Chun-Wei Lin, Jerry

Flath, Christoph M.

Srivastava, Gautam

Zhang, Yu-Dong

English

AIS Electronic Library (AISeL)

EDITORIALChallenges and Outcomes Using Big Data as a ServiceGautam Srivastava • Christoph M. Flath • Jerry Chun-Wei Lin • Yu-Dong ZhangPublished online: 4 October 2023 The Author(s), under exclusive licence to Springer Fachmedien Wiesbaden GmbH 2023Big Data as a Service (BDaaS) encompasses software, datawarehousing, infrastructure, and platform service models todeliver advanced analysis of large data sets, generallythrough a cloud-based network. It is a solution-based sys-tem designed to provide organizations with wide-rangingcapabilities to gain insights from data. With a rapidlygrowing amount of data, businesses require advancedtechnologies to harness the power of data science effec-tively. These convergent technological trends will signifi-cantly redefine business applications for both e-businessand more traditional sectors such as manufacturing orlogistics. As enterprises acquire the opportunity to storeand analyze gigantic amounts of data, ranging fromdetailed customer behavior traces to demographic shifts ofclientele in emerging markets, they play a significant rolein creating novel business opportunities. Further, it helpsthem to disparate data to drive insights through which thebusinesses can make efficient decisions and create a com-petitive advantage. Practically, big data services are mostimportant for business applications, not just because thedata is big but also because its potential or impact istremendous. Despite the advantages, the major challenge indealing with big data applications is that it requires moreinnovative technological solutions that replace traditionaldatabases with more scalable architectures. This is wherebig data as a service (BDaaS) can make a difference forbusiness applications. BDaaS has been commercialized andimplemented in many business platforms. The cloudinfrastructure required to use BDaaS is provided by bothMicrosoft Azure and Google BigQuery, which are bothdata services. The cloud data lake, data warehousing, anddata sharing are located on top of the data services layer.Their purpose is to offer the analytics and sharing bridgesthat lead to end-user analytics.BDaaS solutions implementations take entire businessIT platforms to the next level. They integrate varioussoftware services and apply them to massively large data-sets in turn helping organizations meet evolving needsfaster, more accurately and more cost-effectively. Fur-thermore, offloading data management across BDaaSplatforms provides more internal capacity for businessdevelopment. With BDaaS, businesses are no longerassociated with their local servers and storage systems. Itenables them to securely store their data and access itacross remote locations, such that they can efficientlycollaborate with their business partners and extractimportant business insights to drive their business. In theshort term, BDaaS enables access to business data fromanywhere in the world and at any time. So that businessesof all kinds and sizes, particularly E-business applications,can acquire maximum benefit from using BDaaS tools.Some of the considerable advantages include seamless dataaccess, easier setup, remote collaboration, data crash pro-tection, improved financial reporting, cost-effectiveness,and providing more advanced insights for various businessimprovements. An e-business application can regularlyG. Srivastava (&)Brandon University, Brandon, Canadae-mail: srivastavag@brandonu.caC. M. FlathUniversity of Würzburg, Würzburg, Germanye-mail: christoph.flath@uni-wuerzburg.deJ. C.-W. LinSilesian University of Technology, Gliwice, Polande-mail: Jerry.Chun-Wei.Lin@polsl.plY.-D. ZhangUniversity of Leicester, Leicester, UKe-mail: yudongzhang@ieee.org123Bus Inf Syst Eng 66(1):1–2 (2024)https://doi.org/10.1007/s12599-023-00835-6track user activities without compromising security andimplement the extracted insights in a meaningful way toacquire benefits in offering more advanced services to theend-users.This special issue explores the advances in BDaaS toenhance the customer relationship measures across theentire spectrum of business applications. The special issuesaw a total of 10 submissions, from which 2 papers arepublished. It was intentional to adhere to a strict acceptancerate (20%) and ensure that only the best papers in the scopeof the special issue were accepted. The following fewparagraphs summarize the contributions that our specialissue collection presents.In ‘‘Financing Decisions and the Role of CSR inDonation-Based Crowdfunding – Evidence from Pakistanand Indonesia’’, Usman et al. investigate the associationbetween fundraisers in donation-based crowdfundingactivities, which becomes a potential Corporate SocialResponsibility (CSR) activity. Donation-based crowd-funding and corporate social responsibility activities havepotential symbiotic ramifications to raise funds, but cam-paigners confront challenges and competition to accom-plish their charitable target. For example, CSR activitiescould warrant the possibility of using crowdfunding to raisemoney. On the other hand, a company’s CSR objectivescan be achieved by using crowdfunding to micro-fundvarious social initiatives. This paper contributes to thedonation-based crowdfunding literature to develop a vividunderstanding of different CSR activities and their impacton the project’s success. The work in this paper is one ofthe first to examine the significance of CSR activities andhow it may enrich the body of knowledge regardingcrowdfunding in diverse economies.In ‘‘Dynamic Circular Network-Based Federated Dual-View Learning for Multivariate Time Series AnomalyDetection’’, Zhang et al. look into multivariate time-seriesdata and how it exhibits intricate correlations in bothtemporal and spatial dimensions. Existing network archi-tectures often overlook dependencies in the spatialdimension and struggle to strike a balance between long-term and short-term patterns when extracting features fromany data. Furthermore, industries within the businesscommunity are hesitant to share their raw data, whichhinders anomaly prediction accuracy and detection per-formance. To address these challenges, the authors pro-posed a dynamic circular network-based federated dual-view learning approach. Experimental results on four open-source datasets demonstrate that the presented methodoutperforms existing methods in terms of accuracy, recall,and F1-score for anomaly detection.The editors would like to thank Ulrich Hasenkamp andthe rest of the editorial office for their countless hours spenton this Special Issue as well as Christof Weinhardt, currentEditor-In-Chief of BISE (Business and Information Sys-tems Engineering) journal for responding to all queries andconcerns raised by us as editors to ensure the Special Issueis an overwhelming success.1232 G. Srivastava et al.: Challenges and Outcomes Using Big Data as a Service, Bus Inf Syst Eng 66(1):1–2 (2024)

Challenges and Outcomes Using Big Data as a Service

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Challenges and Outcomes Using Big Data as a Service

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