Related papers: Rumble: Data Independence for Large Messy Data Set…

Intel Array Building Blocks is a high-level data-parallel programming environment designed to produce scalable and portable results on existing and upcoming multi- and many-core platforms. We have chosen several mathematical kernels - a…

JSON Lines (JSONL) is widely used for managing large collections of semi-structured data, ranging from large language model (LLM) prompts to chemical compound records and geospatial datasets. A key operation is substructure search, which…

In this paper, we present an implementation of JSON-diff framework JYCM, extending the existing framework by introducing the concept of "unordered" comparisons and allowing users to customize their comparison scenarios flexibly.…

WebAssembly (Wasm) has become a key compilation target for portable and efficient execution across diverse platforms. Benchmarking its performance, however, is a multi-dimensional challenge: it depends not only on the choice of runtime…

Despite significant progress having been made in question answering on tabular data (Table QA), it's unclear whether, and to what extent existing Table QA models are robust to task-specific perturbations, e.g., replacing key question…

One of the most important issues in the path to the convergence of HPC and Big Data is caused by the differences in their software stacks. Despite some research efforts, the interoperability between their programming models and languages is…

The demanding requirements of the new Big Data intensive era raised the need for flexible storage systems capable of handling huge volumes of unstructured data and of tackling the challenges that traditional databases were facing. NoSQL…

Cloud service provider propose services to insensitive customers to use their platform. Different services can achieve the same result at different cost. In this paper, we study the efficiency of a serverless architecture for running highly…

The distributed data analytic system -- Spark is a common choice for processing massive volumes of heterogeneous data, while it is challenging to tune its parameters to achieve high performance. Recent studies try to employ auto-tuning…

In the process of knowledge discovery and representation in large datasets using formal concept analysis, complexity plays a major role in identifying all the formal concepts and constructing the concept lattice(digraph of the concepts).…

Independence testing plays a central role in statistical and causal inference from observational data. Standard independence tests assume that the data samples are independent and identically distributed (i.i.d.) but that assumption is…

Database queries traditionally operate under the closed-world assumption, providing no answers to questions that require information beyond the data stored in the database. Hybrid querying using SQL offers an alternative by integrating…

Explaining closed-source Large Language Model (LLM) outputs is challenging because API access prevents gradient-based attribution, while perturbation methods are costly and noisy when they depend on regenerated text. We introduce…

A blockchain is a decentralised linked data structure that is characterised by its inherent resistance to data modification, but it is deficient in search queries, primarily due to its inferior data formatting. A distributed database is…

Many distributed machine learning frameworks have recently been built to speed up the large-scale data learning process. However, most distributed machine learning used in these frameworks still uses an offline algorithm model which cannot…

While large language models (LLMs) have demonstrated impressive performance in question-answering tasks, their performance is limited when the questions require knowledge that is not included in the model's training data and can only be…

The integration of heterogeneous databases into a unified querying framework remains a critical challenge, particularly in resource-constrained environments. This paper presents a novel Small Language Model(SLM)-driven system that…

Deploying large language models (LLMs) on edge devices is crucial for delivering fast responses and ensuring data privacy. However, the limited storage, weight, and power of edge devices make it difficult to deploy LLM-powered applications.…

The exponential growth of data necessitates distributed storage models, such as peer-to-peer systems and data federations. While distributed storage can reduce costs and increase reliability, the heterogeneity in storage capacity, I/O…

Significant obstacles exist in scientific domains including genetics, climate modeling, and astronomy due to the management, preprocess, and training on complicated data for deep learning. Even while several large-scale solutions offer…