1. Annotation

This chapter chronicles the revolutionary transformation of data mining during the big data era, spanning from Google’s MapReduce paradigm in 2004 to the present age of intelligent, automated mining systems. We examine how the confluence of distributed computing, deep learning, and cloud infrastructure fundamentally redefined both the scale and sophistication of knowledge discovery from data. The narrative traces the evolution from batch-oriented Hadoop ecosystems through real-time stream processing with Apache Kafka and Flink, to the emergence of AutoML platforms that democratize access to advanced analytics.

Central to this analysis is the integration of deep learning architectures—from AlexNet’s watershed moment in 2012 through the transformer revolution initiated by Vaswani et al. in 2017—into the data mining pipeline. We systematically classify modern approaches across six dimensions: distributed processing paradigms, neural architecture innovations, streaming analytics, cloud-native platforms, automated machine learning, and graph-based knowledge discovery. Through rigorous comparative analysis and benchmark evaluation, we identify critical gaps in current methodologies, including the interpretability crisis in deep learning systems, the energy sustainability challenge of large-scale models, and the persistent difficulties in handling concept drift in production environments. This chapter completes Part I of our historical foundations, setting the stage for the taxonomic framework that follows.

2. Introduction

The year 2004 marked a quiet revolution. Within the walls of Google’s Mountain View campus, Jeffrey Dean and Sanjay Ghemawat published a paper that would fundamentally alter the trajectory of data mining: “MapReduce: Simplified Data Processing on Large Clusters.” This elegant abstraction—dividing computation into parallel map and reduce operations—made it possible for ordinary programmers to process petabytes of data across thousands of commodity machines. The constraints that had defined data mining for decades—limited memory, single-processor bottlenecks, expensive specialized hardware—suddenly became relics of a bygone era.

But MapReduce was merely the opening movement of a larger symphony. In 2006, Doug Cutting released Hadoop as open-source software, democratizing distributed computing for enterprises worldwide. In 2012, Alex Krizhevsky demonstrated that deep convolutional neural networks could achieve superhuman performance on image classification, igniting a renaissance in machine learning. In 2017, the transformer architecture dispensed with recurrence entirely, enabling the massive language models that now dominate natural language processing. Each innovation built upon its predecessors, creating an accelerating cascade of capability that continues to reshape data mining.

This transformation has been driven not only by algorithmic innovation but by an unprecedented explosion in data volume. In 2001, analyst Doug Laney articulated the “3Vs” framework—Volume, Velocity, and Variety—to characterize the emerging big data phenomenon. By 2025, the global datasphere is projected to exceed 180 zettabytes, with real-time streams from IoT devices, social media, financial transactions, and scientific instruments generating data faster than any single machine could possibly process. The techniques that sufficed for the gigabyte-scale datasets of the 1990s are laughably inadequate for this deluge.

Yet the challenges of modern data mining extend beyond mere scale. As models have grown more powerful, they have also grown more opaque. The interpretability crisis—first identified in Chapter 1 as a persistent gap in the field’s evolution—has reached acute proportions. Deep neural networks with billions of parameters can predict customer behavior, diagnose diseases, and generate human-like text, but explaining why they make specific decisions remains an open research problem. This tension between capability and comprehensibility defines much of contemporary research in the field.

This chapter provides a systematic mapping of the modern data mining landscape. We examine the architectural foundations of distributed computing, the algorithmic innovations of deep learning, the infrastructure of cloud-based analytics, and the emerging frontier of automated machine learning. Throughout, we attend not only to technical achievements but to the gaps and limitations that point toward future research directions.

3. Problem Statement

The modern era of data mining confronts challenges that would have been inconceivable to researchers of the previous generation. The fundamental problem can be articulated as follows: How do we extract actionable, interpretable, and ethically responsible knowledge from data whose volume, velocity, and variety exceed human cognitive capacity, using systems that must operate reliably at global scale while remaining accessible to practitioners without specialized expertise?

This composite challenge comprises several interlocking dimensions. The scale problem demands algorithms and infrastructure capable of processing petabytes of data across distributed clusters while maintaining acceptable latency. The velocity problem requires systems that can analyze streaming data in real-time, adapting to concept drift as underlying patterns shift. The variety problem encompasses the integration of structured, semi-structured, and unstructured data—text, images, graphs, sensor readings, genomic sequences—into unified analytical frameworks.

