Challenging Big Data Engineering: Positioning of Current and Future Development

Volk, Matthias; Staegemann, Daniel; Pohl, Matthias; Turowski, Klaus

doi:10.5220/0007748803510358

Cited by 22 publications

(11 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is exacerbated by the socio-technical nature of big data applications, combining the capabilities of the involved people, the injected data and the technical implementation [21]. The latter, whose realization can be summarized under the term big data engineering [22], represents one of the most important dimensions. It includes, inter alia, the planning and structuring of the system under development, as well as the capabilities that are to be provided.…”

Section: Big Data Analyticsmentioning

confidence: 99%

“…As it was exhibited in the literature review and is also validated by quantitative studies [13] and the conducted expert interviews, BDA can provide significant advantages to a business. Though, the practical incorporation is generally accompanied by many obstacles [22] and this especially applies to highly dynamic business environments, which pose additional challenges, since they require the analysis solution to be constantly adjusted regarding the new circumstances [9], [61]. However, doing so can be a costly endeavor.…”

Section: Big Data Analytics Solutions In Dynamic Business Environmentsmentioning

confidence: 99%

See 1 more Smart Citation

Discussing Relations Between Dynamic Business Environments and Big Data Analytics

Staegemann

Volk

Daase

et al. 2020

CSIMQ

Self Cite

View full text Add to dashboard Cite

Big data attracts researchers and practitioners around the globe in their desire to effectively manage the data deluge resulting from the ongoing evolution of the information systems domain. Consequently, many decision makers attempt to harness the potentials arising with the use of those modern technologies in a multitude of application scenarios. As a result, big data has gained an important role for many businesses. However, as of today, the developed solutions are oftentimes perceived as completed products, without considering that the application in highly dynamic environments might benefit from a deviation of this approach. Relevant data sources as well as the questions that are supposed to be answered by their analysis may change rapidly and so do subsequently the requirements regarding the functionalities of the system. To our knowledge, while big data itself is a prominent topic, fields of application that are likely to evolve in a short period of time and the resulting consequences were not specifically investigated until now. Therefore, this research aims to overcome this paucity by clarifying the relation between dynamic business environments and big data analytics (BDA), sensitizing researchers and practitioners for future big data engineering activities. Apart from a thorough literature review, expert interviews are conducted that evaluate the made inferences regarding dynamic and stable influencing factors, the influence of dynamic environments on BDA applications as well as possible countermeasures. The ascertained insights are condensed into a proposal for decision making, facilitating the alignment of BDA and business needs in dynamic business environments.

show abstract

Section: Big Data Analyticsmentioning

confidence: 99%

Section: Big Data Analytics Solutions In Dynamic Business Environmentsmentioning

confidence: 99%

Discussing Relations Between Dynamic Business Environments and Big Data Analytics

Staegemann

Volk

Daase

et al. 2020

CSIMQ

Self Cite

View full text Add to dashboard Cite

show abstract

“…By considering the shortage of qualified experts in this domain and the concurrent demand [21,22], independent from the actual size of the enterprise, it appears to be reasonable to support concerned decision-makers and technicians. This applies especially to fundamental tasks like the design of the underlying technical architecture [23]. Hence, a thorough description of corresponding use cases could facilitate the realization of those kinds of complex projects by providing a suitable source of information.…”

Section: Introductionmentioning

confidence: 99%

Identifying Similarities of Big Data Projects–A Use Case Driven Approach

et al. 2020

Self Cite

View full text Add to dashboard Cite

Big data is considered as one of the most promising technological advancements in the last decades. Today it is used for a multitude of data intensive projects in various domains and also serves as the technical foundation for other recent trends in the computer science domain. However, the complexity of its implementation and utilization renders its adoption a sophisticated endeavor. For this reason, it is not surprising that potential users are often overwhelmed and tend to rely on existing guidelines and best practices to successfully realize and monitor their projects. A valuable source of knowledge are use case descriptions, of which a multitude exists, each of them with a varying information density. In this design science research endeavor, 43 use cases are identified by conducting a thorough literature review in combination with the application and adaption of a corresponding template for big data projects. By a subsequent categorization, which is performed by identifying and employing a hierarchical clustering algorithm, nine different standard use cases emerge, as the contribution's artifact. This provides decision-makers with an initial entry point, which can be utilized to shape their project ideas, not only by identifying the general meaningfulness of their potential big data project but also in terms of concrete implementation details.

show abstract

“…Big data is a technology that deals with data sets that are too large or complex to handle with traditional data processing techniques for capturing, storing, analyzing, searching, sharing, transferring, visualizing, querying, and updating of data. The main characteristics of big data technologies are 5V's: volume, velocity, variety, volatility, and variability [4,[10][11][12]. Volume refers to the massive amount of data; Velocity refers to the high growth rate of incoming data that needs to be processed and analyzed; Variety refers to many different forms of data; Volatility refers to the duration which data is valid and should be stored in the repository; Variability refers to data whose context changes invariably.…”

Section: Introductionmentioning

confidence: 99%

REBD: A Conceptual Framework for Big Data Requirements Engineering

Kourla¹,

Putti²,

Maleki³

2020

Computer Science &Amp; Information Technology

View full text Add to dashboard Cite

Requirements engineering (RE), as a part of the project development life cycle, has increasingly been recognized as the key to ensuring on-time, on-budget, and goal-based delivery of software projects;compromising this vital phase is nothing but project failures. RE of big data projects is even more crucial because of the main characteristics of big data, including high volume, velocity, and variety. As the traditional RE methods and tools are user-centric rather than data-centric, employing these methodologies is insufficient to fulfill the RE processes for big data projects. Because of the importance of RE and limitations of traditional RE methodologies in the context of big data software projects, in this paper, a big data requirements engineering framework, named REBD, has been proposed. This conceptual framework describes the systematic plan to carry out big data projects starting from requirements engineering to the development, assuring successful execution, and increased productivity of the big data projects.

show abstract

Challenging Big Data Engineering: Positioning of Current and Future Development

Cited by 22 publications

References 24 publications

Discussing Relations Between Dynamic Business Environments and Big Data Analytics

Discussing Relations Between Dynamic Business Environments and Big Data Analytics

Identifying Similarities of Big Data Projects–A Use Case Driven Approach

REBD: A Conceptual Framework for Big Data Requirements Engineering

Contact Info

Product

Resources

About