相关论文: Scalable Blocking for Very Large Databases

The problem of fast items retrieval from a fixed collection is often encountered in most computer science areas, from operating system components to databases and user interfaces. We present an approach based on hash tables that focuses on…

In this paper, we present BlinkDB, a massively parallel, sampling-based approximate query engine for running ad-hoc, interactive SQL queries on large volumes of data. The key insight that BlinkDB builds on is that one can often make…

Array operations are one of the most concise ways of expressing common filtering and simple aggregation operations that is the hallmark of the first step of a particle physics analysis: selection, filtering, basic vector operations, and…

We proposed a data-driven approach to dissect multivariate time series in order to discover multiple phases underlying dynamics of complex systems. This computing approach is developed as a multiple-dimension version of Hierarchical Factor…

Predicting the right number of TVs (Device Reach) in real-time based on a user-specified targeting attributes is imperative for running multi-million dollar ADs business. The traditional approach of SQL queries to join billions of records…

Deep hashing is an effective approach for large-scale image retrieval. Current methods are typically classified by their supervision types: point-wise, pair-wise, and list-wise. Recent point-wise techniques (e.g., CSQ, MDS) have improved…

High-Level Synthesis (HLS) frameworks allow to easily specify a large number of variants of the same hardware design by only acting on optimization directives. Nonetheless, the hardware synthesis of implementations for all possible…

The LHCb Stripping project is a pivotal component of the experiment's data processing framework, designed to refine vast volumes of collision data into manageable samples for offline analysis. It ensures the re-analysis of Runs 1 and 2…

In e-commerce industry, user behavior sequence data has been widely used in many business units such as search and merchandising to improve their products. However, it is rarely used in financial services not only due to its 3V…

Byte-addressable persistent memory (PM) brings hash tables the potential of low latency, cheap persistence and instant recovery. The recent advent of Intel Optane DC Persistent Memory Modules (DCPMM) further accelerates this trend. Many new…

Hashing has been recognized as an efficient representation learning method to effectively handle big data due to its low computational complexity and memory cost. Most of the existing hashing methods focus on learning the low-dimensional…

In semi-supervised learning, methods that rely on confidence learning to generate pseudo-labels have been widely proposed. However, increasing research finds that when faced with noisy and biased data, the model's representation network is…

In unsupervised domain adaptation (UDA), directly adapting from the source to the target domain usually suffers significant discrepancies and leads to insufficient alignment. Thus, many UDA works attempt to vanish the domain gap gradually…

Database search and clustering are fundamental components of many data analytics problems, such as mass spectrometry-driven proteomics. Traditional full clustering and search algorithms suffer from high resource usage and long latencies. We…

Dataset deduplication is widely recognized as a crucial preprocessing step that enhances data quality and improves the performance of large language models. A commonly used method for this process is the MinHash Locality-Sensitive Hashing…

Distributed RDF systems partition data across multiple computer nodes (workers). Some systems perform cheap hash partitioning, which may result in expensive query evaluation, while others apply heuristics aiming at minimizing inter-node…

Modern distributed systems produce massive, heterogeneous logs essential for reliability, security, and anomaly detection. Converting these free-form messages into structured templates (log parsing) is challenging due to evolving formats…

Semantic operators have increasingly become integrated within data systems to enable processing data using Large Language Models (LLMs). Despite significant recent effort in improving these operators, their accuracy is limited due to a…

DNA barcoding has emerged as a cost-effective approach for species identification. However, the scarcity of tools used for searching the booming reference database becomes an obstacle, currently with BLAST as the only practical choice.…

Iterative methods for solving large sparse systems of linear equations are widely used in many HPC applications. Extreme scaling of these methods can be difficult, however, since global communication to form dot products is typically…