Collaboratively Patching Linked Data

Today's Web of Data is noisy. Linked Data often needs extensive preprocessing to enable efficient use of heterogeneous resources. While consistent and valid data provides the key to efficient data processing and aggregation we are facing two main challenges: (1st) Identification of erroneous facts and tracking their origins in dynamically connected datasets is a difficult task, and (2nd) efforts in the curation of deficient facts in Linked Data are exchanged rather rarely. Since erroneous data often is duplicated and (re-)distributed by mashup applications it is not only the responsibility of a few original publishers to keep their data tidy, but progresses to be a mission for all distributers and consumers of Linked Data too. We present a new approach to expose and to reuse patches on erroneous data to enhance and to add quality information to the Web of Data. The feasibility of our approach is demonstrated by example of a collaborative game that patches statements in DBpedia data and provides notifications for relevant changes.Comment: 2nd International Workshop on Usage Analysis and the Web of Data (USEWOD2012) in the 21st International World Wide Web Conference (WWW2012), Lyon, France, April 17th, 201

DataHub: Collaborative Data Science & Dataset Version Management at Scale

Relational databases have limited support for data collaboration, where teams collaboratively curate and analyze large datasets. Inspired by software version control systems like git, we propose (a) a dataset version control system, giving users the ability to create, branch, merge, difference and search large, divergent collections of datasets, and (b) a platform, DataHub, that gives users the ability to perform collaborative data analysis building on this version control system. We outline the challenges in providing dataset version control at scale.Comment: 7 page

Introducing Access Control in Webdamlog

We survey recent work on the specification of an access control mechanism in a collaborative environment. The work is presented in the context of the WebdamLog language, an extension of datalog to a distributed context. We discuss a fine-grained access control mechanism for intentional data based on provenance as well as a control mechanism for delegation, i.e., for deploying rules at remote peers.Comment: Proceedings of the 14th International Symposium on Database Programming Languages (DBPL 2013), August 30, 2013, Riva del Garda, Trento, Ital

Research Objects: Towards Exchange and Reuse of Digital Knowledge

What will researchers be publishing in the future? Whilst there is little question that the Web will be the publication platform, as scholars move away from paper towards digital content, there is a need for mechanisms that support the production of self-contained units of knowledge and facilitate the publication, sharing and reuse of such entities.

 In this paper we discuss the notion of _research objects_, semantically rich aggregations of resources, that can possess some scientific intent or support some research objective. We present a number of principles that we expect such objects and their associated services to follow

Research Objects: Towards Exchange and Reuse of Digital Knowledge

What will researchers be publishing in the future? Whilst there is little question that the Web will be the publication platform, as scholars move away from paper towards digital content, there is a need for mechanisms that support the production of self-contained units of knowledge and facilitate the publication, sharing and reuse of such entities. In this paper we discuss the notion of research objects, semantically rich aggregations of resources, that possess some scientifi?c intent or support some research objective. We present a number of principles that we expect such objects and their associated services to follow

Provenance in Collaborative Data Sharing

This dissertation focuses on recording, maintaining and exploiting provenance information in Collaborative Data Sharing Systems (CDSS). These are systems that support data sharing across loosely-coupled, heterogeneous collections of relational databases related by declarative schema mappings. A fundamental challenge in a CDSS is to support the capability of update exchange --- which publishes a participant\u27s updates and then translates others\u27 updates to the participant\u27s local schema and imports them --- while tolerating disagreement between them and recording the provenance of exchanged data, i.e., information about the sources and mappings involved in their propagation. This provenance information can be useful during update exchange, e.g., to evaluate provenance-based trust policies. It can also be exploited after update exchange, to answer a variety of user queries, about the quality, uncertainty or authority of the data, for applications such as trust assessment, ranking for keyword search over databases, or query answering in probabilistic databases. To address these challenges, in this dissertation we develop a novel model of provenance graphs that is informative enough to satisfy the needs of CDSS users and captures the semantics of query answering on various forms of annotated relations. We extend techniques from data integration, data exchange, incremental view maintenance and view update to define the formal semantics of unidirectional and bidirectional update exchange. We develop algorithms to perform update exchange incrementally while maintaining provenance information. We present strategies for implementing our techniques over an RDBMS and experimentally demonstrate their viability in the Orchestra prototype system. We define ProQL, a query language for provenance graphs that can be used by CDSS users to combine data querying with provenance testing as well as to compute annotations for their data, based on their provenance, that are useful for a variety of applications. Finally, we develop a prototype implementation ProQL over an RDBMS and indexing techniques to speed up provenance querying, evaluate experimentally the performance of provenance querying and the benefits of our indexing techniques

Towards Exascale Scientific Metadata Management

Advances in technology and computing hardware are enabling scientists from all areas of science to produce massive amounts of data using large-scale simulations or observational facilities. In this era of data deluge, effective coordination between the data production and the analysis phases hinges on the availability of metadata that describe the scientific datasets. Existing workflow engines have been capturing a limited form of metadata to provide provenance information about the identity and lineage of the data. However, much of the data produced by simulations, experiments, and analyses still need to be annotated manually in an ad hoc manner by domain scientists. Systematic and transparent acquisition of rich metadata becomes a crucial prerequisite to sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and domain-agnostic metadata management infrastructure that can meet the demands of extreme-scale science is notable by its absence. To address this gap in scientific data management research and practice, we present our vision for an integrated approach that (1) automatically captures and manipulates information-rich metadata while the data is being produced or analyzed and (2) stores metadata within each dataset to permeate metadata-oblivious processes and to query metadata through established and standardized data access interfaces. We motivate the need for the proposed integrated approach using applications from plasma physics, climate modeling and neuroscience, and then discuss research challenges and possible solutions

Making Linked Open Data Writable with Provenance Semirings

Linked Open Data cloud (LOD) is essentially read-only, re- straining the possibility of collaborative knowledge construction. To sup- port collaboration, we need to make the LOD writable. In this paper, we propose a vision for a writable linked data where each LOD participant can define updatable materialized views from data hosted by other par- ticipants. Consequently, building a writable LOD can be reduced to the problem of SPARQL self-maintenance of Select-Union recursive mate- rialized views. We propose TM-Graph, an RDF-Graph annotated with elements of a specialized provenance semiring to maintain consistency of these views and we analyze complexity in space and traffic

AiiDA: Automated Interactive Infrastructure and Database for Computational Science

Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA's design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA's design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows.Comment: 30 pages, 7 figure