Monitoring SOA-based applications with business provenance

Cruz, Sérgio Manuel Serra da; Costa, Raimundo M.; Manhaes, Mary; Zavaleta, Jorge

doi:10.1145/2480362.2480718

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Metodologi tersebut fokus pada pencarian penyebab tidak adanya kualitas dari data. Prosesnya dengan mengukur dan meningkatkan kegiatan pada satu set dimensi kualitas (misalnya, akurasi, ketepatan waktu, kelengkapan) dan menganalisis ketergantungan antara dimensi dengan menggunakan sifat entropi (Shariat, et al, 2013).…”

Section: Dimensi Kualitas Dataunclassified

Metode Penilaian Kualitas Data Sebagai Rekomendasi Sistem Repositori Ilmiah Nasional

et al. 2020

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High quality data and data quality assessment which efficiently needed to data standardization in the research data repository. Three attributes most used i.e: completeness, accuracy, and timeliness are dimensions to data quality assessment. The purposes of the research are to increase knowledge and discuss in depth of research done. To support the research, we are using traditional review method on the Scopus database to identify relevant research. The literature review is limited for the type of documents i.e: articles, books, proceedings, and reviews. The result of document searching is filtered using some keywords i.e: data quality, data quality assessment, data quality dimensions, quality assessment, data accuracy, dan data completeness. The document that found be analyzed based on relevant research. Then, these documents compare to find out different of concept and method which used in the data quality metric. The result of analysis could be used as a recommendation to implement in the data quality assessment in the National Scientific Repository. ABSTRAKData berkualitas tinggi dan penilaian kualitas data yang efektif dibutuhkan untuk standaridasi data dalam repositori data penelitian. Tiga atribut yang paling banyak digunakan, yaitu kelengkapan, akurasi, dan ketepatan waktu. Tiga atribut tersebut merupakan beberapa dimensi untuk penilaian kualitas data. Penelitian ini bertujuan untuk meningkatkan pengetahuan dan membahas secara mendalam terhadap penelitian yang akan dilakukan. Untuk menunjang penelitian, kami menggunakan metode tinjauan pustaka secara tradisional pada database Scopus dan beberapa website terkemuka untuk mengidentifikasi penelitian yang relevan. Studi pustaka dibatasi pada jenis dokumen, yaitu artikel, buku, prosiding, dan tinjauan. Hasil pencarian dokumen disaring menggunakan beberapa kata kunci, yaitu data quality, data quality assessment, data quality dimensions, quality assessment, data accuracy, dan data completeness. Dokumen yang telah diperoleh selanjutnya dianalisis berdasarkan penelitian yang relevan. Selanjutnya, data dianalisis dan dibandingkan untuk mengetahui perbedaan konsep dan metode yang digunakan dalam mengukur kualitas data. Hasil analisis digunakan sebagai rekomendasi untuk diterapkan dalam menilai kualitas data pada sistem Repositori Ilmiah Nasional.

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Section: Dimensi Kualitas Dataunclassified

Metode Penilaian Kualitas Data Sebagai Rekomendasi Sistem Repositori Ilmiah Nasional

et al. 2020

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“…An interesting approach appears in Mukhi [36], which posits a probabilistic provenance model to deal with unmanaged business processes. In spite of these proposals, da Cruz et al [15] state: 'monitoring business provenance has received insignificant attention despite its relevance.' This study also discusses the importance of monitoring service-oriented architecture applications from a provenance perspective.…”

Section: Related Workmentioning

confidence: 99%

“…One of the techniques in which process management and provenance are more highly intertwined is business process monitoring [4,13,15,36], which sometimes falls under the slightly more general term business activity monitoring [12,16]. Business process monitoring scrutinizes the status of process instances, by providing an integrated view on process and application data, or enabling business performance supervision [38].…”

Section: Introductionmentioning

confidence: 99%

Developing provenance-aware query systems: an occurrence-centric approach

et al. 2016

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In recent years, research on provenance has increased exponentially, and such studies in the field of business process monitoring have been especially remarkable. Business process monitoring deals with recording information about the actual execution of processes to then extract valuable knowledge that can be utilized for business process quality improvement. In prior research, we developed an occurrence-centric approach built on our notion of occurrence that provides a holistic perspective of system dynamics. Based on this concept, more complex structures are defined herein, namely Occurrence Base (OcBase) and Occurrence Management System (OcSystem), which serve as scaffolding to develop business process monitoring systems. This paper focuses primarily on the critical provenance task of extracting valuable knowledge from such systems by proposing an Occurrence Query Framework that includes the definition of an Occurrence Base Metamodel and an Occurrence Query Language based on this metamodel. Our framework provides a way of working for the construction of business process monitoring systems that are provenance aware. As a proof of concept, a tool implementing the various components of the framework is presented. This tool has been tested against a real system in the context of biobanks.

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Provenance as a Service: A Data-centric Approach for Real-Time Monitoring

Hammad

2014

2014 IEEE International Congress on Big Data

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Monitoring SOA-based applications with business provenance

Cited by 4 publications

References 17 publications

Metode Penilaian Kualitas Data Sebagai Rekomendasi Sistem Repositori Ilmiah Nasional

Metode Penilaian Kualitas Data Sebagai Rekomendasi Sistem Repositori Ilmiah Nasional

Developing provenance-aware query systems: an occurrence-centric approach

Provenance as a Service: A Data-centric Approach for Real-Time Monitoring

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