--The increasing adoption of large language models (LLMs) with extended context windows necessitates efficient Key-V alue Cache (KVC) management to optimize inference performance. We analyze real-world KVC access patterns using publicly available traces and evaluate commercial key-value stores like Redis and state-of-the-art RDMA-based systems (CHIME [1] and Sherman [2]) for KVC metadata management. Our work demonstrates the lack of tailored storage solution for KVC prefilling, underscores the need for an efficient distributed caching system with optimized metadata management for LLM workloads, and provides insights into designing improved KVC management systems for scalable, low-latency inference. Large Language Models (LLMs) have shown remarkable ability in tasks like text generation, translation, and question-answering, but their attention architecture introduces significant challenges. The use of key-value caches (KVC) in attention layer of transformer models, while essential for efficient token generation, requires substantial memory resources.

Metadata management plays a critical role in data governance, resource discovery, and decision-making in the data-driven era. While traditional metadata approaches have primarily focused on organization, classification, and resource reuse, the integration of modern artificial intelligence (AI) technologies has significantly transformed these processes. This paper investigates both traditional and AI-driven metadata approaches by examining open-source solutions, commercial tools, and research initiatives. A comparative analysis of traditional and AI-driven metadata management methods is provided, highlighting existing challenges and their impact on next-generation datasets. The paper also presents an innovative AI-assisted metadata management framework designed to address these challenges. This framework leverages more advanced modern AI technologies to automate metadata generation, enhance governance, and improve the accessibility and usability of modern datasets. Finally, the paper outlines future directions for research and development, proposing opportunities to further advance metadata management in the context of AI-driven innovation and complex datasets.

Most machine learning models require many iterations of hyper-parameter tuning, feature engineering, and debugging to produce effective results. As machine learning models become more complicated, this pipeline becomes more difficult to manage effectively. In the physical sciences, there is an ever-increasing pool of metadata that is generated by the scientific research cycle. Tracking this metadata can reduce redundant work, improve reproducibility, and aid in the feature and training dataset engineering process. In this case study, we present a tool for machine learning metadata management in dynamic radiography. We evaluate the efficacy of this tool against the initial research workflow and discuss extensions to general machine learning pipelines in the physical sciences.

How does Spotify battle against a giant like Apple? With machine learning and AI, Spotify creates value for its users by providing a more personalized and bespoke experience. Let's take a quick look at the layers of aggregate information that are used to enhance their platform: The core data here is in the music – the basic components of songs like the title, artist, and duration. Choosing a song to listen to sets the baseline (and maybe you like it for its bass line). Everything else can be seen as metadata: additional elements about how one listens, how the song is composed, and what other music it sounds like.

Artificial intelligence (AI) has evolved from hype to reality over the past few years. Algorithmic advances in machine learning and deep learning, significant increases in computing power and storage, and huge amounts of data generated by digital transformation efforts make AI a game-changer across all industries.8 AI has the potential to radically improve business processes with, for instance, real-time quality prediction in manufacturing, and to enable new business models, such as connected car services and self-optimizing machines. Traditional industries, such as manufacturing, machine building, and automotive, are facing a fundamental change: from the production of physical goods to the delivery of AI-enhanced processes and services as part of Industry 4.0.25 This paper focuses on AI for industrial enterprises with a special emphasis on machine learning and data mining. Despite the great potential of AI and the large investments in AI technologies undertaken by industrial enterprises, AI has not yet delivered on the promises in industry practice. The core business of industrial enterprises is not yet AI-enhanced. AI solutions instead constitute islands for isolated cases--such as the optimization of selected machines in the factory--with varying success. According to current industry surveys, data issues constitute the main reasons for the insufficient adoption of AI in industrial enterprises.27,35 In general, it is nothing new that data preparation and data quality are key for AI and data analytics, as there is no AI without data. This has been an issue since the early days of business intelligence (BI) and data warehousing.3 However, the manifold data challenges of AI in industrial enterprises go far beyond detecting and repairing dirty data. This article profoundly investigates these challenges and rests on our practical real-world experiences with the AI enablement of a large industrial enterprise--a globally active manufacturer.

