Looking Inside the Black-Box: Capturing Data Provenance Using Dynamic Instrumentation

Stamatogiannakis, Manolis; Groth, Paul; Bos, Herbert

doi:10.1007/978-3-319-16462-5_12

Cited by 34 publications

(35 citation statements)

References 26 publications

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“…3) Dynamic Instruction-Level Solution -DataTracker: DataTracker [12] is a tool that captures provenance using Dynamic Taint Analysis (DTA). The analysis is applied as Dynamic Binary Instrumentation (DBI) using the Intel Pin [13] and libdft [14] frameworks.…”

Section: B Provenance Collection Methods and Reportersmentioning

confidence: 99%

See 1 more Smart Citation

Trade-Offs in Automatic Provenance Capture

Stamatogiannakis

Kazmi

Sharif

et al. 2016

Lecture Notes in Computer Science

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Abstract-Automatic provenance capture from arbitrary applications is a challenging problem. Different approaches to tackle this problem have evolved, most notably a. system-event trace analysis, b. compile-time static instrumentation, and c. taint flow analysis using dynamic binary instrumentation. Each of these approaches offers different trade-offs in terms of the granularity of captured provenance, integration requirements, and runtime overhead. While these aspects have been discussed separately, a systematic and detailed study, quantifying and elucidating them, is still lacking. To fill this gap, we begin to explore these trade-offs for representative examples of these approaches for automatic provenance capture by means of evaluation and measurement. We base our evaluation on UnixBench-a widely used benchmark suite within systems research. We believe this approach will make our results easier to compare with future studies.

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Section: B Provenance Collection Methods and Reportersmentioning

confidence: 99%

“…In [12], sets of <file descriptor, offset> pairs are used for tracking the provenance of each memory location. In this work, we instead opted to use bitsets-where each bit represents a file descriptor.…”

Section: B Provenance Collection Methods and Reportersmentioning

confidence: 99%

Trade-Offs in Automatic Provenance Capture

Stamatogiannakis

Kazmi

Sharif

et al. 2016

Lecture Notes in Computer Science

Self Cite

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“…Finally in the reproducibility discussion, three papers were presented: [85] is an approach to capturing data provenance based on taint tracking. [89] generates electronic notebook documentation from multienvironment workflows by using provenance represented in the W3C PROV model.…”

Section: Provenancementioning

confidence: 99%

Provenance in Open Data Entity-Centric Aggregation

Giunchiglia

Reyad

2015

Lecture Notes in Computer Science

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An increasing number of web services these days require combining data from several data providers into an aggregated database. Usually this aggregation is based on the linked data approach. On the other hand, the entity-centric model is a promising data model that outperforms the linked data approach because it solves the lack of explicit semantics and the semantic heterogeneity problems. However, current open data which is available on the web as raw datasets can not be used in the entity-centric model before processing them with an import process to extract the data elements and insert them correctly in the aggregated entity-centric database. It is essential to certify the quality of these imported data elements, especially the background knowledge part which acts as input to semantic computations, because the quality of this part affects directly the quality of the web services which are built on top of it. Furthermore, the aggregation of entities and their attribute values from different sources raises three problems: the need to trace the source of each element, the need to trace the links between entities which can be considered equivalent and the need to handle possible conflicts between different values when they are imported from various data sources. In this thesis, we introduce a new model to certify the quality of a back ground knowledge base which separates linguistic and language independent elements. We also present a pipeline to import entities from open data repositories to add the missing implicit semantics and to eliminate the semantic heterogeneity. Finally, we show how to trace the source of attribute values coming from different data providers; how to choose a strategy for handling possible conflicts between these values; and how to keep the links between identical entities which represent the same real world entity.

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“…In noWorkflow [24] authors analyse Python scripts to extract function-call hierarchies, which they use to create a view over provenance traces collected by run-time instrumentation of the functions' reads and writes to the file system. In [25] authors employ a taint tracking framework, which instruments programme executions and records which computations are affected by tainted data sources. Here the authors use names of files read by programmes as taint marks and show how such an approach can create fine grained lineage among files, where the programme during its execution writes to one file the data read from another file.…”

Section: Related Workmentioning

confidence: 99%

Static analysis of Taverna workflows to predict provenance patterns

Alper

Belhajjame

Goble

2017

Future Generation Computer Systems

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Workflows have found adoption in scientific domains particularly due to their automation and provenance features. Using workflows scientists can repeat analyses with different input parameters to later use provenance to access and compare results based on respective parameters. An assumption that is often made is that by designing an analysis as a workflow, we get parameterto-result traceability for free with workflow provenance. This assumption holds for cases of coarse-grained traceability, where an entire workflow is subjected to repetition and all workflow parameters contribute to all results. On the other hand for cases requiring finer grained traceability, where a workflow is configured with collections of parameters and analyses within a workflow are repeated with combinations of parameters from collections, this assumption is not guaranteed to hold. In this paper we identify two dimensions that affect fine-grained traceability as: 1) the level of granularity supported by a workflow system in modelling parameters/data in workflows and in provenance, which we name as the level of support for Factorial Design, and 2) the practice of scientists in successfully encoding Factorial Design into workflows. Taverna is a workflow system that provides extensive features for factorial design, meanwhile it provides an uncontrolled approach to workflow design; meaning scientists may create workflows, which, when run, could break traceability in provenance. Using a real-world Taverna workflow we show how broken traceability manifests in provenance and how it can render provenance practically useless for accessing workflow outputs derived from particular input parameters. In order to prevent broken traceability from occurring, we describe a rule-based static analysis technique, which operates over workflow descriptions and anticipates patterns in provenance. Our rules exploit the well-defined execution behaviour in the Taverna system. In order to understand Factorial Design support in workflow systems in general, we provide a comparative survey. We conclude that other workflow systems also provide constructs for Factorial Design, and, similar to Taverna, they too are prone to broken traceability.

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Looking Inside the Black-Box: Capturing Data Provenance Using Dynamic Instrumentation

Cited by 34 publications

References 26 publications

Trade-Offs in Automatic Provenance Capture

Trade-Offs in Automatic Provenance Capture

Provenance in Open Data Entity-Centric Aggregation

Static analysis of Taverna workflows to predict provenance patterns

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