The Ode active database: trigger semantics and implementation

Lieuwen, B.F.; Gehani, Narain; Arlein, Robert M.

doi:10.1109/icde.1996.492190

Cited by 30 publications

(21 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Event languages for active databases [14,13,6,23,22,36] offer temporal operators including sequencing and Kleene closure, but do not support complex predicates to compare events. As we showed in this paper, such predicates are crucial in pattern definition.…”

Section: Related Workmentioning

confidence: 99%

Efficient pattern matching over event streams

Agrawal

Diao

Gyllstrom

et al. 2008

Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data

297

381

View full text Add to dashboard Cite

Pattern matching over event streams is increasingly being employed in many areas including financial services, RFIDbased inventory management, click stream analysis, and electronic health systems. While regular expression matching is well studied, pattern matching over streams presents two new challenges: Languages for pattern matching over streams are significantly richer than languages for regular expression matching. Furthermore, efficient evaluation of these pattern queries over streams requires new algorithms and optimizations: the conventional wisdom for stream query processing (i.e., using selection-join-aggregation) is inadequate.In this paper, we present a formal evaluation model that offers precise semantics for this new class of queries and a query evaluation framework permitting optimizations in a principled way. We further analyze the runtime complexity of query evaluation using this model and develop a suite of techniques that improve runtime efficiency by exploiting sharing in storage and processing. Our experimental results provide insights into the various factors on runtime performance and demonstrate the significant performance gains of our sharing techniques.

show abstract

Section: Related Workmentioning

confidence: 99%

Efficient pattern matching over event streams

Agrawal

Diao

Gyllstrom

et al. 2008

Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data

297

381

View full text Add to dashboard Cite

show abstract

“…If the corresponding event occurs, the event mask is evaluated, and if it evaluates to true then the trigger is fired. Given that Ode identifies both complex and primitive events using the same extended finite state machine mechanism [29], it takes exactly the same amount of time to detect that an event of interest has occurred whether the event is simple or complex unless masks (conditions) must be evaluated. 6 If a mask involves an expensive computation or if several masks must be evaluated, identifying a composite event will take proportionately more time.…”

Section: Results For Odementioning

confidence: 99%

“…The systems support different sets of composite event constructors; however most of those required for the BEAST tests can be expressed in the rule definition language of each system. The systems use four different techniques for composite event detection: arrays (ACOOD, [15]), extended finite state machines (Ode, [19,29]), event graphs (NAOS, [10]), and Petri Nets (SAMOS, [17]). The provided consumption modes [9] are chronicle (Ode, SAMOS), continuous (NAOS), and recent (ACOOD).…”

Section: The Tested Prototypesmentioning

confidence: 99%

“…• ACOOD [2,15] is built on top of the commercial OODBMS ONTOS DB 3.0, • NAOS [11,10] which extends the OODBMS O 2 [1] (version 4.5.6), • Ode [29] exists in two variants: a disk-based version built on EOS [4] and a main-memory version built on Dali [26] (the disk-based one was benchmarked), and • SAMOS [18] uses the commercial OODBMS ObjectStore as a platform.…”

Section: The Tested Prototypesmentioning

confidence: 99%

“…In this paper we therefore first identify performance-critical aspects of ADBMS and then introduce the BEAST benchmark [22] (BEnchmark for Active database SysTems). We also report on results obtained from applying BEAST to four object-oriented ADBMS (ACOOD [2], NAOS [11], Ode [29], and SAMOS [18]). 1 We interpret the benchmark results obtained for each system and make some general conclusions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance evaluation of object-oriented active database systems using the BEAST benchmark

Geppert

Berndtsson

Lieuwen³

et al. 1998

Theory Pract. Obj. Syst.

View full text Add to dashboard Cite

Ode Triggers: Monitoring the Stock Market

Gehani¹,

Lieuwen²

1997

Softw: Pract. Exper.

View full text Add to dashboard Cite

SUMMARYThe Ode object-oriented database provides sophisticated trigger facilities. We illustrate their use and capabilities by writing a non-trivial trigger application-a stock market monitoring application. We will then describe our experiences in using the Ode trigger facilities to build the stock monitoring application. Our experiences with the performance and expressiveness of Ode triggers reveal advantages and limitations of the current system. Finally, we propose future work to remove the limitations.

show abstract

The Ode active database: trigger semantics and implementation

Cited by 30 publications

References 9 publications

Efficient pattern matching over event streams

Efficient pattern matching over event streams

Performance evaluation of object-oriented active database systems using the BEAST benchmark

Ode Triggers: Monitoring the Stock Market

Contact Info

Product

Resources

About