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On static determination of temporal relevance for incremental evaluation of complex event queries
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Source Distributed event-based systems; Vol. 332 archive
Proceedings of the second international conference on Distributed event-based systems table of contents
Rome, Italy
SESSION: Complex event processing and streaming queries table of contents
Pages 289-300  
Year of Publication: 2008
ISBN:978-1-60558-090-6
Authors
François Bry  University of Munich
Michael Eckert  University of Munich
Sponsors
: IEEE
: ACM
: USENIX
IFIP : International Federation for Information Processing
SIGSOFT: ACM Special Interest Group on Software Engineering
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
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ABSTRACT

Evaluation of complex event queries over time involves storing information about those events that are relevant for, i.e., might contribute to, future answers. We call the period of time for which an event or an intermediate result must (at least) be stored its temporal relevance. This paper pioneers a precise definition of temporal relevance and develops a method for statically (i.e., at compile time) determining it. During query evaluation (i.e., at run time), this enables garbage collection of events that become irrelevant as time progresses. Temporal relevance is also important at compile time for cost-based query planning.


REFERENCES

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1
R. Adaikkalavan and S. Chakravarthy. SnoopIB: Interval-based event specification and detection for active databases. Data and Knowledge Eng., 59(1), 2006.
 
2
A. Adi and O. Etzion. Amit --- the situation manager. Int. J. on Very Large Data Bases, 13(2), 2004.
 
3
J. F. Allen. Maintaining Knowledge About Temporal Intervals. Communications of the ACM, 26(11), 1983.
 
4
A. Arasu, S. Babu, and J. Widom. The CQL continuous query language: Semantic foundations and query execution. VLDB Journal, 15(2):121--142, 2006.
 
5
S. Babu, U. Srivastava, and J. Widom. Exploiting k-constraints to reduce memory overhead in continuous queries over data streams. ACM Trans. Database Syst., 29(3):545--580, 2004.
 
6
F. Bry and M. Eckert. A high-level query language for events. In Proc. Int. Workshop on Event-driven Architecture, Processing and Systems, 2006.
 
7
F. Bry and M. Eckert. Rule-Based Composite Event Queries: The Language XChangeEQ and its Semantics. In Proc. Int. Conf. on Web Reasoning and Rule Systems, 2007.
 
8
F. Bry and M. Eckert. Towards formal foundations of event queries and rules. In Proc. Int. Workshop on Event-Driven Architecture, Processing and Systems, 2007.
 
9
F. Bry, M. Eckert, and P.-L. Pǎtrânjan. Querying composite events for reactivity on the Web. In Proc. Int. Workshop on XML Research and Applications, 2006.
 
10
A. P. Buchmann, J. Zimmermann, J. A. Blakeley, and D. L. Wells. Building an integrated active OODBMS: Requirements, architecture, and design decisions. In Proc. Int. Conf. on Data Engineering, 1995.
 
11
J. Carlson and B. Lisper. An event detection algebra for reactive systems. In Proc. ACM Int. Conf. On Embedded Software, pages 147--154, 2004.
 
12
S. Chakravarthy, V. Krishnaprasad, E. Anwar, and S.-K. Kim. Composite events for active databases: Semantics, contexts and detection. In Proc. Int. Conf. on Very Large Data Bases, 1994.
 
13
M. Fisher, D. Gabbay, and L. Vila, editors. Handbook of Temporal Reasoning in Artificial Intelligence. Elsevier, 2005.
 
14
C. L. Forgy. A fast algorithm for the many pattern/many object pattern match problem. Artif. Intelligence, 19(1), 1982.
 
15
H. Garcia-Molina, J. Ullman, and J. Widom. Database Systems: The Complete Book. Prentice Hall, 2001.
 
16
S. Gatziu and K. R. Dittrich. Events in an active object-oriented database system. In Proc. Int. Workshop on Rules in Database Systems, 1993.
 
17
N. H. Gehani, H. V. Jagadish, and O. Shmueli. Composite event specification in active databases: Model & implementation. In Proc. Int. Conf. on Very Large Data Bases, 1992.
 
18
T. Griffin and L. Libkin. Incremental maintenance of views with duplicates. In Proc. Int. Conf. on Management of Data (SIGMOD), 1995.
 
19
A. Hinze and A. Voisard. A parameterized algebra for event notification services. In Proc. Int. Symp. on Temporal Representation and Reasoning, 2002.
 
20
D. P. Miranker. TREAT: A better match algorithm for AI production system matching. In Proc. AAAI Natl. Conf. on Artificial Intelligence, 1987.
 
21
R. Sedgewick. Algorithms in C. Addison Wesley, 1990.
 
22
D. Zhu and A. S. Sethi. SEL, a new event pattern specification language for event correlation. In Proc. Int. Conf. on Computer Communications and Networks, 2001.

Collaborative Colleagues:
François Bry: colleagues
Michael Eckert: colleagues