Physical database design for relational databases
ACM Transactions on Database Systems (TODS)
Algorithms for creating indexes for very large tables without quiescing updates
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
A time indexed formulation of non-preemptive single machine scheduling problems
Mathematical Programming: Series A and B
AutoAdmin “what-if” index analysis utility
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Tabu Search
Constraint-Based Scheduling
Selection of Views to Materialize Under a Maintenance Cost Constraint
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Automated Selection of Materialized Views and Indexes in SQL Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
On Schema Evolution in Multidimensional Databases
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Automatic physical design tuning: workload as a sequence
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Physical design refinement: The ‘merge-reduce’ approach
ACM Transactions on Database Systems (TODS)
DB2 design advisor: integrated automatic physical database design
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
An Integer Linear Programming Approach to Database Design
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
On-Line Index Selection for Shifting Workloads
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Correlation maps: a compressed access method for exploiting soft functional dependencies
Proceedings of the VLDB Endowment
Index interactions in physical design tuning: modeling, analysis, and applications
Proceedings of the VLDB Endowment
Constraint-Based Local Search
CORADD: correlation aware database designer for materialized views and indexes
Proceedings of the VLDB Endowment
CoPhy: a scalable, portable, and interactive index advisor for large workloads
Proceedings of the VLDB Endowment
An integer programming approach for the view and index selection problem
Data & Knowledge Engineering
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Many database applications deploy hundreds or thousands of indexes to speed up query execution. Despite a plethora of prior work on index selection, designing and deploying indexes remains a difficult task for database administrators. First, real-world businesses often require online index deployment, and the traditional off-line approach to index selection ignores intermediate workload performance during index deployment. Second, recent work on on-line index selection does not address effects of complex interactions that manifest during index deployment. In this paper, we propose a new approach that incorporates transitional design performance into the overall problem of physical database design. We call our approach Incremental Database Design. As the first step in this direction, we study the problem of ordering index deployment. The benefits of a good index deployment order are twofold: (1) a prompt query runtime improvement and (2) a reduced total time to deploy the design. Finding an effective deployment order is difficult due to complex index interaction and a factorial number of possible solutions. We formulate a mathematical model to represent the index ordering problem and demonstrate that Constraint Programming (CP) is a more efficient solution compared to other methods such as mixed integer programming and A * search. In addition to exact search techniques, we also study local search algorithms that make significant improvements over a greedy solution with minimal computational overhead. Our empirical analysis using the TPC-H dataset shows that our pruning techniques can reduce the size of the search space by many orders of magnitude. Using the TPC-DS dataset, we verify that our local search algorithm is a highly scalable and stable method for quickly finding the best known solutions.