Efficient processing of multiple nested event pattern queries over multi-dimensional event streams based on a triaxial hierarchical model☆
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/
References (46)
- et al.
Heart monitoring systems – a review
Comput Biol Med
(2014) - et al.
Smart medical environment at the point of care: auto-tracking clinical interventions at the bed side using RFID technology
Comput Biol Med
(2010) - et al.
Monitoring streams: a new class of data management applications
- et al.
Tracking the social dimensions of RFID systems in hospitals
Int J Med Inform
(2008) - et al.
Real-time location and inpatient care systems based on passive RFID
J Netw Comput Appl
(2011) Patient care information systems and health care work: a sociotechnical approach
Int J Med Inform
(1999)Implementing information systems in health care organizations: myths and challenges
Int J Med Inform
(2001)Health information systems-past, present, future
Int J Med Inform
(2006)- et al.
Temporal abstraction and temporal Bayesian networks in clinical domains: a survey
Artif Intell Med
(2014) - et al.
Temporal abstraction in intelligent clinical data analysis: a survey
Artif Intell Med
(2007)