Sample-driven schema mapping

Abstract

End-users increasingly find the need to perform light-weight, customized schema mapping. State-of-the-art tools provide powerful functions to generate schema mappings, but they usually require an in-depth understanding of the semantics of multiple schemas and their correspondences, and are thus not suitable for users who are technically unsophisticated or when a large number of mappings must be performed.We propose a system for sample-driven schema mapping. It automatically constructs schema mappings, in real time, from userinput sample target instances. Because the user does not have to provide any explicit attribute-level match information, she is isolated from the possibly complex structure and semantics of both the source schemas and the mappings. In addition, the user never has to master any operations specific to schema mappings: she simply types data values into a spreadsheet-style interface. As a result, the user can construct mappings with a much lower cognitive burden.In this paper we present MWEAVER, a prototype sample-driven schema mapping system. It employs novel algorithms that enable the system to obtain desired mapping results while meeting interactive response performance requirements. We show the results of a user study that compares MWEAVER with two state-of-theart mapping tools across several mapping tasks, both real and synthetic. These suggest that the MWEAVER system enables users to perform practical mapping tasks in about 1/5th the time needed by the state-of-the-art tools.

Privacy Preserving Data Integration Through L-Diversity

“…Fagin, R., Kolaitis, P. G., and Popa, L [9] consider the following scenario for a mapping system: given a source schema, target schemas, and a set of value correspondences between these two schemas, generate an executable transformation to compute target instances from source instances.…”

“…These tables are treated as constraints (aka mapping constraints) on the exchange of data between peers. In [9], a sample-driven schema mapping technique is proposed that builds the schema mappings from sample target instances. This technique generates mapping expressions given each pair of sample source and target instances.…”

Discovering queries based on example tuples

“…Existing approaches on keyword search in databases [1,10,2,14,9,19,4,6,15,12] and sample driven schema mapping [18] focus on the single input tuple scenario. A naive adaptation to our problem is to first compute the set valid minimal queries for each row in ET and then intersect those sets.…”

Online Social Network Profile Linkage

“…Surveys [8,9]. review the various approaches, including named attributes computations [5], schema mapping [2,17] and duplicate detection in hierarchical data [10], all which inform the construction of profile linkage techniques.…”