AI Insights

• The proposed controller prevents both under-utilization (too many idle GPUs) and overload (insufficient capacity). [3]
• The proposed hierarchical control framework integrates workload utilization, cluster-level provisioning, MILP-based frequency tuning, computing power consumption, and cooling regulation. [2]

Abstract
The AI datacenters are currently being deployed on a large scale to support the training and deployment of power-intensive large-language models (LLMs). Extensive amount of computation and cooling required in datacenters increase concerns about the energy use and carbon emissions of AI datacenters. Although current state-of-the-art has examined the energy efficiency of LLM inference, most prior research focused on optimizing compute-side scheduling without considering thermal objectives or constraints. Since GPU-intensive inference generates substantial heat that can degrade datacenter performance, ignoring thermal effects can increase total energy consumption and reduce the efficiency of LLM serving. To fill this gap, we profile the characteristics of GPU servers under varying cooling and AI jobs, and develop a joint cooling and computing modeling approach for AI datacenters. Built upon such workload and thermal dynamics models, a novel hierarchical control framework is proposed to co-optimize computing and thermal management by identifying the optimal GPU parallelism, frequency (DVFS), and cooling control knobs. Using real Azure inference traces and detailed GPU profiling, our model balances serving latency and thermal constraints in AI datacenters while significantly improving AI datacenters' energy efficiency.

Why we are recommending this paper?
Due to your Interest in AI for Data Science Management

This paper directly addresses the management of AI infrastructure, a key concern given your interest in managing data science teams and AI systems. The focus on energy efficiency and datacenter operations aligns strongly with your broader interests in optimizing tech teams and their impact.

AI Insights

• HDC generation involves extracting representative entities for each database to facilitate efficient data exploration across multiple databases. [3]
• It also includes a self-refinement chain to correct errors in generated SQL statements. [3]
• The system demonstrates its capabilities through two real-world scenarios: the Financial dataset and the Bird dataset, showcasing its ability to provide insights and facilitate user-system interaction. [3]
• HDC: Hierarchical Data Context - a summary of the data that includes a description, keywords, table information, and more. [3]
• TiChart: Chart Selection - a component that selects the most suitable chart type to present analysis results by visualization. [3]
• Exploration Efficiency: The ability of the system to efficiently explore data across multiple databases. [3]
• TiInsight is a SQL-based automated cross-domain exploratory data analysis system that utilizes large language models to facilitate user-system interaction and provide powerful hierarchical data context (HDC) generation, text-to-SQL (TiSQL), chart selection (TiChart), and exploration efficiency. [2]
• TiSQL is a schema filtering framework based on the map-reduce paradigm that filters tables and columns using clarified questions and cosine similarity. [1]

Abstract
The SQL-based exploratory data analysis has garnered significant attention within the data analysis community. The emergence of large language models (LLMs) has facilitated the paradigm shift from manual to automated data exploration. However, existing methods generally lack the ability for cross-domain analysis, and the exploration of LLMs capabilities remains insufficient. This paper presents TiInsight, an SQL-based automated cross-domain exploratory data analysis system. First, TiInsight offers a user-friendly GUI enabling users to explore data using natural language queries. Second, TiInsight offers a robust cross-domain exploratory data analysis pipeline: hierarchical data context (i.e., HDC) generation, question clarification and decomposition, text-to-SQL (i.e., TiSQL), and data visualization (i.e., TiChart). Third, we have implemented and deployed TiInsight in the production environment of PingCAP and demonstrated its capabilities using representative datasets. The demo video is available at https://youtu.be/JzYFyYd-emI.

Why we are recommending this paper?
Due to your Interest in Data Science Management

This research explores the use of LLMs for automated data analysis, a technique relevant to streamlining data science workflows and improving team efficiency. The SQL-based approach is particularly valuable for managing and understanding complex datasets, aligning with your interest in data science engineering management.

