Springer High-Performance In-Memory Genome Data Analysis: How In-Memory Database Technology Accelerates Personalized Medicine (In-Memory Data Management Research)

Recent achievements in hardware and software developments have enabled the introduction of a revolutionary technology: in-memory data management. This technology supports the flexible and extremely fast analysis of massive amounts of data, such as diagnoses, therapies, and human genome data. This book shares the latest research results of applying in-memory data management to personalized medicine, changing it from computational possibility to clinical reality. The authors provide details on innovative approaches to enabling the processing, combination, and analysis of relevant data in real-time. The book bridges the gap between medical experts, such as physicians, clinicians, and biological researchers, and technology experts, such as software developers, database specialists, and statisticians. Topics covered in this book include - amongst others - modeling of genome data processing and analysis pipelines, high-throughput data processing, exchange of sensitive data and protection of intellectual property. Beyond that, it shares insights on research prototypes for the analysis of patient cohorts, topology analysis of biological pathways, and combined search in structured and unstructured medical data, and outlines completely new processes that have now become possible due to interactive data analyses.  From the Back Cover  Recent achievements in hardware and software developments have enabled the introduction of a revolutionary technology: in-memory data management. This technology supports the flexible and extremely fast analysis of massive amounts of data, such as diagnoses, therapies, and human genome data. This book shares the latest research results of applying in-memory data management to personalized medicine, changing it from computational possibility to clinical reality. The authors provide details on innovative approaches to enabling the processing, combination, and analysis of relevant data in real-time. The book bridges the gap between medical experts, such as physicians, clinicians, and biological researchers, and technology experts, such as software developers, database specialists, and statisticians. Topics covered in this book include - amongst others - modeling of genome data processing and analysis pipelines, high-throughput data processing, exchange of sensitive data and protection of intellectual property. Beyond that, it shares insights on research prototypes for the analysis of patient cohorts, topology analysis of biological pathways, and combined search in structured and unstructured medical data, and outlines completely new processes that have now become possible due to interactive data analyses. About the Author Prof. Dr. h.c. Hasso Plattner is the chair of the "Enterprise Platform and Integration Concepts" research group at the Hasso Plattner Institute (HPI), which focuses mainly on in-memory data management for enterprise architectures. He is author and co-author of over 50 scientific publications and has served conferences, such as ACM SIGMOD, as keynote speaker. He is author of "In-Memory Data Management" and currently serves as a visiting professor at the Stanford University Institute of Design. Hasso Plattner is also a co-founder of SAP AG, where he served as CEO until 2003 and has since been chairman of the supervisory board. SAP AG today is the leading vendor of enterprise software solutions. In his role as chief software advisor, he concentrates on defining the mid- and long-term technology strategy and direction of SAP. Dr. Matthieu-P. Schapranow is the principal investigator of life sciences at the chair of Prof. Plattner at Hasso Plattner Institute. He holds a PhD, MSc and BSc in Software Engineering. In this position, he is responsible for research efforts applying in-memory technology to scientific areas in life sciences. In addition, he is a valued member of the Berlin Cancer Society.