4,904 research outputs found
Leveraging Semantic Web Service Descriptions for Validation by Automated Functional Testing
Recent years have seen the utilisation of Semantic Web Service descriptions for automating a wide range of service-related activities, with a primary focus on service discovery, composition, execution and mediation. An important area which so far has received less attention is service validation, whereby advertised services are proven to conform to required behavioural specifications. This paper proposes a method for validation of service-oriented systems through automated functional testing. The method leverages ontology-based and rule-based descriptions of service inputs, outputs, preconditions and effects (IOPE) for constructing a stateful EFSM specification. The specification is subsequently utilised for functional testing and validation using the proven Stream X-machine (SXM) testing methodology. Complete functional test sets are generated automatically at an abstract level and are then applied to concrete Web services, using test drivers created from the Web service descriptions. The testing method comes with completeness guarantees and provides a strong method for validating the behaviour of Web services
ArCo: the Italian Cultural Heritage Knowledge Graph
ArCo is the Italian Cultural Heritage knowledge graph, consisting of a
network of seven vocabularies and 169 million triples about 820 thousand
cultural entities. It is distributed jointly with a SPARQL endpoint, a software
for converting catalogue records to RDF, and a rich suite of documentation
material (testing, evaluation, how-to, examples, etc.). ArCo is based on the
official General Catalogue of the Italian Ministry of Cultural Heritage and
Activities (MiBAC) - and its associated encoding regulations - which collects
and validates the catalogue records of (ideally) all Italian Cultural Heritage
properties (excluding libraries and archives), contributed by CH administrators
from all over Italy. We present its structure, design methods and tools, its
growing community, and delineate its importance, quality, and impact
Semantic Support for Log Analysis of Safety-Critical Embedded Systems
Testing is a relevant activity for the development life-cycle of Safety
Critical Embedded systems. In particular, much effort is spent for analysis and
classification of test logs from SCADA subsystems, especially when failures
occur. The human expertise is needful to understand the reasons of failures,
for tracing back the errors, as well as to understand which requirements are
affected by errors and which ones will be affected by eventual changes in the
system design. Semantic techniques and full text search are used to support
human experts for the analysis and classification of test logs, in order to
speedup and improve the diagnosis phase. Moreover, retrieval of tests and
requirements, which can be related to the current failure, is supported in
order to allow the discovery of available alternatives and solutions for a
better and faster investigation of the problem.Comment: EDCC-2014, BIG4CIP-2014, Embedded systems, testing, semantic
discovery, ontology, big dat
LiteMat: a scalable, cost-efficient inference encoding scheme for large RDF graphs
The number of linked data sources and the size of the linked open data graph
keep growing every day. As a consequence, semantic RDF services are more and
more confronted with various "big data" problems. Query processing in the
presence of inferences is one them. For instance, to complete the answer set of
SPARQL queries, RDF database systems evaluate semantic RDFS relationships
(subPropertyOf, subClassOf) through time-consuming query rewriting algorithms
or space-consuming data materialization solutions. To reduce the memory
footprint and ease the exchange of large datasets, these systems generally
apply a dictionary approach for compressing triple data sizes by replacing
resource identifiers (IRIs), blank nodes and literals with integer values. In
this article, we present a structured resource identification scheme using a
clever encoding of concepts and property hierarchies for efficiently evaluating
the main common RDFS entailment rules while minimizing triple materialization
and query rewriting. We will show how this encoding can be computed by a
scalable parallel algorithm and directly be implemented over the Apache Spark
framework. The efficiency of our encoding scheme is emphasized by an evaluation
conducted over both synthetic and real world datasets.Comment: 8 pages, 1 figur
Data protection regulation ontology for compliance
The GDPR is the current data protection regulation in Europe. A significant market demand has been created ever since GDPR came into force. This is mostly due to the fact that it can go outside of European borders if the data processed belongs to European citizens. The number of companies who require some type of
regulation or standard compliance is ever-increasing and the need for cyber security and
privacy specialists has never been greater.
Moreover, the GDPR has inspired a series of similar regulations all over the world. This further increases the market demand and makes the work of companies who work internationally more complicated and difficult to scale.
The purpose of this thesis is to help consultancy companies to automate their work by using semantic structures known as ontologies. By doing this, they can increase productivity and reduce costs. Ontologies can
store data and their semantics (meaning) in a machine-readable format.
In this thesis, an ontology has been designed which is meant to help consultants generate checklists (or runbooks) which they
are required to deliver to their clients. The ontology is designed to handle concepts such as security measures, company information, company architecture, data sensitivity, privacy mechanisms, distinction between technical and organisational measures, and even conditionality.
The ontology was evaluated using a litmus test. In the context of this ontology, the litmus test was composed of a collection of competency questions. Competency questions were collected based on the use-cases of the ontology. These questions were later translated to SPARQL queries
which were run against a test ontology. The ontology has successfully passed the given litmus test. Thus, it can be concluded that the implemented functionality matches the proposed design
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