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1 – 10 of over 1000Martin Nečaský, Petr Škoda, David Bernhauer, Jakub Klímek and Tomáš Skopal
Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking…
Abstract
Purpose
Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.
Design/methodology/approach
In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.
Findings
The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.
Originality/value
To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.
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Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Anne H.H. Ngu and Yihong Zhang
This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase.
Abstract
Purpose
This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase.
Design/methodology/approach
In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed.
Findings
Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed.
Originality/value
To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.
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Yasuhiro Fukushima, Gakushi Ishimura, Andrew James Komasinski, Reiko Omoto and Shunsuke Managi
This paper aims to suggest the structure of a platform for education and capacity building for Future Earth, which is an intensive program open to the eight stakeholders and which…
Abstract
Purpose
This paper aims to suggest the structure of a platform for education and capacity building for Future Earth, which is an intensive program open to the eight stakeholders and which utilizes existing research programs/facilities associated with Future Earth. An intention of this paper is to facilitate a policy brief for projects associated with Future Earth.
Design/methodology/approach
This paper reviewed backgrounds and necessary items for education and capacity buildings in Future Earth projects by implementing three main priorities in Future Earth and current surrounding environments.
Findings
This paper then suggested a possible structure, competencies, contents and human resources for education and capacity building and education for Future Earth.
Originality/value
The suggestions can be implemented in capacity building and education programs associated with Future Earth.
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In this study, reasons for proving the relevancy of personalisation of e-learning systems to act as a knowledge management system in which tacit to tacit type of knowledge…
Abstract
In this study, reasons for proving the relevancy of personalisation of e-learning systems to act as a knowledge management system in which tacit to tacit type of knowledge (socialisation) can be delivered, are being provided. Nonaka’s knowledge conversion model is being used as the basis of the investigation. The relationship between ‘the strategic knowledge conversion model’ drawn from the ‘identifying list of strategies’ and ‘an individual’s decision-making method’ has been investigated in relation to knowledge transferring systems and individual’s learning styles. The outcome of the qualitative as well as quantitative investigation defines a set of frameworks in which different types of e-learning systems utilizing different learning philosophies and learners learning preferences to support the learner’s learning curve.
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Farag Edghiem, Xiuli Guo, Carl Bridge and Martin McAreavey
Based on initial observation, this paper aims to explore the current practices of collaborative knowledge sharing (KS) between North West Universities and highlight new avenues of…
Abstract
Purpose
Based on initial observation, this paper aims to explore the current practices of collaborative knowledge sharing (KS) between North West Universities and highlight new avenues of future relevant research.
Design/methodology/approach
A netnographic observation was conducted to unveil the current practices of KS between North West Universities.
Findings
The paper concludes that there is little or no evidence of collaborative KS practices amongst North West Universities in response to the present Covid-19 transition.
Practical implications
This paper provides useful, practical insight that may assist decision-makers to establish KS initiatives within North West Universities and beyond. A strategy is also proposed to nurture collaborative KS amongst North West Universities and within wider work-applied management practice.
Originality/value
This paper presents an unconventional conceptualisation of KS practices amid the present Covid-19 pandemic with the fresh perspective of North West England Universities.
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Giuseppe Festa, Matteo Rossi, Ashutosh Kolte and Mario Situm
This study aims to analyze the territory as a distinctive factor through which the concept and practice of “Made in Italy” operates. Specifically, the study considers the role of…
Abstract
Purpose
This study aims to analyze the territory as a distinctive factor through which the concept and practice of “Made in Italy” operates. Specifically, the study considers the role of local and sub-national entrepreneurial collaborations that preserve and enhance factors such as history, style and talent as the essence of Italian “quality” and as the pillar of Italian territorial capitalism.
Design/methodology/approach
The research examines this Italian phenomenon by investigating small and medium enterprises (SMEs) that successfully compete abroad (and also in the domestic market) with a “glocal” approach, adopting the entrepreneurial formula of industrial districts.
