726 research outputs found
PReServ: Provenance Recording for Services
The importance of understanding the process by which a result was generated in an experiment is fundamental to science. Without such information, other scientists cannot replicate, validate, or duplicate an experiment. We define provenance as the process that led to a result. With large scale in-silico experiments, it becomes increasingly difficult for scientists to record process documentation that can be used to retrieve the provenance of a result. Provenance Recording for Services (PReServ) is a software package that allows developers to integrate process documentation recording into their applications. PReServ has been used by several applications and its performance has been benchmarked
The Origin of Data: Enabling the Determination of Provenance in Multi-institutional Scientific Systems through the Documentation of Processes
The Oxford English Dictionary defines provenance as (i) the fact of coming from some particular source or quarter; origin, derivation. (ii) the history or pedigree of a work of art, manuscript, rare book, etc.; concr., a record of the ultimate derivation and passage of an item through its various owners. In art, knowing the provenance of an artwork lends weight and authority to it while providing a context for curators and the public to understand and appreciate the work’s value. Without such a documented history, the work may be misunderstood, unappreciated, or undervalued. In computer systems, knowing the provenance of digital objects would provide them with greater weight, authority, and context just as it does for works of art. Specifically, if the provenance of digital objects could be determined, then users could understand how documents were produced, how simulation results were generated, and why decisions were made. Provenance is of particular importance in science, where experimental results are reused, reproduced, and verified. However, science is increasingly being done through large-scale collaborations that span multiple institutions, which makes the problem of determining the provenance of scientific results significantly harder. Current approaches to this problem are not designed specifically for multi-institutional scientific systems and their evolution towards greater dynamic and peer-to-peer topologies. Therefore, this thesis advocates a new approach, namely, that through the autonomous creation, scalable recording, and principled organisation of documentation of systems’ processes, the determination of the provenance of results produced by complex multi-institutional scientific systems is enabled. The dissertation makes four contributions to the state of the art. First is the idea that provenance is a query performed over documentation of a system’s past process. Thus, the problem is one of how to collect and collate documentation from multiple distributed sources and organise it in a manner that enables the provenance of a digital object to be determined. Second is an open, generic, shared, principled data model for documentation of processes, which enables its collation so that it provides high-quality evidence that a system’s processes occurred. Once documentation has been created, it is recorded into specialised repositories called provenance stores using a formally specified protocol, which ensures documentation has high-quality characteristics. Furthermore, patterns and techniques are given to permit the distributed deployment of provenance stores. The protocol and patterns are the third contribution. The fourth contribution is a characterisation of the use of documentation of process to answer questions related to the provenance of digital objects and the impact recording has on application performance. Specifically, in the context of a bioinformatics case study, it is shown that six different provenance use cases are answered given an overhead of 13% on experiment run-time. Beyond the case study, the solution has been applied to other applications including fault tolerance in service-oriented systems, aerospace engineering, and organ transplant management
Architecture for Provenance Systems
This document covers the logical and process architectures of provenance systems. The logical architecture identifies key roles and their interactions, whereas the process architecture discusses distribution and security. A fundamental aspect of our presentation is its technology-independent nature, which makes it reusable: the principles that are exposed in this document may be applied to different technologies
An Architecture for Provenance Systems
This document covers the logical and process architectures of provenance systems. The logical architecture identifies key roles and their interactions, whereas the process architecture discusses distribution and security. A fundamental aspect of our presentation is its technology-independent nature, which makes it reusable: the principles that are exposed in this document may be applied to different technologies
Provenance-based validation of E-science experiments
E-Science experiments typically involve many distributed services maintained by different organisations. After an experiment has been executed, it is useful for a scientist to verify that the execution was performed correctly or is compatible with some existing experimental criteria or standards. Scientists may also want to review and verify experiments performed by their colleagues. There are no existing frameworks for validating such experiments in today's e-Science systems. Users therefore have to rely on error checking performed by the services, or adopt other ad hoc methods. This paper introduces a platform-independent framework for validating workflow executions. The validation relies on reasoning over the documented provenance of experiment results and semantic descriptions of services advertised in a registry. This validation process ensures experiments are performed correctly, and thus results generated are meaningful. The framework is tested in a bioinformatics application that performs protein compressibility analysis
GitTables: A Large-Scale Corpus of Relational Tables
The success of deep learning has sparked interest in improving relational
table tasks, like data preparation and search, with table representation models
trained on large table corpora. Existing table corpora primarily contain tables
extracted from HTML pages, limiting the capability to represent offline
database tables. To train and evaluate high-capacity models for applications
beyond the Web, we need resources with tables that resemble relational database
tables. Here we introduce GitTables, a corpus of 1M relational tables extracted
from GitHub. Our continuing curation aims at growing the corpus to at least 10M
tables. Analyses of GitTables show that its structure, content, and topical
coverage differ significantly from existing table corpora. We annotate table
columns in GitTables with semantic types, hierarchical relations and
descriptions from Schema.org and DBpedia. The evaluation of our annotation
pipeline on the T2Dv2 benchmark illustrates that our approach provides results
on par with human annotations. We present three applications of GitTables,
demonstrating its value for learned semantic type detection models, schema
completion methods, and benchmarks for table-to-KG matching, data search, and
preparation. We make the corpus and code available at
https://gittables.github.io
Inductive Entity Representations from Text via Link Prediction
Knowledge Graphs (KG) are of vital importance for multiple applications on
the web, including information retrieval, recommender systems, and metadata
annotation. Regardless of whether they are built manually by domain experts or
with automatic pipelines, KGs are often incomplete. Recent work has begun to
explore the use of textual descriptions available in knowledge graphs to learn
vector representations of entities in order to preform link prediction.
However, the extent to which these representations learned for link prediction
generalize to other tasks is unclear. This is important given the cost of
learning such representations. Ideally, we would prefer representations that do
not need to be trained again when transferring to a different task, while
retaining reasonable performance.
In this work, we propose a holistic evaluation protocol for entity
representations learned via a link prediction objective. We consider the
inductive link prediction and entity classification tasks, which involve
entities not seen during training. We also consider an information retrieval
task for entity-oriented search. We evaluate an architecture based on a
pretrained language model, that exhibits strong generalization to entities not
observed during training, and outperforms related state-of-the-art methods (22%
MRR improvement in link prediction on average). We further provide evidence
that the learned representations transfer well to other tasks without
fine-tuning. In the entity classification task we obtain an average improvement
of 16% in accuracy compared with baselines that also employ pre-trained models.
In the information retrieval task, we obtain significant improvements of up to
8.8% in NDCG@10 for natural language queries. We thus show that the learned
representations are not limited KG-specific tasks, and have greater
generalization properties than evaluated in previous work
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