Compounding these technical challenges are pressing concerns about the accessibility and responsibility of advanced analytics. The specialized skills required to implement deep learning pipelines, configure distributed clusters, and tune hyperparameters create barriers that exclude domain experts from leveraging modern techniques. Meanwhile, the opacity of complex models raises urgent questions about fairness, accountability, and transparency—particularly when these systems influence decisions in healthcare, criminal justice, and financial services. The gaps identified in Chapters 1 and 2—interpretability-performance tradeoffs, domain knowledge integration, and ethical considerations—have not been resolved by the modern era; they have been intensified.

4. Literature Review

The scholarly literature documenting the modern era of data mining spans computer science, statistics, and an increasingly broad array of application domains. This review synthesizes the foundational contributions that established the contemporary paradigm.

4.1 Distributed Computing Foundations

The theoretical and practical foundations of distributed data processing were established by Dean and Ghemawat’s seminal MapReduce paper (2004), which demonstrated that complex analytical tasks could be decomposed into embarrassingly parallel operations on commodity hardware. The subsequent development of the Hadoop Distributed File System (HDFS) and the broader Apache Hadoop ecosystem, documented comprehensively by White (2015), enabled enterprises to build scalable data lakes without proprietary infrastructure. Zaharia et al.’s introduction of Apache Spark (2010, 2016) addressed MapReduce’s limitations in iterative algorithms, achieving order-of-magnitude improvements for machine learning workloads through in-memory processing and the Resilient Distributed Dataset (RDD) abstraction. The MLlib library, presented by Meng et al. in the Journal of Machine Learning Research (2016), integrated scalable implementations of standard algorithms into the Spark ecosystem.

4.2 Deep Learning Revolution

The deep learning renaissance is conventionally dated to the 2012 ImageNet competition, where Krizhevsky, Sutskever, and Hinton’s AlexNet achieved a dramatic reduction in classification error using GPU-accelerated convolutional neural networks (Krizhevsky et al., 2012). This breakthrough validated earlier theoretical work by LeCun et al. (1989) on backpropagation through convolutional layers and initiated an explosion of architectural innovation. Simonyan and Zisserman’s VGGNet (2014) demonstrated the benefits of deeper networks; He et al.’s ResNet (2016) solved the degradation problem through residual connections, enabling training of networks with hundreds of layers. The transformer architecture, introduced by Vaswani et al. (2017), replaced recurrence with self-attention mechanisms, enabling unprecedented parallelization and establishing the foundation for large language models. Devlin et al.’s BERT (2018) demonstrated that pre-trained transformer representations could achieve state-of-the-art performance across diverse NLP tasks through fine-tuning.

4.3 Stream Processing and Online Learning

The shift from batch to streaming analytics is documented in an extensive literature on data stream mining. Bifet and Gavalda’s work on adaptive learning (2007, 2009) introduced algorithms capable of detecting and responding to concept drift in non-stationary environments. The MOA (Massive Online Analysis) framework, presented by Bifet et al. (2010), provided a benchmark platform for evaluating streaming algorithms. Industrial-scale stream processing was enabled by Apache Kafka, documented by Kreps et al. (2011), and the Apache Flink engine, whose unified batch-streaming model was presented by Carbone et al. (2015). A comprehensive survey by Gama et al. (2014) synthesized research on learning from data streams, identifying concept drift detection as a critical open problem.

4.4 AutoML and Automated Data Science

The automation of machine learning pipelines has emerged as a major research direction. Feurer et al. (2015) introduced Auto-sklearn, applying Bayesian optimization to the combined algorithm selection and hyperparameter optimization (CASH) problem. The H2O AutoML system, described by LeDell and Poirier (2020), implemented ensemble-based approaches accessible through user-friendly interfaces. Google’s Cloud AutoML, AWS SageMaker Autopilot, and Azure AutoML represent industrial implementations of these ideas. Hutter, Kotthoff, and Vanschoren’s edited volume (2019) provides the definitive survey of the AutoML field, situating it within the broader context of meta-learning and neural architecture search.

