145 research outputs found
Commonsense Knowledge in Sentiment Analysis of Ordinance Reactions for Smart Governance
Smart Governance is an emerging research area which has attracted scientific as well as policy interests, and aims to improve collaboration between government and citizens, as well as other stakeholders. Our project aims to enable lawmakers to incorporate data driven decision making in enacting ordinances. Our first objective is to create a mechanism for mapping ordinances (local laws) and tweets to Smart City Characteristics (SCC). The use of SCC has allowed us to create a mapping between a huge number of ordinances and tweets, and the use of Commonsense Knowledge (CSK) has allowed us to utilize human judgment in mapping.
We have then enhanced the mapping technique to link multiple tweets to SCC. In order to promote transparency in government through increased public participation, we have conducted sentiment analysis of tweets in order to evaluate the opinion of the public with respect to ordinances passed in a particular region.
Our final objective is to develop a mapping algorithm in order to directly relate ordinances to tweets. In order to fulfill this objective, we have developed a mapping technique known as TOLCS (Tweets Ordinance Linkage by Commonsense and Semantics). This technique uses pragmatic aspects in Commonsense Knowledge as well as semantic aspects by domain knowledge. By reducing the sample space of big data to be processed, this method represents an efficient way to accomplish this task.
The ultimate goal of the project is to see how closely a given region is heading towards the concept of Smart City
ΠΠΊΡΡΠΆΠ΅ΡΠ΅ Π·Π° Π°Π½Π°Π»ΠΈΠ·Ρ ΠΈ ΠΎΡΠ΅Π½Ρ ΠΊΠ²Π°Π»ΠΈΡΠ΅ΡΠ° Π²Π΅Π»ΠΈΠΊΠΈΡ ΠΈ ΠΏΠΎΠ²Π΅Π·Π°Π½ΠΈΡ ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ°
Linking and publishing data in the Linked Open Data format increases the interoperability
and discoverability of resources over the Web. To accomplish this, the process comprises
several design decisions, based on the Linked Data principles that, on one hand, recommend to
use standards for the representation and the access to data on the Web, and on the other hand
to set hyperlinks between data from different sources.
Despite the efforts of the World Wide Web Consortium (W3C), being the main international
standards organization for the World Wide Web, there is no one tailored formula for publishing
data as Linked Data. In addition, the quality of the published Linked Open Data (LOD) is a
fundamental issue, and it is yet to be thoroughly managed and considered.
In this doctoral thesis, the main objective is to design and implement a novel framework for
selecting, analyzing, converting, interlinking, and publishing data from diverse sources,
simultaneously paying great attention to quality assessment throughout all steps and modules
of the framework. The goal is to examine whether and to what extent are the Semantic Web
technologies applicable for merging data from different sources and enabling end-users to
obtain additional information that was not available in individual datasets, in addition to the
integration into the Semantic Web community space. Additionally, the Ph.D. thesis intends to
validate the applicability of the process in the specific and demanding use case, i.e. for creating
and publishing an Arabic Linked Drug Dataset, based on open drug datasets from selected
Arabic countries and to discuss the quality issues observed in the linked data life-cycle. To that
end, in this doctoral thesis, a Semantic Data Lake was established in the pharmaceutical domain
that allows further integration and developing different business services on top of the
integrated data sources. Through data representation in an open machine-readable format, the
approach offers an optimum solution for information and data dissemination for building
domain-specific applications, and to enrich and gain value from the original dataset. This thesis
showcases how the pharmaceutical domain benefits from the evolving research trends for
building competitive advantages. However, as it is elaborated in this thesis, a better
understanding of the specifics of the Arabic language is required to extend linked data
technologies utilization in targeted Arabic organizations.ΠΠΎΠ²Π΅Π·ΠΈΠ²Π°ΡΠ΅ ΠΈ ΠΎΠ±ΡΠ°Π²ΡΠΈΠ²Π°ΡΠ΅ ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° Ρ ΡΠΎΡΠΌΠ°ΡΡ "ΠΠΎΠ²Π΅Π·Π°Π½ΠΈ ΠΎΡΠ²ΠΎΡΠ΅Π½ΠΈ ΠΏΠΎΠ΄Π°ΡΠΈ" (Π΅Π½Π³.