Vienna, November 19th of 2019, Semantic Web Company (Austria) and PANTOPIX (Germany) have announced a comprehensive cooperation to provide the machinery industry with expertise in metadata management and structured information. Semantic Web Company (SWC), based in Vienna, is the leading provider of graph-based metadata management. The Germany Company PANTOPIX is a high-end specialist for improving information processes, developing data models as well as providing intelligent information for technical documentation. The key pillar of the partnership is to develop taxonomies, ontologies and large-scale Enterprise Knowledge Graphs to make target-oriented technical content available to internal and external customers. Knowledge Graphs enable companies to process large amounts of data from various silos and adding value to it so that it can be used in meaningful and more intelligent ways. It provides a structure and common interface for all data and enables the creation of smart multilateral relations throughout databases.

Extraction, transformation and load (ETL) became a familiar concept in the 1990s, when data warehousing became a well known business intelligence (BI) concept. The advent of the web, and the vast volume of data took many organizations' focus away from ETL to data lakes. Too many people disparaged ETL as a tool of the past. However, as IT has always been aware, data lakes aren't a solution all to themselves and rebranding to ELT doesn't change the fact that there are now far more sources and targets than there ever were. Data movement is still a complex problem and metadata management (MDM), and it's a problem becoming even more challenging as regulatory requirements for privacy mean data must be better tracked and controlled.

The first panel depicts a collection of talks by Doug Pontious of Amerisure titled Cultivating an Analytics-Driven Culture to Ensure Successful Insight Generation, and Jacob Ablowitz and William Hickson at dmi.io titled "What's My Data Worth?" Pontious' session discussed some best practices learned at Amerisure as they unified many different data sources into an enterprise repository. Ablowitz and Hickson's presentation covered the fundamentals of commercializing data. Bradley A. Rhine of Fulton Financial Corporation and Kristin M. Love of GSK – Not the Return on Investment: Alternatives to Measuring Your Data Integration Strategy; Peter Haynes Aiken at Data Blueprint, Ed Kelly at the State of Texas, Jeffrey Kriseman at the State of Tennessee, and Michael Leahy at the State of Maryland – Challenges Facing the "First" State CDO (Not Initially Different from the Private Sector); JG Cowper of Healthbridge – How Prescribing "Data Glasses" to Eye Surgeons Is Transforming How They See Their Industry; Michael Scofield of Loma Linda University – Good Data, Bad Information – Why the Disconnect; and, Ian Rowlands of ASG Technologies – Data for Everyone: A Changing Data World. Cathy S Normand of ExxonMobil – Making Metadata Valuable – ExxonMobil's Journey Collecting and Cataloging Metadata; Lori Hurley and Denise Janci at Allstate – Divergent Approaches to Metadata Management: Lessons Learned; Ron Klein at Klein Admonition – Deriving New Business Terms from Technical Metadata; Liju Fan of OFR – Semantic Metadata Management: Leveraging Intuitive Ontologies Developed with Best Practices; David N Plotkin of MUFG – Metadata Quality: Ignore at Your Own Risk!; and, Susan Swanson at HCSC – Leveraging the Enterprise Metadata Repository for Data Governance Oversight and Data Quality Monitoring.

Google Inc.'s efforts to establish itself as a major player in cloud computing received a small boost Wednesday when it was named by research firm Forrester Research Inc. as the undisputed leader in a part of the market Forrester calls "Insight Platform as a Service." It should be noted that Insight PaaS is just a small subsection of the overall PaaS market, so Google still has some way to go. Forrester defines the Insight PaaS category as an integrated set of data management, analytics and insight application development and management components, offered as a platform. PaaS more generally is the middle layer of cloud computing, customarily defined as services to allow software developers to create applications that run on the Internet. In its report, Forrester also laid out the benefits Insight PaaS platforms provide to organizations.