Towards hybrid online on-demand querying of realtime data with stateful complex event processing

Zhou, Qunzhi; Simmhan, Yogesh; Prasanna, Viktor K.

doi:10.1109/bigdata.2013.6691575

Cited by 12 publications

(8 citation statements)

References 16 publications

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“…Event algebras are also proposed in related approaches by Zimmer and Unland [25], Eckert et al [8] and Anicic et al [2]. Events are typically modeled as data tuples consisting of attributes, values and timestamps, such as the 3-tuple by Voisard and Ziekow [21], the 2-tuple by Zhou et al [24] or XML schema [3]. The E* event model [9] is useful for modeling multimedia events.…”

Section: Related Workmentioning

confidence: 97%

The process-oriented event model (PoEM)

Patri

Sorathia²,

Panangadan

et al. 2014

Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems

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The paper presents a comprehensive theoretical framework for modeling events and semantics of event processing at a level of abstraction that captures the different processes in industrial applications but is not limited to a specific application domain. The model, called Process-oriented Event Model (PoEM), provides a formal approach to model realworld entities and their interrelationships, and specifies the process of moving from data streams to event detection to event-based goal planning. The model links event detection to states, actions, and roles, enabling event notification, filtering, context awareness and escalation. PoEM defines event and non-event concepts and combines information from them to build an event processing workflow. Usage of the PoEM model is illustrated in case studies from the oil and gas industry and maritime piracy events.

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Section: Related Workmentioning

confidence: 97%

The process-oriented event model (PoEM)

Patri

Sorathia²,

Panangadan

et al. 2014

Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems

View full text Add to dashboard Cite

show abstract

“…It supports reasoning on both functional and non-functional properties (including geospatial reasoning), but the composition relies on the stream type specification, not the exact processing pattern. In H2O [51] a hybrid processing mechanism is proposed, in which persistent queries that keeps monitoring fine-grained data (online queries) and infrequent queries over occasional events (on-demand queries) are modelled and processed on different levels. The online queries provide partial results to be used by on-demand queries.…”

Section: On-demand/unified Event Stream Processingmentioning

confidence: 99%

Automated discovery and integration of semantic urban data streams: The ACEIS middleware

Gao

Ali

Curry

et al. 2017

Future Generation Computer Systems

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With the growing popularity of Internet of Things (IoT) technologies and sensors deployment, more and more cities are leaning towards smart cities solutions that can leverage this rich source of streaming data to gather knowledge that can be used to solve domain-specific problems. A key challenge that needs to be faced in this respect is the ability to automatically discover and integrate heterogeneous sensor data streams on the fly for applications to use them. To provide a domain-independent platform and take full benefits from semantic technologies, in this paper we present an Automated Complex Event Implementation System (ACEIS), which serves as a middleware between sensor data streams and smart city applications. ACEIS not only automatically discovers and composes IoT streams in urban infrastructures for users' requirements expressed as complex event requests, but also automatically generates stream queries in order to detect the requested complex events, bridging the gap between high-level application users and low-level information sources. We also demonstrate the use of ACEIS in a smart travel planner scenario using real-world sensor devices and datasets.

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“…Such an integrated analysis of historical and streaming data is required by many emerging applications including network monitoring for intrusion detection [23,4], financial trading, real-time bidding [28], and traffic monitoring [25]. To address the demands of such applications, data stream warehousing systems, such as TidalRace [16], have recently emerged.…”

Section: Introductionmentioning

confidence: 99%

Estimating quantiles from the union of historical and streaming data

2016

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Modern enterprises generate huge amounts of streaming data, for example, micro-blog feeds, financial data, network monitoring and industrial application monitoring. While Data Stream Management Systems have proven successful in providing support for real-time alerting, many applications, such as network monitoring for intrusion detection and real-time bidding, require complex analytics over historical and real-time data over the data streams. We present a new method to process one of the most fundamental analytical primitives, quantile queries, on the union of historical and streaming data. Our method combines an index on historical data with a memory-efficient sketch on streaming data to answer quantile queries with accuracy-resource tradeoffs that are significantly better than current solutions that are based solely on disk-resident indexes or solely on streaming algorithms. Disciplines Electrical and Computer Engineering CommentsThis is a manuscript of a proceeding published as Singh, Sneha Aman, Divesh Srivastava, and Srikanta Tirthapura. "Estimating quantiles from the union of historical and streaming data. Systems have proven successful in providing support for real-time alerting, many applications, such as network monitoring for intrusion detection and real-time bidding, require complex analytics over historical and real-time data over the data streams. We present a new method to process one of the most fundamental analytical primitives, quantile queries, on the union of historical and streaming data. Our method combines an index on historical data with a memory-efficient sketch on streaming data to answer quantile queries with accuracy-resource tradeoffs that are significantly better than current solutions that are based solely on disk-resident indexes or solely on streaming algorithms.

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Towards hybrid online on-demand querying of realtime data with stateful complex event processing

Cited by 12 publications

References 16 publications

The process-oriented event model (PoEM)

The process-oriented event model (PoEM)

Automated discovery and integration of semantic urban data streams: The ACEIS middleware

Estimating quantiles from the union of historical and streaming data

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