SRQL: Sorted Relational Query Language
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
Similarity based retrieval from sequence databases using automata as queries
Proceedings of the eleventh international conference on Information and knowledge management
Indexing web access-logs for pattern queries
Proceedings of the 4th international workshop on Web information and data management
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Efficiently Discovering Recent Frequent Items in Data Streams
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
A skip-list approach for efficiently processing forecasting queries
Proceedings of the VLDB Endowment
Identifying streaming frequent items in ad hoc time windows
Data & Knowledge Engineering
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Financial mathematicians think they can predict the future by looking at time series of trades and quotes (called ticks) from the past. The main evidence for this hypothesis is that prices fluctuate only by a small amount in a given day and more or less obey the mathematics of a random walk. The hypothesis allows traders to price options and to speculate on stocks. This demonstration presents a query language and a parallel database (50-way parallelism) to support traders who want to analyze every tick, not just end-of-day ticks, using temporal statistical queries such as time-delayed correlations and tick trends. This is the first attempt that we know of to store and analyze hundreds of gigabytes of time series data and to query that data using a declarative time series extension to SQL (available at www.kx.com).