Adaptive Architectures and Real-time Decision Support Systems: Integrating Streaming Analytics for Next-Generation Business Intelligence

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@article{1703230,
      author = {Guru Prasad Selvarajan},
      title = {Adaptive Architectures and Real-time Decision Support Systems: Integrating Streaming Analytics for Next-Generation Business Intelligence},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {5},
      number = {9},
      pages = {651-662},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703230.pdf},
      abstract = {The development of new technologies within the last few years and the enormous increase in the availability of real-time data have changed the concept of Business Intelligence (BI). Difficulty in providing real-time, aggregated OLAP information with slow data mining limited ability to accommodate strategic decision support needs through traditional BI systems. This paper looks at the role that streaming analytics can have in serving as an extension of adaptive BI to foster the evolution of RT-DSS. Like using real-time data from IoT, financial transactions data, and social media data, next-generation BI systems can provide predictive and prescriptive analytics information in real-time. The work highlights the drawbacks of the conventional BI architectures and demonstrates the adaptive architectures that can scale depending on the data traffic and various processing loads. Event stream processing, real-time data pipeline, and machine learning algorithms are analyzed as the core technologies enabling efficient and scalable, non-faulty systems. Examples from the financial, healthcare, and retail sectors include showing how RT-DSS can create an environment that promotes anticipatory action and organizational performance. While real-time analytics has been postulated, providing analytical responses to business questions in almost real-time, there are essential limitations yet to be effectively addressed, including latency issues, data integration, and the questions of ethical AI decision-making. Finally, this paper provides insights on where the BI systems are going in the future and focuses on the ongoing adaptation of BI architectures to use real-time data.},
      keywords = {Streaming Analytics, Real-time Decision Support Systems (RT-DSS), Adaptive Architectures, Business Intelligence (BI), Data Integration},
      month = {March},
  }