Linked Open Data) ΠΏΠΎΠ²Π΅ΡΠ°Π²Π° ΠΈΠ½ΡΠ΅ΡΠΎΠΏΠ΅ΡΠ°Π±ΠΈΠ»Π½ΠΎΡΡ ΠΈ ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡΠΈ Π·Π° ΠΏΡΠ΅ΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ΅ ΡΠ΅ΡΡΡΡΠ°
ΠΏΡΠ΅ΠΊΠΎ Web-Π°. ΠΡΠΎΡΠ΅Ρ ΡΠ΅ Π·Π°ΡΠ½ΠΎΠ²Π°Π½ Π½Π° Linked Data ΠΏΡΠΈΠ½ΡΠΈΠΏΠΈΠΌΠ° (W3C, 2006) ΠΊΠΎΡΠΈ ΡΠ° ΡΠ΅Π΄Π½Π΅
ΡΡΡΠ°Π½Π΅ Π΅Π»Π°Π±ΠΎΡΠΈΡΠ° ΡΡΠ°Π½Π΄Π°ΡΠ΄Π΅ Π·Π° ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΡΠ°ΡΠ΅ ΠΈ ΠΏΡΠΈΡΡΡΠΏ ΠΏΠΎΠ΄Π°ΡΠΈΠΌΠ° Π½Π° WΠ΅Π±Ρ (RDF, OWL,
SPARQL), Π° ΡΠ° Π΄ΡΡΠ³Π΅ ΡΡΡΠ°Π½Π΅, ΠΏΡΠΈΠ½ΡΠΈΠΏΠΈ ΡΡΠ³Π΅ΡΠΈΡΡ ΠΊΠΎΡΠΈΡΡΠ΅ΡΠ΅ Ρ
ΠΈΠΏΠ΅ΡΠ²Π΅Π·Π° ΠΈΠ·ΠΌΠ΅ΡΡ ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ°
ΠΈΠ· ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΡ
ΠΈΠ·Π²ΠΎΡΠ°.
Π£ΠΏΡΠΊΠΎΡ Π½Π°ΠΏΠΎΡΠΈΠΌΠ° W3C ΠΊΠΎΠ½Π·ΠΎΡΡΠΈΡΡΠΌΠ° (W3C ΡΠ΅ Π³Π»Π°Π²Π½Π° ΠΌΠ΅ΡΡΠ½Π°ΡΠΎΠ΄Π½Π° ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΡΠ° Π·Π°
ΡΡΠ°Π½Π΄Π°ΡΠ΄Π΅ Π·Π° Web-Ρ), Π½Π΅ ΠΏΠΎΡΡΠΎΡΠΈ ΡΠ΅Π΄ΠΈΠ½ΡΡΠ²Π΅Π½Π° ΡΠΎΡΠΌΡΠ»Π° Π·Π° ΠΈΠΌΠΏΠ»Π΅ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΡ ΠΏΡΠΎΡΠ΅ΡΠ°
ΠΎΠ±ΡΠ°Π²ΡΠΈΠ²Π°ΡΠ΅ ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° Ρ Linked Data ΡΠΎΡΠΌΠ°ΡΡ. Π£Π·ΠΈΠΌΠ°ΡΡΡΠΈ Ρ ΠΎΠ±Π·ΠΈΡ Π΄Π° ΡΠ΅ ΠΊΠ²Π°Π»ΠΈΡΠ΅Ρ
ΠΎΠ±ΡΠ°Π²ΡΠ΅Π½ΠΈΡ
ΠΏΠΎΠ²Π΅Π·Π°Π½ΠΈΡ
ΠΎΡΠ²ΠΎΡΠ΅Π½ΠΈΡ
ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° ΠΎΠ΄Π»ΡΡΡΡΡΡΠΈ Π·Π° Π±ΡΠ΄ΡΡΠΈ ΡΠ°Π·Π²ΠΎΡ Web-Π°, Ρ ΠΎΠ²ΠΎΡ
