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A rational function is a ratio of two polynomials, where the denominator is not zero. It is defined for all real numbers except those that make the denominator zero, which are called the function's vertical asymptotes or points of discontinuity.
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Job scheduling is a critical process in computing and operations management that involves allocating system resources to various tasks over time, optimizing for performance metrics like throughput and latency. Effective Job scheduling ensures that tasks are executed in an efficient manner, balancing load and minimizing resource contention, which is essential for achieving high system performance and reliability.
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Data throughput refers to the rate at which data is successfully transferred from one location to another, measured in bits per second (bps). It's a critical measure of network performance and efficiency, impacting everything from internet speed to the performance of data-driven applications.
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Automation refers to the use of technology to perform tasks with minimal human intervention, enhancing efficiency and consistency across various industries. It plays a crucial role in increasing productivity, reducing operational costs, and enabling new capabilities through advanced technologies like robotics and artificial intelligence.
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Resource optimization involves the efficient and effective allocation and use of resources to achieve the best possible outcomes in terms of productivity, cost, and sustainability. It is a critical aspect of operations management, ensuring that resources such as time, money, labor, and materials are utilized to their fullest potential without waste.
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Parallel processing is a computational approach where multiple processors execute or process an application or computation simultaneously. This method significantly increases computational speed and efficiency by dividing tasks into smaller sub-tasks that are processed concurrently, leveraging the power of multi-core processors or distributed computing systems.
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Queue management involves the efficient handling of waiting lines to enhance customer satisfaction and operational efficiency. It encompasses strategies and techniques to minimize wait times and optimize service delivery in various settings, from retail to telecommunications.
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A batch window is a designated time period during which batch processing is performed on a computer system, typically when the system is underutilized, such as during off-peak hours. It is crucial for optimizing resource usage and ensuring that large volumes of data are processed efficiently without impacting real-time operations.
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Latency refers to the delay between a user's action and the corresponding response in a system, crucial in determining the perceived speed and efficiency of interactions. It is a critical factor in network performance, affecting everything from web browsing to real-time applications like gaming and video conferencing.
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Scalability refers to the ability of a system, network, or process to handle a growing amount of work or its potential to accommodate growth. It is a critical factor in ensuring that systems can adapt to increased demands without compromising performance or efficiency.
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Error handling is a crucial aspect of software development that involves anticipating, detecting, and resolving errors or exceptions that occur during a program's execution. Effective Error handling improves program stability and user experience by ensuring that errors are managed gracefully and do not lead to application crashes or data corruption.
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Time-sharing systems allow multiple users to interact with a computer simultaneously by rapidly switching between tasks, maximizing CPU utilization and reducing idle time. This approach revolutionized computing by making it more accessible and efficient, paving the way for modern operating systems and cloud computing services.
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I/O Scheduling is a critical component of operating systems that manages the order and priority of input/output operations, optimizing the performance and efficiency of data access. By determining the sequence in which I/O requests are processed, it minimizes latency and maximizes throughput, ensuring balanced resource utilization and system responsiveness.
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Production scheduling is the process of organizing, optimizing, and controlling the production process to ensure that goods are produced efficiently, on time, and within budget. It involves allocating resources, setting timelines, and coordinating tasks to meet customer demand while minimizing costs and maximizing productivity.
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Inference speed refers to how quickly a machine learning model can make predictions or generate outputs once it has been trained. It is a critical aspect for real-time applications and can be influenced by factors such as model complexity, hardware capabilities, and optimization techniques.
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Scripting automation involves writing scripts to automate repetitive tasks, increasing efficiency and reducing the potential for human error. It is widely used in various fields such as IT, software development, and data analysis to streamline processes and improve productivity.
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Net settlement is a payment system process where only the net difference between total debits and credits is transferred between financial institutions, reducing the number of transactions and associated costs. This system enhances efficiency and liquidity management in financial markets by minimizing the need for large cash reserves and decreasing settlement risk.
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Big data processing involves the collection, organization, and analysis of vast amounts of data to extract valuable insights and support decision-making. It requires specialized tools and techniques to efficiently handle the volume, velocity, and variety of data that traditional data processing methods cannot manage effectively.
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Apache Flink is a powerful open-source stream processing framework for real-time data analytics, known for its high-throughput, low-latency processing capabilities. It supports both batch and stream processing, making it versatile for handling a wide range of data processing tasks in distributed environments.
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Data latency refers to the time delay between when data is generated and when it is available for use or analysis. Minimizing Data latency is crucial for real-time applications, as it directly impacts the speed and efficiency of data-driven decision-making processes.
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Data pipelines are automated processes that move data from one system to another, transforming and processing it along the way to ensure it is ready for analysis or further use. They are essential for managing large volumes of data efficiently, ensuring data quality, and enabling real-time analytics in modern data-driven environments.
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The SQL INSERT statement is used to add new rows of data to a table within a database, making it essential for populating tables with initial or additional data. It requires specifying the table name and the values for each column, and can be used in conjunction with SELECT statements to insert data from other tables.
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Inference acceleration refers to the techniques and technologies used to speed up the process of making predictions or decisions based on machine learning models, particularly in real-time applications. This is crucial for deploying AI systems efficiently in environments where latency and computational resources are critical constraints.
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Batch extraction is a process in which multiple data elements are extracted from a source in a single operation, enhancing efficiency and reducing processing time. It is commonly used in data processing, ETL (Extract, Transform, Load) operations, and large-scale data integration tasks to handle substantial volumes of data efficiently.
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The Automated Clearing House (ACH) is a centralized electronic network used for financial transactions, allowing for the efficient transfer of funds between banks in the United States. It is a crucial component of the financial infrastructure, facilitating direct deposits, bill payments, and other automated money transfers securely and reliably.
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Data chunking is a process of breaking down large datasets into smaller, more manageable pieces to optimize processing and improve performance. It is widely used in data storage, transmission, and processing to enhance efficiency and ensure scalability in handling big data.
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The lot system is a method of organizing and managing inventory or production by grouping items into batches or 'lots' for processing, tracking, and quality control. It is commonly used in manufacturing, supply chain management, and agriculture to improve efficiency, traceability, and compliance with regulations.
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Data parallelism is a technique in computing where a dataset is divided into smaller chunks, and computations on these chunks are executed simultaneously across multiple processors to expedite processing time. It is commonly used in parallel computing environments to optimize performance, especially in tasks like machine learning model training and large-scale data processing.
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JDBC, or Java Database Connectivity, is a Java-based API that allows Java applications to interact with various databases in a standardized way. It provides methods for querying and updating data in a database, making it a crucial tool for database-driven applications in Java.
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Model training is the process of teaching a machine learning algorithm to make predictions or decisions based on data. It involves optimizing the model's parameters to minimize error and improve accuracy using a training dataset.
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Time-sharing is a computing technique that allows multiple users to access a computer system concurrently by rapidly switching between them, maximizing the system's efficiency and resource utilization. It laid the groundwork for modern multi-user and multitasking operating systems, enabling more interactive and cost-effective computing environments.
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