Spatial Perspectives in Event Processing

Etzion, Opher; Zolotorvesky, Nir

doi:10.1007/978-3-642-17226-7_6

Cited by 14 publications

(18 citation statements)

References 12 publications

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“…Event-driven architectures adopt a message-based approach to decouple service providers from consumers [ 35 ]. In these service-oriented architectures, event detection is essential to get a reliable stream of changed data [ 36 ]. Event-driven architectures are used for direct responses to various events and for coordination with business process integration in ubiquitous scenarios.…”

Section: Resultsmentioning

confidence: 99%

An automated real-time integration and interoperability framework for bioinformatics

Lopes

Oliveira

2015

BMC Bioinformatics

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BackgroundIn recent years data integration has become an everyday undertaking for life sciences researchers. Aggregating and processing data from disparate sources, whether through specific developed software or via manual processes, is a common task for scientists. However, the scope and usability of the majority of current integration tools fail to deal with the fast growing and highly dynamic nature of biomedical data.ResultsIn this work we introduce a reactive and event-driven framework that simplifies real-time data integration and interoperability. This platform facilitates otherwise difficult tasks, such as connecting heterogeneous services, indexing, linking and transferring data from distinct resources, or subscribing to notifications regarding the timeliness of dynamic data. For developers, the framework automates the deployment of integrative and interoperable bioinformatics applications, using atomic data storage for content change detection, and enabling agent-based intelligent extract, transform and load tasks.ConclusionsThis work bridges the gap between the growing number of services, accessing specific data sources or algorithms, and the growing number of users, performing simple integration tasks on a recurring basis, through a streamlined workspace available to researchers and developers alike.

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Section: Resultsmentioning

confidence: 99%

An automated real-time integration and interoperability framework for bioinformatics

Lopes

Oliveira

2015

BMC Bioinformatics

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“…In CEP, an event is considered as an occurrence that has happened in a domain [37]. The video event is a high-level semantic concept captured due to the change of state in video content while observed over time [32].…”

Section: A Video Event Definitionmentioning

confidence: 99%

VidCEP: Complex Event Processing Framework to Detect Spatiotemporal Patterns in Video Streams

Yadav

Curry

2019

2019 IEEE International Conference on Big Data (Big Data)

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Video data is highly expressive and has traditionally been very difficult for a machine to interpret. Querying event patterns from video streams is challenging due to its unstructured representation. Middleware systems such as Complex Event Processing (CEP) mine patterns from data streams and send notifications to users in a timely fashion. Current CEP systems have inherent limitations to query video streams due to their unstructured data model and lack of expressive query language. In this work, we focus on a CEP framework where users can define high-level expressive queries over videos to detect a range of spatiotemporal event patterns. In this context, we propose-i) VidCEP, an in-memory, on the fly, near real-time complex event matching framework for video streams. The system uses a graphbased event representation for video streams which enables the detection of high-level semantic concepts from video using cascades of Deep Neural Network models, ii) a Video Event Query language (VEQL) to express high-level user queries for video streams in CEP, iii) a complex event matcher to detect spatiotemporal video event patterns by matching expressive user queries over video data. The proposed approach detects spatiotemporal video event patterns with an F-score ranging from 0.66 to 0.89. VidCEP maintains near real-time performance with an average throughput of 70 frames per second for 5 parallel videos with sub-second matching latency.

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“…Following, a sensor data cleaning process takes place for improving the overall data quality through the usage of the FIWARE Complex Event Processing (CEP) enabler called PROTON [ 18 ]. CEP focuses on timely processing streams of information items, so-called events, like filtering or aggregation by means of predefined rules following the event-condition-action paradigm [ 19 ]. A filtering mechanism is implemented that discards extreme outliers of the different attributes that a sensor measurement contains, avoiding the further transmission of erroneous data.…”

Section: Energy-aware It Ecosystem Architectural Approachmentioning

confidence: 99%

Providing Personalized Energy Management and Awareness Services for Energy Efficiency in Smart Buildings

Fotopoulou

Zafeiropoulos

Terroso-Sáenz

et al. 2017

Sensors

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Considering that the largest part of end-use energy consumption worldwide is associated with the buildings sector, there is an inherent need for the conceptualization, specification, implementation, and instantiation of novel solutions in smart buildings, able to achieve significant reductions in energy consumption through the adoption of energy efficient techniques and the active engagement of the occupants. Towards the design of such solutions, the identification of the main energy consuming factors, trends, and patterns, along with the appropriate modeling and understanding of the occupants’ behavior and the potential for the adoption of environmentally-friendly lifestyle changes have to be realized. In the current article, an innovative energy-aware information technology (IT) ecosystem is presented, aiming to support the design and development of novel personalized energy management and awareness services that can lead to occupants’ behavioral change towards actions that can have a positive impact on energy efficiency. Novel information and communication technologies (ICT) are exploited towards this direction, related mainly to the evolution of the Internet of Things (IoT), data modeling, management and fusion, big data analytics, and personalized recommendation mechanisms. The combination of such technologies has resulted in an open and extensible architectural approach able to exploit in a homogeneous, efficient and scalable way the vast amount of energy, environmental, and behavioral data collected in energy efficiency campaigns and lead to the design of energy management and awareness services targeted to the occupants’ lifestyles. The overall layered architectural approach is detailed, including design and instantiation aspects based on the selection of set of available technologies and tools. Initial results from the usage of the proposed energy aware IT ecosystem in a pilot site at the University of Murcia are presented along with a set of identified open issues for future research.

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Spatial Perspectives in Event Processing

Cited by 14 publications

References 12 publications

An automated real-time integration and interoperability framework for bioinformatics

An automated real-time integration and interoperability framework for bioinformatics

VidCEP: Complex Event Processing Framework to Detect Spatiotemporal Patterns in Video Streams

Providing Personalized Energy Management and Awareness Services for Energy Efficiency in Smart Buildings

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