Paper Info
Title
Data-driven understanding and refinement of schema mappings
Abstract
At the heart of many data-intensive applications is the problem of quickly and accurately transforming data into a new form. Database researchers have long advocated the use of declarative queries for this process. Yet tools for creating, managing and understanding the complex queries necessary for data transformation are still too primitive to permit widespread adoption of this approach. We present a new framework that uses data examples as the basis for understanding and refining declarative schema mappings. We identify a small set of intuitive operators for manipulating examples. These operators permit a user to follow and refine an example by walking through a data source. We show that our operators are powerful enough both to identify a large class of schema mappings and to distinguish effectively between alternative schema mappings. These operators permit a user to quickly and intuitively build and refine complex data transformation queries that map one data source into another.
Year
DOI
Venue
2001
10.1145/375663.375729
SIGMOD Conference
Keywords
Field
DocType
complex data,range query,random sampling,data transformation
Data mining,Data-driven,Star schema,Computer science,Range query (data structures),Complex data type,Theoretical computer science,Operator (computer programming),Sampling (statistics),Small set,Schema (psychology),Database
Conference
Volume
Issue
ISSN
30
2
0163-5808
ISBN
Citations 
PageRank 
1-58113-332-4
108
13.45
References 
Authors
11
4
Search Limit
100108
Name
Order
Citations
PageRank
Ling-ling Yan139659.31
Renée J. Miller23545373.59
Laura M. Haas338721128.30
Ronald Fagin488082643.66