Large-scale Machine Research Articles

Design and Experiments of the Data Acquisition System for Bale Rolling Characteristic Parameters on a Large-Scale Round Bale Machine

The parameters of the roll characteristics of a large-scale round bale machine were collected in real time to investigate the bale rolling mechanism. This investigation develops a set of adaptable and highly integrated data acquisition systems for the bale rolling performance parameters of large-type round bale machines. A rolling experiment is conducted using sunflower straw as the material, and the power consumption and radial tension of the roller-round bale machine during the bale rolling process are studied. In the grass core formation stage, the round bale machine’s torque need was minimal, the radial tension of the bale remained nearly constant, and the bale chamber was primarily filled with loose sunflower straw. The motor torque and the straw bale’s radial tension both showed a tendency of gradual increase when the round bale machine was in the grass-filling stage. The motor torque and bale radial tension displayed a roughly linear trend of rapid rise as the sunflower straw continued to enter the rolling bale chamber; this was when the round bale machine was in the compressed bale rolling stage. When the power consumption of the round bale machine was measured using the data acquisition system during the test bench empty run and core-creation stage, the energy consumption comparison analysis produced a relative error of 5.8%. During the stage of bale rolling and compression, the data acquisition system monitored the power consumption of the round bale machine. The relative error was 9.5%. The data acquisition system of the round bale machine test bed has an accuracy of 90.5%–94.2% when measuring the machine’s power consumption, indicating that it is a stable and efficient system. This study provides a foundation for further research on intelligent the roller-round bale machine.

Towards Robust Multi-Agent Reinforcement Learning

Stochastic gradient descent (SGD) is at the heart of large-scale distributed machine learning paradigms such as federated learning (FL). In these applications, the task of training high-dimensional weight vectors is distributed among several workers that exchange information over networks of limited bandwidth. While parallelization at such an immense scale helps to reduce the computational burden, it creates several other challenges: delays, asynchrony, and most importantly, a significant communication bottleneck. The popularity and success of SGD can be attributed in no small part to the fact that it is extremely robust to such deviations from ideal operating conditions. Inspired by these findings, we ask: Are common reinforcement learning (RL) algorithms also robust to similarly structured perturbations? Perhaps surprisingly, despite the recent surge of interest in multi-agent/federated RL, almost nothing is known about the above question. This paper collects some of our recent results in filling this void.

A K-means-Teaching Learning based optimization algorithm for parallel machine scheduling problem

With the continuous increase of workshop production scale, traditional heuristic algorithms in solving the scheduling problem have the defects of unsatisfactory computing time and insufficient stability of the solution result. However, data mining has a good performance in solving large-scale scheduling problems. A data mining method was proposed for industrial big data to solve the problem of large-scale parallel machines scheduling. This methodology can obtain an effective initial solution for single-operation parallel machine scheduling problem by exploring the effective information in the historical scheduling data. Based on historical customer orders, the offline learning was used to continuously generate simulated data for learning, which makes up for the shortcomings of insufficient data. A TLBO framework (teaching-learning-based optimization) hybrid K-means algorithm was redesigned to enhance the accuracy of offline learning and the efficiency of data searching. In the online operation part, according to the optimal solutions for high-similarity manufacturing orders are the approximate solutions, the new customer order will be quickly matched with the most similar manufacturing order through similarity calculation, and then and then local search is performed. Finally, the globally optimal solution is obtained after screening. Experimental results show that the hybrid teaching–learning methodology can solve the large-scale parallel machines scheduling problem with a better learning performance and computational efficiency.

Maximizing user experience with LLMOps-driven personalized recommendation systems

The integration of LLMOps into personalized recommendation systems marks a significant advancement in managing LLM-driven applications. This innovation presents both opportunities and challenges for enterprises, requiring specialized teams to navigate the complexity of engineering technology while prioritizing data security and model interpretability. By leveraging LLMOps, enterprises can enhance the efficiency and reliability of large-scale machine learning models, driving personalized recommendations aligned with user preferences. Despite ethical considerations, LLMOps is poised for widespread adoption, promising more efficient and secure machine learning services that elevate user experience and shape the future of personalized recommendation systems.

A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research.

