Sketch algorithms are crucial for identifying top-k items in large-scale data streams. Existing methods often compromise between performance and accuracy, unable to efficiently handle increasing data volumes with limited memory. We present Bubble Sketch, ...

Estimating cardinality, i.e., the number of distinct elements, of a data stream is a fundamental problem in areas like databases, computer networks, and information retrieval. This study delves into a broader scenario where each element carries a ...

Sketches are useful for network measurement thanks to their low resource overheads and theoretically bounded accuracy. However, their network-wide deployment suffers from the trade-off between optimality and scalability: (1) Most solutions rely on mixed ...

We introduce an algorithm for sketch vectorization with state-of-the-art accuracy and capable of handling complex sketches. We approach sketch vectorization as a surface extraction task from an unsigned distance field, which is implemented using a two-...

Data stream processing plays a pivotal role in various web-related applications, including click fraud detection, anomaly identification, and recommendation systems. Accurate and fast detection of items relevant to such tasks within data streams, e.g., ...

In this paper, we propose the first geometry and texture (double) referenced interactive 2D and 3D caricature generating and editing method. The main challenge of caricature generation lies in the fact that it not only exaggerates the facial geometry but ...

Recently, the growing memory demands of embedding tables in Deep Learning Recommendation Models (DLRMs) pose great challenges for model training and deployment. Existing embedding compression solutions cannot simultaneously meet three key design ...

In this study, we present BasketballFlow, a system designed to generate diverse basketball gameplays based on a pre-determined strategy sketch. A strategy sketch is a graphical representation that coaches use to outline their planned tactics, ...

Heavy-hitter detection is a fundamental task in network traffic measurement and security. Existing work faces the dilemma of suffering dynamic and imbalanced traffic characteristics or lowering the detection efficiency and flexibility. In this paper, we ...

Recent developments in AI have made it possible to design new proteins that are crucial to meeting humanity’s needs. However, tools for exploring the 3D structures of proteins in the early stages of AI-based protein design are lacking, leading to many ...

Recent remarkable advances in large-scale text-to-image diffusion models have inspired a significant breakthrough in text-to-3D generation, pursuing 3D content creation solely from a given text prompt. However, existing text-to-3D techniques lack a ...

With the recent surge in the use of touchscreen devices, free-hand sketching has emerged as a promising modality for human-computer interaction. While previous research has focused on tasks such as recognition, retrieval, and generation of familiar ...

Sketch algorithms have been widely deployed for network measurement as they achieve high accuracy with restricted resource usage. They store measurement results compactly in fixed-size counters. However, as sketch counters are skewed towards low values, ...

Network measurement is critical to many network applications. There are mainly two kinds of flow-level measurement tasks: 1) packet accumulation tasks and 2) packet loss tasks. In practice, the two kinds of tasks are often required at the same time, but ...

¹Estimating the quantile of distribution, especially tail distribution, is an interesting topic in data stream models, and has obtained extensive interest from many researchers. In this paper, we propose a novel sketch, namely SketchPolymer to accurately ...

Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges and subgraphs in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? For example, in intrusion detection, existing ...

High-accuracy real-time data stream estimations are critical for various applications, and sliding-window-based techniques have attracted wide attention. However, existing solutions struggle to achieve high accuracy, generality, and low memory usage ...

Sketching algorithms are considered as promising solutions for answering approximate query on massive data stream. In real scenarios, a large number of problems can be abstracted as subset query over multiple attributes. Existing sketches are designed ...

The bumps and dips in data streams are valuable patterns for data mining and networking scenarios such as online advertising and botnet detection. In this paper, we define the wave, a data stream pattern with a serious deviation from the stable arrival ...

Inner-product estimation is the base of many important tasks in a variety of big data scenarios, including measuring similarity of streams in data stream processing, estimating join size in database, and analyzing cosine similarity in various ...