The first international workshop on Big Data eXploration, Compression and Systems (BDXCS) is an international workshop held in conjunction with SCA/HPCAsia 2026, focusing on big data, data compression, and their associated systems.

Home address: https://sites.google.com/view/bdxcs2026/home
Paper submission deadline: October 21, 2025
Notification of acceptance: November 19, 2025
Camera-ready paper deadline: December 15, 2025

Objectives

In addition to traditional applications, the rise of AI and cloud computing has significantly increased the volume of data processing and communication required in high-performance computing (HPC).

Efficient data analytics and data movement across distributed and parallel environments (e.g., the Internet, inter-node networks, and system interconnects) have become critical factors in determining the performance and energy efficiency of supercomputers, data centers, and cloud platforms.

This workshop aims to address key research challenges related to big data from multiple perspectives, including data exploration, data compression, and big data systems.

To tackle these challenges, the workshop will aim to explore practical and effective approaches to data analytics and mining, big data visualization, data integration, scalable data compression, and storage/processing systems for big data.

These investigations will consider both the characteristics of large-scale data workloads and the constraints of modern hardware architectures.

In particular, the workshop will emphasize optimization strategies for big data processing, adaptive and general-purpose compression techniques, and high-performance systems designed for high-throughput, low-latency, and hardware-efficient data operations.


Scopes

-- Big Data Exploration

Data Analytics and Mining: Statistical and Machine Learning methods for Big Data, Graph Analytics and Network Mining, Pattern Recognition and Anomaly Detection, Time Series and Spatial Data Analysis

Interactive Visualization and Visual Analytics: Real-time and Interactive Visualization Techniques, Scalable Visualization Algorithms, Exploratory Data Analysis, Visualization of Complex Data Structures

Data Integration and Fusion: Multi-source and Multi-modal Data Integration, Schema Matching and Data Harmonization, Semantic Web and Knowledge Graphs, Data Fusion Techniques for Heterogeneous Data

-- Big Data Compression

Lossless and Lossy Data Compression: Compression Techniques for Structured and Unstructured Scientific Data, Multimedia Data Compression, Time-series Data Compression, Textual Data Compression

Compression Algorithms and Techniques: Quantization, Predictive Coding, Transform-based Compression, Dictionary/Entropy-based Compression, Tensor Decomposition and Low-rank Approximations

Compression and Analytics Integration: Compression-aware Data Mining and Machine Learning, Performance investigation by applying compression, Analysis of power consumption associated with compression

Compression/Reduction-conscious Architecture: Offloading data compression/reduction to the network, Data reduction in smart NICs, Adaptive compression with dedicated hardware, Online data compression methods

-- Big Data Systems

Scalable and Distributed Systems: Distributed Storage Systems (e.g., Hadoop, HDFS, Ceph), Distributed and Parallel Computing Frameworks (e.g., Spark, Flink, MPI), Cloud and Edge Computing Platforms for Big Data,

Performance and Optimization: Big Data-based Resource Scheduling and Load Balancing, Hardware-accelerated Big Data Processing (GPU, FPGA), Energy-efficient and Cost-efficient Big Data Processing

Reliability, Privacy, and Security: Fault Tolerance and Reliability in Big Data Systems, Data Security and Encryption in Large-scale Storage, Privacy-preserving Analytics and Differential Privacy

Architecture and Middleware: Big Data Workflow Management, Middleware Systems for Data-intensive Computing, Containers and Virtualization for Big Data Applications