Π΄ΠΎΠΊΡΠΎΡΡΠΊΠΎΡ Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠΈ, Π³Π»Π°Π²Π½ΠΈ ΡΠΈΡ ΡΠ΅ (1) Π΄ΠΈΠ·Π°ΡΠ½ ΠΈ ΠΈΠΌΠΏΠ»Π΅ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΠ° ΠΈΠ½ΠΎΠ²Π°ΡΠΈΠ²Π½ΠΎΠ³ ΠΎΠΊΠ²ΠΈΡΠ°
Π·Π° ΠΈΠ·Π±ΠΎΡ, Π°Π½Π°Π»ΠΈΠ·Ρ, ΠΊΠΎΠ½Π²Π΅ΡΠ·ΠΈΡΡ, ΠΌΠ΅ΡΡΡΠΎΠ±Π½ΠΎ ΠΏΠΎΠ²Π΅Π·ΠΈΠ²Π°ΡΠ΅ ΠΈ ΠΎΠ±ΡΠ°Π²ΡΠΈΠ²Π°ΡΠ΅ ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° ΠΈΠ·
ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΡ
ΠΈΠ·Π²ΠΎΡΠ° ΠΈ (2) Π°Π½Π°Π»ΠΈΠ·Π° ΠΏΡΠΈΠΌΠ΅Π½Π° ΠΎΠ²ΠΎΠ³ ΠΏΡΠΈΡΡΡΠΏΠ° Ρ ΡΠ°ΡΠΌΠ°ΡeΡΡΡΠΊΠΎΠΌ Π΄ΠΎΠΌΠ΅Π½Ρ.
ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° Π΄ΠΎΠΊΡΠΎΡΡΠΊΠ° Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠ° Π΄Π΅ΡΠ°ΡΠ½ΠΎ ΠΈΡΡΡΠ°ΠΆΡΡΠ΅ ΠΏΠΈΡΠ°ΡΠ΅ ΠΊΠ²Π°Π»ΠΈΡΠ΅ΡΠ° Π²Π΅Π»ΠΈΠΊΠΈΡ
ΠΈ
ΠΏΠΎΠ²Π΅Π·Π°Π½ΠΈΡ
Π΅ΠΊΠΎΡΠΈΡΡΠ΅ΠΌΠ° ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° (Π΅Π½Π³. Linked Data Ecosystems), ΡΠ·ΠΈΠΌΠ°ΡΡΡΠΈ Ρ ΠΎΠ±Π·ΠΈΡ
ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡ ΠΏΠΎΠ½ΠΎΠ²Π½ΠΎΠ³ ΠΊΠΎΡΠΈΡΡΠ΅ΡΠ° ΠΎΡΠ²ΠΎΡΠ΅Π½ΠΈΡ
ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ°. Π Π°Π΄ ΡΠ΅ ΠΌΠΎΡΠΈΠ²ΠΈΡΠ°Π½ ΠΏΠΎΡΡΠ΅Π±ΠΎΠΌ Π΄Π° ΡΠ΅
ΠΎΠΌΠΎΠ³ΡΡΠΈ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠΈΠΌΠ° ΠΈΠ· Π°ΡΠ°ΠΏΡΠΊΠΈΡ
Π·Π΅ΠΌΠ°ΡΠ° Π΄Π° ΡΠΏΠΎΡΡΠ΅Π±ΠΎΠΌ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠΊΠΈΡ
Π²Π΅Π± ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ°
