TY - GEN
T1 - Preference query evaluation over expensive attributes
AU - Levandoski, Justin J.
AU - Mokbel, Mohamed F.
AU - Khalefa, Mohamed E.
PY - 2010
Y1 - 2010
N2 - Most database systems allow query processing over attributes that are derived at query runtime (e.g., user-defined functions and remote data calls to web services), making them expensive to compute relative to relational data stored in a heap or index. In addition, core support for efficient preference query processing has become an important objective in database systems. This paper addresses an important problem at the intersection of these two query processing objectives: efficient preference query evaluation involving expensive attributes. We explore an efficient framework for processing skyline and multi-objective queries in a database when the data involves a mix of "cheap" and "expensive" attributes. Our solution involves a three-phase approach that evaluates a correct final preference answer while aiming to minimizing the number of expensive attributes computations. Unlike previous works for distributed preference algorithms that assume sorted access over each attribute, our framework assumes expensive attribute requests are stateless, i.e., know nothing previous requests. Thus, the proposed approach is more in line with realistic system architectures. Our framework is implemented inside the query processor of PostgreSQL, and evaluated over both synthetic and real data sets involving computation of expensive attributes over real web-service data (e.g., Microsoft MapPoint).
AB - Most database systems allow query processing over attributes that are derived at query runtime (e.g., user-defined functions and remote data calls to web services), making them expensive to compute relative to relational data stored in a heap or index. In addition, core support for efficient preference query processing has become an important objective in database systems. This paper addresses an important problem at the intersection of these two query processing objectives: efficient preference query evaluation involving expensive attributes. We explore an efficient framework for processing skyline and multi-objective queries in a database when the data involves a mix of "cheap" and "expensive" attributes. Our solution involves a three-phase approach that evaluates a correct final preference answer while aiming to minimizing the number of expensive attributes computations. Unlike previous works for distributed preference algorithms that assume sorted access over each attribute, our framework assumes expensive attribute requests are stateless, i.e., know nothing previous requests. Thus, the proposed approach is more in line with realistic system architectures. Our framework is implemented inside the query processor of PostgreSQL, and evaluated over both synthetic and real data sets involving computation of expensive attributes over real web-service data (e.g., Microsoft MapPoint).
KW - Algorithms
KW - Design
KW - Performance
UR - http://www.scopus.com/inward/record.url?scp=78651315372&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78651315372&partnerID=8YFLogxK
U2 - 10.1145/1871437.1871481
DO - 10.1145/1871437.1871481
M3 - Conference contribution
AN - SCOPUS:78651315372
SN - 9781450300995
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 319
EP - 328
BT - CIKM'10 - Proceedings of the 19th International Conference on Information and Knowledge Management and Co-located Workshops
T2 - 19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10
Y2 - 26 October 2010 through 30 October 2010
ER -