14,507 research outputs found
Enhancing Workflow with a Semantic Description of Scientific Intent
Peer reviewedPreprin
Measuring usability for application software using the quality in use integration measurement model
User interfaces of application software are designed to make user interaction as efficient and as simple as possible. Market accessibility of any application software is determined by the usability of its user interfaces. A poorly designed user interface will have little value no matter how powerful the program is. Thus, it is significantly important to measure usability during the system development lifecycle in order to avoid user disappointment. Various methods and standards that help measure usability have been developed. However, these methods define usability inconsistently, which makes software engineers hesitant in implementing these methods or standards. The Quality in Use Integrated Measurement (QUIM) model is a consolidated approach for measuring usability through 10 factors, 26 criteria, and 127 metrics. It decomposes usability into factors, criteria, and metrics, and it is a hierarchical model that helps developers with no or little background of usability metrics. Among 127 metrics of QUIM, essential efficiency (EE) is the most specific metric used to measure the usability of user interfaces through an equation. This study involves a comparative analysis between three case studies that use the QUIM model to measure usability in terms of EE for three case studies: (1) Public University Registration System, (2) Restaurant Menu Ordering System, and (3) ATM system. A comparison is made based on the percentage of EE for each element of the use cases in each use case diagram. The results obtained revealed that the user interface design for Restaurant Menu Ordering System scored the highest percentage of EE, thus proving to be the most user-friendly application software among its counterparts
Km4City Ontology Building vs Data Harvesting and Cleaning for Smart-city Services
Presently, a very large number of public and private data sets are available
from local governments. In most cases, they are not semantically interoperable
and a huge human effort would be needed to create integrated ontologies and
knowledge base for smart city. Smart City ontology is not yet standardized, and
a lot of research work is needed to identify models that can easily support the
data reconciliation, the management of the complexity, to allow the data
reasoning. In this paper, a system for data ingestion and reconciliation of
smart cities related aspects as road graph, services available on the roads,
traffic sensors etc., is proposed. The system allows managing a big data volume
of data coming from a variety of sources considering both static and dynamic
data. These data are mapped to a smart-city ontology, called KM4City (Knowledge
Model for City), and stored into an RDF-Store where they are available for
applications via SPARQL queries to provide new services to the users via
specific applications of public administration and enterprises. The paper
presents the process adopted to produce the ontology and the big data
architecture for the knowledge base feeding on the basis of open and private
data, and the mechanisms adopted for the data verification, reconciliation and
validation. Some examples about the possible usage of the coherent big data
knowledge base produced are also offered and are accessible from the RDF-Store
and related services. The article also presented the work performed about
reconciliation algorithms and their comparative assessment and selection
Dimensional enrichment of statistical linked open data
On-Line Analytical Processing (OLAP) is a data analysis technique typically used for local and well-prepared data. However, initiatives like Open Data and Open Government bring new and publicly available data on the web that are to be analyzed in the same way. The use of semantic web technologies for this context is especially encouraged by the Linked Data initiative. There is already a considerable amount of statistical linked open data sets published using the RDF Data Cube Vocabulary (QB) which is designed for these purposes. However, QB lacks some essential schema constructs (e.g., dimension levels) to support OLAP. Thus, the QB4OLAP vocabulary has been proposed to extend QB with the necessary constructs and be fully compliant with OLAP. In this paper, we focus on the enrichment of an existing QB data set with QB4OLAP semantics. We first thoroughly compare the two vocabularies and outline the benefits of QB4OLAP. Then, we propose a series of steps to automate the enrichment of QB data sets with specific QB4OLAP semantics; being the most important, the definition of aggregate functions and the detection of new concepts in the dimension hierarchy construction. The proposed steps are defined to form a semi-automatic enrichment method, which is implemented in a tool that enables the enrichment in an interactive and iterative fashion. The user can enrich the QB data set with QB4OLAP concepts (e.g., full-fledged dimension hierarchies) by choosing among the candidate concepts automatically discovered with the steps proposed. Finally, we conduct experiments with 25 users and use three real-world QB data sets to evaluate our approach. The evaluation demonstrates the feasibility of our approach and shows that, in practice, our tool facilitates, speeds up, and guarantees the correct results of the enrichment process.Peer ReviewedPostprint (author's final draft
Bridging the Semantic Gap in Multimedia Information Retrieval: Top-down and Bottom-up approaches
Semantic representation of multimedia information is vital for enabling the kind of multimedia search capabilities that professional searchers require. Manual annotation is often not possible because of the shear scale of the multimedia information that needs indexing. This paper explores the ways in which we are using both top-down, ontologically driven approaches and bottom-up, automatic-annotation approaches to provide retrieval facilities to users. We also discuss many of the current techniques that we are investigating to combine these top-down and bottom-up approaches
Multilingual search for cultural heritage archives via combining multiple translation resources
The linguistic features of material in Cultural Heritage (CH) archives may be in various languages requiring a facility for effective multilingual search. The specialised
language often associated with CH content introduces problems for automatic translation to support search applications. The MultiMatch project is focused on enabling
users to interact with CH content across different media types and languages. We present results from a MultiMatch study exploring various translation techniques for
the CH domain. Our experiments examine translation techniques for the English language CLEF 2006 Cross-Language
Speech Retrieval (CL-SR) task using Spanish, French and German queries. Results compare effectiveness of our query
translation against a monolingual baseline and show improvement when combining a domain-specific translation lexicon with a standard machine translation system
Strategies for Searching Video Content with Text Queries or Video Examples
The large number of user-generated videos uploaded on to the Internet
everyday has led to many commercial video search engines, which mainly rely on
text metadata for search. However, metadata is often lacking for user-generated
videos, thus these videos are unsearchable by current search engines.
Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity
problem by directly analyzing the visual and audio streams of each video. CBVR
encompasses multiple research topics, including low-level feature design,
feature fusion, semantic detector training and video search/reranking. We
present novel strategies in these topics to enhance CBVR in both accuracy and
speed under different query inputs, including pure textual queries and query by
video examples. Our proposed strategies have been incorporated into our
submission for the TRECVID 2014 Multimedia Event Detection evaluation, where
our system outperformed other submissions in both text queries and video
example queries, thus demonstrating the effectiveness of our proposed
approaches
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