An algebra for pattern matching, time-aware aggregates and partitions on relational data streams

Herbst, Sebastian; Pollner, Niko; Tenschert, Johannes; Lauterwald, Frank; Endler, Gregor; Meyer-Wegener, Klaus

doi:10.1145/2675743.2771830

Cited by 5 publications

(8 citation statements)

References 19 publications

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“…Users request their queries in the Cayuga Event Language (CEL) format. Unlike other stream database systems that support query processing for sliding windows (such as TelegraphCQ), Cayuga does not support sliding windows [Herbst et al 2015]. Although, Cayuga supports detection of sequential tuples for event streams [Demers et al 2007], it can not detect successive events within a speci c time interval [Li et al 2011].…”

Section: F Stream Processing Systems and Enginesmentioning

confidence: 99%

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

2018

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Network-enabled sensing and actuation devices are key enablers to connect real-world objects to the cyber world. The Internet of Things (IoT) consists of the network-enabled devices and communication technologies that allow connectivity and integration of physical objects (Things) into the digital world (Internet). Enormous amounts of dynamic IoT data are collected from Internet-connected devices. IoT data is usually multi-variant streams that are heterogeneous, sporadic, multi-modal and spatio-temporal. IoT data can be disseminated with di erent granularities and have diverse structures, types and qualities. Dealing with the data deluge from heterogeneous IoT resources and services imposes new challenges on indexing, discovery and ranking mechanisms that will allow building applications that require on-line access and retrieval of ad-hoc IoT data. However, the existing IoT data indexing and discovery approaches are complex or centralised which hinders their scalability. The primary objective of this paper is to provide a holistic overview of the state-of-the-art on indexing, discovery and ranking of IoT data. The paper aims to pave the way for researchers to design, develop, implement and evaluate techniques and approaches for on-line large-scale distributed IoT applications and services.

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Section: F Stream Processing Systems and Enginesmentioning

confidence: 99%

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

2018

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“…We argued [15] that some aggregates require the window operation to keep the time information. To compute the average speed for instance, the time stamps of the measurements are needed.…”

Section: Time-based Windowsmentioning

confidence: 99%

“…They consume a stream of events of interest and create a stream of complex events. As we elaborated [15], it is useful to split pattern matching into three logical units:…”

Section: Pattern Matchingmentioning

confidence: 99%

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Sequences, yet Functions: The Dual Nature of Data-Stream Processing

Herbst,

Tenschert,

Wahl

et al. 2018

Preprint

Self Cite

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Data-stream processing has continuously risen in importance as the amount of available data has been steadily increasing over the last decade. Besides traditional domains such as data-center monitoring and click analytics, there is an increasing number of network-enabled production machines that generate continuous streams of data.Due to their continuous nature, queries on data-streams can be more complex, and distinctly harder to understand then database queries. As users have to consider operational details, maintenance and debugging become challenging.Current approaches model data-streams as sequences, because this is the way they are physically received. These models result in an implementation-focused perspective.We explore an alternate way of modeling data-streams by focusing on time-slicing semantics. This focus results in a model based on functions, which is better suited for reasoning about query semantics.By adapting the definitions of relevant concepts in stream processing to our model, we illustrate the practical usefulness of our approach. Thereby, we link data-streams and query primitives to concepts in functional programming and mathematics. Most noteworthy, we prove that data-streams are monads, and show how to derive monad definitions for current data-stream models.We provide an abstract, yet practical perspective on datastream related subjects based on a sound, consistent query model. Our work can serve as solid foundation for future data-stream query-languages.

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“…These models attach semantics to queries written using these languages, but they generally cannot be applied to other contexts without significant adaptation. More recent research has targeted the development of language-independent formalisms for CEP [Krämer and Seeger 2009;Herbst et al 2015]. These authors recognized the importance of a generic model to enable formal analysis of user-defined queries.…”

Section: Cep Formal Modelsmentioning

confidence: 99%

Attributed Graph Rewriting for Complex Event Processing Self-Management

Higashino

Eichler²,

Capretz

et al. 2016

ACM Trans. Auton. Adapt. Syst.

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The use of Complex Event Processing (CEP) and Stream Processing (SP) systems to process high-volume, high-velocity Big Data has renewed interest in procedures for managing these systems. In particular, selfmanagement and adaptation of runtime platforms have been common research themes, as most of these systems run under dynamic conditions. Nevertheless, the research landscape in this area is still young and fragmented. Most research is performed in the context of specific systems, and it is difficult to generalize the results obtained to other contexts. To enable generic and reusable CEP/SP system management procedures and self-management policies, this research introduces the Attributed Graph Rewriting for Complex Event Processing Management (AGeCEP) formalism. AGeCEP represents queries in a language-and technologyagnostic fashion using attributed graphs. Query reconfiguration capabilities are expressed through standardized attributes, which are defined based on a novel classification of CEP query operators. By leveraging this representation, AGeCEP also proposes graph rewriting rules to define consistent reconfigurations of queries. To demonstrate AGeCEP feasibility, this research has used it to design an autonomic manager and to define a selected set of self-management policies. Finally, experiments demonstrate that AGeCEP can indeed be used to develop algorithms that can be integrated into diverse CEP systems.

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An algebra for pattern matching, time-aware aggregates and partitions on relational data streams

Cited by 5 publications

References 19 publications

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

Sequences, yet Functions: The Dual Nature of Data-Stream Processing

Attributed Graph Rewriting for Complex Event Processing Self-Management

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