ΠΏΠΎΠ²Π΅ΠΆΡ ΡΠ²ΠΎΡΠ΅ ΠΏΠΎΠ΄Π°ΡΠΊΠ΅ ΡΠ° ΠΎΡΠ²ΠΎΡΠ΅Π½ΠΈΠΌ ΠΏΠΎΠ΄Π°ΡΠΈΠΌΠ°, ΠΊΠ°ΠΎ Π½ΠΏΡ. DBpedia-ΡΠΎΠΌ. Π¦ΠΈΡ ΡΠ΅ Π΄Π° ΡΠ΅ ΠΈΡΠΏΠΈΡΠ°
Π΄Π° Π»ΠΈ ΠΎΡΠ²ΠΎΡΠ΅Π½ΠΈ ΠΏΠΎΠ΄Π°ΡΠΈ ΠΈΠ· ΠΡΠ°ΠΏΡΠΊΠΈΡ
Π·Π΅ΠΌΠ°ΡΠ° ΠΎΠΌΠΎΠ³ΡΡΠ°Π²Π°ΡΡ ΠΊΡΠ°ΡΡΠΈΠΌ ΠΊΠΎΡΠΈΡΠ½ΠΈΡΠΈΠΌΠ° Π΄Π° Π΄ΠΎΠ±ΠΈΡΡ
Π΄ΠΎΠ΄Π°ΡΠ½Π΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡΠ΅ ΠΊΠΎΡΠ΅ Π½ΠΈΡΡ Π΄ΠΎΡΡΡΠΏΠ½Π΅ Ρ ΠΏΠΎΡΠ΅Π΄ΠΈΠ½Π°ΡΠ½ΠΈΠΌ ΡΠΊΡΠΏΠΎΠ²ΠΈΠΌΠ° ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ°, ΠΏΠΎΡΠ΅Π΄
ΠΈΠ½ΡΠ΅Π³ΡΠ°ΡΠΈΡΠ΅ Ρ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠΊΠΈ WΠ΅Π± ΠΏΡΠΎΡΡΠΎΡ.
ΠΠΎΠΊΡΠΎΡΡΠΊΠ° Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠ° ΠΏΡΠ΅Π΄Π»Π°ΠΆΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΡΡ Π·Π° ΡΠ°Π·Π²ΠΎΡ Π°ΠΏΠ»ΠΈΠΊΠ°ΡΠΈΡΠ΅ Π·Π° ΡΠ°Π΄ ΡΠ°
ΠΏΠΎΠ²Π΅Π·Π°Π½ΠΈΠΌ (Linked) ΠΏΠΎΠ΄Π°ΡΠΈΠΌΠ° ΠΈ ΠΈΠΌΠΏΠ»Π΅ΠΌΠ΅Π½ΡΠΈΡΠ° ΡΠΎΡΡΠ²Π΅ΡΡΠΊΠΎ ΡΠ΅ΡΠ΅ΡΠ΅ ΠΊΠΎΡΠ΅ ΠΎΠΌΠΎΠ³ΡΡΡΡΠ΅
ΠΏΡΠ΅ΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ΅ ΠΊΠΎΠ½ΡΠΎΠ»ΠΈΠ΄ΠΎΠ²Π°Π½ΠΎΠ³ ΡΠΊΡΠΏΠ° ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° ΠΎ Π»Π΅ΠΊΠΎΠ²ΠΈΠΌΠ° ΠΈΠ· ΠΈΠ·Π°Π±ΡΠ°Π½ΠΈΡ
Π°ΡΠ°ΠΏΡΠΊΠΈΡ
Π·Π΅ΠΌΠ°ΡΠ°. ΠΠΎΠ½ΡΠΎΠ»ΠΈΠ΄ΠΎΠ²Π°Π½ΠΈ ΡΠΊΡΠΏ ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° ΡΠ΅ ΠΈΠΌΠΏΠ»Π΅ΠΌΠ΅Π½ΡΠΈΡΠ°Π½ Ρ ΠΎΠ±Π»ΠΈΠΊΡ Π‘Π΅ΠΌΠ°Π½ΡΠΈΡΠΊΠΎΠ³ ΡΠ΅Π·Π΅ΡΠ°
ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° (Π΅Π½Π³. Semantic Data Lake).
