One-Pot Synthesis of Requirements Elicitation for Operational BI (OBI) System: in the Context of the Modern Business Environment
Bibliographic record
Abstract
Background: Requirement elicitation is the first step for any project. The available BI requirement elicitation approaches are focused more towards: the top pyramid of the management, less focus on the business aspect of an organization, historical in nature, emphasis on data mining and data warehousing aspects, no clear separation between requirements, and lack of proper linkage between the requirements. The demand of BI shifts towards the operational front for last couple of years. The use of Operational BI is gaining more popularity among industry and business communities because of increased demand of real time BI. It provides a powerful analysis of both operational and business information in current time for all levels of the users in the organization.Objective: In the modern business environment, the business operates on networks that demands multi-level decision-making capabilities as compared to the traditional business approaches. Operational BI is one of the business information systems that support the modern business environment and provides timely decision-making information to all the users in the organization. The requirement elicitation methodology for Operational BI system is found open for research. A new approach for requirement elicitation for an Operational BI system is presented in this paper, which highly suits to the organizations in the modern business environment.Methods: A top down technique is employed in the proposed requirements methodology that focuses on the business context of an organization. The proposed requirement elicitation approach is highly suited for the organizations that operate in the modern business environment. This approach overcomes several limitations in the existing BI requirement approaches. A case study is presented to support the proposed requirement elicitation approach for OBI system.Conclusion: This approach has several advantages like fast development, clear definition, classification of various types of requirements and proper linkage between the requirements without any loss or missing of gathering requirements. Finally, it is to conclude that the proposed approach acts as a one-pot synthesis of requirements elicitation for Operational BI system.Keywords: Business Context, Business, Intelligence, Business Networks, Protocols, Modern Business, Environment, Operational Business, Intelligence Requirement, Elicitation, Requirement, Methodology
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".