arXiv subjects

Abdullah Al-Mamun

Publications and source records attributed to Abdullah Al-Mamun.

12 recordsLinked to original sources

arXiv2026

iFVS: Towards Instance-Optimized Filtered Vector Search

Filtered vector search (FVS) is increasingly important in modern AI + DB systems, where vector similarity search is combined with relational predicates. Quantization plays a vital role in these systems by enabling query processing over large vector datasets. However, lossy approaches, e.g., Product Quantization (PQ), incur a precision penalty during distance calculation, thereby negatively impacting the query recall performance. This problem becomes more challenging in FVS because the relevant vector space can change with the relational predicate and selectivity. Motivated by the success of instance-optimized database system components, we introduce iFVS, an Instance-Optimized Filtered Vector Search technique. Given a fixed, quantized vector dataset, and a representative workload of filtered vector queries, iFVS adopts a query-specific codebook generation approach for FVS that is instance-optimized towards a certain dataset and query workload. Instead of using a fixed codebook for all queries, iFVS conditions distance estimation on both the query vector and the filter predicate. This enables more accurate ranking over compressed vectors while preserving compact per-vector storage. Experiments show that iFVS improves the Queries Per Second (QPS)-recall tradeoff across several filter selectivity bins compared with fixed-codebook quantized FVS baselines.

cs.DB↗

arXiv2026

Binary Gaussian Copula Synthesis: an LLM-powered data augmentation framework for early dialysis prediction in chronic kidney disease

Only a small fraction of patients with chronic kidney disease (CKD) progress to dialysis, creating severe class imbalance that limits the performance of machine learning models for early dialysis prediction. This challenge is compounded by the binary structure of electronic health record (EHR) data, for which most existing augmentation methods were not designed. We propose Binary Gaussian Copula Synthesis (BGCS), a two-stage data augmentation method tailored to binary clinical data. BGCS first generates synthetic minority-class samples using a Gaussian copula framework that explicitly models pairwise dependencies among binary features, then applies a fine-tuned GPT-2 classifier to filter out clinically implausible samples before training. We evaluated BGCS on a real-world EHR dataset of 15,169 patients with CKD from West Virginia collected between 2008 and 2022, benchmarking it against SMOTE, CTGAN, and standard Gaussian Copula across four machine learning classifiers over 25 independent runs. BGCS consistently outperformed all comparison methods, achieving the highest minority-class recall for 90-day dialysis prediction, with median values ranging from 0.78 to 0.87 across classifiers, and the strongest distributional fidelity to real data, with a mean p-value of 0.68 across features. The best-performing BGCS-augmented model was integrated into an interpretable decision tree-based clinical decision support system for dialysis risk stratification, with electrolyte imbalances, cardiovascular comorbidities, and renal monitoring indicators emerging as the most influential predictive features. These findings suggest that augmentation methods designed for the structural properties of binary EHR data can meaningfully improve early dialysis risk prediction and support the development of interpretable clinical decision-support tools for CKD care.

stat.AP↗

arXiv2025

Provenance-Driven Reliable Semantic Medical Image Vector Reconstruction via Lightweight Blockchain-Verified Latent Fingerprints

Medical imaging is essential for clinical diagnosis, yet real-world data frequently suffers from corruption, noise, and potential tampering, challenging the reliability of AI-assisted interpretation. Conventional reconstruction techniques prioritize pixel-level recovery and may produce visually plausible outputs while compromising anatomical fidelity, an issue that can directly impact clinical outcomes. We propose a semantic-aware medical image reconstruction framework that integrates high-level latent embeddings with a hybrid U-Net architecture to preserve clinically relevant structures during restoration. To ensure trust and accountability, we incorporate a lightweight blockchain-based provenance layer using scale-free graph design, enabling verifiable recording of each reconstruction event without imposing significant overhead. Extensive evaluation across multiple datasets and corruption types demonstrates improved structural consistency, restoration accuracy, and provenance integrity compared with existing approaches. By uniting semantic-guided reconstruction with secure traceability, our solution advances dependable AI for medical imaging, enhancing both diagnostic confidence and regulatory compliance in healthcare environments.

cs.CV↗

arXiv2025

Tradeoffs in Processing Queries and Supporting Updates over an ML-Enhanced R-tree