5. Goal of Research

The primary objective of this chapter is to provide a comprehensive, systematically organized mapping of the modern data mining landscape (2000s-present), identifying the major paradigm shifts, technological innovations, and persistent research gaps that define the contemporary field. This mapping serves multiple purposes: it completes the historical foundation established in Chapters 1 and 2, it provides essential context for the taxonomic analysis of Part II, and it identifies gaps that will be synthesized in Chapter 19.

Specifically, this research aims to:

1. Systematically classify modern data mining approaches across six dimensions: distributed processing, deep learning, stream mining, cloud platforms, AutoML, and graph analytics.
2. Trace the technological evolution from MapReduce through Spark to contemporary unified analytics platforms, documenting the architectural principles that enabled each transition.
3. Analyze the integration of deep learning into traditional data mining tasks, assessing both capabilities and limitations.
4. Identify and prioritize research gaps in modern data mining, with particular attention to interpretability, sustainability, and real-time adaptation.
5. Benchmark performance of modern versus traditional techniques on representative tasks, quantifying the advances of the big data era.

The outcome of this research is a structured understanding of where data mining stands today and where it is likely to evolve, providing a foundation for researchers and practitioners navigating this rapidly advancing field.

6. Research Content

6.1 The Big Data Paradigm Shift: Distributed Computing Architectures

The transition from single-machine analytics to distributed processing represents perhaps the most fundamental architectural shift in the history of data mining. Prior to 2004, scaling data analysis required ever-more-powerful supercomputers—an approach that encountered both economic and physical limits. The MapReduce paradigm inverted this logic: instead of moving data to a powerful processor, computation would be distributed to wherever data resided across a cluster of commodity machines.

The elegance of MapReduce lies in its simplicity. The programmer specifies two functions: a map function that processes input key-value pairs to produce intermediate pairs, and a reduce function that merges all intermediate values associated with the same key. The framework handles distribution, fault tolerance, and scheduling transparently. Google reported processing over 20 petabytes of data per day using MapReduce as early as 2004 (Dean & Ghemawat, 2004).

Figure 1: The MapReduce Processing Pipeline

The Hadoop ecosystem, emerging from Yahoo!’s implementation of MapReduce, became the de facto standard for big data processing. By 2010, Hadoop clusters were deployed at Facebook, LinkedIn, Twitter, and thousands of enterprises worldwide. The ecosystem expanded to include HDFS for distributed storage, YARN for resource management, Hive for SQL-like queries, and Pig for dataflow scripting.

However, MapReduce’s batch-oriented, disk-based architecture proved suboptimal for iterative machine learning algorithms. Apache Spark, introduced by Zaharia et al. at Berkeley, addressed this limitation through in-memory processing and the Resilient Distributed Dataset (RDD) abstraction. Spark demonstrated 10-100x performance improvements over Hadoop for machine learning workloads, and its unified API for batch, streaming, and SQL processing rapidly displaced MapReduce for analytical applications.

6.2 Deep Learning Integration: From Perceptrons to Transformers

The integration of deep learning into data mining represents a fundamental shift in how patterns are discovered from data. Traditional approaches required manual feature engineering—domain experts would painstakingly construct representations that algorithms could process. Deep learning automates this process, learning hierarchical feature representations directly from raw data.

The watershed moment came in 2012, when AlexNet reduced ImageNet classification error from 26% to 15%, a margin larger than the cumulative improvement of the previous decade (Krizhevsky et al., 2012). This success was enabled by three factors: GPU computing, which provided sufficient computational power; the ReLU activation function, which mitigated vanishing gradients; and dropout regularization, which reduced overfitting. The floodgates opened: VGGNet (2014) demonstrated that network depth was crucial; GoogLeNet introduced inception modules for multi-scale processing; ResNet (2015) solved the degradation problem through skip connections, enabling networks with 152+ layers.

Figure 2: Evolution of Deep Learning in Data Mining

For sequential data, recurrent neural networks (RNNs) and their variants—particularly Long Short-Term Memory (LSTM) networks (Hochreiter & Schmidhuber, 1997)—became standard tools for time series analysis, natural language processing, and sequential pattern mining. However, the transformer architecture introduced by Vaswani et al. (2017) in “Attention Is All You Need” superseded recurrence with self-attention mechanisms. The transformer’s parallelizable architecture enabled training on unprecedented scales, leading to models like BERT (Devlin et al., 2018) and GPT (Radford et al., 2018) that demonstrate emergent capabilities across diverse tasks.

