Ant Colony Clusters for Fast Execution of Large Datasets

被引:0
|
作者
Sreenu, Konda [1 ]
Reddy, Boddu Raja Srinivasa [2 ]
机构
[1] Sir CR Reddy Coll Engn, Dept CSE, Eluru, Andhra Pradesh, India
[2] Ramachandra Coll Engn, Dept CSE, Eluru, Andhra Pradesh, India
关键词
Large databases; Deep analytics; Datasets; Columnar; Run-length environment; Ant colony;
D O I
10.1007/978-981-10-7868-2_27
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Today's world is fully dependent upon computer systems. As the population is growing, the database is also growing. We are rich in information but unable to access them fast and clearly. Business experts want data within a fraction of seconds, which is not possible because of large volumes. It takes a time to view, select, or accumulate large datasets. It is important to have information fastly, clearly, and accurately. This paper focuses on those issues. Deep analytics is the application of sophisticated data processing techniques on the large collection of multi-source datasets. These datasets may be semi-structured or structured or unstructured. For data mining, there should be mining expert and a business analyst. This process may take a long day or more. We need not wait for mining expert; a business user can himself interface with the databases. Paper focuses on methods like columnar databases, run-length environment technique, and ant colony clusters. The business user can interact with database management system easily. The business user can extract domain knowledge related to the current system.
引用
收藏
页码:271 / 280
页数:10
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