AI Insights

• Limited generalizability due to the small sample size. [3]
• Organizational readiness has become the binding constraint for enterprise XR adoption, surpassing technology barriers. [2]

Abstract
Extended Reality (XR) offers transformative potential for industrial support, training, and maintenance; yet, widespread adoption lags despite demonstrated occupational value and hardware maturity. Organizations successfully implement XR in isolated pilots, yet struggle to scale these into sustained operational deployment, a phenomenon we characterize as the ``Pilot Trap.'' This study examines this phenomenon through a qualitative ecosystem analysis of 17 expert interviews across technology providers, solution integrators, and industrial adopters. We identify a ``Great Inversion'' in adoption barriers: critical constraints have shifted from technological maturity to organizational readiness (e.g., change management, key performance indicator alignment, and political resistance). While hardware ergonomics and usability remain relevant, our findings indicate that systemic misalignments between stakeholder incentives are the primary cause of friction preventing enterprise integration. We conclude that successful industrial XR adoption requires a shift from technology-centric piloting to a problem-first, organizational transformation approach, necessitating explicit ecosystem-level coordination.

Why we are recommending this paper?
Due to your Interest in Managing tech teams

The paper’s focus on scaling XR adoption within industrial settings is pertinent to managing tech teams implementing innovative solutions. Understanding the organizational challenges in deploying XR aligns with your interest in managing tech teams and their projects.

AI Insights

• The results showed that PBL with technology is still a trend in research. [3]
• The analysis used bibliometric mapping and VOS Viewer software to visualize the relationships between variables studied by researchers. [3]
• The study suggests that PBL with technology is still a popular area of research, but there is room for improvement in determining student success and motivation. [3]
• The study analyzed the development of project-based learning (PBL) research by integrating technology from 2015 to 2024. [2]

Abstract
This study aims to conduct a bibliometric analysis of scientific publications discussing technology integration in the Project-Based Learning (PjBL) model during the 2015-2024 period. With the evolving 21st century educational paradigm, PjBL has emerged as one of the most promising pedagogical approaches. In this context, the integration of technology in PjBL becomes an important catalyst that expands the scope and depth of learning experiences while preparing students to face challenges in the digital era. This research applies a bibliometric analysis methodology and systematic literature review, utilizing the Publish or Perish (POP) application for data collection and VOSviewer for data analysis and visualization. The results reveal emerging research trends, collaboration patterns, and focus areas in the scientific literature related to technology integration in PjBL. The bibliometric analysis uncovers research dynamics and knowledge gaps that need to be addressed. These findings provide valuable insights for researchers, practitioners, and policymakers in optimizing the implementation of technology-based PjBL and formulating educational policies responsive to technological developments and 21st-century learning needs. The results of this study are expected to contribute significantly to the academic community and educational practitioners in developing more effective technology based PjBL implementation strategies and fostering interdisciplinary collaboration to advance education in the digital age.

Why we are recommending this paper?
Due to your Interest in Managing tech teams

This paper investigates technology integration within a specific educational model, offering insights into evolving pedagogical approaches. Given your interest in managing data science teams, this could inform strategies for integrating new technologies into training and development programs.

AI Insights

• The paper discusses the challenges of dataset licensing and attribution in AI research, highlighting the need for more transparent and equitable practices. [3]
• Attribution: The act of acknowledging the source of a dataset or model used in AI research. [3]
• The paper assumes that all datasets are available for use, which may not be the case in practice. [3]
• The authors propose a framework for optimal data selection from multiple sources, which can improve performance scaling and reduce computational costs. [2]

Abstract
We argue that the machine learning value chain is structurally unsustainable due to an economic data processing inequality: each state in the data cycle from inputs to model weights to synthetic outputs refines technical signal but strips economic equity from data generators. We show, by analyzing seventy-three public data deals, that the majority of value accrues to aggregators, with documented creator royalties rounding to zero and widespread opacity of deal terms. This is not just an economic welfare concern: as data and its derivatives become economic assets, the feedback loop that sustains current learning algorithms is at risk. We identify three structural faults - missing provenance, asymmetric bargaining power, and non-dynamic pricing - as the operational machinery of this inequality. In our analysis, we trace these problems along the machine learning value chain and propose an Equitable Data-Value Exchange (EDVEX) Framework to enable a minimal market that benefits all participants. Finally, we outline research directions where our community can make concrete contributions to data deals and contextualize our position with related and orthogonal viewpoints.

Why we are recommending this paper?
Due to your Interest in AI for Data Science Management

This paper addresses the critical issue of data equity within the AI ecosystem, a factor increasingly relevant to responsible data science management. Understanding the economic implications of data access aligns with your broader interest in AI for Data Science Management.