On the Expressiveness of Languages for Complex Event Recognition

Grez, Alejandro; Riveros, Cristian; Ugarte, Martín; Vansummeren, Stijn

doi:10.4230/lipics.icdt.2020.15

Cited by 7 publications

(4 citation statements)

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“…However, the two have fundamental differences. Of interest to the present discussion are the following: languages used in CEP are not based on temporal logic (in fact, they often lack clear semantics [53]) which makes a direct comparison difficult; the capability of CEP to evaluate sequential patterns is typically limited, while of central importance to RV [57]-we refer to [57] for a more detailed discussion. Therefore, RV is considerably more relevant to InTempo.…”

Section: Related Workmentioning

confidence: 99%

Incremental execution of temporal graph queries over runtime models with history and its applications

et al. 2021

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Modern software systems are intricate and operate in highly dynamic environments for which few assumptions can be made at design-time. This setting has sparked an interest in solutions that use a runtime model which reflects the system state and operational context to monitor and adapt the system in reaction to changes during its runtime. Few solutions focus on the evolution of the model over time, i.e., its history, although history is required for monitoring temporal behaviors and may enable more informed decision-making. One reason is that handling the history of a runtime model poses an important technical challenge, as it requires tracing a part of the model over multiple model snapshots in a timely manner. Additionally, the runtime setting calls for memory-efficient measures to store and check these snapshots. Following the common practice of representing a runtime model as a typed attributed graph, we introduce a language which supports the formulation of temporal graph queries, i.e., queries on the ordering and timing in which structural changes in the history of a runtime model occurred. We present a querying scheme for the execution of temporal graph queries over history-aware runtime models. Features such as temporal logic operators in queries, the incremental execution, the option to discard history that is no longer relevant to queries, and the in-memory storage of the model, distinguish our scheme from relevant solutions. By incorporating temporal operators, temporal graph queries can be used for runtime monitoring of temporal logic formulas. Building on this capability, we present an implementation of the scheme that is evaluated for runtime querying, monitoring, and adaptation scenarios from two application domains.

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Section: Related Workmentioning

confidence: 99%

Incremental execution of temporal graph queries over runtime models with history and its applications

et al. 2021

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“…A transition is followed if its formula, applied to the current input, evaluates to true. The work presented in [10,29] may also be categorized under this class of "unary" symbolic automata (or transducers, to be more precise). Contrary to register automata, symbolic automata have nice closure properties, but their formulas are unary and thus can only be applied to a single element from the input string.…”

Section: Related Workmentioning

confidence: 99%

Symbolic Register Automata for Complex Event Recognition and Forecasting

Alevizos¹,

Artikis²,

Παλιούρας³

2021

Preprint

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We propose an automaton model which is a combination of symbolic and register automata, i.e., we enrich symbolic automata with memory. We call such automata Symbolic Register Automata (SRA). SRA extend the expressive power of symbolic automata, by allowing Boolean formulas to be applied not only to the last element read from the input string, but to multiple elements, stored in their registers. SRA also extend register automata, by allowing arbitrary Boolean formulas, besides equality predicates. We study the closure properties of SRA under union, intersection, concatenation, Kleene closure, complement and determinization and show that SRA, contrary to symbolic automata, are not in general closed under complement and they are not determinizable. However, they are closed under these operations when a window operator, quintessential in Complex Event Recognition, is used. We show how SRA can be used in Complex Event Recognition in order to detect patterns upon streams of events, using our framework that provides declarative and compositional semantics, and that allows for a systematic treatment of such automata. We also show how the behavior of SRA, as they consume streams

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“…Flink includes a native API, namely FlinkCEP [10], for CEP analytics. FlinkCEP provides a CEP language of high expressive power [7,15] and also allows for parallel processing to ensure rapid delivery of CEP analytics. Nonetheless, there are certain barriers in FlinkCEP's adoption.…”

mentioning

confidence: 99%

“…To overcome the barrier posed by coding directly in FlinkCEP, we build a novel, logical CEP operator that receives as input CEP pattern queries in the form of extended regular expressions [1,2,6,15,22,25] and seamlessly re-writes them to FlinkCEP programs. To offload domain experts from computer cluster administration decisions, we build a CEP Optimizer that automatically decides good configurations for executing the transformed FlinkCEP code on a computer cluster.…”

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confidence: 99%

EasyFlinkCEP

Giatrakos

Kougioumtzi

Kontaxakis

et al. 2021

Proceedings of the 30th ACM International Conference on Information &Amp; Knowledge Management

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FlinkCEP is the Complex Event Processing (CEP) API of the Flink Big Data platform. The high expressive power of the language of FlinkCEP comes at the cost of cumbersome parameterization of the queried patterns, acting as a barrier for FlinkCEP's adoption. Moreover, properly configuring a FlinkCEP program to run over a computer cluster requires advanced skills on modern hardware administration which non-expert programmers do not possess. In this work (i) we build a novel, logical CEP operator that receives CEP pattern queries in the form of extended regular expressions and seamlessly re-writes them to FlinkCEP programs, (ii) we build a CEP Optimizer that automatically decides good job configurations for these FlinkCEP programs. We also present an experimental evaluation which demonstrates the significant benefits of our approach.

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On the Expressiveness of Languages for Complex Event Recognition

Cited by 7 publications

References 0 publications

Incremental execution of temporal graph queries over runtime models with history and its applications

Incremental execution of temporal graph queries over runtime models with history and its applications

Symbolic Register Automata for Complex Event Recognition and Forecasting

EasyFlinkCEP

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