A time-and-value centric provenance model and architecture for medical event streams

Wang, Min; Blount, Marion; Davis, John S.; Misra, Archan; Sow, Daby

doi:10.1145/1248054.1248082

Cited by 17 publications

(11 citation statements)

References 7 publications

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“…The TVC model [17] specifies a set of primitives that are used to define a causative relationship between the data elements generated at the output port of a PE and the data elements arriving at its input ports. The TVC model differs from conventional annotation-based approaches for data provenance, which would need to embed a potentially large set of input data identifiers as metadata in every output data element (owing to the statefulness of stream operators, which implies that each output may be influenced by a large number of input data samples).…”

Section: The Tvc Model For Century and Resulting Limitationsmentioning

confidence: 99%

“…Our earlier work in [17] was one of the first to explore a model-based solution for data provenance in stream computing platforms.…”

Section: Related Workmentioning

confidence: 99%

“…To support data provenance for such high throughput environments, we have previously introduced a model-based provenance solution, called Time-Value-Centric (TVC) provenance [17], which uses an explicit specification of the dependency relationship between input and output streams at every node (hereafter called a Processing Element or PE) in the processing graph. The notion of data provenance, involving the identification of multiple stream elements at upstream PEs belonging to a processing graph, is graphically illustrated in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Advances and Challenges for Scalable Provenance in Stream Processing Systems

Misra¹,

Blount²,

Kementsietsidis³

et al. 2008

Lecture Notes in Computer Science

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Abstract. While data provenance is a well-studied topic in both database and workflow systems, its support within stream processing systems presents a new set of challenges. Part of the challenge is the high stream event rate and the low processing latency requirements imposed by many streaming applications. For example, emerging streaming applications in healthcare or finance call for data provenance, as illustrated in the Century stream processing infrastructure that we are building for supporting online healthcare analytics. At anytime, given an output data element (e.g., a medical alert) generated by Century, the system must be able to retrieve the input and intermediate data elements that led to its generation. In this paper, we describe the requirements behind our initial implementation of Century's provenance subsystem. We then analyze its strengths and limitations and propose a new provenance architecture to address some of these limitations. The paper also includes a discussion on the open challenges in this area.

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Section: The Tvc Model For Century and Resulting Limitationsmentioning

confidence: 99%

“…Our earlier work in [17] was one of the first to explore a model-based solution for data provenance in stream computing platforms.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Advances and Challenges for Scalable Provenance in Stream Processing Systems

Misra¹,

Blount²,

Kementsietsidis³

et al. 2008

Lecture Notes in Computer Science

Self Cite

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“…allocated to each procedure performed on a patient. This information must be stored in retrievable fashion for legal or analysis purposes (Wang et al, 2007).…”

Section: Alpha-3i and Cimag-ramentioning

confidence: 99%

CRISTAL-iSE - Provenance Applied in Industry

Shamdasani

Branson

McClatchey

et al. 2014

Proceedings of the 16th International Conference on Enterprise Information Systems

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Abstract:This paper presents the CRISTAL-iSE project as a framework for the management of provenance information in industry. The project itself is a research collaboration between academia and industry. A key factor in the project is the use of a system known as CRISTAL which is a mature system based on proven description driven principles. A crucial element in the description driven approach is that the fact that objects (Items) are described at runtime enabling managed systems to be both dynamic and flexible. Another factor is the notion that all Items in CRISTAL are stored and versioned, therefore enabling a provenance collection system. In this paper a concrete application, called Agilium, is briefly described and a future application CIMAG-RA is presented which will harness the power of both CRISTAL and Agilium.

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“…To this end, they introduce the Time-Value-Centric (TVC) model [287,396,228] which is an algebraic approach to compute all the ancestor events an event depends on.…”

Section: Data Collections and Streamsmentioning

confidence: 99%

The Foundations for Provenance on the Web

Moreau

2010

FNT in Web Science

172

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Provenance, i.e., the origin or source of something, is becoming an important concern, since it offers the means to verify data products, to infer their quality, to analyse the processes that led to them, and to decide whether they can be trusted. For instance, provenance enables the reproducibility of scientific results; provenance is necessary to track attribution and credit in curated databases; and, it is essential for reasoners to make trust judgements about the information they use over the Semantic Web.As the Web allows information sharing, discovery, aggregation, filtering and flow in an unprecedented manner, it also becomes very difficult to identify, reliably, the original source that produced an information item on the Web. Since the emerging use of provenance in niche applications is undoubtedly demonstrating the benefits of provenance, we contend that provenance can and should reliably be tracked and exploited on the Web, and we survey the necessary foundations to achieve such a vision.Using multiple data sources, we have compiled the largest bibliographical database on provenance so far. This large corpus allows us to analyse emerging trends in the research community. Specifically, using the CiteSpace tool, we identify clusters of papers that constitute research fronts, from which we derive characteristics that we use to structure our foundational framework for provenance on the Web. We note that such an endeavour requires a multi-disciplinary approach, since it requires contributions from many computer science sub-disciplines, but also other non-technical fields given the human challenge that is anticipated.To develop our vision, it is necessary to provide a definition of provenance that applies to the Web context. Our conceptual definition of provenance is expressed in terms of processes, and is shown to generalise various definitions of provenance commonly encountered. Furthermore, by bringing realistic distributed systems assumptions, we refine our definition as a query over assertions made by processes.Given that the majority of work on provenance has been undertaken by the database, workflow and e-science communities, we review some of their work, contrasting approaches, and focusing on important topics we believe to be crucial for bringing provenance to the Web, such as abstraction, collections, storage, queries, workflow evolution, semantics and activities involving human interactions.However, provenance approaches developed in the context of databases and workflows essentially deal with closed systems. By that, we mean that workflow or database management systems are in full control of the data they manage, and track their provenance within their own scope, but not beyond. In the context of the Web, a broader approach is required by which chunks of provenance representation can be brought together to describe the provenance of information flowing across multiple systems. This is the specific purpose of the Open Provenance Vision, which is an approach that consists of controlled voc...

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A time-and-value centric provenance model and architecture for medical event streams

Cited by 17 publications

References 7 publications

Advances and Challenges for Scalable Provenance in Stream Processing Systems

Advances and Challenges for Scalable Provenance in Stream Processing Systems

CRISTAL-iSE - Provenance Applied in Industry

The Foundations for Provenance on the Web

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