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"Bioinformatics: Concepts, Methodologies, Tools, and Applications highlights the area of bioinformatics and its impact over the medical community with its innovations that change how we recognize and care for illnesses"--Provided by publisher.
Although AI has incredible potential, it has three weak links: 1. Blackbox, lack of explainability2. Silos, slews of siloed systems across the AI ecosystem3. Low-performance, most of ML/DL based AI systems are SLOW.Fixing these problems will pave the road to strong and effective AI. Graph databases, particularly high-performance graph database or graph computing, should allow this to happen.The Essential Criteria of Graph Databases simply broadens the horizon of graph applications. The book collects several truly innovative graph applications in asset-liability and liquidity risk management, which hopefully will spark readers' interest in further broaden the reach and applicable domains of graph systems. - Presents updates on the essential criteria of graph database(s) and how they are quite different from traditional relational database or other types of NoSQL DBMS or any of those big-data frameworks (i.e., Hadoop, Spark, etc.) - Clearly points out the key criteria that readers should pay attention to - Teaches users how to avoid common mistakes and how to get hands-on with system architecture design, benchmarking or selection of an appropriate graph platform/vendor-system
The rise of intelligence and computation within technology has created an eruption of potential applications in numerous professional industries. Techniques such as data analysis, cloud computing, machine learning, and others have altered the traditional processes of various disciplines including healthcare, economics, transportation, and politics. Information technology in today’s world is beginning to uncover opportunities for experts in these fields that they are not yet aware of. The exposure of specific instances in which these devices are being implemented will assist other specialists in how to successfully utilize these transformative tools with the appropriate amount of discretion, safety, and awareness. Considering the level of diverse uses and practices throughout the globe, the fifth edition of the Encyclopedia of Information Science and Technology series continues the enduring legacy set forth by its predecessors as a premier reference that contributes the most cutting-edge concepts and methodologies to the research community. The Encyclopedia of Information Science and Technology, Fifth Edition is a three-volume set that includes 136 original and previously unpublished research chapters that present multidisciplinary research and expert insights into new methods and processes for understanding modern technological tools and their applications as well as emerging theories and ethical controversies surrounding the field of information science. Highlighting a wide range of topics such as natural language processing, decision support systems, and electronic government, this book offers strategies for implementing smart devices and analytics into various professional disciplines. The techniques discussed in this publication are ideal for IT professionals, developers, computer scientists, practitioners, managers, policymakers, engineers, data analysts, and programmers seeking to understand the latest developments within this field and who are looking to apply new tools and policies in their practice. Additionally, academicians, researchers, and students in fields that include but are not limited to software engineering, cybersecurity, information technology, media and communications, urban planning, computer science, healthcare, economics, environmental science, data management, and political science will benefit from the extensive knowledge compiled within this publication.
This book covers cutting-edge and advanced research on data processing techniques and applications for Cyber-Physical Systems. Gathering the proceedings of the International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019), held in Shanghai, China on November 15–16, 2019, it examines a wide range of topics, including: distributed processing for sensor data in CPS networks; approximate reasoning and pattern recognition for CPS networks; data platforms for efficient integration with CPS networks; and data security and privacy in CPS networks. Outlining promising future research directions, the book offers a valuable resource for students, researchers and professionals alike, while also providing a useful reference guide for newcomers to the field.
This book presents a comprehensive overview of fundamental issues and recent advances in graph data management. Its aim is to provide beginning researchers in the area of graph data management, or in fields that require graph data management, an overview of the latest developments in this area, both in applied and in fundamental subdomains. The topics covered range from a general introduction to graph data management, to more specialized topics like graph visualization, flexible queries of graph data, parallel processing, and benchmarking. The book will help researchers put their work in perspective and show them which types of tools, techniques and technologies are available, which ones could best suit their needs, and where there are still open issues and future research directions. The chapters are contributed by leading experts in the relevant areas, presenting a coherent overview of the state of the art in the field. Readers should have a basic knowledge of data management techniques as they are taught in computer science MSc programs.
