This paper provides a comprehensive analysis of architectural patterns and optimization techniques for time-series data processing, centered on the replacement of native DATE/TIMESTAMP types with integerbased surrogate keys. We demonstrate that employing 32-bit (YYYYMMDD) and 64-bit integer formats for datetime representation, coupled with specialized algorithms for indexing, range search, and aggregation, yields substantial performance gains. Empirical evaluations confirm a 30–60% reduction in storage footprint, a 25–40% acceleration in query execution, and up to an eightfold increase in system throughput through batched operations. Beyond these metrics, the study delves into advanced practical implementations across high-frequency trading, telecommunications, and industrial IoT, detailing extended use cases such as real-time fraud detection and predictive maintenance. The paper further introduces a set of actionable implementation guidelines, including hybrid data models and optimized partitioning strategies, to facilitate adoption. Finally, we explore the application of International Atomic Time (TAI) to eliminate temporal ambiguities and outline future research directions integrating this approach with machine learning pipelines and edge computing architectures. The collective findings position integer-based timestamp storage as a foundational element in the design of high-performance, scalable, and reliable time-series data warehouses.
In this study, we optimize SQL+ML queries on top of OpenMLDB, an open-source database that seamlessly integrates offline and online feature computations. The work used feature-rich synthetic dataset experiments in Docker, which acted like production environments that processed 100 to 500 records per batch and 6 to 12 requests per batch in parallel. Efforts have been concentrated in the areas of better query plans, cached execution plans, parallel processing, and resource management. The experimental results show that OpenMLDB can support approximately 12,500 QPS with less than 1 ms latency, outperforming SparkSQL and ClickHouse by a factor of 23 and PostgreSQL and MySQL by 3.57 times. This study assessed the impact of optimization and showed that query plan optimization accounted for 35
Enterprises pursuing AI-driven transformation face a critical tradeoff: centralized consistency vs. decentralized scalability. The "Data Platform Unification Paradox" captures this dilemma. Building on our prior NLPI 2025 paper, this extended version integrates technical depth, mathematical models, and concrete architectures, especially for integrating Data Mesh with Quantum Databases and LLM Agents. A federated architecture is proposed using graph-theoretic models and entropy-based data valuation. We introduce a formal structure to evaluate platform complexity and propose intelligent agent-based governance models to operationalize data sharing across domains. This work aims to move beyond conceptual frameworks by proposing actionable blueprints for next-generation, intelligent data ecosystems.
Agile methodologies have transformed organizational management by prioritizing team autonomy and iterative learning cycles. However, these approaches often lack structured mechanisms for knowledge retention and interoperability, leading to fragmented decision-making, information silos, and strategic misalignment. This study proposes an alternative approach to knowledge management in Agile environments by integrating Ikujiro Nonaka and Hirotaka Takeuchi’s theory of knowledge creation— specifically the concept of Ba, a shared space where knowledge is created and validated—with Jürgen Habermas’s Theory of Communicative Action, which emphasizes deliberation as the foundation for trust and legitimacy in organizational decision-making. To operationalize this integration, we propose the Deliberative Permeability Metric (DPM), a diagnostic tool that evaluates knowledge flow and the deliberative foundation of organizational decisions, and the Communicative Rationality Cycle (CRC), a structured feedback model that extends the DPM, ensuring long-term adaptability and data governance. This model was applied at Livelo, a Brazilian loyalty program company, demonstrating that structured deliberation improves operational efficiency and reduces knowledge fragmentation. The findings indicate that institutionalizing deliberative processes strengthens knowledge interoperability, fostering a more resilient and adaptive approach to data governance in complex organizations.
Business process intelligence improves operational efficiency that is essential for achieving business objectives, besides facilitating competitive advantage. As organizations operate through inter-connected business processes, insights into their process performance through related business rules are essential to achieve business objectives. This paper outlines an approach for developing insight-driven business rules using the concept of buckets, which can lead to the development of a repository of business knowledge for business process operations. The proposed concepts are demonstrated through a prototype modeled on a hypothetical customer mortgage lending process, implemented using Oracle’s PL/SQL database language.
