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

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.

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.

DOOD'95 Post-Conference Workshop on Temporal Reasoning in Deductive and Object-Oriented Databases

1996 ◽  
Vol 11 (2) ◽  
pp. 191-192 ◽  
Author(s):  
Stefan Conrad
Keyword(s):  
Knowledge Discovery ◽  
Object Oriented ◽  
Temporal Reasoning ◽  
International Conference ◽  
First Time ◽  
Object Oriented Databases

For the first time, post-conference workshops were organised for the International Conference on Deductive and Object-Oriented Databases (DOOD). There were two workshops focusing on knowledge discovery and temporal reasoning. This report is dedicated to one dealing with temporal reasoning.

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