ΠΠ²Π° ΡΠ΅Π·Π° ΠΏΠΎΠΊΠ°Π·ΡΡΠ΅ ΠΊΠ°ΠΊΠΎ ΡΠ°ΡΠΌΠ°ΡΠ΅ΡΡΡΠΊΠ° ΠΈΠ½Π΄ΡΡΡΡΠΈΡΠ° ΠΈΠΌΠ° ΠΊΠΎΡΠΈΡΡΠΈ ΠΎΠ΄ ΠΏΡΠΈΠΌΠ΅Π½Π΅
ΠΈΠ½ΠΎΠ²Π°ΡΠΈΠ²Π½ΠΈΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ° ΠΈ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠΊΠΈΡ
ΡΡΠ΅Π½Π΄ΠΎΠ²Π° ΠΈΠ· ΠΎΠ±Π»Π°ΡΡΠΈ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠΊΠΈΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ°. ΠΠ΅ΡΡΡΠΈΠΌ, ΠΊΠ°ΠΊΠΎ ΡΠ΅ Π΅Π»Π°Π±ΠΎΡΠΈΡΠ°Π½ΠΎ Ρ ΠΎΠ²ΠΎΡ ΡΠ΅Π·ΠΈ, ΠΏΠΎΡΡΠ΅Π±Π½ΠΎ ΡΠ΅ Π±ΠΎΡΠ΅ ΡΠ°Π·ΡΠΌΠ΅Π²Π°ΡΠ΅
ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ½ΠΎΡΡΠΈ Π°ΡΠ°ΠΏΡΠΊΠΎΠ³ ΡΠ΅Π·ΠΈΠΊΠ° Π·Π° ΠΈΠΌΠΏΠ»Π΅ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΡ Linked Data Π°Π»Π°ΡΠ° ΠΈ ΡΡΡ
ΠΎΠ²Ρ ΠΏΡΠΈΠΌΠ΅Π½Ρ
ΡΠ° ΠΏΠΎΠ΄Π°ΡΠΈΠΌΠ° ΠΈΠ· ΠΡΠ°ΠΏΡΠΊΠΈΡ
Π·Π΅ΠΌΠ°ΡΠ°
Recommending on graphs: a comprehensive review from a data perspective
Recent advances in graph-based learning approaches have demonstrated their
effectiveness in modelling users' preferences and items' characteristics for
Recommender Systems (RSS). Most of the data in RSS can be organized into graphs
where various objects (e.g., users, items, and attributes) are explicitly or
implicitly connected and influence each other via various relations. Such a
graph-based organization brings benefits to exploiting potential properties in
graph learning (e.g., random walk and network embedding) techniques to enrich
the representations of the user and item nodes, which is an essential factor
for successful recommendations. In this paper, we provide a comprehensive
survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we
start from a data-driven perspective to systematically categorize various
graphs in GLRSs and analyze their characteristics. Then, we discuss the
state-of-the-art frameworks with a focus on the graph learning module and how
they address practical recommendation challenges such as scalability, fairness,
diversity, explainability and so on. Finally, we share some potential research
directions in this rapidly growing area.Comment: Accepted by UMUA
D5.2: Digital-Twin Enabled multi-physics simulation and model matching
This deliverable presents a report on the developed actions and results concerning Digital-Twin-enabled multi-physics simulations and model matching. Enabling meaningful simulations within new human-infrastructure interfaces such as Digital twins is paramount. Accessing the power of simulation opens manifold new ways for observation, understanding, analysis and prediction of numerous scenarios to which the asset may be faced. As a result, managers can access countless ways of acquiring synthetic data for eventually taking better, more informed decisions. The tool MatchFEM is conceived as a fundamental part of this endeavour. From a broad perspective, the tool is aimed at contextualizing information between multi-physics simulations and vaster information constructs such as digital twins. 3D geometries, measurements, simulations, and asset management coexist in such information constructs. This report provides guidance for the generation of comprehensive adequate initial conditions of the assets to be used during their life span using a DT basis. From a more specific focus, this deliverable presents a set of exemplary recommendations for the development of DT-enabled load tests of assets in the form of a white paper. The deliverable also belongs to a vaster suit of documents encountered in WP5 of the Ashvin project in which measurements, models and assessments are described thoroughly.Objectius de Desenvolupament Sostenible::9 - IndΓΊstria, InnovaciΓ³ i InfraestructuraPreprin
Scalable Quality Assessment of Linked Data
In a world where the information economy is booming, poor data quality can lead to adverse consequences, including social and economical problems such as decrease in revenue. Furthermore, data-driven indus- tries are not just relying on their own (proprietary) data silos, but are also continuously aggregating data from different sources. This aggregation could then be re-distributed back to βdata lakesβ. However, this data (including Linked Data) is not necessarily checked for its quality prior to its use. Large volumes of data are being exchanged in a standard and interoperable format between organisations and published as Linked Data to facilitate their re-use. Some organisations, such as government institutions, take a step further and open their data. The Linked Open Data Cloud is a witness to this. However, similar to data in data