Complex event processing over streaming multi-cloud platforms

Flouris, Ioannis; Manikaki, Vasiliki; Giatrakos, Nikos; Deligiannakis, Antonios; Garofalakis, Minos; Mock, Michael; Bothe, Sebastian; Skarbovsky, Inna; Fournier, Fabiana; Stajcer, Marko; Krizan, Tomislav; Yom-Tov, Jonathan; Volarević, Marijo

doi:10.1145/2933267.2933289

Cited by 10 publications

(27 citation statements)

References 2 publications

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“…The approach in FERARI [12,13] will attempt to avoid continuous central data collection by constructing in-situ filters that are not uncertainty-aware, in the sense that they consider all potentially premium locations as surely premium. Assuming a city with N antennas, an in-situ filter for each site will be: each antenna A i suppresses communication if the local count X i of calls of callerID is at most T N , where T is the threshold on the call count in the given query.…”

Section: Overview and Motivationmentioning

confidence: 99%

“…As a proof-of-concept of the applicability of our approach on real CEP engines, we comment on how our techniques bridge the gap between two recent prototypes that extend the same CEP engine, that is IBM Proton [1], one towards uncertainty handling [7], while the other [12,13] towards distributed settings.…”

Section: Proof-of-conceptmentioning

confidence: 99%

“…Complementarily to the approach by Correia et al [7], which enriches Proton with uncertainty-aware querying and processing functionality, the FERARI platform [12,13] presents extensions to Proton that enable it to operate both (a) exploiting parallelization opportunities within corporate data centers, public or hybrid clouds and (b) in a geo-distributed manner, i.e., performing CEP across a network of sites.…”

Section: The Ferari Platformmentioning

confidence: 99%

“…Our approach is backed by probability theory and communication protocols. Past research efforts and prototypes have focused on one of these aspects, either reducing communication during CEP procedures over distributed settings [12,13] or synthesizing probabilities of SDEs so as to produce an overall probability of the output CE (for instance, see [9,32]). Hence, to our knowledge, our techniques are the first that elaborate on communication-efficient, uncertainty-aware event analytics over distributed settings.…”

Section: Introductionmentioning

confidence: 99%

“…• As a proof-of-concept of the applicability of our techniques in real systems, we show how we bridge the gap between two recent prototypes. The FERARI streaming multi-cloud platform [12,13], which provides communication-efficient event analytics over distributed settings but neglects uncertainty aspects, and the work by Correia et al [7] which elaborates on handling event uncertainty but requires continuous event transmissions, not accounting for efficiency over distributed settings. Both prototypes utilize IBM PROactive Technology ONline (Proton) [1] as their CEP engine and each extend it towards the respective aspect.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Uncertainty-Aware Event Analytics over Distributed Settings

Giatrakos

Artikis

Deligiannakis

et al. 2019

Proceedings of the 13th ACM International Conference on Distributed and Event-Based Systems

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In complex event processing (CEP), simple derived event (SDE) tuples are combined in pattern matching procedures to derive complex events (CEs) of interest. Big Data applications analyze event streams online and extract CEs to support decision making procedures. At massive scale, such applications operate over distributed networks of sites where efficient CEP requires reducing communication as much as possible. Besides, events often encompass various types of uncertainty assigned on event attribute values, occurrence or detection rules. Therefore, massively distributed Big event Data applications in a world of uncertain events call for communicationefficient, uncertainty-aware CEP solutions, which is the focus of this work. As a proof-of-concept, we show how we bridge the gap between two recent CEP prototypes which utilize IBM PROactive Technology ONline as their CEP engine and each extend it towards only one of the dimensions of distribution and uncertainty. CCS CONCEPTS • Information systems → Data streams; • Applied computing → Event-driven architectures; • Computer systems organization → Distributed architectures.

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Section: Overview and Motivationmentioning

confidence: 99%

Section: Proof-of-conceptmentioning

confidence: 99%

Section: The Ferari Platformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Uncertainty-Aware Event Analytics over Distributed Settings

Giatrakos

Artikis

Deligiannakis

et al. 2019

Proceedings of the 13th ACM International Conference on Distributed and Event-Based Systems

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Event-Driven Business Process Management Enhancing IoT – A Systematic Literature Review and Development of Research Agenda

Stoiber

Schönig

2021

Lecture Notes in Information Systems and Organisation

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The integration of high frequency event data from Internet of Things (IoT) devices into existing complex and mature Business Process Management Systems (BPMS) constitutes a major hurdle for many organizations. Event-Driven Business Process Management (EDBPM) is a paradigm to tackle this hurdle and to lever the enhancement of industrial IoT applications. Existing literature regarding EDBPM and its underlying technologies and methods form a heterogenous set of approaches, frameworks and applications that lacks standardization and maturity. In this context, the literature review of the work at hand conducts a survey about EDBPM focusing on its capabilities to be a lever for the scale of IoT applications. First, we perform an extensive literature research on EDBPM and related topics. Second, a literature analysis and synthesis are presented by summarizing and clustering the discovered publications. Furthermore, a future research agenda is formulated that addresses the main existing research gaps and challenges of EDBPM.

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Distributed Complex Event Processing in Multiclouds

Stefanidis

Verginadis

Patiniotakis

et al. 2018

Lecture Notes in Computer Science

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The last few years, the generation of vast amounts of heterogeneous data with different velocity and veracity and the requirement to process them, has significantly challenged the computational capacity and efficiency of the modern infrastructural resources. The propagation of Big Data among different processing and storage architectures, has amplified the need for adequate and costefficient infrastructures to host them. An overabundance of cloud service offerings is currently available and is being rapidly adopted by small and medium enterprises based on its many benefits to traditional computing models. However, at the same time the Big Data computing requirements pose new research challenges that question the adoption of single cloud provider resources. Nowadays, we discuss the emerging data-intensive applications that necessitate the wide adoption of multicloud deployment models, in order to use all the advantages of cloud computing. A key tool for managing such multicloud applications and guarantying their quality of service, even in extreme scenarios of workload fluctuations, are adequate distributed monitoring mechanisms. In this work, we discuss a distributed complex event processing architecture that follows automatically the big data application deployment in order to efficiently monitor its health status and detect reconfiguration opportunities. This proposal is examined against an illustrative scenario and is preliminary evaluated for revealing its performance results.

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Complex event processing over streaming multi-cloud platforms

Cited by 10 publications

References 2 publications

Uncertainty-Aware Event Analytics over Distributed Settings

Uncertainty-Aware Event Analytics over Distributed Settings

Event-Driven Business Process Management Enhancing IoT – A Systematic Literature Review and Development of Research Agenda

Distributed Complex Event Processing in Multiclouds

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