Membership in ACM includes a subscription to Communications of the ACM (CACM), the computing industry's most trusted source for staying connected to the world of advanced computing. Technical Perspective: Where Is My Data? Smash: Flexible, Fast, and Resource-Efficient Placement and Lookup of Distributed Storage, by Yi Liu et al., introduces a storage system that is the first of its kind to apply minimal perfect hashing (MPH) to storage research. When making a storage system distributed across many machines, designers are faced with a critical question: Where is my data? At the heart of every storage system is a mechanism for determining where to find data based on an identifier.

Large warehouse-scale computers (WSCs) underpin all the cloud computing services we use daily--whether it is Web search, video streaming, social networks, or even emerging AI chatbots or agents. The memory subsystem in these computers poses one of the biggest challenges in their design and operation: Across the industry, Big Tech companies such as Amazon, Google, Meta, and Microsoft spend billions of dollars buying memory and consume hundreds of megawatts powering them. Sadly, this problem is only getting worse, exacerbated by slowing of technology scaling trends (like Moore's law) and exploding demand for more data and correspondingly more memory--for example, artificial intelligence (AI) workloads. One approach to address the costs of memory is to use tiers. Most workloads have a working dataset that includes both hot (more frequently used) and cold (less frequently used) data.

Interest in miniature radar systems has grown dramatically in recent years as they enable rich interaction and health monitoring in everyday settings. By 2025, industrial radar applications are anticipated to encompass 10 million devices, whereas the consumer market will reach a substantial 250 million. The applications are diverse--for example, Google's Pixel phones incorporated radar for gesture control, while small radar sensors are being deployed in homes to monitor elderly residents' movements and detect falls, offering more privacy than camera-based solutions. However, conventional radar architectures rely on complex RF front ends with power amplifiers, low-noise amplifiers, and phase-locked loops, collectively consuming hundreds of milliwatts of power. This makes radar sensing impractical for battery-powered or self-powered Internet of Things (IoT) devices and wearables.

Exceptional added value may lie in connecting two complementary areas of computer science. This statement is particularly true when applying mature techniques developed in one area to solve complex problems that arise in a new area. The accompanying paper, "An Automata-Based Framework for Verification and Bug Hunting in Quantum Circuits" by Lengál et al., is a case in point. It applies techniques developed in logic, automata, and symbolic verification to analyze the correctness of quantum programs. The current quest of quantum computing is achieving quantum supremacy--that is, to reach the point where we solve problems that are practically unsolvable using conventional computing.

What is a mathematical proof? It can be described as a sequence of logical steps and calculations that serve as evidence of the correctness of a statement. The steps must follow rules that are accepted as correct by the community. One might think there is a set of universal rules. However, this is far from being the case.

Most database research papers are prescriptive. They identify a technical problem and show us how to solve it. Other papers follow a different path; they are descriptive rather than prescriptive. They tell us how data systems behave in practice and how they are actually used. They employ a different set of tools, such as surveys, software analyses, or user studies.

Phishing attacks continue to impose a significant threat on digital communication and online transactions, costing organizations and individuals billions of dollars each year. According to the Anti-Phishing Working Group (APWG), phishing incidents increased by over 25% in 2022 compared to previous years, with attackers refining their methods to mimic trusted brands and deceive users into revealing sensitive information Anti-Phishing Working Group [2022]. This alarming increase not only highlights the ingenuity of cybercriminals but also emphasizes the critical need for more advanced detection systems. In response, researchers and cybersecurity professionals have increasingly turned to artificial intelligence (AI) and deep learning (DL) techniques to build more accurate and adaptable detection systems capable of identifying subtle cues in phishing attempts. Historically, phishing detection relied on signature-based methods and blacklists, which, although useful, could not keep pace with the rapid evolution of phishing tactics. Traditional approaches often suffered from high false-positive rates and were unable to adapt to new, previously unseen attack vectors.

NP-hard problems are assumed to be computationally intractable, meaning that no efficient (polynomial time) algorithm is guaranteed to correctly solve every input instance. One way of coping with NP-hardness is via the use of reliable heuristics. These are efficient algorithms that might not solve every input instance, but when they claim a solution, the solution is guaranteed to be correct. Consider the canonical NP-complete problem of SAT (determining whether a Boolean formula of the form (x1 x2 x3) (x2 x4) . . . A heuristic for finding satisfying assignments will naturally be reliable, because one can efficiently check whether the assignment found indeed satisfies all clauses of the input formula.

From the demographic statistics produced by national census bodies to the complex predictive models built by companies in "Big Tech" and finance, data is the fuel that powers these applications. Most such use cases rely on data derived from the properties and actions of individual people. This data is therefore considered sensitive and in need of protections to prevent inappropriate use or disclosure. Some protections come from enforcing policies, access control, and contractual agreements. But we also seek technical interventions--definitions and algorithms that can be applied by computer systems to protect private information while still enabling the intended use.

In recent years, enormous progress has been made in the field of large language models (LLMs). Based on neural network architectures, specifically transformer models, they have proven highly effective in natural language processing (NLP). The models are designed to understand, generate, and work with human language. Trained on large datasets consisting of text from the Internet, books, articles, and many other data sources, the model learns to predict the next word in a sentence based on previous words. LLMs are not only able to generate human language but can also generate source code to support humans in the implementation of software systems.