406 research outputs found
Yavaa: supporting data workflows from discovery to visualization
Recent years have witness an increasing number of data silos being opened up both within organizations and to the general public: Scientists publish their raw data as supplements to articles or even standalone artifacts to enable others to verify and extend their work. Governments pass laws to open up formerly protected data treasures to improve accountability and transparency as well as to enable new business ideas based on this public good. Even companies share structured information about their products and services to advertise their use and thus increase revenue. Exploiting this wealth of information holds many challenges for users, though. Oftentimes data is provided as tables whose sheer endless rows of daunting numbers are barely accessible. InfoVis can mitigate this gap. However, offered visualization options are generally very limited and next to no support is given in applying any of them. The same holds true for data wrangling. Only very few options to adjust the data to the current needs and barely any protection are in place to prevent even the most obvious mistakes. When it comes to data from multiple providers, the situation gets even bleaker. Only recently tools emerged to search for datasets across institutional borders reasonably. Easy-to-use ways to combine these datasets are still missing, though. Finally, results generally lack proper documentation of their provenance. So even the most compelling visualizations can be called into question when their coming about remains unclear. The foundations for a vivid exchange and exploitation of open data are set, but the barrier of entry remains relatively high, especially for non-expert users. This thesis aims to lower that barrier by providing tools and assistance, reducing the amount of prior experience and skills required. It covers the whole workflow ranging from identifying proper datasets, over possible transformations, up until the export of the result in the form of suitable visualizations
Exploiting Context-Dependent Quality Metadata for Linked Data Source Selection
The traditional Web is evolving into the Web of Data which consists of huge collections
of structured data over poorly controlled distributed data sources. Live
queries are needed to get current information out of this global data space. In live
query processing, source selection deserves attention since it allows us to identify the
sources which might likely contain the relevant data. The thesis proposes a source
selection technique in the context of live query processing on Linked Open Data,
which takes into account the context of the request and the quality of data contained in
the sources to enhance the relevance (since the context enables a better interpretation
of the request) and the quality of the answers (which will be obtained by processing
the request on the selected sources). Specifically, the thesis proposes an extension of
the QTree indexing structure that had been proposed as a data summary to support
source selection based on source content, to take into account quality and contextual
information. With reference to a specific case study, the thesis also contributes an approach,
relying on the Luzzu framework, to assess the quality of a source with respect
to for a given context (according to different quality dimensions). An experimental
evaluation of the proposed techniques is also provide
Web
The Web of Linked Data grows rapidly and already contains data originating from hundreds of data sources. The quality of data from those sources is very diverse, as values may be out of date, incomplete or incorrect. Moreover, data sources may provide conflicting values for a single real-world object. In order for Linked Data applications to consume data from this global data space in an integrated fashion, a number of challenges have to be overcome. One of these challenges is to rate and to integrate data based on their quality. However, quality is a very subjective matter, and finding a canonic judgement that is suitable for each and every task is not feasible. To simplify the task of consuming high-quality data, we present Sieve, a framework for flexibly expressing quality assessment methods as well as fusion methods. Sieve is integrated into the Linked Data Integration Framework (LDIF), which handles Data Access, Schema Mapping and Identity Resolution, all crucial preliminaries for quality assessment and fusion. We demonstrate Sieve in a data integration scenario importing data from the English and Portuguese versions of DBpedia, and discuss how we increase completeness, conciseness and consistency through the use of our framework
Provenance-aware knowledge representation: A survey of data models and contextualized knowledge graphs
Expressing machine-interpretable statements in the form of subject-predicate-object triples is a well-established practice for capturing semantics of structured data. However, the standard used for representing these triples, RDF, inherently lacks the mechanism to attach provenance data, which would be crucial to make automatically generated and/or processed data authoritative. This paper is a critical review of data models, annotation frameworks, knowledge organization systems, serialization syntaxes, and algebras that enable provenance-aware RDF statements. The various approaches are assessed in terms of standard compliance, formal semantics, tuple type, vocabulary term usage, blank nodes, provenance granularity, and scalability. This can be used to advance existing solutions and help implementers to select the most suitable approach (or a combination of approaches) for their applications. Moreover, the analysis of the mechanisms and their limitations highlighted in this paper can serve as the basis for novel approaches in RDF-powered applications with increasing provenance needs
