TY - GEN
T1 - PrefJoin
T2 - 2011 IEEE 27th International Conference on Data Engineering, ICDE 2011
AU - Khalefa, Mohamed E.
AU - Mokbel, Mohamed F.
AU - Levandoski, Justin J.
PY - 2011
Y1 - 2011
N2 - Preference queries are essential to a wide spectrum of applications including multi-criteria decision-making tools and personalized databases. Unfortunately, most of the evaluation techniques for preference queries assume that the set of preferred attributes are stored in only one relation, waiving on a wide set of queries that include preference computations over multiple relations. This paper presents PrefJoin, an efficient preference-aware join query operator, designed specifically to deal with preference queries over multiple relations. PrefJoin consists of four main phases: Local Pruning, Data Preparation, Joining, and Refining that filter out, from each input relation, those tuples that are guaranteed not to be in the final preference set, associate meta data with each non-filtered tuple that will be used to optimize the execution of the next phases, produce a subset of join result that are relevant for the given preference function, and refine these tuples respectively. An interesting characteristic of PrefJoin is that it tightly integrates preference computation with join hence we can early prune those tuples that are guaranteed not to be an answer, and hence it saves significant unnecessary computations cost. PrefJoin supports a variety of preference function including skyline, multi-objective and k-dominance preference queries. We show the correctness of PrefJoin. Experimental evaluation based on a real system implementation inside PostgreSQL shows that PrefJoin consistently achieves from one to three orders of magnitude performance gain over its competitors in various scenarios.
AB - Preference queries are essential to a wide spectrum of applications including multi-criteria decision-making tools and personalized databases. Unfortunately, most of the evaluation techniques for preference queries assume that the set of preferred attributes are stored in only one relation, waiving on a wide set of queries that include preference computations over multiple relations. This paper presents PrefJoin, an efficient preference-aware join query operator, designed specifically to deal with preference queries over multiple relations. PrefJoin consists of four main phases: Local Pruning, Data Preparation, Joining, and Refining that filter out, from each input relation, those tuples that are guaranteed not to be in the final preference set, associate meta data with each non-filtered tuple that will be used to optimize the execution of the next phases, produce a subset of join result that are relevant for the given preference function, and refine these tuples respectively. An interesting characteristic of PrefJoin is that it tightly integrates preference computation with join hence we can early prune those tuples that are guaranteed not to be an answer, and hence it saves significant unnecessary computations cost. PrefJoin supports a variety of preference function including skyline, multi-objective and k-dominance preference queries. We show the correctness of PrefJoin. Experimental evaluation based on a real system implementation inside PostgreSQL shows that PrefJoin consistently achieves from one to three orders of magnitude performance gain over its competitors in various scenarios.
UR - http://www.scopus.com/inward/record.url?scp=79957877042&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79957877042&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2011.5767894
DO - 10.1109/ICDE.2011.5767894
M3 - Conference contribution
AN - SCOPUS:79957877042
SN - 9781424489589
T3 - Proceedings - International Conference on Data Engineering
SP - 995
EP - 1006
BT - 2011 IEEE 27th International Conference on Data Engineering, ICDE 2011
Y2 - 11 April 2011 through 16 April 2011
ER -