Front Matter

FlatStor: An Efficient Embedded-Index Based Columnar Data Layout for Multimodal Data Workloads

TIMEST: Temporal Information Motif Estimator Using Sampling Trees

ConANN: Conformal Approximate Nearest Neighbor Search

RayDB: Building Databases with Ray Tracing Cores

NeutronCloud: Resource-Aware Distributed GNN Training in Fluctuating Cloud Environments

CLaP - State Detection from Time Series

Modern data lakes have become essential for storing, managing, and analyzing massive amounts of heterogeneous data. As production data increasingly exhibits multimodal storage characteristics and multi-purpose access patterns, efficient management of such complexities becomes critical. However, current hybrid storage system-based data lakes face persistent challenges, including synchronization overhead, data correlation disruption, and escalating storage costs due to the involvement of multiple underlying storage systems. While columnar storage, central to data lakes, addresses hybrid-system inefficiencies, it struggles with the complexities of multimodal data storage and multi-purpose access.
To tackle these challenges, we analyze access patterns across various scenarios and assess the issues in storing multimodal data. Based on these insights, we propose FlatStor, a FlatBuffers-based columnar Storage format with embedded indexing. It supports point access through indexing and handles multimodal data by vertically partitioning and treating each modality as a byte stream for storage. It also applies FSST compression, reducing storage overhead significantly. Benchmark evaluations reveal that FlatStor reduces the access latency by 99.6% and the storage overhead by 91.3% compared to Parquet in inference workloads. Furthermore, FlatStor outperforms LanceV2 with a 41.3% latency improvement, maintaining minimal additional overhead.

The mining of pattern subgraphs, known as motifs, is a core task in the field of graph mining. Edges in real-world networks often have timestamps, so there is a need for temporal motif mining. A temporal motif is a richer structure that imposes timing constraints on the edges of the motif. Temporal motifs have used to analyze social networks, financial transactions, and biological networks.
Motif counting in temporal graphs is particularly challenging. A graph with millions of edges can have trillions of temporal motifs, since the same edge can occur with multiple timestamps. There is a combinatorial explosion of possibilities, and state-of-the-art algorithms cannot manage motifs with more than four vertices.
In this work, we present TIMEST: a general, fast, and accurate estimation algorithm to count temporal motifs of arbitrary sizes in temporal networks. Our approach introduces a temporal spanning tree sampler that leverages weighted sampling to generate substructures of target temporal motifs. This method carefully takes a subset of temporal constraints of the motif that can be jointly and efficiently sampled. TIMEST uses randomized estimation techniques to obtain accurate estimates of motif counts.
We give theoretical guarantees on the running time and approximation guarantees of TIMEST. We perform an extensive experimental evaluation and show that TIMEST is both faster and more accurate than previous algorithms. Our CPU implementation exhibits an average speedup of 28X over state-of-the-art GPU implementation of the exact algorithm, and 6X speedup over SOTA approximate algorithms while consistently showcasing less than 5% error in most cases. For example, TIMEST can count the number of instances of a financial fraud temporal motif in four minutes with 0.6% error, while exact methods take more than two days.

Approximate Nearest Neighbor (ANN) search is widely used in applications such as recommendation systems, search engines, and natural language processing. Indexing techniques like the Inverted File (IVF) offer efficiency at the cost of accuracy, yet lack formal mechanisms to quantify or control approximation error. Existing approaches that attempt to provide such guarantees typically rely on restrictive assumptions about underlying data distributions, which limits their generalizability. We introduce ConANN, the first framework to provide formal, distribution-free error guarantees for IVF-based ANN search by leveraging recent advances in Conformal Risk Control. Empirical evaluation across five standard benchmarks demonstrates that ConANN: (1) tightly controls approximation error, achieving a worst-case False Negative Rate deviation within 0.03 percentage points of the target; (2) provides formal guarantees without requiring expansion of the search space, and in some cases even reduces the number of probed clusters; (3) dynamically adapts the cluster probes required per query; and (4) incurs negligible overheads when compared to existing state-of-the-art baselines. ConANN is integrated into the FAISS vector search library, facilitating adoption in real-world ANN systems.

Ray tracing (RT) cores are a new type of hardware that have been actively integrated into modern GPUs. Recent studies have demonstrated that RT cores can deliver much higher performance than GPU CUDA cores and CPUs for general-purpose data processing tasks like table scan. In this paper, we propose RayDB, the first query engine that leverages RT cores to accelerate database query processing. RayDB transforms the core part of the query execution that involves multiple operators into one single ray tracing job. With a set of proposed encoding and ray launching techniques, RayDB effectively exploits RT cores to accelerate diverse workloads. Experimental results show that RayDB outperforms the state-of-the-art GPU-based query engine by up to 18.3×.

Graph Neural Networks (GNNs) are widely employed to learn representations from graph-structured data. To support large-scale graph training, researchers use distributed techniques, partitioning the graph across multiple computing nodes and performing parallel training by exchanging dependency vertex information via cross-node communication. However, existing GNN training systems operate on statically partitioned subgraphs, making them difficult to adapt to resource fluctuations. In practice, resource fluctuations in cloud environments often cause variability in compute and communication resources, posing challenges for aligning each worker's workload to its available resources during GNN training.
In this paper, we propose NeutronCloud, a system designed for efficient GNN training in cloud environments. First, we adopt a resource-aware workload adjustment strategy. It builds on hybrid dependency handling by obtaining dependency information through both local computation and remote communication. During training, it dynamically adjusts the ratio between locally computed and remotely fetched dependencies based on each worker's available resources, ensuring workload-resource alignment. Second, we employ a dependency-aware partial-reduce approach reusing historical vertex embeddings and skipping the stragglers during gradient aggregation to address extreme resource fluctuations that cause some workers to lag significantly behind others in the cluster. Experimental results on the resource-fluctuating environment demonstrate that NeutronCloud achieves 1.83X-4.43X speedup compared to state-of-the-art distributed GNN systems.

The ever-growing amount of sensor data from machines, smart devices, and the environment leads to an abundance of high-resolution, unannotated time series (TS). These recordings encode recognizable properties of latent states and transitions from physical phenomena that can be modelled as abstract processes. The unsupervised localization and identification of these states and their transitions is the task of time series state detection (TSSD). Current TSSD algorithms employ classical unsupervised learning techniques, to infer state membership directly from feature space. This limits their predictive power, compared to supervised learning methods, which can exploit additional label information. We introduce CLaP, a new, highly accurate and efficient algorithm for TSSD. It leverages the predictive power of time series classification for TSSD in an unsupervised setting by applying novel self-supervision techniques to detect whether data segments emerge from the same state. To this end, CLaP cross-validates a classifier with segment-labelled subsequences to quantify confusion between segments. It merges labels from segments with high confusion, representing the same latent state, if this leads to an increase in overall classification quality. We conducted an experimental evaluation using 405 TS from five benchmarks and found CLaP to be significantly more precise in detecting states than six state-of-the-art competitors. It achieves the best accuracy-runtime tradeoff and is scalable to large TS. We provide a Python implementation of CLaP, which can be deployed in TS analysis workflows.

Volume 19, 2025-2026

Volume 19, No. 1

Start

Current Submission

All Volumes

Reproducibility

General Information

Volume 19, 2025-2026

Volume 19, No. 1