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      <div class="kicker"><span></span> BOOKS / SPRINGER</div>
      <h1>Featured Book</h1>

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          <div class="kicker"><span></span> SYNTHESIS LECTURES ON DATA MANAGEMENT · SPRINGER</div>
          <h3 style="margin-top:14px"><a href="https://link.springer.com/book/10.1007/978-3-031-01872-5" target="_blank" rel="noreferrer">Data-Intensive Workflow Management</a></h3>
          <p class="book-authors">Daniel de Oliveira, Ji Liu and Esther Pacitti</p>
          <p>I would like to highlight the book <strong><a href="https://link.springer.com/book/10.1007/978-3-031-01872-5" target="_blank">Data-Intensive Workflow Management</a></strong>, co-authored by <strong>Daniel de Oliveira, Ji Liu, and Esther Pacitti</strong>. This work provides a thorough exploration of managing data-intensive workflows in scientific research, focusing on reproducibility, transparency, and efficiency.</p>
          <div class="kicker" style="margin-top:28px">KEY TOPICS COVERED</div>
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          <li>Workflow management principles and best practices</li>
          <li>Workflow Execution in a Single-Site Cloud</li>
          <li>Workflow Execution in a Multi-Site Cloud</li>
          <li>Real-world case studies in bioinformatics, astronomy, and engineering</li>
          <li>Workflow Execution in DISC Environments</li>
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          <p>This book is a valuable resource for researchers, data scientists, and professionals interested in practical and theoretical insights on scientific workflow management.</p>
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