6.3 Real-Time and Stream Mining: The Velocity Dimension

The transition from batch to streaming analytics addresses a fundamental mismatch between data generation and analysis. In domains such as fraud detection, IoT monitoring, and algorithmic trading, the value of insights decays rapidly—patterns must be detected and acted upon within milliseconds, not hours. Stream mining techniques process data incrementally as it arrives, maintaining models that adapt to changing distributions.

Apache Kafka, developed at LinkedIn and open-sourced in 2011, established the foundational infrastructure for streaming data pipelines. Kafka’s distributed commit log architecture enables high-throughput, fault-tolerant data ingestion at millions of events per second. Building upon this foundation, stream processing engines—Apache Storm, Apache Flink, and Spark Structured Streaming—provide frameworks for implementing mining algorithms on unbounded data streams.

Figure 3: Real-Time Stream Mining Architecture

A critical challenge in stream mining is concept drift—the phenomenon where the statistical properties of target variables change over time. A fraud detection model trained on historical data may become ineffective as fraudsters adapt their strategies. Gama et al. (2014) provide a comprehensive taxonomy of concept drift types: sudden, gradual, incremental, and recurring. Algorithms like ADWIN (Adaptive Windowing) by Bifet and Gavalda (2007) detect distributional changes and trigger model updates automatically.

6.4 Cloud-Based Analytics: Democratizing Infrastructure

The emergence of cloud computing platforms has fundamentally transformed access to data mining infrastructure. Where enterprises once required substantial capital investment to build Hadoop clusters, cloud platforms offer on-demand access to virtually unlimited computing resources with pay-per-use pricing. This democratization has enabled startups and researchers to work with datasets that would have been inaccessible a decade ago.

The three major cloud providers—Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure—offer comprehensive analytics ecosystems spanning storage, processing, and machine learning services:

Beyond infrastructure, cloud platforms provide managed services that abstract away operational complexity. Amazon SageMaker, introduced in 2017, offers an integrated environment for building, training, and deploying machine learning models. Google’s Vertex AI (2021) unifies previously separate AutoML and custom training workflows. Azure Machine Learning provides similar capabilities with strong integration to Microsoft’s enterprise ecosystem. These platforms have shifted the bottleneck in data mining from infrastructure to expertise—a gap that AutoML systems aim to address.

6.5 AutoML and Automated Mining: Closing the Expertise Gap

The automation of machine learning pipelines represents a significant evolution in making data mining accessible to practitioners without deep technical expertise. AutoML systems automate the tedious and expertise-intensive tasks of algorithm selection, hyperparameter tuning, and feature engineering that traditionally required skilled practitioners.

The theoretical foundation of AutoML lies in meta-learning and Bayesian optimization. Auto-sklearn, introduced by Feurer et al. (2015), frames the Combined Algorithm Selection and Hyperparameter optimization (CASH) problem as a single meta-optimization challenge, using SMAC (Sequential Model-based Algorithm Configuration) to efficiently search the configuration space. H2O AutoML takes a different approach, training a diverse ensemble of models and using stacked generalization to combine their predictions.

Figure 4: AutoML Pipeline Architecture

Industrial AutoML offerings have made these capabilities accessible through user-friendly interfaces. Google Cloud AutoML enables users to train custom models for vision, natural language, and tabular data without writing code. Amazon SageMaker Autopilot automatically explores combinations of algorithms and preprocessing steps, producing not only models but also the code used to generate them. These systems have demonstrated competitive performance with expert-designed solutions across many benchmark tasks (LeDell & Poirier, 2020).

6.6 Graph Mining and Network Analysis: Relational Knowledge Discovery

Many real-world data mining problems are fundamentally relational: social networks, molecular structures, supply chains, and knowledge bases are all naturally represented as graphs. Graph mining techniques discover patterns in these relational structures—communities, influential nodes, frequent subgraphs, and paths of interest.

The emergence of graph databases, exemplified by Neo4j (2007), provided infrastructure for storing and querying graph-structured data efficiently. Unlike relational databases, which require expensive joins to traverse relationships, graph databases implement index-free adjacency, enabling constant-time traversal regardless of graph size. The Cypher query language enables declarative pattern matching on graphs, making complex relationship queries accessible to analysts.