Security Aspects in Web of Data Based on Trust Principles. A brief of Literature Review
Within scientific community, there is a certain consensus to define "Big Data" as a global set, through a complex integration that embraces several dimensions from using of research data, Open Data, Linked Data, Social Network Data, etc. These data are scattered in different sources, which suppose a mix that respond to diverse philosophies, great diversity of structures, different denominations, etc. Its management faces great technological and methodological challenges: The discovery and selection of data, its extraction and final processing, preservation, visualization, access possibility, greater or lesser structuring, between other aspects, which allow showing a huge domain of study at the level of analysis and implementation in different knowledge domains. However, given the data availability and its possible opening: What problems do the data opening face? This paper shows a literature review about these security aspects
Security Aspects in Web of Data Based on Trust Principles. A brief of Literature Review
Within scientific community, there is a certain consensus to define "Big Data" as a global set, through a complex integration that embraces several dimensions from using of research data, Open Data, Linked Data, Social Network Data, etc. These data are scattered in different sources, which suppose a mix that respond to diverse philosophies, great diversity of structures, different denominations, etc. Its management faces great technological and methodological challenges: The discovery and selection of data, its extraction and final processing, preservation, visualization, access possibility, greater or lesser structuring, between other aspects, that allow showing a huge domain of study at the level of analysis and implementation in different knowledge domains. However, given the data availability and its possible opening: What problems do the data opening face? This paper shows a literature review about these security aspects
A semantic and agent-based approach to support information retrieval, interoperability and multi-lateral viewpoints for heterogeneous environmental databases
PhDData stored in individual autonomous databases often needs to be combined and
interrelated. For example, in the Inland Water (IW) environment monitoring domain,
the spatial and temporal variation of measurements of different water quality indicators
stored in different databases are of interest. Data from multiple data sources is more
complex to combine when there is a lack of metadata in a computation forin and when
the syntax and semantics of the stored data models are heterogeneous. The main types
of information retrieval (IR) requirements are query transparency and data
harmonisation for data interoperability and support for multiple user views. A
combined Semantic Web based and Agent based distributed system framework has
been developed to support the above IR requirements. It has been implemented using
the Jena ontology and JADE agent toolkits. The semantic part supports the
interoperability of autonomous data sources by merging their intensional data, using a
Global-As-View or GAV approach, into a global semantic model, represented in
DAML+OIL and in OWL. This is used to mediate between different local database
views. The agent part provides the semantic services to import, align and parse
semantic metadata instances, to support data mediation and to reason about data
mappings during alignment. The framework has applied to support information
retrieval, interoperability and multi-lateral viewpoints for four European environmental
agency databases.
An extended GAV approach has been developed and applied to handle queries that can
be reformulated over multiple user views of the stored data. This allows users to
retrieve data in a conceptualisation that is better suited to them rather than to have to
understand the entire detailed global view conceptualisation. User viewpoints are
derived from the global ontology or existing viewpoints of it. This has the advantage
that it reduces the number of potential conceptualisations and their associated
mappings to be more computationally manageable. Whereas an ad hoc framework
based upon conventional distributed programming language and a rule framework
could be used to support user views and adaptation to user views, a more formal
framework has the benefit in that it can support reasoning about the consistency,
equivalence, containment and conflict resolution when traversing data models. A
preliminary formulation of the formal model has been undertaken and is based upon
extending a Datalog type algebra with hierarchical, attribute and instance value
operators. These operators can be applied to support compositional mapping and
consistency checking of data views. The multiple viewpoint system was implemented
as a Java-based application consisting of two sub-systems, one for viewpoint
adaptation and management, the other for query processing and query result
adjustment
- …