Findings
The results indicate that international expansion is becoming increasingly more complex (as is every growth/development strategy) but that “glocalism” could represent a potential driver for the success of internationalization strategies. Specifically, for SMEs operating in industrial districts, territorial capitalism could emerge as a unique competitive factor, because it is a component of local structural capital and global reputational capital, as in the case of “Made in Italy.”
Originality/value
In an increasingly globalized market environment, many companies look to foreign markets to maintain and expand competitive advantage and business performance. Once the companies embark on this endeavor, organizations are involved in governing and managing these networks of finance, production and communication and the distribution-related relationships that constitute globalization. The push to engage in international development is currently imperative for SMEs, which need to extend their business engagement beyond conventional local markets and identify and exploit their distinctive competitive advantage to be able to succeed. One possible way of achieving this is the close interaction with the local territories in which these enterprises reside.
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Fung Yuen Chin, Kong Hoong Lem and Khye Mun Wong
The amount of features in handwritten digit data is often very large due to the different aspects in personal handwriting, leading to high-dimensional data. Therefore, the…
Abstract
Purpose
The amount of features in handwritten digit data is often very large due to the different aspects in personal handwriting, leading to high-dimensional data. Therefore, the employment of a feature selection algorithm becomes crucial for successful classification modeling, because the inclusion of irrelevant or redundant features can mislead the modeling algorithms, resulting in overfitting and decrease in efficiency.
Design/methodology/approach
The minimum redundancy and maximum relevance (mRMR) and the recursive feature elimination (RFE) are two frequently used feature selection algorithms. While mRMR is capable of identifying a subset of features that are highly relevant to the targeted classification variable, mRMR still carries the weakness of capturing redundant features along with the algorithm. On the other hand, RFE is flawed by the fact that those features selected by RFE are not ranked by importance, albeit RFE can effectively eliminate the less important features and exclude redundant features.
Findings
The hybrid method was exemplified in a binary classification between digits “4” and “9” and between digits “6” and “8” from a multiple features dataset. The result showed that the hybrid mRMR + support vector machine recursive feature elimination (SVMRFE) is better than both the sole support vector machine (SVM) and mRMR.
Originality/value
In view of the respective strength and deficiency mRMR and RFE, this study combined both these methods and used an SVM as the underlying classifier anticipating the mRMR to make an excellent complement to the SVMRFE.
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Aya Rizk, Anna Ståhlbröst and Ahmed Elragal
Within digital innovation, there are two significant consequences of the pervasiveness of digital technology: (1) the increasing connectivity is enabling a wider reach and scope…
Abstract
Purpose
Within digital innovation, there are two significant consequences of the pervasiveness of digital technology: (1) the increasing connectivity is enabling a wider reach and scope of innovation structures, such as innovation networks and (2) the unprecedented availability of digital data is creating new opportunities for innovation. Accordingly, there is a growing domain for studying data-driven innovation (DDI), especially in contemporary contexts of innovation networks. The purpose of this study is to explore how DDI processes take form in a specific type of innovation networks, namely federated networks.
Design/methodology/approach
A multiple case study design is applied in this paper. We draw our analysis from data collected over six months from four cases of DDI. The within-analysis is aimed at constructing the DDI process instance in each case, while the crosscase analysis focuses on pattern matching and cross-case synthesis of common and unique characteristics in the constructed processes.
Findings
Evidence from the crosscase analysis suggests that the widely accepted four-phase digital innovation process (including discovery, development, diffusion and post-diffusion) does not account for the explorative nature of data analytics and DDI. We propose an extended process comprising an explicit exploration phase before development, where refinement of the innovation concept and exploring social relationships are essential. Our analysis also suggests two modes of DDI: (1) asynchronous, i.e. data acquired before development and (2) synchronous, i.e. data acquired after (or during) development. We discuss the implications of these modes on the DDI process and the participants in the innovation network.