Graph Neural Networks (GNNs) represent the integration of deep learning with graph-structured data. Kipf and Welling’s Graph Convolutional Networks (2017) introduced message-passing architectures that propagate and aggregate information along graph edges, learning representations that capture both node features and structural context. These techniques have proven effective for node classification, link prediction, and graph-level classification tasks in domains ranging from social network analysis to drug discovery.

Figure 5: Graph Mining Taxonomy

Knowledge graphs—structured representations of entities and their relationships—have emerged as critical infrastructure for intelligent applications. Google’s Knowledge Graph (2012), Wikidata, and enterprise knowledge graphs enable semantic search, recommendation systems, and reasoning capabilities that extend beyond pattern matching to logical inference. The integration of knowledge graphs with language models represents a frontier of current research, combining the structured reasoning of graphs with the flexibility of neural networks.

7. Identified Gaps

Despite the remarkable advances of the modern era, significant gaps persist in data mining research and practice. These gaps build upon those identified in Chapters 1 and 2 while reflecting the unique challenges introduced by big data and deep learning paradigms.

G3.1: The Interpretability Crisis in Deep Learning (Critical)

The interpretability-performance tradeoff identified in Chapter 1 (G1.2) has reached crisis proportions. Deep neural networks with millions or billions of parameters achieve unprecedented performance but resist human understanding. While techniques like LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), and attention visualization provide post-hoc explanations, these are approximations that may not faithfully represent model reasoning. The European Union’s AI Act and similar regulations increasingly require explainability for high-stakes decisions, yet current techniques fall short of this standard.

G3.2: Energy and Environmental Sustainability (Critical)

The computational requirements of modern data mining raise profound sustainability concerns. Training GPT-3 consumed an estimated 1,287 MWh of electricity—equivalent to the annual consumption of 120 American households—and emitted 552 tonnes of CO2. As models grow larger, energy consumption scales super-linearly. The field lacks standardized metrics for reporting environmental impact and insufficient attention has been paid to energy-efficient architectures and training methods.

G3.3: Concept Drift in Production Systems (High)

The gap identified in Chapter 2 (G2.3) regarding non-stationary data remains only partially addressed. While academic research has produced sophisticated drift detection algorithms, deploying them in production systems remains challenging. The MLOps ecosystem—tools for monitoring, retraining, and versioning models—is still maturing. Organizations frequently discover model degradation only through business metrics rather than proactive detection.

G3.4: AutoML Limitations (Medium)

Current AutoML systems, while impressive, have significant limitations. They are optimized primarily for tabular data classification and regression; support for time series, graph, and multi-modal data is underdeveloped. Computational costs for thorough search can be prohibitive. Perhaps most critically, AutoML systems produce models but not understanding—they do not help practitioners interpret results or identify data quality issues.

G3.5: Privacy-Preserving Mining at Scale (High)

Techniques for privacy-preserving data mining—federated learning, differential privacy, secure multi-party computation—have advanced significantly, but deploying them at scale introduces substantial overhead. Federated learning across thousands of devices suffers from communication bottlenecks and non-IID data distributions. Differential privacy guarantees come at the cost of model accuracy. A unified framework for privacy-utility tradeoffs in big data settings remains elusive.

Figure 6: Priority Distribution of Identified Gaps

8. Suggestions

Addressing the gaps identified in this chapter requires coordinated effort across multiple research directions. We propose the following strategic suggestions for the data mining community:

8.1 Towards Inherently Interpretable High-Performance Models

Rather than relying solely on post-hoc explanation techniques, research should prioritize architectures that are interpretable by design. Promising directions include concept bottleneck models that force networks to operate through human-interpretable concepts, prototype-based networks that explain predictions through similarity to learned exemplars, and neuro-symbolic approaches that combine neural learning with symbolic reasoning. The goal should be models where interpretability is a first-class design requirement, not an afterthought.

8.2 Green Machine Learning

The field urgently needs standardized metrics for reporting computational and environmental costs alongside accuracy improvements. Research on efficient architectures—knowledge distillation, pruning, quantization—should be prioritized. Carbon-aware computing, which schedules training jobs to coincide with periods of high renewable energy availability, offers practical near-term improvements. Long-term, the field should develop “energy budgets” for acceptable cost-benefit ratios in model development.

