Fine-Grained Provenance for Matching &amp; ETL

Zheng, Nan; Alawini, Abdussalam

doi:10.1109/icde.2019.00025

Cited by 9 publications

(7 citation statements)

References 25 publications

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“…We found that the execution cost of function operator with an expensive UDF tends to be dominant in the overall processing time compared with other relational operators. To reduce the time of rerunning tasks and segments in Rerun and FM, we utilize the semi-join pushdown optimization used in [57]. When we get output tuples O of a task and Ō ⊆ O are specified as targets for the augmented lineage derivation, we apply the semi-join O Ō and then push it down along the operator tree.…”

Section: Deployment Of Intermediate Resultsmentioning

confidence: 99%

“…When we get output tuples O of a task and Ō ⊆ O are specified as targets for the augmented lineage derivation, we apply the semi-join O Ō and then push it down along the operator tree. Although [57] does not consider the difference operator, the pushdown transformation…”

Section: Deployment Of Intermediate Resultsmentioning

confidence: 99%

“…Moreover, they did not cover the reasoning basis for AI/ML decisions. [57] proposed a method to track information about which parts of the content data were extracted by data extraction processing represented by UDFs as well as the traditional tuple-level provenance based on the semiring model. Even this framework cannot provide the basis for decisions made by AI/ML processing.…”

Section: Related Workmentioning

confidence: 99%

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Augmented lineage: traceability of data analysis including complex UDF processing

et al. 2022

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Data lineage allows information to be traced to its origin in data analysis by showing how the results were derived. Although many methods have been proposed to identify the source data from which the analysis results are derived, analysis is becoming increasingly complex both with regard to the target (e.g., images, videos, and texts) and technology (e.g., AI and machine learning (ML)). In such complex data analysis, simply showing the source data may not ensure traceability. For example, ML analysts building image classifier models often need to know which parts of images are relevant to the output and why the classifier made a decision. Recent studies have intensively investigated interpretability and explainability in the AI/ML domain. Integrating these techniques into the lineage framework will help analysts understand more precisely how the analysis results were derived and how the results are trustful. In this paper, we propose the concept of augmented lineage for this purpose, which is an extended lineage, and an efficient method to derive the augmented lineage for complex data analysis. We express complex data analysis flows using relational operators by combining user-defined functions (UDFs). UDFs can represent invocations of AI/ML models within the data analysis. Then, we present a method taking UDFs into consideration to derive the augmented lineage for arbitrarily chosen tuples among the analysis results. We also experimentally demonstrate the efficiency of the proposed method.

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Section: Deployment Of Intermediate Resultsmentioning

confidence: 99%

Section: Deployment Of Intermediate Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Augmented lineage: traceability of data analysis including complex UDF processing

et al. 2022

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“…DISC systems natively support nested data formats such as JSON, XML, Parquet, or Protocol Buffers. Provenance capture for DISC systems has been studied in, e.g., [1,15,21,22,28,42]. Why-not explanations are practically relevant in these systems.…”

Section: Related Workmentioning

confidence: 99%

To not miss the forest for the trees -- a holistic approach for explaining missing answers over nested data (extended version)

Diestelkaemper¹,

Lee²,

Herschel³

et al. 2021

Preprint

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Query-based explanations for missing answers identify which operators of a query are responsible for the failure to return a missing answer of interest. This type of explanations has proven to be useful in a variety of contexts including debugging of complex analytical queries. Such queries are frequent in big data systems such as Apache Spark. We present a novel approach for producing querybased explanations. Our approach is the first to support nested data and to consider operators that modify the schema and structure of the data (e.g., nesting and projections) as potential causes of missing answers. To efficiently compute explanations, we propose a heuristic algorithm that applies two novel techniques: (i) reasoning about multiple schema alternatives for a query and (ii) re-validating at each step whether an intermediate result can contribute to the missing answer. Using an implementation of our approach on Spark, we demonstrate that it is the first to scale to large datasets and that it often finds explanations that existing techniques fail to identify.

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“…The main difference is that we have focused on a restricted set of core operators (with some of those in [32] missing and others combined in one) with the specific goal of providing a solid basis to an effective technique for capturing data provenance of classical preprocessing operators. We point out that our algebra can be easily extended to include operators implementing other ETL/ELT-like transformations, such as join, intersection, and union, whose fine-grained provenance capture have been described elsewhere [50].…”

Section: Data Manipulation Modelmentioning

confidence: 99%

Capturing and querying fine-grained provenance of preprocessing pipelines in data science

et al. 2020

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Data processing pipelines that are designed to clean, transform and alter data in preparation for learning predictive models, have an impact on those models' accuracy and performance, as well on other properties, such as model fairness. It is therefore important to provide developers with the means to gain an in-depth understanding of how the pipeline steps affect the data, from the raw input to training sets ready to be used for learning. While other efforts track creation and changes of pipelines of relational operators, in this work we analyze the typical operations of data preparation within a machine learning process, and provide infrastructure for generating very granular provenance records from it, at the level of individual elements within a dataset. Our contributions include: (i) the formal definition of a core set of preprocessing operators, and the definition of provenance patterns for each of them, and (ii) a prototype implementation of an application-level provenance capture library that works alongside Python. We report on provenance processing and storage overhead and scalability experiments, carried out over both real ML benchmark pipelines and over TCP-DI, and show how the resulting provenance can be used to answer a suite of provenance benchmark queries that underpin some of the developers' debugging questions, as expressed on the Data Science Stack Exchange.

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Fine-Grained Provenance for Matching & ETL

Cited by 9 publications

References 25 publications

Augmented lineage: traceability of data analysis including complex UDF processing

Augmented lineage: traceability of data analysis including complex UDF processing

To not miss the forest for the trees -- a holistic approach for explaining missing answers over nested data (extended version)

Capturing and querying fine-grained provenance of preprocessing pipelines in data science

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