Provenance-Aware Query Optimization

Niu, Xing; Kapoor, Raghav; Glavic, Boris; Gawlick, Dieter; Liu, Zhen Hua; Radhakrishnan, Venkatesh Babu

doi:10.1109/icde.2017.104

Cited by 26 publications

(23 citation statements)

References 18 publications

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“…Provenance analysis is a well-known topic in data-centric domains such as the data annotation, management, and warehousing [18][19][20][21]. However, the problem of IPA is not intensively studied so far in the literature.…”

Section: Related Workmentioning

confidence: 99%

Media Forensics Challenge image provenance evaluation and state-of-the-art analysis on large-scale benchmark datasets

Jin¹,

Lee²,

Fiscus³

et al. 2020

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With the development of storage, transmission, editing, and sharing tools, digital forgery images are propagating rapidly. The need for image provenance analysis has never been more timely. Typical applications are content tracking, copyright enforcement, and forensics reasoning. However, large-scale image provenance datasets, which contain diverse manipulation history graphs with various manipulation operations and rich metadata, are still needed to facilitate the research. It is one of the major factors that hinders the development of techniques for image provenance analysis. To address this issue, we introduce large-scale benchmark datasets for provenance analysis, namely Media Forensics Challenge-Provenance (MFC-Prov) datasets. Two provenance tasks are designed along with evaluation metrics. Furthermore, extensive analysis is conducted for system performance in terms of accuracy on our datasets.

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Section: Related Workmentioning

confidence: 99%

Media Forensics Challenge image provenance evaluation and state-of-the-art analysis on large-scale benchmark datasets

Jin¹,

Lee²,

Fiscus³

et al. 2020

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“…Visualization systems typically implement this by annotating records as they flow through the visualization workflow so that the output is annotated with the input records [8]. However, annotations [7,36] are only one mechanism to answer fine-grained provenance queries. They can also be computed by evaluating the provenance predicates above, or by explicitly materializing input-to-output record dependency information as explicit index data structures when executing the visualization workflow [45,62].…”

Section: Listing 1: Example Database Schemamentioning

confidence: 99%

Provenance for Interactive Visualizations

Psallidas

2018

Proceedings of the Workshop on Human-in-the-Loop Data Analytics

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“…Furthermore, the user should be able to explore the effect of hypothetical changes to data or transaction statements (what-if ). In this paper, we present a novel debugger for transactions that uses reenactment [1,2,5], a declarative replay techniques we have developed, to recreate the state of the database observed by the original execution of a transaction including all its interactions with other transactions from the history that executed concurrently. Using reenactment, we can overcome the challenges discussed above.…”

Section: C3 Dataflow Within Sql Statements Is Opaquementioning

confidence: 99%

“…The resulting SQL query is sent to the backend database and its results are passed on to the user. By applying provenancespecific optimizations [1,5] we can reenact complex transactions over tables with millions of rows within seconds.…”

Section: The Gprom Approachmentioning

confidence: 99%

Debugging transactions and tracking their provenance with reenactment

et al. 2017

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Debugging transactions and understanding their execution are of immense importance for developing OLAP applications, to trace causes of errors in production systems, and to audit the operations of a database. However, debugging transactions is hard for several reasons: 1) after the execution of a transaction, its input is no longer available for debugging, 2) internal states of a transaction are typically not accessible, and 3) the execution of a transaction may be affected by concurrently running transactions. We present a debugger for transactions that enables non-invasive, postmortem debugging of transactions with provenance tracking and supports what-if scenarios (changes to transaction code or data). Using reenactment, a declarative replay technique we have developed, a transaction is replayed over the state of the DB seen by its original execution including all its interactions with concurrently executed transactions from the history. Importantly, our approach uses the temporal database and audit logging capabilities available in many DBMS and does not require any modifications to the underlying database system nor transactional workload.

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Provenance-Aware Query Optimization

Cited by 26 publications

References 18 publications

Media Forensics Challenge image provenance evaluation and state-of-the-art analysis on large-scale benchmark datasets

Media Forensics Challenge image provenance evaluation and state-of-the-art analysis on large-scale benchmark datasets

Provenance for Interactive Visualizations

Debugging transactions and tracking their provenance with reenactment

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