Adriane Chapman

University of Southampton

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Co-Authors

M. David Allen

Provo College

Barbara Blaustein

Mitre (United States)

Len Seligman

Mitre (United States)

H. V. Jagadish

University of Michigan–Ann Arbor

Arnon Rosenthal

Tel Aviv University

George Roussos

Birkbeck, University of London

Thanassis Tiropanis

University of Southampton

Alexandra Poulovassilis

Birkbeck, University of London

Mohamed Ragab

University of Southampton

James Cheney

University of Edinburgh

Cooperative Institutions

University of Southampton

University of Michigan–Ann Arbor

University of Oxford

NIHR Southampton Biomedical Research Centre

King's College London

University of Manchester

Mitre (United States)

Primary Health Care

University College London

Université de Versailles Saint-Quentin-en-Yvelines

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A Provenance Model for Manually Curated Data

Lecture notes in computer science (2006)

Peter Buneman Adriane Chapman James Cheney Stijn Vansummeren

Copying

Data model (GIS)

10.1007/11890850_17

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Citations (45)

Effective Integration of Protein Data through Better Data Modeling

OMICS A Journal of Integrative Biology (2003)

Adriane Chapman Cong Yu H. V. Jagadish

Protein data, from sequence and structure to interaction, is being generated through many diverse methodologies; it is stored and reported in numerous forms and multiple places. The magnitude of the data limits researchers abilities to utilize all information generated. Effective integration of protein data can be accomplished through better data modeling. We demonstrate this through the MIPD project.

Sequence (biology)

10.1089/153623103322006706

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Citations (3)

Extending the FHIR standard to handle provenance

John Moehrke Arnon Rosenthal Adriane Chapman

Source

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Citations (1)

Provenance tipping point

David Gammack Adriane Chapman

Capture is a known, difficult problem for provenance. Obtaining from the systems and programs exactly what happened has been a continuing struggle outside of database and workflow systems. The provenance research community has created libraries to log provenance, and has also embedded instances of capture agents within operating systems, specific programs, etc. However, it is impossible to know if we are inserting capture agents at both the optimal location and frequency in a given system for a high quality provenance graph. In this work, we develop an initial agent based model to simulate Activity and Entity interactions in a complex system of software. Using this model, we can attempt to define some generalized principles about type, frequency and distribution of provenance capture agents given a new system.

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Citations (1)

Do You Know Where Your Data’s Been? – Tamper-Evident Database Provenance

Lecture notes in computer science (2009)

Jing Zhang Adriane Chapman Kristen LeFevre

Checksum

Bloom filter

10.1007/978-3-642-04219-5_2

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Citations (33)

PLUS: Provenance for life, the universe and stuff

Adriane Chapman M. David Allen Barbara Blaustein Len Seligman Chris Wolf

In this demonstration, we exhibit a new type of provenance system, one that is not tied to any particular domain, closed-world system or use. The PLUS provenance system was inspired by government requirements to enable provenance capture, storage and use across multi-organizational systems. PLUS is general enough to interact across open-world distributed systems, often without administrative access to those underlying distributed systems. It captures and stores provenance, permits user annotations, and provides tools for analyzing the provenance on the basis of those annotations. Due to the need to share provenance across many organizations, much attention has been paid to provenance access and security. We highlight all of these features via a demonstration using an Emergency Preparedness and Response (EP&R) scenario.

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Citations (2)

What Can Knowledge Bring to Machine Learning?—A Survey of Low-shot Learning for Structured Data

ACM Transactions on Intelligent Systems and Technology (2022)

Yang Hu Adriane Chapman Guihua Wen Dame Wendy Hall

Supervised machine learning has several drawbacks that make it difficult to use in many situations. Drawbacks include heavy reliance on massive training data, limited generalizability, and poor expressiveness of high-level semantics. Low-shot Learning attempts to address these drawbacks. Low-shot learning allows the model to obtain good predictive power with very little or no training data, where structured knowledge plays a key role as a high-level semantic representation of human. This article will review the fundamental factors of low-shot learning technologies, with a focus on the operation of structured knowledge under different low-shot conditions. We also introduce other techniques relevant to low-shot learning. Finally, we point out the limitations of low-shot learning, the prospects and gaps of industrial applications, and future research directions.

One shot

Representation

10.1145/3510030

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Citations (16)

Scalable access controls for lineage

Arnon Rosenthal Len Seligman Adriane Chapman Barbara Blaustein

Lineage stores often contain sensitive information that needs protection from unauthorized access. We build on prior work for security and privacy of lineage information, focusing on complex conditions and scalable administration. We use Attribute-Based Access Control (ABAC) to express conditions based on many attributes, instead of roles. We then make administration and management more scalable, instead of managing large, monolithic access predicates for each object. To do so, we first support modular traceability and maintainability for separate concerns (e.g. security, legally mandated privacy, organizationally mandated privacy). We then provide constructs to manage authority when multiple administrators must collaborate. We show that these security techniques are needed for easy lineage security administration.

Traceability

Lineage (genetic)

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Citations (26)

Provenance: Information for Shared Understanding

M. David Allen Adriane Chapman Barbara Blaustein

Source

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Citations (2)

Enabling personal consent in databases

Proceedings of the VLDB Endowment (2021)

George Konstantinidis Jet Holt Adriane Chapman

Users have the right to consent to the use of their data, but current methods are limited to very coarse-grained expressions of consent, as "opt-in/opt-out" choices for certain uses. In this paper we identify the need for fine-grained consent management and formalize how to express and manage user consent and personal contracts of data usage in relational databases. Unlike privacy approaches, our focus is not on preserving confidentiality against an adversary, but rather cooperate with a trusted service provider to abide by user preferences in an algorithmic way. Our approach enables data owners to express the intended data usage in formal specifications, that we call consent constraints , and enables a service provider that wants to honor these constraints, to automatically do so by filtering query results that violate consent; rather than both sides relying on "terms of use" agreements written in natural language. We provide formal foundations (based on provenance), algorithms (based on unification and query rewriting), connections to data privacy, and complexity results for supporting consent in databases. We implement our framework in an open source RDBMS, and provide an evaluation against the most relevant privacy approach using the TPC-H benchmark, and on a real dataset of ICU data.

Benchmark (surveying)

10.14778/3489496.3489516

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Citations (7)