Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering

Souza, Renan; Valduriez, Patrick; Mattoso, Marta; Cerqueira, Renato; Netto, Marco A. S.; Azevedo, Leonardo Guerreiro; Lourenço, Vítor; Soares, Elton; Thiago, Raphael Melo; Brandão, Rafael; Civitarese, Daniel; Brazil, Emilio Vital; Moreno, Márcio Ferreira

doi:10.1109/works49585.2019.00006

Cited by 32 publications

(41 citation statements)

References 17 publications

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“…Considering performance, we validated ProvLake in another ML scenario, also in O&G industry, evaluating the performance with 48 GPUs in parallel, and the data capture overhead was less than 1% [Souza et al 2019a].…”

Section: Discussionmentioning

confidence: 99%

“…While capturing the data being processed in the lifecycle, ProvLake logically integrates and ingests them into a provenance database, named ProvLake Data View (PLView), ready for analyses at runtime [Souza et al 2019b]. The tool captures provenance of the three phases of ML lifecycle: data curation, data preparation for learning, and learning [Souza et al 2019a]. It then gives an integrated view of domain data, execution data, and ML data in multiworkflows supporting queries and analysis on such data.…”

Section: Introductionmentioning

confidence: 99%

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Experiencing ProvLake to Manage the Data Lineage of AI Workflows

Azevedo¹,

Souza²,

Thiago³

et al. 2020

Anais Estendidos Do XVI Simpósio Brasileiro De Sistemas De Informação (Anais Estendidos Do SBSI 2020)

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Machine Learning (ML) is a core concept behind Artificial Intelligence systems, which work driven by data and generate ML models. These models are used for decision making, and it is crucial to trust their outputs by, e.g., understanding the process that derives them. One way to explain the derivation of ML models is by tracking the whole ML lifecycle, generating its data lineage, which may be accomplished by provenance data management techniques. In this work, we present the use of ProvLake tool for ML provenance data management in the ML lifecycle for Well Top Picking, an essential process in Oil and Gas exploration. We show how ProvLake supported the validation of ML models, the understanding of whether the ML models generalize respecting the domain characteristics, and their derivation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experiencing ProvLake to Manage the Data Lineage of AI Workflows

Azevedo¹,

Souza²,

Thiago³

et al. 2020

Anais Estendidos Do XVI Simpósio Brasileiro De Sistemas De Informação (Anais Estendidos Do SBSI 2020)

Self Cite

View full text Add to dashboard Cite

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“…Data provenance (or data lineage) methods aim to improve replication, tracing, quality assessment in data use and data transformation processes [19]. Several researchers have proposed data provenance and lineage solutions for the tracking of data and data transformations during the machine learning lifecycle [43,44,51]. Further, Bertino et al [8] proposes the use of blockchain technology to encourage data transparency and ensure that data collection and utilization coincide with ethical principles.…”

Section: Data Transparencymentioning

confidence: 99%

Know Your Model (KYM): Increasing Trust in AI and Machine Learning

Roszel,

Norvill,

Hilger

et al. 2021

Preprint

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The widespread utilization of AI systems has drawn attention to the potential impacts of such systems on society. Of particular concern are the consequences that prediction errors may have on realworld scenarios, and the trust humanity places in AI systems. It is necessary to understand how we can evaluate trustworthiness in AI and how individuals and entities alike can develop trustworthy AI systems. In this paper, we analyze each element of trustworthiness and provide a set of 20 guidelines that can be leveraged to ensure optimal AI functionality while taking into account the greater ethical, technical, and practical impacts to humanity. Moreover, the guidelines help ensure that trustworthiness is provable and can be demonstrated, they are implementation agnostic, and they can be applied to any AI system in any sector.

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“…Thus, we propose another aspect for building a context from the bottom up and separating it into a similar method of abstraction, while focusing on generating it as a knowledge base. For solving the issues or hurdles mentioned above, studies with Automated Machine Learning (AutoML) 34 and workflow components for machine learning (ML) applications 35,36 have been conducted. Particularly Auto-WEKA 37 or auto-sklearn 38 are the ones of AutoML concepts to automate analysis tasks.…”

Section: Related Workmentioning

confidence: 99%

A method to generate context information sets from analysis results with a unified abstraction model based on an extension of data enrichment scheme

Park

Mun

Choi

et al. 2020

Concurrency and Computation

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Context-awareness techniques generally deal with steps for processing information that can be gathered from perspectives of objects in real-world scenes in accordance with resolving heterogeneous devices and providing meaningful context information in recent computing domains such as Internet of Things, cloud computing, edge computing, and big data analysis. However, analysis applications are necessary to be improved in a way of cooperative approaches because they are independently executed far away from context-aware systems. This means that the stages are applied to process the data or the derived results, are isolated. That leads difficulties of management and integration for other software modules. As a result, a computing paradigm called edge computing has been suggested to compromise such isolations. For processing context information, it is necessary to establish knowledge parts with validation processes against the isolations. In this paper, we suggest a method for processing datasets in a view of building knowledge parts. The suggested method uses an extensible data enrichment scheme, which represents the relations for the object. In the experiment section, applications of the proposed method are shown in one of the healthcare community centers, and the data abstraction with the analysis jobs can be processed for each real-world object. K E Y W O R D Scontext-awareness, data abstraction, edge computing environment, embedding convergence, knowledge representation INTRODUCTIONRecent improvements in computational methodologies and technologies such as cloud computing, Internet of Things (IoT), 5G networks, and big data have enabled to build meaningful knowledge for artificial intelligence. [1][2][3][4][5] Among these improvements, there has been innovative approaches to develop the subsets of an architecture for application in computation domains. It is important to consider that bringing the advantages provides benefits to lives for human being and society for transformative innovation. [6][7][8] Because recent trends and challenges have focused on processing massive amounts of data, also known as big data, so that different approaches related to the collection, management, and application of such datasets have been conducted. Therefore, it is necessary to design the stages that are highly associated with handling these datasets. 9,10 As the result, both processing and providing meaningful context information are important techniques where massive data can be generated on state-of-the-art domains such as IoT, edge computing, or big data analysis. For context-aware systems, despite combining the key techniques mentioned above, there remain certain difficulties to overcome. [11][12][13] In general, context-awareness consists of resolving hardware heterogeneity, data acquisition, collection of

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Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering

Cited by 32 publications

References 17 publications

Experiencing ProvLake to Manage the Data Lineage of AI Workflows

Experiencing ProvLake to Manage the Data Lineage of AI Workflows

Know Your Model (KYM): Increasing Trust in AI and Machine Learning

A method to generate context information sets from analysis results with a unified abstraction model based on an extension of data enrichment scheme

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