lakes, it is challenging to determine the quality of this heterogeneous data, and subsequently to make this information explicit to data consumers. Despite the availability of a number of tools and frameworks to assess Linked Data quality, the current solutions do not aggregate a holistic approach that enables both the assessment of datasets and also provides consumers with quality results that can then be used to find, compare and rank datasetsβ fitness for use. In this thesis we investigate methods to assess the quality of (possibly large) linked datasets with the intent that data consumers can then use the assessment results to find datasets that are fit for use, that is; finding the right dataset for the task at hand. Moreover, the benefits of quality assessment are two-fold: (1) data consumers do not need to blindly rely on subjective measures to choose a dataset, but base their choice on multiple factors such as the intrinsic structure of the dataset, therefore fostering trust and reputation between the publishers and consumers on more objective foundations; and (2) data publishers can be encouraged to improve their datasets so that they can be re-used more. Furthermore, our approach scales for large datasets. In this regard, we also look into improving the efficiency of quality metrics using various approximation techniques. However the trade-off is that consumers will not get the exact quality value, but a very close estimate which anyway provides the required guidance towards fitness for use. The central point of this thesis is not on data quality improvement, nonetheless, we still need to understand what data quality means to the consumers who are searching for potential datasets. This thesis looks into the challenges faced to detect quality problems in linked datasets presenting quality results in a standardised machine-readable and interoperable format for which agents can make sense out of to help human consumers identifying the fitness for use dataset. Our proposed approach is more consumer-centric where it looks into (1) making the assessment of quality as easy as possible, that is, allowing stakeholders, possibly non-experts, to identify and easily define quality metrics and to initiate the assessment; and (2) making results (quality metadata and quality reports) easy for stakeholders to understand, or at least interoperable with other systems to facilitate a possible data quality pipeline. Finally, our framework is used to assess the quality of a number of heterogeneous (large) linked datasets, where each assessment returns a quality metadata graph that can be consumed by agents as Linked Data. In turn, these agents can intelligently interpret a datasetβs quality with regard to multiple dimensions and observations, and thus provide further insight to consumers regarding its fitness for use
Semantics-based platform for context-aware and personalized robot interaction in the internet of robotic things
Robots are moving from well-controlled lab environments to the real world, where an increasing number of environments has been transformed into smart sensorized IoT spaces. Users will expect these robots to adapt to their preferences and needs, and even more so for social robots that engage in personal interactions. In this paper, we present declarative ontological models and a middleware platform for building services that generate interaction tasks for social robots in smart IoT environments. The platform implements a modular, data-driven workflow that allows developers of interaction services to determine the appropriate time, content and style of human-robot interaction tasks by reasoning on semantically enriched loT sensor data. The platform also abstracts the complexities of scheduling, planning and execution of these tasks, and can automatically adjust parameters to the personal profile and current context. We present motivational scenarios in three environments: a smart home, a smart office and a smart nursing home, detail the interfaces and executional paths in our platform and present a proof-of-concept implementation. (C) 2018 Elsevier Inc. All rights reserved
Engineering Agile Big-Data Systems
To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
Linked Open Data - Creating Knowledge Out of Interlinked Data: Results of the LOD2 Project
Database Management; Artificial Intelligence (incl. Robotics); Information Systems and Communication Servic
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