Database management systems will continue to manage large data volumes. Thus, efficient
algorithms for accessing and manipulating large sets and sequences will be required to …

As the data management field has diversified to consider settings in which queries are
increasingly complex, statistics are less available, or data is stored remotely, there has been …

The first edition of this book appeared in 1991 when the technology was new and there were
not too many products. In the Preface to the first edition, we had quoted Michael Stonebraker …

This paper describes the basic processing model and architecture of Aurora, a new system
to manage data streams for monitoring applications. Monitoring applications differ …

At Berkeley, we are developing TelegraphCQ [1, 2], a dataflow system for processing
continuous queries over data streams. TelegraphCQ is based on a novel, highly-adaptive …

Aggregation in traditional database systems is performed in batch mode: a query is
submitted, the system processes a large volume of data over a long period of time, and …

Traditional databases store sets of relatively static records with no pre-defined notion of time,
unless timestamp attributes are explicitly added. While this model adequately represents …

In large federated and shared-nothing databases, resources can exhibit widely fluctuating
characteristics. Assumptions made at the time a query is submitted will rarely hold …

Streams are continuous data feeds generated by such sources as sensors, satellites, and
stock feeds. Monitoring applications track data from numerous streams, filtering them for …

In many recent applications, data may take the form of continuous data streams, rather than
finite stored data sets. Several aspects of data management need to be reconsidered in the …