Owing to their distinct physical and chemical properties, inorganic nanoparticles (NPs) have shown promising results in preclinical cancer therapy, but designing and engineering them for effective therapeutic purposes remains a challenge. Although a comprehensive database of inorganic NP research is not currently available, it is crucial for developing effective cancer therapies. In this context, machine learning (ML) has emerged as a transformative tool, but its adaptation to nanomedicine is hindered by inexistent or small datasets. Here we assembled a large database of inorganic NPs, comprising experimental datasets from 745 preclinical studies in cancer nanomedicine. Using descriptive statistics and explainable ML models we mined this database to gain knowledge of inorganic NP design patterns and inform future NP research for cancer treatment. Our analyses suggest that NP shape and therapy type are prominent features in determining in vivo efficacy, measured as a percentage of tumour reduction. Moreover, our database provides a large-scale open-access resource for discriminative ML that the broader nanotechnology community can utilize. Our work blueprints data mining for translational cancer research and offers evidence for standardizing NP reporting to accelerate and de-risk inorganic NP-based drug delivery, which may help to improve patient outcomes in clinical settings.

On the Model Update Strategies for Supervised Learning in AIOps Solutions

AIOps (Artificial Intelligence for IT Operations) solutions leverage the massive data produced during the operation of large-scale systems and machine learning models to assist software engineers in their system operations. As operation data produced in the field are constantly evolving due to factors such as the changing operational environment and user base, the models in AIOps solutions need to be constantly maintained after deployment. While prior works focus on innovative modeling techniques to improve the performance of AIOps models before releasing them into the field, when and how to update AIOps models remain an under-investigated topic. In this work, we performed a case study on three large-scale public operation data: two trace datasets from the cloud computing platforms of Google and Alibaba and one disk stats dataset from the BackBlaze cloud storage data center. We empirically assessed five different types of model update strategies for supervised learning regarding their performance, updating cost, and stability. We observed that active model update strategies (e.g., periodical retraining, concept drift guided retraining, time-based model ensembles, and online learning) achieve better and more stable performance than a stationary model. Particularly, applying sophisticated model update strategies (e.g., concept drift detection, time-based ensembles, and online learning) could provide better performance, efficiency, and stability than simply retraining AIOps models periodically. In addition, we observed that, although some update strategies (e.g., time-based ensemble and online learning) can save model training time, they significantly sacrifice model testing time, which could hinder their applications in AIOps solutions where the operation data arrive at high pace and volume and where immediate inferences are required. Our findings highlight that practitioners should consider the evolution of operation data and actively maintain AIOps models over time. Our observations can also guide researchers and practitioners in investigating more efficient and effective model update strategies that fit in the context of AIOps.

Robustness evaluation of large-scale machine learning-based reduced order models for reproducing flow fields

The robustness of an artificial neural network that performs model order reduction for flow field data is studied. The network is trained with a large-scale distributed learning approach using up to 6259 nodes of the supercomputer Fugaku. Flow around two square cylinders with a varying distance between their centers is investigated. The network is trained and tested with data from numerical simulations. First, the capability to reproduce flow fields with 2, 12, and 24 modes is investigated by comparing the reconstructed flow data to simulated data. It is shown, that reconstructions based on 2 modes cannot capture both, low- and high-frequency flow structures correctly, whereas predictions based on 12 and 24 modes yield improved flow fields, especially in the case of high-frequency waves in the vicinity of the square cylinders. Reconstructions with 24 modes provide smooth velocity fields that reproduce all relevant low- and high-frequency waves for all variations of the distance between the two square cylinders. Second, the performance of the machine learning-based reconstructions are compared to proper orthogonal decomposition, which is a commonly used reduced order model technique. The comparison only includes flow fields based on 24 modes. For all geometric variations, the mean squared errors of the reconstructions by the conventional method are higher than those of the machine learning model. This underlines the advantage of artificial neural networks over linear methods like proper orthogonal decomposition for tasks like reconstructing flow fields that are characterized by non-linear governing equations.

