Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Combining fuzzy information from multiple systems
Journal of Computer and System Sciences
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Materialized view selection and maintenance using multi-query optimization
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Optimizing Multi-Feature Queries for Image Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Multiple aggregations over data streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Supporting ad-hoc ranking aggregates
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Continuous monitoring of top-k queries over sliding windows
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Extracting k most important groups from data efficiently
Data & Knowledge Engineering
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
Efficient Aggregate Computation over Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
The gist of everything new: personalized top-k processing over web 2.0 streams
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
MaSM: efficient online updates in data warehouses
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Efficient computation of frequent and top-k elements in data streams
ICDT'05 Proceedings of the 10th international conference on Database Theory
MonetDB/DataCell: online analytics in a streaming column-store
Proceedings of the VLDB Endowment
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We consider the problem of maintaining a large set of top-k rankings over the update stream of a database. The rankings stem from top-k aggregation queries that are given a-priori based on the application scenario, for instance created along dimensions of a traditional data warehouse, for efficient automated reporting/detection of changes. The focus on only the top part of a ranking enables efficient buffering techniques to limit expensive interactions with the underlying database, while still guaranteeing correct top-k rankings at all times. This is achieved by employing conservative rank (score) estimates of previously unseen items that are not in the top-k result so far. The proposed family of maintenance algorithms further exploits the relations between the monitored rankings known from multi-query optimisation. We present results of a preliminary experimental evaluation using TPC-H data to study the performance of our algorithms.