Big Data Analysis

Domingue, John; Lasierra, Nelia; Fensel, Dieter; Kasteren, Tim van; Strohbach, Martin; Thalhammer, Andreas

doi:10.1007/978-3-319-21569-3_5

Cited by 16 publications

(16 citation statements)

References 29 publications

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“…Currently, reengineering is the only way to recover intelligence, so there it is thus required to define new policies about how original files are kept, as well as to develop technology to ensure interoperability among different formats. In any case, linked data technologies need to be simplified in order to make them easily adoptable [28]. Also, semantic search, schema matching and mapping, and ontology alignment have to be addressed [29].…”

Section: Data Managementmentioning

confidence: 99%

“…It was mentioned that focus should be given on data that already exists rather than on data that needs to be created in the future, and thus data creation, with a focus on surfacing already existing data, is a priority within this topic. There is more data out there than what people realise, and it should be made easier to find [28]. This could be assisted by developing search engines for datasets with ranking, in order to drive owners to publish better datasets, following the improvement of websites that want to appear high in Google rankings [28].…”

Section: Data Managementmentioning

confidence: 99%

“…There is more data out there than what people realise, and it should be made easier to find [28]. This could be assisted by developing search engines for datasets with ranking, in order to drive owners to publish better datasets, following the improvement of websites that want to appear high in Google rankings [28]. Adaptive data detection and acquisition is needed in e.g.…”

Section: Data Managementmentioning

confidence: 99%

“…the finance and insurance sector [27]. Other relevant subtopics are data discovery, datasets crawlers, metadata, dataset ranking [28]. Data curation by demonstration, in analogy to programming by example or by demonstration, would also for the distribution and scalability of the system.…”

Section: Data Managementmentioning

confidence: 99%

“…We thus recommend to add an open data and data creation priority within the area of data management aimed at surfacing already existing data. Public funding should keep prioritising processes that support open data initiatives [28]. Tracking and recognition of data and infrastructure should be improved in academic research [29].…”

Section: Data Managementmentioning

confidence: 99%

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The societal impact of big data: A research roadmap for Europe

Cuquet¹,

Fensel²

2018

Technology in Society

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With its rapid growth and increasing adoption, big data is producing a substantial impact in society. Its usage is opening both opportunities such as new business models and economic gains and risks such as privacy violations and discrimination. Europe is in need of a comprehensive strategy to optimise the use of data for a societal benefit and increase the innovation and competitiveness of its productive activities. In this paper, we contribute to the definition of this strategy with a research roadmap to capture the economic, social and ethical, legal and political benefits associated with the use of big data in Europe. The present roadmap considers the positive and negative externalities associated with big data, maps research and innovation topics in the areas of data management, processing, analytics, protection, visualisation, as well as non-technical topics, to the externalities they can tackle, and provides a time frame to address these topics in order to deliver social impact, skills development and standardisation. Finally, it also identifies what sectors will be most benefited by each of the research efforts. The goal of the roadmap is to guide European research efforts to develop a socially responsible big data economy, and to allow stakeholders to identify and meet big data challenges and proceed with a shared understanding of the societal impact, positive and negative externalities and concrete problems worth investigating in future programmes. IntroductionThe volume of data is growing exponentially, and is expected to reach the tens of zettabytes in 2020, of which a third is expected to be valuable if analysed, and about 40% will require protection [1]. The acquisition, analysis, curation, storage and usage of such big data may result in effects experienced by third parties that had no direct involvement in the activity itself. These externalities-positive if the action causes a positive effect or benefit to the third party, negative if it causes cost or harm-arise from decisions, activities or products by stakeholders such as industry, researchers and policy-makers.The present document contributes to the formulation of a strategy to define research and innovation efforts necessary for the realisation of a European big data economy by capturing and addressing the positive and negative societal externalities associated with the use of big data. It complements the technical challenges already identified [2] by taking a special focus on societal impacts, skills development and standardisation, and has been developed in parallel with a policy roadmap in the context of a multi-disciplinary study of the societal impacts of big data in seven European sectors aimed to define a roadmap and create a community that address and optimise these impacts [3].The term big data has received numerous definitions [4,5]. To develop the roadmap, we considered as a working definition that big data is that which uses big volume, big velocity, big variety data assets to extract value (insight and knowledge), and furthermor...

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Section: Data Managementmentioning

confidence: 99%

Section: Data Managementmentioning

confidence: 99%