Revisiting Arguments for a Three Layered Data Warehousing Architecture in the Context of the Hadoop Platform

Yang, Qishan; Helfert, Markus

doi:10.5220/0005912703290334

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…This research mainly focuses on the reconciled layer. (Yang and Helfert, 2016b) investigates and builds a data warehouse architecture in the context of big data. It is based on the Flume, Hadoop, Hive, HBase, Sqoop etc.…”

Section: Big Data Warehousementioning

confidence: 99%

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Analysis of Data Warehouse Architectures: Modeling and Classification

Yang

Helfert

2019

Proceedings of the 21st International Conference on Enterprise Information Systems

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With decades of development and innovation, data warehouses and their architectures have been extended to a variety of derivatives in various environments to achieve different organisations' requirements. Although there are some ad-hoc studies on data warehouse architecture (DWHA) investigations and classifications, limited research is relevant to systematically model and classify DWHAs. Especially in the big data era, data is generated explosively. More emerging architectures and technologies are leveraged to manipulate and manage big data in this domain. It is therefore valuable to revisit and investigate DWHAs with new innovations. In this paper, we collect 116 publications and model 73 disparate DWHAs using Archimate, then 9 representative DWHAs are identified and summarised into a "big picture". Furthermore, it proposes a new classification model sticking to state-of-the-art DWHAs. This model can guide researchers and practitioners to identify, analyse and compare differences and trends of DWHAs from componental and architectural perspectives.

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Section: Big Data Warehousementioning

confidence: 99%

“…Hadoop), in which the distributed file system and other mechanisms (e.g. MapReduce) are applied to store and deal with data respectively (Yang and Helfert, 2016b). This DWH can address big data issues (e.g.…”

Section: Dwha Overviewmentioning

confidence: 99%

Analysis of Data Warehouse Architectures: Modeling and Classification

Yang

Helfert

2019

Proceedings of the 21st International Conference on Enterprise Information Systems

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“…Big data also represents a challenge for business intelligence systems. Examples of various proposals of new solutions in this field include: implementing data warehouses within NoSQL database management systems [6], parallel construction of OLAP cubes in big data environments [7], data warehousing architecture for big data environment [8], Octopus [9] or Ophidia [10] as BDA engines and Deep Data Warehouse as an integration of warehouse data and unstructured content [11].…”

Section: Almeida and Calistrumentioning

confidence: 99%

Big Data Trends in Domains of Application

Lovrenčić

Šestak

Rabuzin

2018

Recent Advances in IT, Tourism, Economics, Management and Agriculture

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Big data is a well-known area of research due to many challenges, which are constantly appearing. Although specifically big data analytics has been prominently in the focus of interest, big data management as a whole should be guided properly, so that as much information and knowledge as possible can be obtained from data for their intended purpose. There are many proposed solutions that are trying to resolve various issues that arose with big data, for example weak processing capability of traditional systems, such as relational databases, to efficiently manage them. New technologies that are trying to cope with various unstructured or structured forms of data are also being researched, Hadoop and NoSQL databases being some of them. Big data challenges are also connected with requirements in the domain of their usage, such as business and commerce, healthcare, public sector, education, or scientific research. This paper examines current research of big data management, especially according to its various domains of application. It gives an insight into applications of big data in various domains of different background (business, government, etc.), followed by a discussion on issues and challenges in its application, as well as mechanisms developed to resolve them. Additionally, this paper discusses the benefits and values of using big data solutions in selected domains. The contribution of the paper is also an overview of big data associated differences and similarities among domains that are most researched in the context of this area of interest.

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