Machine Learning (ML) techniques have been successfully applied to design various learned database index structures for both the one- and multi-dimensional spaces. Particularly, a class of traditional multi-dimensional indexes has been augmented with ML models to design ML-enhanced variants of their traditional counterparts. This paper focuses on the R-tree multi-dimensional index structure as it is widely used for indexing multi-dimensional data. The R-tree has been augmented with machine learning models to enhance the R-tree performance. The AI+R-tree is an ML-enhanced R-tree index structure that augments a traditional disk-based R-tree with an ML model to enhance the R-tree's query processing performance, mainly, to avoid navigating the overlapping branches of the R-tree that do not yield query results, e.g., in the presence of high-overlap among the rectangles of the R-tree nodes. We investigate the empirical tradeoffs in processing dynamic query workloads and in supporting updates over the AI+R-tree. Particularly, we investigate the impact of the choice of ML models over the AI+R-tree query processing performance. Moreover, we present a case study of designing a custom loss function for a neural network model tailored to the query processing requirements of the AI+R-tree. Furthermore, we present the design tradeoffs for adopting various strategies for supporting dynamic inserts, updates, and deletes with the vision of realizing a mutable AI+R-tree. Experiments on real datasets demonstrate that the AI+R-tree can enhance the query processing performance of a traditional R-tree for high-overlap range queries by up to 5.4X while achieving up to 99% average query recall.

cs.DB↗

arXiv2024

A Survey of Learned Indexes for the Multi-dimensional Space

A recent research trend involves treating database index structures as Machine Learning (ML) models. In this domain, single or multiple ML models are trained to learn the mapping from keys to positions inside a data set. This class of indexes is known as "Learned Indexes." Learned indexes have demonstrated improved search performance and reduced space requirements for one-dimensional data. The concept of one-dimensional learned indexes has naturally been extended to multi-dimensional (e.g., spatial) data, leading to the development of "Learned Multi-dimensional Indexes". This survey focuses on learned multi-dimensional index structures. Specifically, it reviews the current state of this research area, explains the core concepts behind each proposed method, and classifies these methods based on several well-defined criteria. We present a taxonomy that classifies and categorizes each learned multi-dimensional index, and survey the existing literature on learned multi-dimensional indexes according to this taxonomy. Additionally, we present a timeline to illustrate the evolution of research on learned indexes. Finally, we highlight several open challenges and future research directions in this emerging and highly active field.

cs.DB↗

arXiv2023

Multiscale Sampling for the Inverse Modeling of Partial Differential Equations

We are concerned with a novel Bayesian statistical framework for the characterization of natural subsurface formations, a very challenging task. Because of the large dimension of the stochastic space of the prior distribution in the framework, typically a dimensional reduction method, such as a Karhunen-Leove expansion (KLE), needs to be applied to the prior distribution to make the characterization computationally tractable. Due to the large variability of properties of subsurface formations (such as permeability and porosity) it may be of value to localize the sampling strategy so that it can better adapt to large local variability of rock properties. In this paper, we introduce the concept of multiscale sampling to localize the search in the stochastic space. We combine the simplicity of a preconditioned Markov Chain Monte Carlo method with a new algorithm to decompose the stochastic space into orthogonal subspaces, through a one-to-one mapping of the subspaces to subdomains of a non-overlapping domain decomposition of the region of interest. The localization of the search is performed by a multiscale blocking strategy within Gibbs sampling: we apply a KL expansion locally, at the subdomain level. Within each subdomain, blocking is applied again, for the sampling of the KLE random coefficients. The effectiveness of the proposed framework is tested in the solution of inverse problems related to elliptic partial differential equations arising in porous media flows. We use multi-chain studies in a multi-GPU cluster to show that the new algorithm clearly improves the convergence rate of the preconditioned MCMC method. Moreover, we illustrate the importance of a few conditioning points to further improve the convergence of the proposed method.

math.NA↗

arXiv2021

Conditioning by Projection for the Sampling from Prior Gaussian Distributions

In this work we are interested in the (ill-posed) inverse problem for absolute permeability characterization that arises in predictive modeling of porous media flows. We consider a Bayesian statistical framework with a preconditioned Markov Chain Monte Carlo (MCMC) algorithm for the solution of the inverse problem. Reduction of uncertainty can be accomplished by incorporating measurements at sparse locations (static data) in the prior distribution. We present a new method to condition Gaussian fields (the log of permeability fields) to available sparse measurements. A truncated Karhunen-Loève expansion (KLE) is used for dimension reduction. In the proposed method the imposition of static data is made through the projection of a sample (expressed as a vector of independent, identically distributed normal random variables) onto the nullspace of a data matrix, that is defined in terms of the KLE. The numerical implementation of the proposed method is straightforward. Through numerical experiments for a model of second-order elliptic equation, we show that the proposed method in multi-chain studies converges much faster than the MCMC method without conditioning. These studies indicate the importance of conditioning in accelerating the MCMC convergence.

math.NA↗

arXiv2020

Reducing Road Vehicle Fuel Consumption by Exploiting Connectivity and Automation: A Literature Survey

