develop actionable indicators

In data-driven economies, organizations lack systematic frameworks for evaluating and managing data value within internal business processes. To address this gap, this study develops a comprehensive data value assessment framework grounded in the Balanced Scorecard’s internal process perspective, integrating three interrelated dimensions: data quality, governance compliance, and operational efficiency. It introduces a novel, multi-layered taxonomy of data value—spanning technological, organizational, and regulatory dependencies—that resolves metric redundancy and establishes cross-dimensional conceptual linkages. Through systematic literature review, theoretical modeling, indicator clustering, and taxonomy design, the research produces a scalable, reusable data value metrics system. This system underpins standardized data valuation models and decision-support systems, offering both a methodological foundation and actionable implementation pathways for cross-sectoral data assetization. (149 words)

为解决数据分析工程中治理、质量、来源和可重复性的问题，本文提出UnespDataLens-RM参考模型，通过整合技术操作过程和跨领域能力来提高分析流程的可靠性。

Composite indicators in multi-criteria evaluation often suffer from a “black-box” nature, undermining transparency, interpretability, and comprehensibility. Method: This paper proposes a dominance-based rough set approach (DRSA)-driven methodology for constructing interpretable composite indicators. It systematically integrates DRSA with four key interpretability requirements—score explanation, quantile-based classification, preference modeling, and result traceability—via ordinal qualitative coding and threshold-based decision rule induction, yielding human-readable “if–then” rules that explicitly link indicator thresholds to categories or scores. Contribution/Results: Unlike conventional statistical aggregation paradigms, the proposed method enables a paradigm shift toward logic-driven, rule-based evaluation. It significantly enhances transparency, auditability, and the capacity for automatic classification and attribution of new evaluation units, thereby supporting accountable and explainable decision-making in complex multi-criteria assessment contexts.

To address predictive maintenance requirements in Industry 5.0, this paper proposes DT-Create—a digital twin modeling service suite enabling real-time virtual mirroring of physical assets and dynamic decision support. Methodologically, it integrates machine learning with ontology-driven knowledge graphs to construct semantically enriched digital twins; introduces an adaptive mechanism that dynamically selects optimal prediction models based on data characteristics and supports online model updating and inference. Following the design science research paradigm, DT-Create unifies multi-source sensor data acquisition, semantic modeling, machine learning, and logical reasoning. Empirical validation demonstrates significant improvements in data interpretability, model adaptation efficiency, and decision autonomy, confirming its engineering feasibility. The core contribution is a novel adaptive twin modeling framework that orchestrates semantic representation, data processing, and model selection in tight synergy.

Process mining often yields an overwhelming number of candidate process models, creating decision paralysis for managers seeking actionable insights. Method: This paper proposes a multi-criteria decision-making (MCDM) evaluation framework that jointly incorporates quantitative metrics (e.g., fitness, precision) and qualitative factors (e.g., organizational culture alignment). It systematically integrates MCDM techniques—including the Analytic Hierarchy Process (AHP)—into process model prioritization for the first time, moving beyond purely technical, performance-driven selection criteria. Contribution/Results: The framework enables structured, interpretable trade-offs between operational performance and strategic objectives. Evaluated in a logistics case study, it significantly improves contextual sensitivity and managerial alignment in model selection, facilitating robust, transparent decision-making under competing goals.

Organizations struggle to quantify the commercial value of data assets due to fragmented, siloed valuation approaches—divided across economic, governance, and strategic perspectives—and the absence of actionable mechanisms. This paper proposes an integrated data valuation framework that unifies these three perspectives using a Balanced Scorecard–inspired hybrid model. The framework combines qualitative scoring, cost-utility estimation, data quality indexing, and Analytic Network Process (ANP)-based multi-criteria weighting to enhance transparency and strategic alignment. Adopting a design science research methodology, it is iteratively refined through embedded industrial case studies. Empirical evaluation demonstrates that the framework significantly reduces subjectivity in valuation, improves the precision of mapping data assets to organizational strategic objectives, and supports diverse monetization pathways—including Data-as-a-Service (DaaS). It exhibits cross-industry applicability and robustness.

Existing software engineering metrics often fail to effectively support critical development decisions—such as whether refactoring is necessary or whether testing is sufficient—thereby limiting their practical utility. This work addresses this gap by systematically introducing metrological principles (the science of measurement) into the domain of software measurement for the first time. It proposes a metrology-informed approach to metric modeling and evaluation, establishing a rigorous scientific foundation for the design of software metrics. By grounding metric development in established measurement theory, the proposed method substantially enhances the usability, credibility, and decision-support capability of metrics in real-world engineering contexts. This study thus opens a new research direction for software measurement, aligning it more closely with the epistemological standards of empirical science.

This study addresses the limitations of traditional construction quality control, which relies on lagging inspections that hinder timely intervention and often lead to rework and schedule delays. The authors propose the first component-level digital twin framework tailored for the construction phase, integrating inspection records, material production and concrete placement data, early-age sensor measurements, and strength prediction models to enable dynamic representation of quality status and readiness-driven decision support. By shifting quality assessment from passive document review to real-time, data-driven proactive management, the framework facilitates structured decisions—such as releasing or halting components—well before standard strength tests are completed. This approach significantly enhances the timeliness of interventions, traceability, and overall management efficiency in construction quality control.

This study addresses a critical limitation in traditional reproducible research, where sharing only code and results fails to expose the implicit assumptions, expectations, and premises underlying an analyst’s reasoning—thereby hindering thorough evaluation of analytical quality. To overcome this, the paper proposes a formal modeling framework that explicitly translates the analyst’s tacit reasoning process into structured logical representations, statically capturing the construction logic of the analysis. This approach enables systematic scrutiny of the analytical chain of reasoning, assumption sensitivity, and conclusion robustness—even in the absence of the original data. Empirical validation on representative data analysis tasks demonstrates the framework’s effectiveness, achieving both logical visualization and data-free static assessment of analytical integrity.

This work addresses the challenge that domain experts face in translating natural language descriptions of data quality requirements into executable analyses, a process often hindered by reliance on data engineers, resulting in inefficiency and high technical barriers. To overcome this, the paper proposes a no-code, model-driven pipeline that leverages a QPM metamodel to define domain-specific quality analysis templates. Coupled with the Constrainify toolchain, it automatically transforms natural language requirements into executable and reusable analytical logic. By integrating model-driven engineering, metamodeling, and no-code web technologies, the approach significantly reduces dependency on technical expertise, enabling efficient, reproducible, and semantically aligned data quality assessments. This advancement enhances both the accessibility and automation of data quality analysis for non-technical domain practitioners.