Efficient processing of multiple nested event pattern queries over multi-dimensional event streams based on a triaxial hierarchical model

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Highlights

  • A triaxial hierarchy is proposed to realise the relationships among queries.

  • A novel strategy (MQOS) is proposed to find an optimised query execution plan.

  • MQOS strategy outperforms other methods even facing the burst input rates.

Abstract

Objective

For efficient and sophisticated analysis of complex event patterns that appear in streams of big data from health care information systems and support for decision-making, a triaxial hierarchical model is proposed in this paper.

Methods and material

Our triaxial hierarchical model is developed by focusing on hierarchies among nested event pattern queries with an event concept hierarchy, thereby allowing us to identify the relationships among the expressions and sub-expressions of the queries extensively. We devise a cost-based heuristic by means of the triaxial hierarchical model to find an optimised query execution plan in terms of the costs of both the operators and the communications between them. According to the triaxial hierarchical model, we can also calculate how to reuse the results of the common sub-expressions in multiple queries. By integrating the optimised query execution plan with the reuse schemes, a multi-query optimisation strategy is developed to accomplish efficient processing of multiple nested event pattern queries.

Results

We present empirical studies in which the performance of multi-query optimisation strategy was examined under various stream input rates and workloads. Specifically, the workloads of pattern queries can be used for supporting monitoring patients’ conditions. On the other hand, experiments with varying input rates of streams can correspond to changes of the numbers of patients that a system should manage, whereas burst input rates can correspond to changes of rushes of patients to be taken care of. The experimental results have shown that, in Workload 1, our proposal can improve about 4 and 2 times throughput comparing with the relative works, respectively; in Workload 2, our proposal can improve about 3 and 2 times throughput comparing with the relative works, respectively; in Workload 3, our proposal can improve about 6 times throughput comparing with the relative work.

Conclusion

The experimental results demonstrated that our proposal was able to process complex queries efficiently which can support health information systems and further decision-making.

Section snippets

Preliminaries

In this section, we briefly introduce the basic event model, nested pattern query language, the operator types and their formal semantics based on related studies, e.g., [13], [15], [16], [21], [44].

Triaxial hierarchical model

An event concept hierarchy is commonly used to summarise information at different levels of abstraction. We extend the hierarchies in [21] and develop our triaxial hierarchical model. After stating our triaxial hierarchical model in this section, differences of our proposal from [21], [16] will be discussed at the end of this section.

Here, an event type Eni is defined as a finer level (resp. coarser level) of an event type En (resp. Enij) in an event concept hierarchy which can be expressed as E

Query processing plans

For the related query processing plans compared with our MQOS in the experiments, E-cube [21], Naive, and Middle-to-both-sides were chosen and implemented in the experiments. To our knowledge, E-cube is currently the best method for processing multiple pattern queries that have pure SEQ operators. However, E-cube cannot process complex pattern queries involving nests of sequences (SEQ) and conjunctions (AND), which can have negative event type(s). Therefore, we choose the Naive and the

Conclusions

MQOS integrates OLAP with CEP functionalities to realise (i) technologies that allow users to efficiently process large amounts of event stream data in multi-dimensions, each of which could be at different levels of abstraction, and (ii) technologies that allow CEP systems to process nested pattern queries by leveraging appropriate replicas of common operators’ results. The experimental results showed that MQOS has faster processing time and higher throughput than E-cube, Naive and

Acknowledgements

The authors greatly appreciate the reviewers’ valuable comments. This research was partially supported by the Fundamental Research Funds for the Central Universities (No. XDJK2015C107), the Doctoral Program of Higher Education (No. SWU115008), National Natural Science Foundation of China (Grant Nos. 61573290, 61503237), and JSPS KAKENHI Grant (No. 15H02705).

We thank TIBCO StreamBase who provides “StreamBase Education Licensing Program (http://www.streambase.com/community/streambase-university/

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  • Cited by (0)

    This paper is based on our preliminary work [15] published in International Computer Software & Applications Conference (COMPSAC), 2013.

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