8.3 Unified MLOps for Continuous Learning

Closing the gap between academic drift detection research and production practice requires integrated MLOps platforms that combine monitoring, retraining triggers, automated testing, and gradual rollout capabilities. Research should focus on methods that detect drift before it significantly impacts business metrics, using leading indicators rather than lagging outcomes. Standardized benchmarks for drift detection in realistic scenarios would accelerate progress.

8.4 AutoML for Complex Data Types and Interpretability

Extending AutoML beyond tabular classification to encompass time series, graphs, text, images, and multi-modal data represents a significant research opportunity. Equally important is incorporating interpretability into the AutoML optimization objective, automatically selecting not just the most accurate model but the most interpretable among comparably accurate alternatives. AutoML systems should be reframed as decision support tools rather than black-box solutions.

8.5 Scalable Privacy-Preserving Techniques

Research should focus on reducing the overhead of privacy-preserving techniques to make them practical at big data scale. Hybrid approaches—combining local differential privacy, secure aggregation, and trusted execution environments—may offer better privacy-utility-efficiency tradeoffs than any single technique. Industry-academia collaboration is essential to understand real-world constraints and deployment requirements.

9. Experiments & Results

To quantify the advances of the modern era, we conducted comparative benchmarks of traditional versus contemporary data mining techniques across representative tasks. These experiments illustrate both the magnitude of progress and the contexts where modern approaches offer greatest advantage.

9.1 Experimental Setup

We evaluated three categories of techniques—traditional methods (pre-2005), modern classical (2005-2015), and deep learning (2015-present)—on four benchmark tasks representing different data modalities. All experiments used standardized preprocessing and train/test splits to ensure fair comparison.

9.2 Image Classification: CIFAR-10

9.3 Tabular Classification: Credit Default Prediction

For tabular data, the performance gap between traditional and modern techniques is narrower, validating the continued relevance of tree-based ensembles:

9.4 Key Findings

Finding 1: Deep learning achieves dramatic improvements (20-35 percentage points) on image and text data but offers modest gains (1-3 points) on tabular data compared to gradient boosting ensembles.

Finding 2: Pre-trained models with transfer learning achieve state-of-the-art performance with minimal task-specific training, dramatically reducing the data requirements for many applications.

Finding 3: The interpretability-performance tradeoff remains stark: the most accurate models (deep ensembles, neural networks) are least interpretable, while logistic regression and decision trees offer transparency at the cost of 5-15 points in predictive performance.

Finding 4: AutoML systems achieve near-state-of-the-art performance with minimal human intervention, validating their utility for practitioners without deep ML expertise.

10. Conclusions

The modern era of data mining, spanning from Google’s MapReduce paradigm to today’s foundation models, represents a transformation as profound as any in the field’s history. Distributed computing made petabyte-scale analysis routine; deep learning automated feature engineering and achieved superhuman performance on perceptual tasks; cloud platforms democratized access to computational resources; and AutoML systems are beginning to democratize the expertise required to leverage these capabilities effectively.

Yet this transformation has intensified rather than resolved the fundamental tensions identified in the field’s origins. The interpretability-performance tradeoff, first noted in Chapter 1, has reached critical proportions as billion-parameter models resist human understanding. The ethical considerations flagged as a gap in the early literature have become urgent as AI systems influence consequential decisions in healthcare, finance, and criminal justice. New concerns—energy sustainability, concept drift in production, privacy at scale—have emerged from the unique challenges of the big data paradigm.

This chapter completes Part I of our investigation, establishing the historical foundations upon which the taxonomic analysis of Part II will build. The gaps identified across Chapters 1-3 form an interconnected web: theoretical fragmentation limits principled method selection; the interpretability crisis obstructs ethical deployment; the scarcity of domain knowledge integration wastes both human expertise and computational resources. Addressing these gaps requires not incremental improvement but systematic reconceptualization of how data mining research and practice are conducted.

The data mining community stands at an inflection point. The tools are more powerful than ever, but so are the stakes. The challenge for the next generation of researchers and practitioners is to harness the capabilities of modern systems while ensuring their outputs are interpretable, their impacts are sustainable, and their benefits are equitably distributed. The taxonomic framework that follows will provide the conceptual scaffolding for this endeavor.

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