Computational geometry: an introduction
Computational geometry: an introduction
Parallel database systems: the future of high performance database systems
Communications of the ACM
Four results on randomized incremental constructions
Computational Geometry: Theory and Applications
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
Combining fuzzy information from multiple systems (extended abstract)
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
On saying “Enough already!” in SQL
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Computational geometry in C (2nd ed.)
Computational geometry in C (2nd ed.)
Synopsis data structures for massive data sets
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
The onion technique: indexing for linear optimization queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
PREFER: a system for the efficient execution of multi-parametric ranked queries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Minimal probing: supporting expensive predicates for top-k queries
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Top-k selection queries over relational databases: Mapping strategies and performance evaluation
ACM Transactions on Database Systems (TODS)
Probabilistic Optimization of Top N Queries
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Determining the Convex Hull in Large Multidimensional Databases
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
Evaluating top-k queries over web-accessible databases
ACM Transactions on Database Systems (TODS)
Approximating extent measures of points
Journal of the ACM (JACM)
Supporting top-k join queries in relational databases
The VLDB Journal — The International Journal on Very Large Data Bases
Adaptive sampling for geometric problems over data streams
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Continuous monitoring of top-k queries over sliding windows
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Detecting anomalies in cross-classified streams: a Bayesian approach
Knowledge and Information Systems
Stability of feature selection algorithms: a study on high-dimensional spaces
Knowledge and Information Systems
Lottery scheduling: flexible proportional-share resource management
OSDI '94 Proceedings of the 1st USENIX conference on Operating Systems Design and Implementation
Isolation with flexibility: a resource management framework for central servers
ATEC '00 Proceedings of the annual conference on USENIX Annual Technical Conference
Answering ad hoc aggregate queries from data streams using prefix aggregate trees
Knowledge and Information Systems
CPU load shedding for binary stream joins
Knowledge and Information Systems
Robust ensemble learning for mining noisy data streams
Decision Support Systems
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We propose a SAO index to approximately answer arbitrary linear optimization queries in a sliding window of a data stream. It uses limited memory to maintain the most “important” tuples. At any time, for any linear optimization query, we can retrieve the approximate top-K tuples in the sliding window almost instantly. The larger the amount of available memory, the better the quality of the answers is. More importantly, for a given amount of memory, the quality of the answers can be further improved by dynamically allocating a larger portion of the memory to the outer layers of the SAO index.