Heat transfer study of 3D packaging structure with superconducting TSV for practical-scale quantum annealing machines

To avoid the interconnect crowding in a planar structure, three-dimensional (3D) integrated technologies are necessary for realizing practical large-scale quantum annealing (QA) machines. We studied the heat transfer of a 3D packaging structure with superconducting through-silicon via for large-scale QA machines by finite element method simulation. The heat transfer becomes less efficient in the stacked structure. A high temperature of 57.0 mK is observed for the qubit chip, which degrades the quantum coherence of the qubit chip. We propose a heat transfer optimization method by increasing the number of bumps under the active interposer. Furthermore, by shortening the distance to the heat sink, the maximum temperature of the qubit chip is reduced to 18.1 mK. Our proposed heat transfer optimization methods are useful to provide a cryogenic temperature for stable qubit chip operation in a 3D packaging structure to realize practical-scale superconducting QA machines.

Optimal Matrix Sketching over Sliding Windows

Matrix sketching, aimed at approximating a matrix A ∈ R N×d consisting of vector streams of length N with a smaller sketching matrix B ∈ R ℓ×d , ℓ ≪ N , has garnered increasing attention in fields such as large-scale data analytics and machine learning. A well-known deterministic matrix sketching method is the FreqentDirections algorithm, which achieves the optimal [EQUATION] space bound and provides a covariance error guarantee of ε = || A ⊤ A - B ⊤ B || 2 /|| A || 2 F. The matrix sketching problem becomes particularly interesting in the context of sliding windows, where the goal is to approximate the matrix A W , formed by input vectors over the most recent N time units. However, despite recent efforts, whether achieving the optimal [EQUATION] space bound on sliding windows is possible has remained an open question. In this paper, we introduce the DS-FD algorithm, which achieves the optimal [EQUATION] space bound for matrix sketching over row-normalized, sequence-based sliding windows. We also present matching upper and lower space bounds for time-based and unnormalized sliding windows, demonstrating the generality and optimality of DS-FD across various sliding window models. This conclusively answers the open question regarding the optimal space bound for matrix sketching over sliding windows. We conduct extensive experiments with both synthetic and real-world datasets, validating our theoretical claims and thus confirming the correctness and effectiveness of our algorithm, both theoretically and empirically.

Distributed gradient-free and projection-free algorithm for stochastic constrained optimization

Distributed stochastic zeroth-order optimization (DSZO), in which the objective function is allocated over multiple agents and the derivative of cost functions is unavailable, arises frequently in large-scale machine learning and reinforcement learning. This paper introduces a distributed stochastic algorithm for DSZO in a projection-free and gradient-free manner via the Frank-Wolfe framework and the stochastic zeroth-order oracle (SZO). Such a scheme is particularly useful in large-scale constrained optimization problems where calculating gradients or projection operators is impractical, costly, or when the objective function is not differentiable everywhere. Specifically, the proposed algorithm, enhanced by recursive momentum and gradient tracking techniques, guarantees convergence with just a single batch per iteration. This significant improvement over existing algorithms substantially lowers the computational complexity. Under mild conditions, we prove that the complexity bounds on SZO of the proposed algorithm are O(n/ϵ2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\mathcal{O}(n/\\epsilon ^{2})$\\end{document} and O(n(21ϵ))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\mathcal{O}(n(2^{\\frac{1}{\\epsilon}}))$\\end{document} for convex and nonconvex cases, respectively. The efficacy of the algorithm is verified on black-box binary classification problems against several competing alternatives.

Individual and community empowerment improve resource users' perceptions of community-based conservation effectiveness in Kenya and Tanzania.

Community-based conservation has been increasingly recognized as critical to achieve both conservation and socio-economic development goals worldwide. However, the long-term sustainability of community-based conservation programs is dependent on a broadly shared perception among community members that management actions are achieving their stated goals. Thus, understanding the underlying factors driving differences in perceptions of management effectiveness can help managers prioritize the processes and outcomes most valued by resource users and thereby promote sustained support for conservation efforts. Here, we utilize large-scale interview survey data and machine learning to identify the factors most strongly associated with differences in perceived management effectiveness between resource users engaged in marine community-based conservation programs in Kenya and Tanzania. Perceptions of management effectiveness were generally favorable in both countries, and the most important predictors of positive perceptions were associated with community and individual empowerment in resource management and use, but within disparate focal domains. Improved perceptions of management effectiveness in Kenya were closely related to increases in women's empowerment in community-based conservation programs, while inclusionary and transparent governance structures were the most important factors driving improved perceptions in Tanzania. Additionally, the strongest predictors of differences between individuals in both countries often interacted synergistically to produce even higher rates of perceived effectiveness. These findings can help future initiatives in the region tailor management to match community-level priorities and emphasize the need for community-based conservation programs to understand local context to ensure that metrics of "success" are aligned with the needs and desires of local resource users.

