The Fourth International Working Conference on Statistical and Scientific Data Base Management (IV SSDBM) held on June 21-23, 1988 in Rome, Italy, continued the series of conferences initiated in California in December 1981. The purpose of this conference was to bring together database researchers, users and system builders, working in this specific field, to discuss the particular points of interest, to propose new solutions to the problems of the domain and to expand the topics of the previous conferences, both from the theoretical and from the applicational point of view. The papers of four scientific sessions dealt with the following topics: knowledge base and expert system, data model, natural language processing, query language, time performance, user interface, heterogeneous data classification, storage constraints, automatic drawing, ranges and trackers, and arithmetic coding. Two other special sessions presented work on progress papers on geographical data modelling, spatial database queries, user interface in an Object Oriented SDB, interpretation of queries, graphical query language and knowledge browsing front ends. The conference also had three invited papers on topics of particular interest such as "Temporal Data", "Statistical Data Management Requirements" and "Knowledge Based Decision Support Systems", included in this volume. The introductory paper by M. Rafanelli provides both an introduction to the general concepts helpful to people outside the field and a survey of all the papers in these Proceedings. Furthermore, there were three open panels. Papers by the chairmen, contributions of the panelists and a summary of the respective discussions are included in this volume, too.

The purpose of the Fifth International Conference on Statistical and Scientific Databases was to bring together database researchers, users, and system builders, to discuss the particular issues of interest and to propose new solutions to the problems of the area, both from the theoretical and from the application point of view. This proceedings volume contains three invited papers as well as the other 13 contributions. The papers cover a wide area of research: object oriented database systems, semantic modelling, deductive mathematical databases, security of statistical databases, implementational issues for scientific databases, temporal summary table management, graphical and visual interfaces, query optimization, distributed databases, and economic and geographical databases.

An All-in-One Resource for Using SAS and R to Carry out Common TasksProvides a path between languages that is easier than reading complete documentationSAS and R: Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in both SAS and R, without having to navigate through the extensive, id

A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data. Researchers in all disciplines are faced with the challenge of managing the growing amounts of digital data that are the foundation of their research. Kristin Briney offers practical advice and clearly explains policies and principles, in an accessible and in-depth text that will allow researchers to understand and achieve the goal of better research data management. Data Management for Researchers includes sections on: * The data problem – an introduction to the growing importance and challenges of using digital data in research. Covers both the inherent problems with managing digital information, as well as how the research landscape is changing to give more value to research datasets and code. * The data lifecycle – a framework for data’s place within the research process and how data’s role is changing. Greater emphasis on data sharing and data reuse will not only change the way we conduct research but also how we manage research data. * Planning for data management – covers the many aspects of data management and how to put them together in a data management plan. This section also includes sample data management plans. * Documenting your data – an often overlooked part of the data management process, but one that is critical to good management; data without documentation are frequently unusable. * Organizing your data – explains how to keep your data in order using organizational systems and file naming conventions. This section also covers using a database to organize and analyze content. * Improving data analysis – covers managing information through the analysis process. This section starts by comparing the management of raw and analyzed data and then describes ways to make analysis easier, such as spreadsheet best practices. It also examines practices for research code, including version control systems. * Managing secure and private data – many researchers are dealing with data that require extra security. This section outlines what data falls into this category and some of the policies that apply, before addressing the best practices for keeping data secure. * Short-term storage – deals with the practical matters of storage and backup and covers the many options available. This section also goes through the best practices to insure that data are not lost. * Preserving and archiving your data – digital data can have a long life if properly cared for. This section covers managing data in the long term including choosing good file formats and media, as well as determining who will manage the data after the end of the project. * Sharing/publishing your data – addresses how to make data sharing across research groups easier, as well as how and why to publicly share data. This section covers intellectual property and licenses for datasets, before ending with the altmetrics that measure the impact of publicly shared data. * Reusing data – as more data are shared, it becomes possible to use outside data in your research. This chapter discusses strategies for finding datasets and lays out how to cite data once you have found it. This book is designed for active scientific researchers but it is useful for anyone who wants to get more from their data: academics, educators, professionals or anyone who teaches data management, sharing and preservation. "An excellent practical treatise on the art and practice of data management, this book is essential to any researcher, regardless of subject or discipline." —Robert Buntrock, Chemical Information Bulletin

Equips social scientists with the tools and techniques to conduct quantitative research in the age of big data.

The Data Book: Collection and Management of Research Data is the first practical book written for researchers and research team members covering how to collect and manage data for research. The book covers basic types of data and fundamentals of how data grow, move and change over time. Focusing on pre-publication data collection and handling, the text illustrates use of these key concepts to match data collection and management methods to a particular study, in essence, making good decisions about data. The first section of the book defines data, introduces fundamental types of data that bear on methodology to collect and manage them, and covers data management planning and research reproducibility. The second section covers basic principles of and options for data collection and processing emphasizing error resistance and traceability. The third section focuses on managing the data collection and processing stages of research such that quality is consistent and ultimately capable of supporting conclusions drawn from data. The final section of the book covers principles of data security, sharing, and archival. This book will help graduate students and researchers systematically identify and implement appropriate data collection and handling methods.

This book constitutes the refereed proceedings of the 21st International Conference on Scientific and Statistical Database Management, SSDBM 2009, held in New Orleans, LA, USA in June 2009. The 29 revised full papers and 12 revised short papers including poster and demo papers presented together with three invited presentations were carefully reviewed and selected from 76 submissions. The papers are organized in topical sections on improving the end-user experience, indexing, physical design, and energy, application experience, workflow, query processing, similarity search, mining, as well as spatial data.

Thirty years after RSA was first publicized, it remains an active research area. Although several good surveys exist, they are either slightly outdated or only focus on one type of attack. Offering an updated look at this field, Cryptanalysis of RSA and Its Variants presents the best known mathematical attacks on RSA and its main variants, includin

Statistical Analysis of Management Data provides a comprehensive approach to multivariate statistical analyses that are important for researchers in all fields of management, including finance, production, accounting, marketing, strategy, technology, and human resources. This book is especially designed to provide doctoral students with a theoretical knowledge of the concepts underlying the most important multivariate techniques and an overview of actual applications. It offers a clear, succinct exposition of each technique with emphasis on when each technique is appropriate and how to use it. This second edition, fully revised, updated, and expanded, reflects the most current evolution in the methods for data analysis in management and the social sciences. In particular, it places a greater emphasis on measurement models, and includes new chapters and sections on: confirmatory factor analysis canonical correlation analysis cluster analysis analysis of covariance structure multi-group confirmatory factor analysis and analysis of covariance structures. Featuring numerous examples, the book may serve as an advanced text or as a resource for applied researchers in industry who want to understand the foundations of the methods and to learn how they can be applied using widely available statistical software.

This book constitutes the refereed proceedings of the 23rd International Conference on Scientific and Statistical Database Management, SSDBM 2011, held in Portland, OR, USA, in July 2011. The 26 long and 12 short papers presented together with 15 posters were carefully reviewed and selected from 80 submissions. The topics covered are ranked search; temporal data and queries; workflow and provenance; querying graphs; clustering and data mining; architectures and privacy; and applications and models.