Approximate query mapping: Accounting for translation closeness

2001 ◽  
Vol 10 (2) ◽  
pp. 155-181 ◽  
Author(s):  
Kevin Chen-Chuan Chang ◽  
Héctor García-Molina
Keyword(s):  
Approximate Query ◽  
Query Mapping

Efficient Approximate Query Processing in Peer-to-Peer Networks

2007 ◽  
Vol 19 (7) ◽  
pp. 919-933 ◽  
Author(s):  
Benjamin Arai ◽  
Gautam Das ◽  
Dimitrios Gunopulos ◽  
Vana Kalogeraki
Keyword(s):  
Query Processing ◽  
Peer To Peer ◽  
Peer Networks ◽  
Approximate Query Processing ◽  
Peer To Peer Networks ◽  
Approximate Query

scholarly journals Sampling Methods in Approximate Query Answering Systems

2011 ◽  
pp. 990-994 ◽  
Author(s):  
Gautam Das
Keyword(s):  
Data Analysis ◽  
Large Data ◽  
Massive Datasets ◽  
Data Repositories ◽  
Large Databases ◽  
Approximate Query Answering ◽  
Very Large Databases ◽  
Approximate Query ◽  
And Storage ◽  
Collection And Management

In recent years, advances in data collection and management technologies have led to a proliferation of very large databases. These large data repositories typically are created in the hope that, through analysis such as data mining and decision support, they will yield new insights into the data and the real-world processes that created them. In practice, however, while the collection and storage of massive datasets has become relatively straightforward, effective data analysis has proven more difficult to achieve. One reason that data analysis successes have proven elusive is that most analysis queries, by their nature, require aggregation or summarization of large portions of the data being analyzed. For multi-gigabyte data repositories, this means that processing even a single analysis query involves accessing enormous amounts of data, leading to prohibitively expensive running times. This severely limits the feasibility of many types of analysis applications, especially those that depend on timeliness or interactivity.

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