Science Gateways with Embedded Ontology-based E-learning Support

Science gateways are widely utilised in a range of scientific disciplines to provide user-friendly access to complex distributed computing infrastructures. The traditional approach in science gateway development is to concentrate on this simplified resource access and provide scientists with a graphical user interface to conduct their experiments and visualise the results. However, as user communities behind these gateways are growing and opening their doors to less experienced scientists or even to the general public as “citizen scientists”, there is an emerging need to extend these gateways with training and learning support capabilities. This paper describes a novel approach showing how science gateways can be extended with embedded e-learning support using an ontology-based learning environment called Knowledge Repository Exchange and Learning (KREL). The paper also presents a prototype implementation of a science gateway for analysing earthquake data and demonstrates how the KREL can extend this gateway with ontology-based embedded e-learning support

Linked Data Meets Big Data: A Knowledge Organization Systems Perspective

The objective of this paper is a) to provide a conceptualanalysis of the term big data and b) to introduce linked dataapplications such as SKOS-based knowledge organizationsystems as new tools for the analysis, organization, representation, visualization and access to big data

Metajournals. A federalist proposal for scholarly communication and data aggregation

While the EU is building an open access infrastructure of archives (e.g. OpenAIRE) and it is trying to implement it in the Horizon 2020 program, the gap between the tools and the human beings – researchers, citizen scientists, students, ordinary people – is still wide. The necessity to dictate open access publishing as a mandate for the EU funded research – ten years after the BOAI - is an obvious symptom of it: there is a chasm between the net and the public use of reason. To escalate the advancement and the reuse of research, we should federate the multitude of already existing open access journals in federal open overlay journals that receive their contents from the member journals and boost it with their aggregation power and their semantic web tools. The article contains both the theoretical basis and the guidelines for a project whose goals are: 1. making open access journals visible, highly cited and powerful, by federating them into wide disciplinary overlay journals; 2. avoiding the traps of the “authors pay” open access business model, by exploiting one of the virtue of federalism: the federate journals can remain little and affordable, if they gain visibility from the power of the federal overlay journal aggregating them; 3. enriching the overlay journals both through semantic annotation tools and by means of open platforms dedicated to host ex post peer review and experts comments; 4. making the selection and evaluation processes and their resulting data as much as possible public and open, to avoid the pitfalls (e. g, the serials price crisis) experienced by the closed access publishing model. It is about time to free academic publishing from its expensive walled gardens and to put to test the tools that can help us to transform it in one open forest, with one hundred flowers – and one hundred trailblazers

Metajournals. A federalist proposal for scholarly communication and data aggregation

Abstract   While the EU is building an open access infrastructure of archives (e.g., Openaire) and it is trying to implement it in the Horizon 2020 program, the gap between the tools and the human beings – researchers, citizen scientists, students, ordinary people – is still wide. The necessity to dictate open access publishing as a mandate for the EU funded research – ten years after the BOAI - is an obvious symptom of it: there is a chasm between the net and the public use of reason. To escalate the advancement and the reuse of research, we should federate the multitude of already existing open access journals in federal open overlay journals that receive their contents from the member journals and boost it with their aggregation power and their semantic web tools.The article contains both the theoretical basis and the guidelines for a project whose goals are:making open access journals visible, highly cited and powerful, by federating them into wide disciplinary overlay journals; avoiding the traps of the “authors pay” open access business model, by exploiting one of the virtue of federalism: the federate journals can remain little and affordable, if they gain visibility from the power of the federal overlay journal aggregating them;enriching the overlay journals both through semantic annotation tools and by means of open platforms dedicated to host ex post peer review and experts comments;making the selection and evaluation processes and their resulting data as much as possible public and open, to avoid the pitfalls (e.g., the serials price crisis) experienced by the closed access publishing model.It is about time to free academic publishing from its expensive walled gardens and to put to test the tools that can help us to transform it in one open forest, with one hundred flowers – and one hundred trailblazers

Improving Academic Natural Language Processing Infrastructures Utilizing Cluster Computation