Originality/value
The paper proposes an extended version of the digital innovation process that is more specifically suited for DDI. We also provide an early explanation to the variation in DDI process complexities by highlighting the different modes of DDI processes. To the best of our knowledge, this is the first empirical investigation of DDI following the process from early stages of discovery till postdiffusion.
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Zhiwen Pan, Jiangtian Li, Yiqiang Chen, Jesus Pacheco, Lianjun Dai and Jun Zhang
The General Society Survey(GSS) is a kind of government-funded survey which aims at examining the Socio-economic status, quality of life, and structure of contemporary society…
Abstract
Purpose
The General Society Survey(GSS) is a kind of government-funded survey which aims at examining the Socio-economic status, quality of life, and structure of contemporary society. GSS data set is regarded as one of the authoritative source for the government and organization practitioners to make data-driven policies. The previous analytic approaches for GSS data set are designed by combining expert knowledges and simple statistics. By utilizing the emerging data mining algorithms, we proposed a comprehensive data management and data mining approach for GSS data sets.
Design/methodology/approach
The approach are designed to be operated in a two-phase manner: a data management phase which can improve the quality of GSS data by performing attribute pre-processing and filter-based attribute selection; a data mining phase which can extract hidden knowledge from the data set by performing data mining analysis including prediction analysis, classification analysis, association analysis and clustering analysis.
Findings
According to experimental evaluation results, the paper have the following findings: Performing attribute selection on GSS data set can increase the performance of both classification analysis and clustering analysis; all the data mining analysis can effectively extract hidden knowledge from the GSS data set; the knowledge generated by different data mining analysis can somehow cross-validate each other.
Originality/value
By leveraging the power of data mining techniques, the proposed approach can explore knowledge in a fine-grained manner with minimum human interference. Experiments on Chinese General Social Survey data set are conducted at the end to evaluate the performance of our approach.
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Laszlo Hetey, Eddy Neefs, Ian Thomas, Joe Zender, Ann-Carine Vandaele, Sophie Berkenbosch, Bojan Ristic, Sabrina Bonnewijn, Sofie Delanoye, Mark Leese, Jon Mason and Manish Patel
This paper aims to describe the development of a knowledge management system (KMS) for the Nadir and Occultation for Mars Discovery (NOMAD) instrument on board the ESA/Roscosmos…
Abstract
Purpose
This paper aims to describe the development of a knowledge management system (KMS) for the Nadir and Occultation for Mars Discovery (NOMAD) instrument on board the ESA/Roscosmos 2016 ExoMars Trace Gas Orbiter (TGO) spacecraft. The KMS collects knowledge acquired during the engineering process that involved over 30 project partners. In addition to the documentation and technical data (explicit knowledge), a dedicated effort was made to collect the gained experience (tacit knowledge) that is crucial for the operational phase of the TGO mission and also for future projects. The system is now in service and provides valuable information for the scientists and engineers working with NOMAD.
Design/methodology/approach
The NOMAD KMS was built around six areas: official documentation, technical specifications and test results, lessons learned, management data (proposals, deliverables, progress reports and minutes of meetings), picture files and movie files. Today, the KMS contains 110 GB of data spread over 11,000 documents and more than 13,000 media files. A computer-aided design (CAD) library contains a model of the full instrument as well as exported sub-parts in different formats. A context search engine for both documents and media files was implemented.
Findings
The conceived KMS design is basic, flexible and very robust. It can be adapted to future projects of a similar size.
Practical implications
The paper provides practical guidelines on how to retain the knowledge from a larger aerospace project. The KMS tool presented here works offline, requires no maintenance and conforms to data protection standards.
Originality/value
This paper shows how knowledge management requirements for space missions can be fulfilled. The paper demonstrates how to transform the large collection of project data into a useful tool and how to address usability aspects.
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