Provenance in Databases: Why, How, and Where

Cheney, James; Chiticariu, Laura; Tan, Wang-Chiew

doi:10.1561/9781601982339

Cited by 41 publications

(60 citation statements)

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“…Intuitively, t = (Michael Jordan) is present in the output Q (I ) for two reasons: (1) the existence of the (ecodd, jumpman) and (jumpman, Michael Jordan) tuples and (2) the existence of the (ecodd, mlpro) and (mlpro, Michael Jordan) tuples. Why-provenance [5,7] formalizes this intuition. The why-provenance 1 of a tuple t with respect to query Q and database instance I , denoted Why(Q,I ,t ), is a set J 1 , .…”

Section: Data Provenancementioning

confidence: 92%

“…Alternatively, consider data provenance in the form of why-provenance [5,7]. Given a relational database, a query issued against the database, and a tuple in the output of the query, why-provenance explains why the output tuple was produced.…”

Section: Introductionmentioning

confidence: 99%

“…For a formal definition of why-provenance, we refer the reader to[7]. For our purposes, an informal understanding of why-provenance is sufficent.…”

mentioning

confidence: 99%

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Debugging Distributed Systems with Why-Across-Time Provenance

Whittaker

Teodoropol

Alvaro

et al. 2018

Proceedings of the ACM Symposium on Cloud Computing

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Systematically reasoning about the fine-grained causes of events in a real-world distributed system is challenging. Causality, from the distributed systems literature, can be used to compute the causal history of an arbitrary event in a distributed system, but the event's causal history is an overapproximation of the true causes. Data provenance, from the database literature, precisely describes why a particular tuple appears in the output of a relational query, but data provenance is limited to the domain of static relational databases. In this paper, we present wat-provenance: a novel form of provenance that provides the benefits of causality and data provenance. Given an arbitrary state machine, watprovenance describes why the state machine produces a particular output when given a particular input. This enables system developers to reason about the causes of events in real-world distributed systems. We observe that automatically extracting the wat-provenance of a state machine is often infeasible. Fortunately, many distributed systems components have simple interfaces from which a developer can directly specify wat-provenance using a technique we call wat-provenance specifications. Leveraging the theoretical foundations of wat-provenance, we implement a prototype distributed debugging framework called Watermelon.Returning to our example above, the why-provenance of the (Michael Jordan) tuple is the set {J 1 , J 2 } where J 1 = {(ecodd, jumpman), (jumpman, Michael Jordan)} J 2 = {(ecodd, mlpro), (mlpro, Michael Jordan)} J 1 and J 2 are minimal witnesses, so the why-provenance and minimal why-provenance of t = (Michael Jordan) are the same.

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Section: Data Provenancementioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Debugging Distributed Systems with Why-Across-Time Provenance

Whittaker

Teodoropol

Alvaro

et al. 2018

Proceedings of the ACM Symposium on Cloud Computing

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“…For an exhaustive survey provenance writing, we allude perusers to [19]. Similarly, Cheney et al give point by point audit provenance inside database network [2]. Extensively, one can sort work into three zones [10]: substance, the board, use.…”

Section: A 4 Analysis Of Exsting Mechanismsmentioning

confidence: 99%

An Extended Native RDF for Provenance-Enabled Queries in Linked Data

Murty,

Rao

2019

IJEAT

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Various thoughts of provenance for database inquiries have been proposed and examined in the previous couple of years. In this article, we detail three primary thoughts of database provenance, portion of their applications, and investigate among them. In particular, we audit why, how, and where provenance, depict the connections among these ideas of provenance, and portray a portion of their applications in certainty calculation, see upkeep and update, troubleshooting, and explanation spread. Provenance in Databases audits explore in course of recent years on why, how, and where provenance, explains connections among these ideas of provenance, and depicts portion of their applications in certainty calculation, see upkeep and update, troubleshooting, and comment engendering. This paper is to give review distributed writing dedicated to the subject of putting away, following, and questioning provenance in connected information It might be utilized as guide for discovering further articles, in any field of study, moderately rapidly. Provenance in Databases is planned for designers and specialists who might want to acclimate themselves with the establishments, just as the numerous difficulties in the field database provenance. Specifically, ability to store, track, and inquiry provenance information is turning into crucial element present day triple stores. We present strategies stretching out local RDF store to proficiently deal with the capacity, following, and questioning of provenance in RDF information. We depict solid and justifiable detail manner in which results were gotten from the information and how specific bits information were joined to answer question. In this manner, we present systems to tailor inquiries with provenance information.

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“…In DataSpaces, a significant challenge is to answer historical queries that applied to heterogenous data. A line of research proposed techniques for modeling and analyzing provenance (also known as lineage and pedigree), uncertainty, and inconsistency of the heterogenous data in DataSpaces . Many provenance models() have been presented, motivated by notions such as influence, dependence, and causality in such systems.…”

Section: Related Workmentioning

confidence: 99%

ProcessAtlas: A scalable and extensible platform for business process analytics

2018

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In today's knowledge-, service-, and cloud-based economy, an overwhelming amount of business-related data are being generated at a fast rate daily from a wide range of sources. These data increasingly show all the typical properties of big data: wide physical distribution, diversity of formats, nonstandard data models, and independently managed and heterogeneous semantics. In this context, there is a need for new scalable and process-aware services for querying, exploration, and analysis of process data in the enterprise because (1) process data analysis services should be capable of processing and querying large amount of data effectively and efficiently and, therefore, have to be able to scale well with the infrastructure's scale and (2) the querying services need to enable users to express their data analysis and querying needs using process-aware abstractions rather than other lower-level abstractions. In this paper, we introduce Proces-sAtlas, ie, an extensible large-scale process data querying and analysis platform for analyzing process data in the enterprise. The ProcessAtlas platform offers an extensible architecture by adopting a service-based model so that new analytical services can be plugged into the platform. In ProcessAtlas, we present a domain-specific model for representing process knowledge, ie, process-level entities, abstractions, and the relationships among them modeled as graphs. We provide services for discovering, extracting, and analyzing process data. We provide efficient mapping and execution of process-level queries into graph-level queries by using scalable process query services to deal with the process data size growth and with the infrastructure's scale. We have implemented ProcessAtlas as a MapReduce-based prototype and report on experiments performed on both synthetic and real-world datasets. goal is to understand how a BP is performed and to identify opportunities for improvement. 2 However, the wide-scale automation in enterprises has led to having BPs implemented over many systems. Therefore, answering questions such as "What are the dependencies between 2 different cases? What is the typical path for a case and how much time and resources can be spent in processing it? What is the story behind the file number #756.1: where is the origin of this file? How it evolved over time? Who was involved in updating this file?" becomes difficult at best.The main barrier for answering questions like those aforementioned is that, in today's knowledge-, service-, and cloud-based economy, the information about process execution is scattered across several systems and data sources. Consequently, process logs increasingly come to show all typical properties of the big data 3 : wide physical distribution, diversity of formats, nonstandard data models, and independently managed and heterogeneous semantics. We use the term Process Data to refer to such large hybrid collections of heterogeneous and partially unstructured process-related execution data. Digitalization of business artifacts (eg, doc...

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Provenance in Databases: Why, How, and Where

Cited by 41 publications

References 0 publications

Debugging Distributed Systems with Why-Across-Time Provenance

Debugging Distributed Systems with Why-Across-Time Provenance

An Extended Native RDF for Provenance-Enabled Queries in Linked Data

ProcessAtlas: A scalable and extensible platform for business process analytics

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