This paper examines the degree to which connectivity and automation can potentially reduce the overall fuel consumption of on-road vehicles. The paper begins with a simulation study highlighting the tradeoff between: (i) the fuel that a vehicle can save through speed trajectory shaping, versus (ii) the additional inter-vehicle spacing needed for this trajectory shaping to be feasible. This study shows that connectivity and automation are essential, rather than merely useful, for substantial reductions in the fuel consumed by fixed on-road vehicle powertrain/chassis configurations in traffic. Motivated by this insight, we survey the literature on the fuel savings achievable through different connected/automated vehicle technologies. This includes optimal vehicle routing, eco-arrival/departure at intersections, platooning, speed trajectory optimization, predictive driveline disengagement, predictive gear shifting, and predictive powertrain accessory control. This survey shows that the ability to shape vehicle speed trajectories collaboratively plays a dominant role in reducing urban/suburban fuel consumption, while platooning plays a dominant role in influencing the attainable fuel savings on the highway. Moreover, the survey shows that the degree to which connectivity/automation can reduce on-road vehicle fuel consumption, in both urban/suburban and highway settings, depends critically on the integration of powertrain- and chassis-level control.

eess.SY↗

arXiv2020

Trustworthy Edge Computing through Blockchains

Edge computing draws a lot of recent research interests because of the performance improvement by offloading many workloads from the remote data center to nearby edge nodes. Nonetheless, one open challenge of this emerging paradigm lies in the potential security issues on edge nodes and end devices, e.g., sensors and controllers. This paper proposes a cooperative protocol, namely DEAN, across edge nodes to prevent data manipulation, and to allow fair data sharing with quick recovery under resource constraints of limited storage, computing, and network capacity. Specifically, DEAN leverages a parallel mechanism equipped with three independent core components, effectively achieving low resource consumption while allowing secured parallel block processing on edge nodes. We have implemented a system prototype based on DEAN and experimentally verified its effectiveness with a comparison with three popular blockchain implementations: Ethereum, Parity, and Hyperledger Fabric. Experimental results show that the system prototype exhibits high resilience to arbitrary failures: the percentile of trusty nodes is much higher than the required 50\% in most cases. Performance-wise, DEAN-based blockchain implementation outperforms the state-of-the-art blockchain systems with up to $25\times$ higher throughput and $18\times$ lower latency on 1,000 nodes.

cs.DC↗

arXiv2020

SciChain: Trustworthy Scientific Data Provenance

The state-of-the-art for auditing and reproducing scientific applications on high-performance computing (HPC) systems is through a data provenance subsystem. While recent advances in data provenance lie in reducing the performance overhead and improving the user's query flexibility, the fidelity of data provenance is often overlooked: there is no such a way to ensure that the provenance data itself has not been fabricated or falsified. This paper advocates to leverage blockchains to deliver immutable and autonomous data provenance services such that scientific data are trustworthy. The challenges for adopting blockchains to HPC include designing a new blockchain architecture compatible with the HPC platforms and, more importantly, a set of new consensus protocols for scientific applications atop blockchains. To this end, we have designed the proof-of-scalable-traceability (POST) protocol and implemented it in a blockchain prototype, namely SciChain, the very first blockchain system for HPC. We evaluated SciChain by comparing it with multiple state-of-the-art systems; Experimental results showed that SciChain guaranteed trustworthy data while incurring orders of magnitude lower overhead.

cs.DC↗

arXiv2020

BAASH: Enabling Blockchain-as-a-Service on High-Performance Computing Systems

The state-of-the-art approach to manage blockchains is to process blocks of transactions in a shared-nothing environment. Although blockchains have the potential to provide various services for high-performance computing (HPC) systems, HPC will not be able to embrace blockchains before the following two missing pieces become available: (i) new consensus protocols being aware of the shared-storage architecture in HPC, and (ii) new fault-tolerant mechanisms compensating for HPC's programming model---the message passing interface (MPI)---that is vulnerable for blockchain-like workloads. To this end, we design a new set of consensus protocols crafted for the HPC platforms and a new fault-tolerance subsystem compensating for the failures caused by faulty MPI processes. Built on top of the new protocols and fault-tolerance mechanism, a prototype system is implemented and evaluated with two million transactions on a 500-core HPC cluster, showing $6\times$, $12\times$, and $75\times$ higher throughput than Hyperldeger, Ethereum, and Parity, respectively.

cs.DC↗

arXiv2019

BlockLite: A Lightweight Emulator for Public Blockchains

Blockchain is an enabler of many emerging decentralized applications in areas of cryptocurrency, Internet of Things, smart healthcare, among many others. Although various open-source blockchain frameworks are available, the infrastructure is complex enough and difficult for many users to modify or test out new research ideas. To make it worse, many advantages of blockchain systems can be demonstrated only at large scales, e.g., thousands of nodes, which are not always available to researchers. This demo paper presents a lightweight single-node emulator of blockchain systems, namely \mbox{BlockLite}, designed to be executing real proof-of-work workload along with peer-to-peer network communications and hash-based immutability. BlockLite employs a preprocessing approach to avoid the per-node computation overhead at runtime and thus scales to thousands of nodes. Moreover, BlockLite offers an easy-to-use programming interface allowing for a Lego-like customization to the system, e.g. new ad-hoc consensus protocols.

cs.DB↗