Research on Algorithmic Gender Bias under the Paradigm of Machine Behavior Studies

In the field of communication studies, machine behavior specifically refers to information dissemination activities involving artificial intelligence technology. As algorithms increasingly become the primary force in information dissemination, their potential gender bias becomes increasingly apparent. This paper, based on three research scopes in machine behavior studies: individual behavior, collective behavior, and human-machine interaction behavior, examines the gender bias exhibited by artificial intelligence entities in algorithms at these three levels. At the individual behavior level, the tendency of algorithm development to simplify features overlooks the diversity present in female society. The inherited data bias and human bias make it difficult to avoid gender discrimination. At the collective behavior level, the creation of opinion leader-type social robots expands the subject of information fog, making the concealed gender discrimination against women more covert. The use of large-scale machine armies manipulates search engine results, leading to severe gender bias in search engine outputs. At the hybrid human-machine behavior level, artificial intelligence shapes female images to construct female cognitive thinking. Algorithms acquire human bias during interaction with users, and social robots amplify gender bias issues through mixed human-machine behavior.

DeFTA: A plug-and-play peer-to-peer decentralized federated learning framework

Federated learning (FL) is a pivotal catalyst for enabling large-scale privacy-preserving distributed machine learning (ML). By eliminating the need for local raw dataset sharing, FL substantially reduces privacy concerns and alleviates the isolated data problem. However, in reality, the success of FL is predominantly attributed to a centralized framework called FedAvg [1], in which workers are responsible for model training, and servers are in control of model aggregation. Nevertheless, FedAvg's centralized worker-server architecture has raised new concerns, including low scalability of the cluster, risk of data leakage, and central server failure or even defection. To overcome these challenges, we propose Decentralized Federated Trusted Averaging (DeFTA), a decentralized FL framework that serves as a plug-and-play replacement for FedAvg, bringing instant improvements to security, scalability, and fault-tolerance in the federated learning process. In essence, it primarily consists of a novel model aggregating formula with theoretical performance analysis, and a decentralized trust system (DTS) to significantly enhance system robustness. Extensive experiments conducted on six datasets and six basic models suggest that DeFTA not only exhibits comparable performance with FedAvg in a more realistic setting, but also achieves remarkable resilience even when 67% of workers are malicious.

System-level time computation and representation in the suprachiasmatic nucleus revealed by large-scale calcium imaging and machine learning

The suprachiasmatic nucleus (SCN) is the mammalian central circadian pacemaker with heterogeneous neurons acting in concert while each neuron harbors a self-sustained molecular clockwork. Nevertheless, how system-level SCN signals encode time of the day remains enigmatic. Here we show that population-level Ca2+ signals predict hourly time, via a group decision-making mechanism coupled with a spatially modular time feature representation in the SCN. Specifically, we developed a high-speed dual-view two-photon microscope for volumetric Ca2+ imaging of up to 9000 GABAergic neurons in adult SCN slices, and leveraged machine learning methods to capture emergent properties from multiscale Ca2+ signals as a whole. We achieved hourly time prediction by polling random cohorts of SCN neurons, reaching 99.0% accuracy at a cohort size of 900. Further, we revealed that functional neuron subtypes identified by contrastive learning tend to aggregate separately in the SCN space, giving rise to bilaterally symmetrical ripple-like modular patterns. Individual modules represent distinctive time features, such that a module-specifically learned time predictor can also accurately decode hourly time from random polling of the same module. These findings open a new paradigm in deciphering the design principle of the biological clock at the system level.

