TY - GEN
T1 - Efficient policy-based inconsistency management in relational knowledge bases
AU - Martinez, Maria Vanina
AU - Parisi, Francesco
AU - Pugliese, Andrea
AU - Simari, Gerardo I.
AU - Subrahmanian, V. S.
PY - 2010
Y1 - 2010
N2 - Real-world databases are frequently inconsistent. Even though the users who work with a body of data are far more familiar not only with that data, but also their own job and the risks they are willing to take and the inferences they are willing to make from inconsistent data, most DBMSs force them to use the policy embedded in the DBMS. Inconsistency management policies (IMPs) were introduced so that users can apply policies that they deem are appropriate for data they know and understand better than anyone else. In this paper, we develop an efficient "cluster table" method to implement IMPs and show that using cluster tables instead of a standard DBMS index is far more efficient when less than about 3% of a table is involved in an inconsistency (which is hopefully the case in most real world DBs), while standard DBMS indexes perform better when the amount of inconsistency in a database is over 3%.
AB - Real-world databases are frequently inconsistent. Even though the users who work with a body of data are far more familiar not only with that data, but also their own job and the risks they are willing to take and the inferences they are willing to make from inconsistent data, most DBMSs force them to use the policy embedded in the DBMS. Inconsistency management policies (IMPs) were introduced so that users can apply policies that they deem are appropriate for data they know and understand better than anyone else. In this paper, we develop an efficient "cluster table" method to implement IMPs and show that using cluster tables instead of a standard DBMS index is far more efficient when less than about 3% of a table is involved in an inconsistency (which is hopefully the case in most real world DBs), while standard DBMS indexes perform better when the amount of inconsistency in a database is over 3%.
UR - http://www.scopus.com/inward/record.url?scp=77958057431&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-15951-0_26
DO - 10.1007/978-3-642-15951-0_26
M3 - Conference contribution
AN - SCOPUS:77958057431
SN - 3642159508
SN - 9783642159503
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 264
EP - 277
BT - Scalable Uncertainty Management - 4th International Conference, SUM 2010, Proceedings
T2 - 4th International Conference on Scalable Uncertainty Management, SUM 2010
Y2 - 27 September 2010 through 29 September 2010
ER -