Incomplete Information in Relational Databases
Journal of the ACM (JACM)
Efficient view maintenance at data warehouses
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Making multiple views self-maintainable in a data warehouse
Data & Knowledge Engineering
Making aggregate views self-maintainable
Data & Knowledge Engineering
Monotonic complements for independent data warehouses
The VLDB Journal — The International Journal on Very Large Data Bases
Incremental maintenance of object-oriented data warehouses
Information Sciences—Informatics and Computer Science: An International Journal
Incremental Maintenance of Schema-Restructuring Views in SchemaSQL
IEEE Transactions on Knowledge and Data Engineering
Incremental maintenance of aggregate and outerjoin expressions
Information Systems
MSMiner-a developing platform for OLAP
Decision Support Systems
Progressive ranking of range aggregates
Data & Knowledge Engineering
Answering ad hoc aggregate queries from data streams using prefix aggregate trees
Knowledge and Information Systems
Efficiency evaluation of data warehouse operations
Decision Support Systems
An efficient method for maintaining data cubes incrementally
Information Sciences: an International Journal
An incremental maintenance scheme of data cubes
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
Multiagent immediate incremental view maintenance for data warehouses
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In general, some simple but very meaningful statistical functions are often used to retrieve valuable summary information in corporate databases. However, it is not uncommon that such information is obtained from computerized information systems which spend a great deal of time calculating the large volume of collected data. In practice, such data is usually stored in a data warehouse in which a large number of summary tables or materialized aggregate views are built in order to improve the system performance. Upon changes, most notable new transactional data are collected from various data sources, and all summary tables in the data warehouse that correspond to the transactional data must be updated accordingly. Since the number of summary tables that need to be maintained is often large, efficiently maintaining these is thus a critical issue for managing a data warehouse. In this study, an efficient maintenance approach to enhance the performance of a data warehouse is proposed, in which some additional auxiliary tables are kept inside a data warehouse with the role of improving the maintenance processes of some statistical functions, such as MIN, MAX, MEAN, and MEDIAN. Finally, a comparative analysis is performed to verify the effectiveness of the proposal method.