Big Data Acquisition

Lyko, Klaus; Nitzschke, Marcus; Ngomo, Axel-Cyrille Ngonga

doi:10.1007/978-3-319-21569-3_4

Cited by 53 publications

(29 citation statements)

References 6 publications

(6 reference statements)

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“…There is also a need to develop easy-to-use reporting tools including semantic annotations that do not add extra work, e.g. to healthcare professionals [27]. This requirement is relevant in conjunction with the topic below.…”

Section: Data Managementmentioning

confidence: 99%

“…Adaptive data detection and acquisition is needed in e.g. the finance and insurance sector [27]. Other relevant subtopics are data discovery, datasets crawlers, metadata, dataset ranking [28].…”

Section: Data Managementmentioning

confidence: 99%

“…Understanding how data expires, what happens with historical data and how it is archived is important. In relation to this, the synchronisation of data and how to update extracted knowledge bases if the sources are changing should also be addressed [27]. Open data is also central to research itself.…”

Section: Data Managementmentioning

confidence: 99%

“…Techniques and tools for processing real-time heterogeneous data. This is particularly needed in the development of new tools for sensor data processing, especially in the manufacturing, retail and transport sectors [27], as well as in the energy sector. Social media mining is also relevant [27].…”

Section: Data Processingmentioning

confidence: 99%

“…Semantic analysis. Examples are sentiment analysis, a relevant subtopic when using social media data for the manufacturing or retail sectors [27], and entity recognition and linking [29].…”

Section: Data Analyticsmentioning

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%

Section: Data Managementmentioning

confidence: 99%

Section: Data Processingmentioning

confidence: 99%

“…Semantic analysis. Examples are sentiment analysis, a relevant subtopic when using social media data for the manufacturing or retail sectors [27], and entity recognition and linking [29].…”

Section: Data Analyticsmentioning

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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Data curation in the Internet of Things: A decision model approach

Haro-Olmo

Valencia-Parra

Varela-Vaca

et al. 2021

Comp and Math Methods

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Current Internet of Things (IoT) scenarios have to deal with many challenges especially when a large amount of heterogeneous data sources are integrated, that is, data curation. In this respect, the use of poor‐quality data (i.e., data with problems) can produce terrible consequence from incorrect decision‐making to damaging the performance in the operations. Therefore, using data with an acceptable level of usability has become essential to achieve success. In this article, we propose an IoT‐big data pipeline architecture that enables data acquisition and data curation in any IoT context. We have customized the pipeline by including the DMN4DQ approach to enable us the measuring and evaluating data quality in the data produced by IoT sensors. Further, we have chosen a real dataset from sensors in an agricultural IoT context and we have defined a decision model to enable us the automatic measuring and assessing of the data quality with regard to the usability of the data in the context.

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Deep Bayesian network architecture for Big Data mining

Njah

Jamoussi

Mahdi

2018

Concurrency and Computation

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Classical Datamining methods are facing various challenges in the era of Big Data. Between the need of fast knowledge extraction and the high flows of data acquired in small slots of time, these methods became shifted. The variability and the veracity of the Big Data perplex the Machine Learning process. The high volume of Big Data yields to a congested learning because the classic methods are designed for small sets of features. Deep Learning has recently emerged in the aim of handling voluminous data. The concept of the Deep induces the conversion of the features into a new abstracted representation in order to optimize an objective. Although the Deep Learning methods are experimentally promising, their parameterization is exhaustive and empirical.To tackle these problems, we utilize the causality and the uncertainty of the Bayesian Network in order to propose a new Deep Bayesian Network architecture. We provide a new learning algorithm for this multi-layered Bayesian Network with latent variables. We evaluate the proposed architecture and learning algorithms over benchmark datasets. We used high-dimensional data in order to simulate the Big Data challenges, which are imposed by the volume and veracity aspects. We demonstrate the effectiveness of our contribution under these constraints.

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Big Data Acquisition

Cited by 53 publications

References 6 publications

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

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

Data curation in the Internet of Things: A decision model approach

Deep Bayesian network architecture for Big Data mining

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