Database Scientist

Design for Sustainable Behavior in Product Design: A Two-Decade Bibliometric and Content Analysis  Theoretical Foundations of Design for Sustainable Behavior Design for Sustainable Behavior (DfSB) is deeply rooted in behavior change theories drawn from psychology, particularly models such as the Theory of Planned Behavior, the Transtheoretical Model, and Fogg’s Behavior Model. These frameworks help designers understand how user decisions and habits can be influenced by altering the physical or digital environment through intentional design. By integrating these psychological principles into design strategies, DfSB aims to promote long-term sustainable habits, reduce energy consumption, and minimize environmental degradation during the product use phase. The theoretical integration of DfSB marks a shift from purely technical approaches to more human-centered strategies in sustainability. Evolution of DfSB in Product Design Since the early 2000s, DfSB has evolved from a marginal con...

 In Recognition of Transformative Leadership and Lasting Impact Introduction The Outstanding Contribution Award celebrates individuals who have made significant and lasting impacts through their dedication, expertise, and innovative efforts. This award acknowledges those whose work transcends ordinary expectations and fosters meaningful progress within their organization or community. Criteria for Selection Recipients of this award are chosen based on their exceptional commitment, ability to inspire others, and measurable achievements that advance the goals of the organization or field. Their contributions often include problem-solving, leadership, collaboration, and a consistent demonstration of excellence. Impact Awardees have not only excelled in their roles but have also influenced peers and stakeholders positively. Their efforts lead to improvements in processes, culture, or outcomes, showcasing the value of passion and perseverance in driving success. Legacy and Inspirat...

Data-Driven Control with Generalization Guarantees Across Probabilistic System Variations Introduction Modern control systems often operate in uncertain and variable environments, where the system dynamics may vary due to manufacturing tolerances, environmental changes, or load variations. Traditional robust control assumes prior knowledge of these variations and builds conservative models to account for them. However, this assumption does not hold in many real-world scenarios, especially when deploying the same controller across a fleet of similar but non-identical systems, such as drones, autonomous vehicles, or wind turbines. To address this, we present a probabilistic data-driven control approach that relies on trajectory data collected from multiple system instances rather than a nominal model. The goal is to synthesize a single controller that stabilizes all such variations with high probability. Problem Setup and Probabilistic Formulation We consider a discrete-time linear time...

Next-Gen Data Management with Vectors: A Review of VDBMSs and Their Real-World Use Vector data refers to numerical representations of unstructured or semi-structured data such as images, text, audio, or video. Each vector is a point in an n-dimensional space, where each dimension corresponds to a feature or attribute of the original data object. For example, a vector representing a sentence may contain hundreds or thousands of dimensions, each capturing linguistic or semantic properties extracted by natural language processing models. Unlike traditional data formats such as rows in relational databases or documents in NoSQL systems, vector data is typically unreadable to humans and cannot be understood without interpretation through algorithms. Evolution and Role of VDBMS Vector Database Management Systems (VDBMSs) are a new generation of data management systems tailored for storing, indexing, querying, and analyzing vector data. Traditional databases fall short when handling high-dim...

Synchronizing Databases with Evolving Feature Configurations in Software Product Lines  Introduction Software Product Line Engineering (SPLE) enables the systematic development of software families through shared core assets and variable features. It promotes large-scale reuse and configurability, allowing organizations to tailor products for different domains or customer needs. However, as business requirements evolve, software systems derived from SPLs must adapt accordingly. While software components can often be regenerated to reflect new configurations, adapting existing, populated databases remains a significantly more intricate task. SPL-DB-Sync proposes a semi-automated approach that bridges this gap, allowing for the controlled evolution of databases based on changes in feature selections. Feature Reconfiguration and Its Impact on Databases As SPL products evolve, new features may be introduced, old ones removed, or existing ones altered. These changes can drastically af...

Artificial Intelligence in Supply Chain Finance: Insights from Industry 5.0 through a Systematic Review Introduction The transformation from Industry 4.0 to Industry 5.0 marks a critical shift in the global industrial paradigm, emphasizing not just technological innovation but also human-centric values, sustainability, and resilience. While Industry 4.0 focused on automation, cyber-physical systems, and digital connectivity, Industry 5.0 brings humans back into the loop, promoting collaborative intelligence between humans and machines. Within this context, artificial intelligence (AI) emerges as a central enabler, capable of optimizing processes, enabling mass personalization, and improving sustainability. One of the emerging areas where AI is expected to make a significant impact is Supply Chain Finance (SCF)—a strategic function that manages financial flows in supply chains to enhance liquidity, reduce risk, and strengthen supplier relationships. However, the integration of AI into ...

Optimized Data Structures for Direction-Agnostic Tridiagonal System Solvers on CPU and GPU Architectures Tridiagonal systems frequently arise in the numerical solution of partial differential equations (PDEs) using structured grids, particularly in high-order compact finite difference schemes and alternating direction implicit (ADI) methods. These systems appear as independent batches along different spatial directions and form the computational backbone of many fluid dynamics simulations. However, performance bottlenecks—especially in solving x-directional systems—are common due to memory layout inefficiencies and limited parallelism. This work introduces a direction-agnostic data structure and a new distributed-memory solver (DistD2-TDS) that address these limitations by optimizing memory access patterns and reducing inter-process communication. Introduction to Tridiagonal Systems in PDE Discretization Tridiagonal systems appear naturally when discretizing PDEs using high-order com...

Lifetime Achievement Award - 2025 Introduction A Lifetime Achievement Award is more than a recognition; it is a celebration of enduring commitment, exceptional accomplishments, and unwavering dedication over the course of a remarkable career. It honors an individual whose influence has not only shaped their field but also inspired generations to come. Career Journey and Milestones The recipient's journey is marked by a series of transformative milestones. From humble beginnings to becoming a respected leader in their domain, their professional life has been a testament to perseverance, vision, and innovation. Each phase of their career has added a layer of depth and distinction, leaving a trail of excellence for others to follow. Contributions to the Field Throughout the years, the recipient has made significant contributions that have advanced their discipline in meaningful ways. Their work has broken new ground, set standards of excellence, and influenced both peers and successo...

Design and Implementation of a Smart Firewall System for Database Protection Using Artificial Intelligence Introduction In today’s digitally connected world, networked databases are critical assets for organizations, holding sensitive and mission-critical data. With the increasing complexity and frequency of cyberattacks, traditional security measures are no longer sufficient. To address these challenges, integrating artificial intelligence (AI) into network database security offers a proactive and intelligent approach. This topic explores the design of an AI-based architecture aimed at enhancing database protection and threat response capabilities in modern networks. AI Integration in Network Security Artificial intelligence introduces advanced capabilities such as machine learning, natural language processing, and behavioral analytics to security systems. In the context of database networks, AI can analyze vast amounts of traffic data to identify patterns, detect anomalies, and rec...