In light of widespread digitization endeavors and ever-growing textual data generation, developing efficient academic Natural Language Processing (NLP) infrastructures, which can deal with large amounts of data, is of particular importance. Novel computation technologies allow tools that support big data and heavy computation while performing timely and cost-effective data processing. This development has led researchers to demand that knowledge be extracted from ever-increasing textual data before it is outdated. Cluster computation is a modern technology for handling big data efficiently. It provides distribution of computing and data over a number of machines in a cluster, as well as efficient use of resources, which are key requirements to process big data in a timely manner. It also assures applications’ high availability and fault tolerance, which are fundamental concerns when dealing with vast amounts of data. In addition, it provides load balancing of data during the execution of tasks, which results in optimal use of resources and enhances efficiency. Data-oriented parallelization is an effective solution to enable the currently available academic NLP infrastructures to process big data. This approach offers a solution to parallelize the NLP tools which comprise identical non-complicated tasks without the expense of changing NLP algorithms. This thesis presents the adaption of cluster computation technology to academic NLP infrastructures to address the notable features that are essential to process vast quantities of text materials efficiently, in terms of both resources and time. Apache Spark on top of Apache Hadoop and its ecosystem have been utilized to develop a set of NLP tools that provide a distributed environment to execute the NLP tasks. Many experiments were conducted to assess the functionality of the designated strategy. This thesis shows that using cluster computation technology and data-oriented parallelization enables academic NLP infrastructures to execute large amounts of textual data in a timely manner while improving the performance of the NLP tools. Moreover, these experiments provide information that brings a more realistic and transparent estimation of workflows’ costs (required hardware resources) and execution time, along with the fastest, optimum, or feasible resource configuration for each individual workflow. This knowledge can be employed by users to trade-off between run-time, size of data, and hardware, and it enables them to design a strategy for data storage, duration of data retention, and delivery time. This has the potential to enhance researchers’ satisfaction when using academic NLP infrastructures. The thesis also shows that a cluster computation approach provides the capacity to adapt NLP services with JIT delivery systems. The proposed strategy assures the reliability and predictability of the services, which are the main characteristics of the services in JIT delivery systems. Defining the relevant parameters, recording the behavior of the services, and analyzing the generated data resulted in the provision of knowledge that can be utilized to create a service catalog—a fundamental requirement for the services in JIT delivery systems—for each service offered. This knowledge also helps to generate the performance profiles for each item mentioned in the service catalog and to update them continuously to cover new experiments and improve service quality

Development of a supervisory internet of things (IoT) system for factories of the future

Big data is of great importance to stakeholders, including manufacturers, business partners, consumers, government. It leads to many benefits, including improving productivity and reducing the cost of products by using digitalised automation equipment and manufacturing information systems. Some other benefits include using social media to build the agile cooperation between suppliers and retailers, product designers and production engineers, timely tracking customers’ feedbacks, reducing environmental impacts by using Internet of Things (IoT) sensors to monitor energy consumption and noise level. However, manufacturing big data integration has been neglected. Many open-source big data software provides complicated capabilities to manage big data software for various data-driven applications for manufacturing. In this research, a manufacturing big data integration system, named as Data Control Module (DCM) has been designed and developed. The system can securely integrate data silos from various manufacturing systems and control the data for different manufacturing applications. Firstly, the architecture of manufacturing big data system has been proposed, including three parts: manufacturing data source, manufacturing big data ecosystem and manufacturing applications. Secondly, nine essential components have been identified in the big data ecosystem to build various manufacturing big data solutions. Thirdly, a conceptual framework is proposed based on the big data ecosystem for the aim of DCM. Moreover, the DCM has been designed and developed with the selected big data software to integrate all the three varieties of manufacturing data, including non-structured, semi-structured and structured. The DCM has been validated on three general manufacturing domains, including product design and development, production and business. The DCM cannot only be used for the legacy manufacturing software but may also be used in emerging areas such as digital twin and digital thread. The limitations of DCM have been analysed, and further research directions have also been discussed

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Open Educational Practices and Resources. OLCOS Roadmap 2012

As a Transversal Action under the European eLearning Programme, the Open e-Learning Content Observatory Services (OLCOS) project carries out a set of activities that aim at fostering the creation, sharing and re-use of Open Educational Resources (OER) in Europe and beyond.OER are understood to comprise content for teaching and learning, software-based tools and services, and licenses that allow for open development and re-use of content, tools and services.The OLCOS road mapping work was conducted to provide decision makers with an overview of current and likely future developments in OER and recommendations on how various challenges in OER could be addressed.While the results of the road mapping will provide some basis for policy and institutional planning, strategic leadership and decision making is needed for implementing measures that are likely to promote a further uptake of open educational practices and resources.OER are understood to be an important element of policies that want to leverage education and lifelong learning for the knowledge economy and society. However, OLCOS emphasises that it is crucial to also promote innovation and change in educational practices.In particular, OLCOS warns that delivering OER to the still dominant model of teachercentred knowledge transfer will have little effect on equipping teachers, students and workers with the competences, knowledge and skills to participate successfully in the knowledge economy and society.This report emphasises the need to foster open practices of teaching and learning that are informed by a competency-based educational framework. However, it is understood that a shift towards such practices will only happen in the longer term in a step-by-step process. Bringing about this shift will require targeted and sustained efforts by educational leaders at all levels