Augmenting Data Warehouses with Big Data

Jukić, Nenad; Sharma, Abhishek; Nestorov, Svetlozar; Jukić, Boris

doi:10.1080/10580530.2015.1044338

Cited by 54 publications

(17 citation statements)

References 4 publications

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“…Variability: refers to variance in meaning, number of inconsistences, multitude of data dimensions, and inconsistent data receiving speeds [51][52][53][54]. Several attempts have been proposed to address this problem; the incorporation of semantic analysis in SBD, for example, reduces the ambiguity of SBD by clarifying the actual context of the users' content.…”

Section: Figure 24: How Much Data Is Generated In Each Minutementioning

confidence: 99%

Introduction to Big Data Technology

Abu-Salih

Wongthongtham

Zhu

et al. 2021

Social Big Data Analytics

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Big data is no more "all just hype" but widely applied in nearly all aspects of our business, governments, and organizations with the technology stack of AI. Its influences are far beyond a simple technique innovation but involves all rears in the world. This chapter will first have historical review of big data; followed by discussion of characteristics of big data, i.e. from the 3V's to up 10V's of big data. The chapter then introduces technology stacks for an organization to build a big data application, from infrastructure/platform/ecosystem to constructional units and components. Finally, we provide some big data online resources for reference.

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Section: Figure 24: How Much Data Is Generated In Each Minutementioning

confidence: 99%

Introduction to Big Data Technology

Abu-Salih

Wongthongtham

Zhu

et al. 2021

Social Big Data Analytics

View full text Add to dashboard Cite

show abstract

“…There is a cost of every hedging strategy and this cost can make the return negative, particularly when indirect cost of missing the potential upside is also included in the calculations. Wang, Alam, and Makar [35] in their study on foreign exchange reporting in annual reports show that although foreign exchange gains and losses on cash flow hedges are shown in Other Comprehensive Income (OCI), investors are unable to make accurate cash flow predictions on account of two reasons: i) hedge ratios are not reported by companies; and ii) offsetting movements in the underlying are not reported along with gains/losses on FX cash flow hedges. According to them, investors tend to assign lower risk premiums to companies reporting FX gains/losses in OCI in the belief that the total FX cash flow exposure has been hedged whereas, in practice, only a small portion of that exposure may have been hedged.…”

Section: Previous Studies On Use Of Gis In Risk Analysis In Foreign Exchange Marketsmentioning

confidence: 99%

Enabling Foreign Exchange Management through GIS

Bhatia

2018

JEMT

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New derivative products and new statistical techniques including big data analysis have brought about a lot of advancements in foreign exchange management. Companies are also disclosing more information on foreign exchange exposures including economic exposure and causes and impact of changes in these exposures on their profitability and asset base. The next step, as advocated in this study, may be to combine spatial visualisation techniques developed with the help of GIS to map out the different types of foreign exchange exposures territory-wise and, thereby, enhance understanding of the strategies required to manage these exposures. This study looks at the previous literature on applications of GIS towards increasing efficiencies in business processes including market share enhancement and optimisation of branch location and conceptualises their use in the field of foreign exchange management similar to their use in the financial derivatives market advocated by earlier researchers.

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“…Studies have been conducted to show that big data leads to complex challenges for libraries. Jukić et al (2015) propose that formal dataset modelling processes cannot be used for big data, which results in the lack of formal metadata on big datasets. In the end, such a situation results in the complexity of accessing, managing, utilising and describing big data.…”

Section: What Is the Importance Of Big Data For Libraries?mentioning

confidence: 99%

Understanding big data in librarianship

Zhan

Widén

2017

Journal of Librarianship and Information Science

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Big data has been widely discussed. The diverse impacts and potential of big data have been pinpointed and empirically proven. Nevertheless, there is no consensus on the understanding of big data. Big data has been used to refer to different things and its characteristics are not universally accepted either. Therefore, this study aims to generate an overall understanding of big data. The domain of the study is limited to librarianship, because of its unique position in managing and utilising big data. Thus, the aim of this study is to understand big data in librarianship according to how it is defined in that profession. Articles containing definitions of big data were reviewed and 35 definitions were collected. Since the number of analysed definitions is small, a combined method was employed. Both a content analysis and a statistical description of these definitions were conducted. Five aspects are summarised based on the analysis of the collected definitions. These aspects help explicate libraries’ current understanding of big data and librarians’ big data skills.

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Augmenting Data Warehouses with Big Data

Cited by 54 publications

References 4 publications

Introduction to Big Data Technology

Introduction to Big Data Technology

Enabling Foreign Exchange Management through GIS

Understanding big data in librarianship

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