TY - GEN
T1 - AFilter
T2 - 32nd International Conference on Very Large Data Bases, VLDB 2006
AU - Candan, K. Selçuk
AU - Hsiung, Wang Pin
AU - Chen, Songting
AU - Tatemura, Junichi
AU - Agrawal, Divyakant
PY - 2006
Y1 - 2006
N2 - XML message filtering problem involves searching for instances of a given, potentially large, set of patterns in a continuous stream of XML messages. Since the messages arrive continuously, it is essential that the filtering rate matches the data arrival rate. Therefore, the given set of filter patterns needs to be indexed appropriately to enable real-time processing of the streaming XML data. In this paper, we propose AFilter, an adaptable, and thus scalable, path expression filtering approach. AFilter has a base memory requirement linear in filter expression and data size. Furthermore, when additional memory is available, AFilter can exploit prefix commonalities in the set of filter expressions using a loosely-coupled prefix caching mechanism as opposed to tightly-coupled active state representation of alternative approaches. Unlike existing systems, AFilter can also exploit suffix-commonalities across filter expressions, while simultaneously leveraging the prefix-commonalities through the cache. Finally, AFilter uses a triggering mechanism to prevent excessive consumption of resources by delaying processing until a trigger condition is observed. Experiment results show that AFilter provides significantly better scalability and runtime performance when compared to state of the art filtering systems.
AB - XML message filtering problem involves searching for instances of a given, potentially large, set of patterns in a continuous stream of XML messages. Since the messages arrive continuously, it is essential that the filtering rate matches the data arrival rate. Therefore, the given set of filter patterns needs to be indexed appropriately to enable real-time processing of the streaming XML data. In this paper, we propose AFilter, an adaptable, and thus scalable, path expression filtering approach. AFilter has a base memory requirement linear in filter expression and data size. Furthermore, when additional memory is available, AFilter can exploit prefix commonalities in the set of filter expressions using a loosely-coupled prefix caching mechanism as opposed to tightly-coupled active state representation of alternative approaches. Unlike existing systems, AFilter can also exploit suffix-commonalities across filter expressions, while simultaneously leveraging the prefix-commonalities through the cache. Finally, AFilter uses a triggering mechanism to prevent excessive consumption of resources by delaying processing until a trigger condition is observed. Experiment results show that AFilter provides significantly better scalability and runtime performance when compared to state of the art filtering systems.
UR - http://www.scopus.com/inward/record.url?scp=84893844864&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893844864&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84893844864
SN - 1595933859
SN - 9781595933850
T3 - VLDB 2006 - Proceedings of the 32nd International Conference on Very Large Data Bases
SP - 559
EP - 570
BT - VLDB 2006 - Proceedings of the 32nd International Conference on Very Large Data Bases
PB - Association for Computing Machinery
Y2 - 12 September 2006 through 15 September 2006
ER -