ◽  
Object Oriented Databases

Superset query is widely used in object-oriented databases, data mining, and many other fields. Trie is an efficient index for superset query, whereas most existing trie index aim at improving query performance while ignoring storage overheads. To solve this problem, in this paper, we propose an efficient extended Level-Ordered Unary Degree Sequence (LOUDS) index: Ext-LOUDS. Ext-LOUDS expresses a trie by 1 integer vector and 3 bit vectors directly map each NodeID to its corresponding position, thus accelerating some key operations needed for superset query. Based on Ext-LOUDS, an efficient superset query algorithm, ELOUDS-Super, is designed. Experimental results on both real and synthetic datasets show that Ext-LOUDS can decrease 50%–60% space overheads compared with trie while maintaining a relative good query performance.

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