Provenance for Online Decision Making

Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer data quality. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. This paper presents an application-independent methodology for analyzing data based on the network metrics of provenance graphs to learn about such patterns and to relate them to data quality in an automated manner. Validating this method on the provenance records of CollabMap, an online crowdsourcing mapping application, we demonstrated an accuracy level of over 95% for the trust classification of data generated by the crowd therein.

2008

Abstract Applications deployed on cyber-infrastructures often rely on multiple data sources and distributed compute resources to access, process, and derive results.

2018

Today, more and more scientific groups are developing citizen science applications. Citizen science is a relatively new domain of science that has already proved to be as beneficial as classical science. One of the major challenges citizen science face is the data quality assurance. It uses several techniques to verify the data quality based on expert evaluation, voting systems, etc. Data provenance is used in many scientific systems and provides reliable mechanism for tracking data history. It includes history of origin, changes, and all interactions between different parts of data. Data provenance by itself has many types such as “Why provenance”, “When provenance”, and “What provenance”. The purpose of this work is to build a prototype of a database with built-in data provenance. Several databases systems and models such as Relational databases, NoSQL databases are taken into consideration. Experiments are been conducted to test limitations of proposed prototype.

2012

Abstract. Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated.

2006

Abstract Provenance is a concept often used in the Art world to refer to the documented history of an artifact, providing information about the artifact's lineage and authenticity. Provenance-aware applications similarly allow their users to have confidence about the data they produce, and can enable users to make judgements relating to notions of trust, accountability, validation, replication and compliance of their data.

Lecture Notes in Computer Science, 2015

Semantic IoT: Theory and Applications, 2021

Provenance as perceived is a trail of a piece of a data item that helps in linking derived pieces of web resources or Internet of Everything (IoE) data to its creators. Provenance allows the software agents and developers to assert that devices across the IoE landscape can be made trustworthy if the same has a valid derivation path that is associated with it and is reliable/trustworthy. Provenance is considered as metadata that must be embedded into an OWL ontology, this metadata supports the semantic agents/reasoners to evaluate the data or workflow trail of the item in question. Contemporary researchers have proposed several models of trust that are based on mathematical calculations, however, the implementation of trust on semantically generated and modified documents i.e. ontologies at large is still evolving. This chapter thus aims to discuss, deliberate, and implement trust in an existing ontology using provenance assertions. This implementation of trust is based on the PROV-DM (Data Model) that has been suggested by the World Wide Web consortium. The chapter illustrates the implementation and inferencing of trust embedded in an OWL-based University Ontology. Provenance assertions using various scenarios and their inference have been highlighted to signify the validity and consistency of the ontology an XML serialized dataset.

IEEE Transactions on Visualization and Computer Graphics, 2018

Visual analytics systems continue to integrate new technologies and leverage modern environments for exploration and collaboration, making tools and techniques available to a wide audience through web browsers. Many of these systems have been developed with rich interactions, offering users the opportunity to examine details and explore hypotheses that have not been directly encoded by a designer. Understanding is enhanced when users can replay and revisit the steps in the sensemaking process, and in collaborative settings, it is especially important to be able to review not only the current state but also what decisions were made along the way. Unfortunately, many web-based systems lack the ability to capture such reasoning, and the path to a result is transient, forgotten when a user moves to a new view. This paper explores the requirements to augment existing client-side web applications with support for capturing, reviewing, sharing, and reusing steps in the reasoning process. Furthermore, it considers situations where decisions are made with streaming data, and the insights gained from revisiting those choices when more data is available. It presents a proof of concept, the Shareable Interactive Manipulation Provenance framework (SIMProv.js), that addresses these requirements in a modern, client-side JavaScript library, and describes how it can be integrated with existing frameworks.

ACM Transactions on Software …, 2009

Data Mining and Knowledge Discovery, 2018

Provenance network analytics is a novel data analytics approach that helps infer properties of data, such as quality or importance, from their provenance. Instead of analysing application data, which are typically domain-dependent, it analyses the data's provenance as represented using the World Wide Web Consortium's domainagnostic PROV data model. Specifically, the approach proposes a number of network metrics for provenance data and applies established machine learning techniques over such metrics to build predictive models for some key properties of data. Applying this method to the provenance of real-world data from three different applications, we show that it can successfully identify the owners of provenance documents, assess the quality of crowdsourced data, and identify instructions from chat messages in an alternate-reality game with high levels of accuracy. By so doing, we demonstrate the different ways the proposed provenance network metrics can be used in analysing data, providing the foundation for provenance-based data analytics.