Gigascope: a stream database for network applications
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
Expressing and optimizing sequence queries in database systems
ACM Transactions on Database Systems (TODS)
StreaMon: an adaptive engine for stream query processing
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Semantics and evaluation techniques for window aggregates in data streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Exploiting predicate-window semantics over data streams
ACM SIGMOD Record
The CQL continuous query language: semantic foundations and query execution
The VLDB Journal — The International Journal on Very Large Data Bases
Cayuga: a high-performance event processing engine
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
CAPE: continuous query engine with heterogeneous-grained adaptivity
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient pattern matching over event streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Out-of-order processing: a new architecture for high-performance stream systems
Proceedings of the VLDB Endowment
AdaptWID: An Adaptive, Memory-Efficient Window Aggregation Implementation
IEEE Internet Computing
Scan Statistics: Methods and Applications
Scan Statistics: Methods and Applications
Supporting views in data stream management systems
ACM Transactions on Database Systems (TODS)
S-OLAP: an OLAP system for analyzing sequence data
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Framing the question: detecting and filling spatial-temporal windows
Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming
High-performance dynamic pattern matching over disordered streams
Proceedings of the VLDB Endowment
Grand challenge: SPRINT stream processing engine as a solution
Proceedings of the 7th ACM international conference on Distributed event-based systems
Hi-index | 0.00 |
We are interested in detecting episodes in a data stream that are characterized by a period of time over which a condition holds, usually with a minimum duration. For example, we might want to know whenever any router has a packet-drop rate above 0.3% continuously for more than two minutes. Such episodes can be interesting in their own right for monitoring purposes, but they can also specify target regions for examination over the original or other stream. For instance, for each router-drop episode we detect, we might want to count the number of control messages the router received. We assert the key requirements are to detect the episodes, detect them accurately, and detect them promptly. Current capabilities for data-stream management systems (DSMSs) include functionality, such as pattern-matching and windowed aggregates, that can help with detecting some kinds of episodes. We offer a third alternative, frames, which generalizes the other two. Frames are intervals that segment a data stream into regions of interest. In contrast to windows, frame boundaries can be data dependent, such as when a predicate holds for a given duration, or the maximum and minimum values of an attribute diverge more than a certain amount. We introduce frames and their theory, plus their implementation in the NiagaraST DSMS. We then demonstrate some advantages of frames versus windows, such as better characterization of episodes, on real data sets and explore an extension, fragments, to deal with long episodes.