Distributed Systems for Machine Learning in Cloud Computing: A Review of Scalable and Efficient Training and Inference

Traditional computer systems have been pushed to their limits as a result of the exponential rise of data and the rising complexity of machine learning (ML) models. As a result of its on-demand scalability and resource agility, cloud computing has emerged as the platform of choice for training and deploying large-scale machine learning models. However, in order to make good use of cloud resources for machine learning, it is necessary to make use of distributed systems. These systems are responsible for coordinating computations over several nodes in order to manage the demanding workloads. The purpose of this paper is to investigate the realm of distributed systems for machine learning in cloud computing, with a particular emphasis on training and inference that is both scalable and efficient. During the discussion on the need of distributed systems in machine learning, it was made clear why conventional single-machine techniques are not enough for the requirements of current machine learning and how distributed systems might help solve these difficulties. Scalability and Efficiency Considerations were reviewed in relation to the primary elements that contribute to the effectiveness of a distributed system for machine learning. These elements include task partitioning, communication overhead, fault tolerance, and resource optimization that were discussed. In the context of cloud computing, the purpose of this review research is to provide a complete overview of the fascinating topic of distributed systems for machine learning. In order to successfully traverse the intricate and ever-changing world of cloud-based machine learning, it provides vital insights and information.

Z-SignFedAvg: A Unified Stochastic Sign-Based Compression for Federated Learning

Federated Learning (FL) is a promising privacy-preserving distributed learning paradigm but suffers from high communi- cation cost when training large-scale machine learning models. Sign-based methods, such as SignSGD, have been proposed as a biased gradient compression technique for reducing the communication cost. However, sign-based algorithms could diverge under heterogeneous data, which thus motivated the de- velopment of advanced techniques, such as the error-feedback method and stochastic sign-based compression, to fix this issue. Nevertheless, these methods still suffer from slower convergence rates, and none of them allows multiple local SGD updates like FedAvg. In this paper, we propose a novel noisy perturbation scheme with a general symmetric noise distribution for sign-based compression, which not only al- lows one to flexibly control the bias-variance tradeoff for the compressed gradient, but also provides a unified viewpoint to existing stochastic sign-based methods. More importantly, the proposed scheme enables the development of the very first sign-based FedAvg algorithm (z-SignFedAvg) to accelerate the convergence. Theoretically, we show that z-SignFedAvg achieves a faster convergence rate than existing sign-based methods and, under the uniformly distributed noise, can enjoy the same convergence rate as its uncompressed counterpart. Extensive experiments are conducted to demonstrate that the z-SignFedAvg can achieve competitive empirical performance on real datasets and outperforms existing schemes.

Abstract 4961: Large-scale CyTOF data modeling of leukemia patient cohorts

Abstract Introduction: Cytometry by Time-of-Flight (CyTOF) is a single-cell proteomic assay that uses antibody-lanthanide metal conjugates to tag surface or intracellular proteins, quantified via mass spectrometry. Compared to flow cytometry, CyTOF significantly reduces overlapping signals and increases the maximum protein features from 8 to 50. CyTOF data is commonly analyzed with the ‘gating’ workflow, which is the iterative process of plotting cells by two protein features to select discrete cell populations. While CyTOF can comprehensively profile nuanced cell types, gating does not scale well in the number of features. Biases in gating often arise from the forced discretization of cell populations, subjectivity of gate boundaries, and intrinsic sample-sample variation. Contemporary challenges in CyTOF data analysis can be attributed to data volume and gating bias, which can lead to oversight of disease-related cell populations while hindering reproducibility. Methods: We developed a computational platform that enables the processing and analysis of large-scale CyTOF data. Our workflow includes modular components for data harmonization, normalization, transformation, batch correction, quality control, subsampling, and dimensionality reduction via UMAP of input sample files in Flow Cytometry Standard (FCS) format. Further, additional analytical modules are currently being integrated to deploy large-scale machine learning models for cell type identification. We use the results of these models as input for multi-scale analysis, where we aim to link single-cell CyTOF data with clinical outcomes. We test our workflow on previously published datasets and apply it to new leukemia patient cohorts of blood and bone marrow samples and a variety of marker panels. Results: We tested our workflow on previously published datasets of 4.7 M peripheral blood mononuclear cells from healthy donors (N= 20) and T cells (N = 8). We observed that samples mixed well upon integration and separated into major cell types like monocytes, CD4+/CD8+ T-cells, naïve/mature B-cells, and natural killer T-cells. We then applied it to a bone marrow cohort of healthy donors (N = 10), where we observe good mixing of samples but less clear separation of cell types. Finally, we applied our workflow to 9.3 M cells from 51 chronic lymphocytic leukemia (CLL) patients and 4 healthy donors, using a lymphocytic marker panel. Here, we observed a clear resolution of a major clonal B-cell population and immune T-cells. We are in the process of applying it to Acute Myeloid Leukemia (AML) patient data with myeloid and immune marker panels. Conclusions: We developed a computational platform that facilitates the simultaneous analysis of large-scale CyTOF data, both in the number of samples and cells per sample, a variety of marker panels, while minimizing gating biases common in traditional gating analysis. Citation Format: Garth Kong, Tania Vu, Evan Lind, Olga H. Nikolova. Large-scale CyTOF data modeling of leukemia patient cohorts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4961.

