A new storm topology for synopsis management in the processing architecture

Diván, Mario José; Martín, M.A.

doi:10.1109/clei.2017.8226398

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Once the collecting has finished, the MA send jointly data (i. Both the Smoothing and analysis process and the decision-making process are embedded in the Apache Storm topology [20]. The smoothing and analysis function uses Redis [21] as a memory cache and R software [12] for the statistical computing.…”

Section: The Processing Architecture Based On Measurement Metadatamentioning

confidence: 99%

A Library for Articulating the Measurement Streams with Columnar Data

DivÃ¡n,

Laura SÃ¡nchez Reynoso

2018

IJET

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The CINCAMI/Measurement Interchange Schema (MIS) organizes jointly data and metadata generated from the heterogeneous measurement devices under the same data stream. The Processing Architecture based on Measurement Metadata (PAbMM) is a data stream engine which processes the data streams organized under the CINCAMI/MIS schema. PAbMM is able to replicate in real-time each measurement stream but limited to C-INCAMI/MIS. This constitutes a use limitation of CINCAMI/MIS because the reading, using and writing of the measures was previously performed just by PAbMM. The CINCAMImisConversor library extracts this functionality with the aim of fostering the using along with any measurement project who need it. The functionality extraction was guided by PAbMMÂ´s internal behavior, which is documented through SPEM metamodel. This allowed defining to the CincamimisConversorâ€™s object model, its implementation and carry forward a discrete simulation for having an associated time reference. As a contribution, the library allows the data format conversion from the CINCAMI/MIS streams to the columnar organization. The library could translate 4900 measures in approximately 11 ms.Â Â

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Section: The Processing Architecture Based On Measurement Metadatamentioning

confidence: 99%

A Library for Articulating the Measurement Streams with Columnar Data

DivÃ¡n,

Laura SÃ¡nchez Reynoso

2018

IJET

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“…El empleo de marcos formales de M&E, permite acomodar los términos, conceptos y las relaciones entre ellos de manera de promover la consistencia, la repetitividad y la extensibilidad del proceso de M&E (M. J. Divan and M. de los A. Martin, 2017), como así también su automatización (Sánchez-Reynoso, 2014).…”

Section: Introductionunclassified

Hacia una Estrategia de Recomendación por Similitud Semántica en Repositorios con Grandes Volúmenes de Datos de Medición y Evaluación

Reynoso

Diván²

2022

AJEA

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Un proceso de medición debiera ser repetible, extensible a nuevos requerimientos y sus medidas debieran poder ser comparables. Esto es así, para poder posibilitar que el mismo proceso de medición pueda ser implementado por diferentes actores, de manera que sus resultados sean comparables a lo largo del tiempo. Definido el proceso de medición, las medidas necesitan ser interpretadas a fin de poder tomar decisiones y a partir de ello recomendar cursos de acción. Puede suceder que dependiendo de la entidad que se encuentre bajo monitoreo, y una situación en particular, no siempre se dispone con antecedentes o conocimientos previos. Ello provocaría que, a pesar de haber identificado la situación, no sería posible brindar recomendaciones o cursos de acción configurado al escenario específico. Adicionalmente el Internet de las Cosas (en inglés, Internet of Things - IoT) ha permitido llevar a cabo el desarrollo de estrategias de recolección de datos, soportando procesos de toma de decisiones en tiempo real. El objetivo consiste en desarrollar una estrategia de recomendación a partir de mediciones, basado en el análisis de similitud semántica que permita brindar experiencias y-o recomendaciones por analogía con otra entidad.

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“…The Data Stream Processing Strategy [6] is a topology based on Apache Storm oriented to the data stream processing, which automatizes the measurement process supported by a measurement and evaluation framework. The measurement project definition is the first stage in where the domain experts define the entity to be monitored, choose the measurable characteristics, define the metric for quantifying each characteristic, choose the device for obtaining the value of each metric, etc.…”

Section: Introductionmentioning

confidence: 99%

Extending the Data Stream Processing Strategy to Scenario Analysis

Diván¹

2019

IJATCSE

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The real-time data processing is continuously demanded by their implications in the real-time decision-making process. The data stream processing strategy implements an automatization of the measurement process based on a measurement and evaluation (M&E) framework for supporting real-time decision making. The decision making is based on the indicators, which have a set of decision criteria established by the experts in the domain in the initial M&E project definition. However, the decision criteria are not isolated from the context in which the decision should be made. This work introduces an extension for the data stream processing strategy, incorporating the scenarios as an interpretation related to the possible states of a context. The transition between scenarios is incorporated through the transition model, obtaining feedback from the processed data for adjusting the decision criteria and their interpretation in terms of the current context. A new complementary schema to the project definition is incorporated for supporting the extensions. In this way, the data stream processing strategy now is able to support the scenario definitions, transition analysis jointly with the possibility of fitting each indicator's decision criteria to each particular scenario. Finally, a scenario analysis based on the monitoring of outpatients is outlined.

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A new storm topology for synopsis management in the processing architecture

Cited by 6 publications

References 17 publications

A Library for Articulating the Measurement Streams with Columnar Data

A Library for Articulating the Measurement Streams with Columnar Data

Hacia una Estrategia de Recomendación por Similitud Semántica en Repositorios con Grandes Volúmenes de Datos de Medición y Evaluación

Extending the Data Stream Processing Strategy to Scenario Analysis

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