Paper Info
Title
Incomplete databases: missing records and missing values
Abstract
Data completeness is an essential aspect of data quality as in many scenarios it is crucial to guarantee the completeness of query answers. Data might be incomplete in two ways: records may be missing as a whole, or attribute values of a record may be absent, indicated by a null. We extend previous work by two of the authors [10] that dealt only with the first aspect, to cover both missing records and missing attribute values. To this end, we refine the formalization of incomplete databases and identify the important special case where values of key attributes are always known. We show that in the presence of nulls, completeness of queries can be defined in several ways.We also generalize a previous approach stating completeness of parts of a database, using so-called table completeness statements. With this formalization in place, we define the main inferences for completeness reasoning over incomplete databases and present first results.
Year
DOI
Venue
2012
10.1007/978-3-642-29023-7_30
DASFAA Workshops
Keywords
Field
DocType
attribute value,missing record,key attribute,data completeness,missing value,completeness reasoning,incomplete databases,data quality,essential aspect,so-called table completeness statement,missing attribute value
Data mining,Conjunctive query,Data quality,Information retrieval,Computer science,Missing data,Completeness (statistics),Database,Special case
Conference
Citations 
PageRank 
References 
2
0.37
13
Authors
3
Name
Order
Citations
PageRank
Werner Nutt12009395.43
Simon Razniewski215727.07
Gil Vegliach3171.50