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

Hammad, Rafat; Wu, Ching-Seh

doi:10.1109/bigdata.congress.2014.46

Cited by 11 publications

(5 citation statements)

References 31 publications

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“…Data provenance is utilized for depicting data evolution, which records the entire data process, including all changes and transformations which have been applied to change data from one state to another [ 49 ]. Zhao et al [ 50 ] introduced different methods for modeling, capturing, and querying provenance data by identifying mapping joins between data generated from different sources related to genomics.…”

Section: Semantics For Healthcare Data Acquisitionmentioning

confidence: 99%

A Semantic-Based Approach for Managing Healthcare Big Data: A Survey

Hammad

Barhoush

Abed-alguni

2020

Journal of Healthcare Engineering

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Healthcare information systems can reduce the expenses of treatment, foresee episodes of pestilences, help stay away from preventable illnesses, and improve personal life satisfaction. As of late, considerable volumes of heterogeneous and differing medicinal services data are being produced from different sources covering clinic records of patients, lab results, and wearable devices, making it hard for conventional data processing to handle and manage this amount of data. Confronted with the difficulties and challenges facing the process of managing healthcare big data such as volume, velocity, and variety, healthcare information systems need to use new methods and techniques for managing and processing such data to extract useful information and knowledge. In the recent few years, a large number of organizations and companies have shown enthusiasm for using semantic web technologies with healthcare big data to convert data into knowledge and intelligence. In this paper, we review the state of the art on the semantic web for the healthcare industry. Based on our literature review, we will discuss how different techniques, standards, and points of view created by the semantic web community can participate in addressing the challenges related to healthcare big data.

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Section: Semantics For Healthcare Data Acquisitionmentioning

confidence: 99%

A Semantic-Based Approach for Managing Healthcare Big Data: A Survey

Hammad

Barhoush

Abed-alguni

2020

Journal of Healthcare Engineering

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“…However, applying data provenance in big data is a fairly new trend that involves continuing development for provenanceaware systems. Therefore, most of the existing studies on data provenance in big-data applications are exploratory [2, 3, 4, 5, and 7], descriptive [11], or case-based research [12]. For example, studies from [13], [14], and [15] focus on the general conceptualization and definition of data provenance, as well as its importance, challenges, and applications.…”

Section: Background and Related Work On Big-data Provenancementioning

confidence: 99%

“…In [25] the authors highlighted the challenge of collecting and merging provenance information from a dynamic environment in which resources are independence; for example, different domains (scientific, business, database), different platforms (windows, Linux), and various applications. Further, computation and storage overhead also presents another challenge [26]. In [27] a framework named CloudProv was developed.…”

Section: B Cloud Computingmentioning

confidence: 99%

Bio-inspired Think-and-Share Optimization for Big Data Provenance in Wireless Sensor Networks

Alkhalil¹,

Ramadan²,

Ahmad³

2019

IJACSA

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Big data systems are being increasingly adopted by the enterprises exploiting big data applications to manage datadriven process, practices, and systems in an enterprise wide context. Specifically, big data systems and their underlying applications empower enterprises with analytical decision making (e.g., recommender/decision support systems) to optimize organizational productivity, competitiveness, and growth. Despite these benefits, big data applications face some challenges that include but not limited to security and privacy, authenticity, and reliability of critical data that may result in propagation of false information across systems. Data provenance as an approach and enabling mechanism (to identify the origin, manage the creation, and track the propagation of information etc.) can be a solution to above mentioned challenges for data management in an enterprise context. Data provenance solution(s) can help stakeholders and enterprises to assess the quality of data along with authenticity, reliability, and trust of information on the basis of identity, reproducibility and integrity of data. Considering the wide spread adoption of big data applications and the needs for data provenance, this paper focuses on (i) analyzing state-of-the-art for holistic presentation of provenance in big-data applications (ii) proposing a bioinspired approach with underlying algorithm that exploits human thinking approach to support data provenance in Wireless Sensor Networks (WSNs). The proposed 'Think-and-Share Optimization' (TaSO) algorithms modularizes and automates data provenance in WSNs that are deployed and operated in enterprises. Evaluation of TaSO algorithm demonstrates its efficiency in terms of connectivity, closeness to the sink node, coverage, and execution time. The proposed research contextualizes bio-inspired computation to enable and optimize data provenance in WSNs. Future research aims to exploit machine learning techniques (with underlying algorithms) to automate data provenance for big data systems in networked environments.

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“…CloudProv [113] is a framework to incorporate, model, and monitor provenance about data coming in real time in the cloud environment. The proposed framework provides public API that can be used to develop an application for sharing and incorporating provenance data.…”

Section: A Taxonomy Of Scientific Platforms Over Provenance Supportmentioning

confidence: 99%

A survey of simulation provenance systems: modeling, capturing, querying, visualization, and advanced utilization

Suh

Lee

2018

Hum. Cent. Comput. Inf. Sci.

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Research and education through computer simulation has been actively conducted in various scientific and engineering fields including computational science engineering. Accordingly, there have been a lot of attentions paid to actively utilize provenance information regarding such computer simulations, particularly conducted on highperformance computing and storage resources. In this manuscript we provide a comprehensive survey of a wide range of existing systems to utilize provenance data produced by simulation. Specifically, we (1) categorize extant provenance research articles into several major themes along with well-motivated criteria, (2) grasp and compare primary functions/features of the existing systems in each category, and (3) then ultimately propose new research directions that have never been pioneered before. In particular, we present a taxonomy of scientific platforms regarding provenance support and holistically tabulate the major functionalities and supporting levels of the studied systems. Finally, we conclude this article with a summary of our contributions.

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

Cited by 11 publications

References 31 publications

A Semantic-Based Approach for Managing Healthcare Big Data: A Survey

A Semantic-Based Approach for Managing Healthcare Big Data: A Survey

Bio-inspired Think-and-Share Optimization for Big Data Provenance in Wireless Sensor Networks

A survey of simulation provenance systems: modeling, capturing, querying, visualization, and advanced utilization

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