TY - GEN
T1 - Efficient pattern detection over a distributed framework
AU - Leghari, Ahmed Khan
AU - Wolf, Martin
AU - Zhou, Yongluan
PY - 2015
Y1 - 2015
N2 - In recent past, work has been done to parallelize pattern detection queries over event stream, by partitioning the event stream on certain keys or attributes. In such partitioning schemes the degree of parallelization totally relies on the available partition keys. A limited number of partitioning keys, or unavailability of such partitioning attributes noticeably affect the distribution of data among multiple nodes, and is a reason of potential data skew and improper resource utilization. Moreover, majority of the past implementations of complex event detection are based on a single machine, hence, they are immune to potential data skew that could be seen in a real distributed environment. In this study, we propose an event stream partitioning scheme that without considering any key attributes partitions the stream over time-windows. This scheme efficiently distributes the event stream partitions across network, and detects pattern sequences in distributed fashion.
AB - In recent past, work has been done to parallelize pattern detection queries over event stream, by partitioning the event stream on certain keys or attributes. In such partitioning schemes the degree of parallelization totally relies on the available partition keys. A limited number of partitioning keys, or unavailability of such partitioning attributes noticeably affect the distribution of data among multiple nodes, and is a reason of potential data skew and improper resource utilization. Moreover, majority of the past implementations of complex event detection are based on a single machine, hence, they are immune to potential data skew that could be seen in a real distributed environment. In this study, we propose an event stream partitioning scheme that without considering any key attributes partitions the stream over time-windows. This scheme efficiently distributes the event stream partitions across network, and detects pattern sequences in distributed fashion.
U2 - 10.1007/978-3-662-46839-5_9
DO - 10.1007/978-3-662-46839-5_9
M3 - Article in proceedings
SN - 978-3-662-46838-8
T3 - Lecture Notes in Business Information Processing
SP - 133
EP - 149
BT - Enabling Real-Time Business Intelligence
A2 - Castellanos, Malu
A2 - Dayal, Umeshwar
A2 - Pedersen, Torben Bach
A2 - Tatbul, Nesime
PB - Springer
T2 - 2014 Workshop on Business Intelligence for the Real-Time Enterprise
Y2 - 1 September 2014 through 1 September 2014
ER -