Poor data quality can seriously hinder or damage the efficiency and effectiveness of organizations and businesses. The growing awareness of such repercussions has led to major public initiatives like the "Data Quality Act" in the USA and the "European 2003/98" directive of the European Parliament. Batini and Scannapieco present a comprehensive and systematic introduction to the wide set of issues related to data quality. They start with a detailed description of different data quality dimensions, like accuracy, completeness, and consistency, and their importance in different types of data, like federated data, web data, or time-dependent data, and in different data categories classified according to frequency of change, like stable, long-term, and frequently changing data. The book's extensive description of techniques and methodologies from core data quality research as well as from related fields like data mining, probability theory, statistical data analysis, and machine learning gives an excellent overview of the current state of the art. The presentation is completed by a short description and critical comparison of tools and practical methodologies, which will help readers to resolve their own quality problems. This book is an ideal combination of the soundness of theoretical foundations and the applicability of practical approaches. It is ideally suited for everyone – researchers, students, or professionals – interested in a comprehensive overview of data quality issues. In addition, it will serve as the basis for an introductory course or for self-study on this topic.
TheexplosivegrowthoftheInternetandtheWebhavecreatedanever-growing demand for information systems, and ever-growing challenges for Information Systems Engineering. The series of Conferences on Advanced Information S- tems Engineering (CAiSE) was launched in Scandinavia by Janis Bubenko and Arne Solvberg in 1989, became an important European conference, and was held annually in major European sites throughout the 1990s. Now, in its 14th year,CAiSEwasheldforthe?rsttimeoutsideEurope,showcasinginternational researchon information systems and their engineering. Not surprisingly, this year the conference enjoyed unprecedented attention. In total, the conference received 173 paper submissions, the highest number ever for a CAiSE conference. Of those, 42 were accepted as regular papers and 26 as short (poster) papers. In addition, the conference received 12 proposals for workshops of which 8 were approved, while 4 tutorials were selected from 15 submissions. The technical program was put together by an international committee of 81 experts. In total, 505 reviews were submitted, with every member of the committeecontributing.Decisionsonallsubmissionswerereachedataprogram committee meeting in Toronto on January 26-27,2002. Workshop and tutorial proposals were handled separately by committees chaired by Patrick Martin (workshops), and Jarek Gryz and Richard Paige (tutorials). We wish to extend a great “THANK YOU!” to all members of the program and organizing committees for their volunteer contributions of time and exp- tise. The fact that so many busy (and famous!) people took the trouble to help uswiththeorganizationofthisconferenceandtheformationofitstechnicalp- gram speaks well for the future of CAiSE and the ?eld of Information Systems Engineering.
At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs. Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You'll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core concepts, you'll explore three end-to-end projects that illustrate architectures, best design practices, optimization approaches, and common pitfalls. Key Features · The lifecycle of a machine learning project · Three end-to-end applications · Graphs in big data platforms · Data source modeling · Natural language processing, recommendations, and relevant search · Optimization methods Readers comfortable with machine learning basics. About the technology By organizing and analyzing your data as graphs, your applications work more fluidly with graph-centric algorithms like nearest neighbor or page rank where it's important to quickly identify and exploit relevant relationships. Modern graph data stores, like Neo4j or Amazon Neptune, are readily available tools that support graph-powered machine learning. Alessandro Negro is a Chief Scientist at GraphAware. With extensive experience in software development, software architecture, and data management, he has been a speaker at many conferences, such as Java One, Oracle Open World, and Graph Connect. He holds a Ph.D. in Computer Science and has authored several publications on graph-based machine learning.
This book constitutes the refereed proceedings of the 14th International Conference entitled Beyond Databases, Architectures and Structures, BDAS 2018, held in Poznań, Poland, in September 2018, during the IFIP World Computer Congress. It consists of 38 carefully reviewed papers selected from 102 submissions. The papers are organized in topical sections, namely big data and cloud computing; architectures, structures and algorithms for efficient data processing; artificial intelligence, data mining and knowledge discovery; text mining, natural language processing, ontologies and semantic web; image analysis and multimedia mining.