A comprehensive error evaluation method for non-rigid body gantry machine tools considering quasi-static crosstalk based on optimized multilateration measurement

For large-scale machine tools, the impact of non-rigid body behavior on volumetric error accuracy cannot be ignored, which hasn't been considered in general volumetric error model. This paper presents a comprehensive error evaluation method for large-scale gantry machine tools considering non-rigid body effect, aiming to provide guidance for volumetric error calibration process. An extended kinematic model is established based on the analysis of crosstalk influence on geometric error motions. An optimized multilateration measurement with improved accuracy and robustness is carried out for volumetric error identification, verified by Monte Carlo simulations. The main novelty of the optimization is to introduce a space uniformity index to generate specialized calibration point sets for any measurement points. A new volumetric error prediction method based on classified descriptions of geometric error motions is proposed, according to their relationship with crosstalk. Experimental results show that the proposed method achieves higher prediction accuracy in crosstalk-sensitive directions.

The harms of terminology: why we should reject so-called “frontier AI”

In the mid-2023, promoters of artificial intelligence (AI) as an “existential risk” coined a new term, “frontier AI,” that refers to “highly capable foundation models that could possess dangerous capabilities sufficient to pose severe risks to public safety.” Promoters of this new term were able to disseminate it via the United Kingdom (UK) government’s Frontier AI Taskforce (formerly the Foundation Models Taskforce) as well as the UK’s AI Safety Summit, held in November 2023.I argue that adoption of the term “frontier AI” is harmful and contributes to AI hype. Promoting this new term is a way for its boosters to focus the public conversation around the AI-related risks they think are most important, namely “existential risk”—a scenario in which AI is able to bring about the destruction of humanity. Simultaneously, “frontier AI” is a re-branding exercise for the large-scale generative machine learning (ML) models that have been shown to cause severe and pervasive harms (including psychological, social, and environmental harms). Unlike “existential risk,” these harms are actual rather than theoretical, whereas the term “frontier AI” moves our collective focus away from actual harms to focus on hypothetical doomsday scenarios.Moreover, “frontier AI” as a term invokes the colonial mindset, further reinscribing the harmful dynamics between the handful of powerful Western companies who produce today’s generative AI models and the people of the “Global South” who are most likely to experience harm as a direct result of the development and deployment of these AI technologies.

Building Flexible, Scalable, and Machine Learning-Ready Multimodal Oncology Datasets.

The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data. Integrating radiological scans, histopathology images, and molecular information with clinical data is essential for developing a holistic understanding of the disease and optimizing treatment. The need for integrating data from multiple sources is further pronounced in complex diseases such as cancer for enabling precision medicine and personalized treatments. This work proposes Multimodal Integration of Oncology Data System (MINDS)-a flexible, scalable, and cost-effective metadata framework for efficiently fusing disparate data from public sources such as the Cancer Research Data Commons (CRDC) into an interconnected, patient-centric framework. MINDS consolidates over 41,000 cases from across repositories while achieving a high compression ratio relative to the 3.78 PB source data size. It offers sub-5-s query response times for interactive exploration. MINDS offers an interface for exploring relationships across data types and building cohorts for developing large-scale multimodal machine learning models. By harmonizing multimodal data, MINDS aims to potentially empower researchers with greater analytical ability to uncover diagnostic and prognostic insights and enable evidence-based personalized care. MINDS tracks granular end-to-end data provenance, ensuring reproducibility and transparency. The cloud-native architecture of MINDS can handle exponential data growth in a secure, cost-optimized manner while ensuring substantial storage optimization, replication avoidance, and dynamic access capabilities. Auto-scaling, access controls, and other mechanisms guarantee pipelines' scalability and security. MINDS overcomes the limitations of existing biomedical data silos via an interoperable metadata-driven approach that represents a pivotal step toward the future of oncology data integration.