Browsing by Author "Rundensteiner, E. A."

Now showing 1 - 4 of 4

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E-Cube: multi-dimensional event sequence processing using concept and pattern hierarchies
(IEEE, 2010) Liu, M.; Rundensteiner, E. A.; Greenfield, K.; Gupta, C.; Wang, S.; Arı, İsmail; Mehta, A.; Computer Science; ARI, Ismail
Many modern applications including tag based mass transit systems, RFID-based supply chain management systems and online financial feeds require special purpose event stream processing technology to analyze vast amounts of sequential multi-dimensional data available in real-time data feeds. Traditional online analytical processing (OLAP) systems are not designed for real-time pattern-based operations, while Complex Event Processing (CEP) systems are designed for sequence detection and do not support OLAP operations. We will demonstrate a novel E-Cube model that combines CEP and OLAP techniques for multi-dimensional event pattern analysis at different abstraction levels. A London transit scenario will be given to demonstrate the utility and performance of this proposed technology.
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High-performance complex event processing using continuous sliding views
(ACM, 2013) Ray, M.; Rundensteiner, E. A.; Liu, M.; Gupta, C.; Wang, S.; Arı, İsmail; Computer Science; ARI, Ismail
Complex Event Processing (CEP) has become increasingly important for tracking and monitoring anomalies and trends in event streams emitted from business processes such as supply chain management to online stores in e-commerce. These monitoring applications submit complex event queries to track sequences of events that match a given pattern. While the state-of-the-art CEP systems mostly focus on the execution of flat sequence queries, we instead support the execution of nested CEP queries specified by the (NEsted Event Language) NEEL. However the iterative execution often results in the repeated recomputation of similar or even identical results for nested subexpressions as the window slides over the event stream. In this work we thus propose to optimize NEEL execution performance by caching intermediate results. In particular we design two methods of applying selective caching of intermediate results. The first is the Continuous Sliding Caching technique. The second is a further optimization of the previous technique which we call the Interval-Driven Semantic Caching. Techniques for incrementally loading, purging and exploiting the cache content are described. Our experimental study using real-world stock trades evaluates the performance of our proposed caching strategies for different query types.
Metadata only
NEEL: The nested complex event language for real-time event analytics
(Springer International Publishing, 2011) Liu, M.; Rundensteiner, E. A.; Dougherty, D.; Gupta, C.; Wang, S.; Arı, İsmail; Mehta, A.; Computer Science; ARI, Ismail
Complex event processing (CEP) over event streams has become increasingly important for real-time applications ranging from health care, supply chain management to business intelligence. These monitoring applications submit complex event queries to track sequences of events that match a given pattern. As these systems mature the need for increasingly complex nested sequence query support arises, while the state-of-art CEP systems mostly support the execution of only flat sequence queries. In this paper, we introduce our nested CEP query language NEEL for expressing nested queries composed of sequence, negation, AND and OR operators. Thereafter, we also define its formal semantics. Subtle issues with negation and predicates within the nested sequence context are discussed. An E-Analytics system for processing nested CEP queries expressed in the NEEL language has been developed. Lastly, we demonstrate the utility of this technology by describing a case study of applying this technology to a real-world application in health care.
Open Access
Processing nested complex sequence pattern queries over event streams
(ACM, 2010) Liu, M.; Ray, M.; Rundensteiner, E. A.; Dougherty, D. J.; Gupta, C.; Wang, S.; Arı, İsmail; Mehta, A.; Computer Science; ARI, Ismail
Complex event processing (CEP) has become increasingly important for tracking and monitoring applications ranging from healthcare, supply chain management to surveillance. These monitoring applications submit complex event queries to track sequences of events that match a given pattern. As these systems mature the needfor increasingly complex nested sequence queries arises, while thestate-of-the-art CEP systems mostly focus on the execution of flat sequence queries only. In this paper, we now introduce an iterative execution strategy for nested CEP queries composed of sequence, negation, AND and OR operators. Lastly the promise of applying selective caching of intermediate results to optimize the execution. Our experimental study using real-world stock trades evaluates the performance of our proposed iterative execution strategy for differentquery types.