This paper explores how cloud-native databases enable AI-driven applications by leveraging purpose-built technologies such as vector databases (pgvector), graph databases (AWS Neptune), NoSQL stores (Amazon DocumentDB, DynamoDB), and relational cloud databases (Aurora MySQL and PostgreSQL). It presents architectural patterns for integrating AI workloads with cloud databases, including Retrieval-Augmented Generation (RAG) [1] with LLMs, real-time data pipelines, AI-driven query optimization, and embeddings-based search. Performance benchmarks, scalability considerations, and cost-efficient strategies are evaluated to guide the design of AI-enabled applications. Real-world case studies from industries such as healthcare, finance, and customer experience illustrate how enterprises utilize cloud databases to enhance AI capabilities while ensuring security, governance, and compliance with enterprise and regulatory standards. By providing a comprehensive analysis of AI and cloud database integration, this paper serves as a practical guide for researchers, architects, and enterprises to build next-generation AI applications that optimize performance, scalability, and cost efficiency in cloud environments.

NPCs in traditional games are often limited by static dialogue trees and a single platform for interaction. To overcome these constraints, this study presents a prototype system that enables large language model (LLM)-powered NPCs to communicate with players both in the game en vironment (Unity) and on a social platform (Discord). Dialogue logs are stored in a cloud database (LeanCloud), allowing the system to synchronize memory between platforms and keep conversa tions coherent. Our initial experiments show that cross-platform interaction is technically feasible and suggest a solid foundation for future developments such as emotional modeling and persistent memory support.

Kurt "The Cyberguy" Knutsson speaks on the anticpation of automated gas stations that are already refueling cars in Finland. Do you find filling up your car with gas a chore? How about letting a robot do it for you? A Denmark based company called Autofuel has introduced a new robotic refueling system that can fill up your car without you ever getting out of the comfort of your front seat. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS When you sign up for the Autofuel system, you put in your car details such as make, model and license plate, what kind of fuel you want, and your payment details.

Hosting database services on cloud systems has become a common practice. This has led to the increasing volume of database workloads, which provides the opportunity for pattern analysis. Discovering workload patterns from a business logic perspective is conducive to better understanding the trends and characteristics of the database system. However, existing workload pattern discovery systems are not suitable for large-scale cloud databases which are commonly employed by the industry. This is because the workload patterns of large-scale cloud databases are generally far more complicated than those of ordinary databases. In this paper, we propose Alibaba Workload Miner (AWM), a real-time system for discovering workload patterns in complicated large-scale workloads. AWM encodes and discovers the SQL query patterns logged from user requests and optimizes the querying processing based on the discovered patterns. First, Data Collection & Preprocessing Module collects streaming query logs and encodes them into high-dimensional feature embeddings with rich semantic contexts and execution features. Next, Online Workload Mining Module separates encoded queries by business groups and discovers the workload patterns for each group. Meanwhile, Offline Training Module collects labels and trains the classification model using the labels. Finally, Pattern-based Optimizing Module optimizes query processing in cloud databases by exploiting discovered patterns. Extensive experimental results on one synthetic dataset and two real-life datasets (extracted from Alibaba Cloud databases) show that AWM enhances the accuracy of pattern discovery by 66% and reduce the latency of online inference by 22%, compared with the state-of-the-arts.

In simple words, data can be facts related to any object in consideration. For example, your name, age, height, weight, etc. are some data related to you. A picture, image, file, pdf, etc. can also be considered data. A database is a systematic collection of data. They support electronic storage and manipulation of data.

MariaDB Corporation and MindsDB, the leader in in-database machine learning, today together announced a technology collaboration that makes machine learning predictions easy and accessible to cloud database users. By using MindsDB in SkySQL, MariaDB's fully managed cloud database service, data science and data engineering teams can increase their organization's predictive capabilities to plan for and address real-world business issues. MariaDB has been downloaded over one billion times and is used by 75% of the Fortune 500, touching the lives of more than a billion people every day. MariaDB database users will now be able to add machine learning based predictions directly into their datasets stored in SkySQL. "We are excited to work with MindsDB to unlock the genius in the cloud by giving customers seamless and easy-to-use machine learning capabilities available through MariaDB SkySQL," said Jags Ramnarayan, Vice President and General Manager of SkySQL, MariaDB Corporation.

PanGEA, the Panoramic Graph Environment Annotation toolkit, is a lightweight toolkit for collecting speech and text annotations in photo-realistic 3D environments. PanGEA immerses annotators in a web-based simulation and allows them to move around easily as they speak and/or listen. It includes database and cloud storage integration, plus utilities for automatically aligning recorded speech with manual transcriptions and the virtual pose of the annotators. Out of the box, PanGEA supports two tasks -- collecting navigation instructions and navigation instruction following -- and it could be easily adapted for annotating walking tours, finding and labeling landmarks or objects, and similar tasks. We share best practices learned from using PanGEA in a 20,000 hour annotation effort to collect the Room-Across-Room dataset. We hope that our open-source annotation toolkit and insights will both expedite future data collection efforts and spur innovation on the kinds of grounded language tasks such environments can support.

Research intended to help scientists maximize data throughput to process microbiome or metagenomics data ended up improving the cloud efficiency of long-running dynamic workloads, saving both cloud providers and users money. The software, called OPTIMUSCLOUD, boosts efficiency for cloud-hosted databases by rightsizing resources. It works by using machine learning to develop algorithms that help optimize the cost and performance of both the virtual machine selection and the database management system options. "Our system takes a look at the hundreds of options available and determines the best one normalized by the dollar cost," said Somali Chaterji, a Purdue University assistant professor of agricultural and biological engineering and OPTIMUSCLOUD team leader. "When it comes to cloud databases and computations, you don't want to buy the whole car when you only need a tire."

Let's start thinking outside-the-box about ways to make money via self-driving driverless autonomous cars. At many of my speaking engagements on self-driving driverless autonomous cars, beyond the usual technology-based questions, I also oftentimes get asked various business-oriented questions about whether or not autonomous cars will be profitable. You might have already assumed that self-driving cars will be profit-making vehicles. Indeed, it is certainly a natural assumption to make. Why in the world would all of these automakers and tech firms be toiling away at trying to make, build, test, and deploy driverless cars unless they felt pretty strongly that there was a decent profit to be made?

Persona is one of the latest fashion magazines in Tokyo. It's printed on heavy stock paper and is full of photos of models and clothing. The only thing missing is text. An app recognizes the images, queries a cloud database and then downloads related information such as pricing and availability of dresses. Other photos feature images of tomatoes and wine, triggering a related vegetable delivery service and wine retailer, as well as online coupons.