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Inicio  /  Applied Sciences  /  Vol: 10 Par: 23 (2020)  /  Artículo
ARTÍCULO
TITULO

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

Lianyin Jia    
Yuna Zhang    
Jiaman Ding    
Jinguo You    
Yinong Chen and Runxin Li    

Resumen

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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