Forthcoming titles in the ACM Books Series are subject to change and will be published as they become available, with 25 titles to be published in Collection I and II. Upon publication, each of the following books will appear in the ACM Digital Library and be accessible to those with full-text access in both PDF and ePub formats. Individual titles will be made available for purchase at Morgan & Claypool and also available at Amazon and Barnes & Noble. Please click on the title name below for more information about each title.
Structural bioinformatics is the field related to the development and application of computational models for the prediction and analysis of macromolecular structures. The unique nature of protein and nucleotide structures has presented many computational challenges over the last three decades. The fast accumulation of data, in addition to the rapid increase in computational power, presents a unique set of challenges and opportunities in the analysis, comparison, modeling, and prediction of macromolecular structures and interactions.
The book is intended as a user's guide for key algorithms to solve problems related to macromolecular structure, with emphasis on protein structure, function and dynamics. It can be used as a textbook for a one-semester graduate course in algorithms in bioinformatics.
It is hard to imagine, but as recently as 1968, computer scientists were uncertain how best to interconnect even two computers. The notion that within a few decades the challenge would be how to interconnect millions of computers around the globe was too farfetched to contemplate. Yet, by 1988, that is precisely what was happening. The products and devices developed in the intervening years—such as modems, multiplexers, local area networks, and routers—became the linchpins of the global digital society. How did such revolutionary innovation occur? This book tells the story of the entrepreneurs who were able to harness and join two factors: the energy of computer science researchers supported by governments and universities, and the tremendous commercial demand for internetworking computers. The centerpiece of this history comes from unpublished interviews from the late 1980s with over 80 computing industry pioneers, including Paul Baran, J.C.R. Licklider, Vint Cerf, Robert Kahn, Larry Roberts, and Robert Metcalfe. These individuals give us unique insights into the creation of multi-billion dollar markets for computer-communications equipment, and they reveal how entrepreneurs struggled with failure, uncertainty, and the limits of knowledge.
Code Nation is a popular history of programming and software culture from the first years of personal computing in the 1970s to the early commercial infrastructure of the World Wide Web. This illustration-rich book offers profiles of ACM members and luminaries who have had an important influence on programming practices, as well as the formative experiences of students, power users, and tinkerers who learned to code on early PCs and built captivating games and applications.
Central to this history is the learn to program movement, an educational agenda that germinated in government labs, gained momentum through business and counterculture experiments, and became a broad-based computer literacy movement in the 1970s and 80s.
Despite conflicts about languages, operating systems, and professional practices, the number of active programmers in America jumped from tens of thousands in the late 1950s to tens of millions by the early 1990s. This surge created a groundswell of popular support for programming culture, resulting in a “Code Nation”—a globally-connected society saturated with computer software and enchanted by its use.
Database replication is widely used for fault-tolerance, scalability, and performance. The failure of one database replica does not stop the system from working as available replicas can take over the tasks of the failed replica. Scalability can be achieved by distributing the load across all replicas, and adding new replicas should the load increase. Finally, database replication can provide fast local access, even if clients are geographically distributed clients, if data copies are located close to clients. Despite its advantages, replication is not a straightforward technique to apply, and there are many hurdles to overcome. At the forefront is replica control: assuring that data copies remain consistent when updates occur. There exist many alternatives in regard to where updates can occur and when changes are propagated to data copies, how changes are applied, where the replication tool is located, etc. A particular challenge is to combine replica control with transaction management as it requires several operations to be treated as a single logical unit, and it provides atomicity, consistency, isolation and durability across the replicated system.
This book provides a categorization of replica control mechanisms, presents several replica and concurrency control mechanisms in detail, and discusses many of the issues that arise when such solutions need to be implemented within or on top of relational database systems. Furthermore, the book presents the tasks that are needed to build a fault-tolerant replication solution, provides an overview of load-balancing strategies that allow load to be equally distributed across all replicas, and introduces the concept of self-provisioning that allows the replicated system to dynamically decide on the number of replicas that are needed to handle the current load. As performance evaluation is a crucial aspect when developing a replication tool, the book presents an analytical model of the scalability potential of various replication solution.
Martin Hellman and Whitfield Diffie won the 2015 Turing Award for the development of public-key cryptography. This book provides original biographies of the award winners and describes the historical and political context and impact of their research, including its influence on the development of internet security, theoretical computer science, and national security. It also summarizes and compiles key documents, including the original research articles that led to the Turing Award, interviews with Hellman and Diffie, and the Turing Award lectures.
In this book we aim to show how data mining and machine learning techniques are used in the context of event mining. We review event recognition and event discovery in different applications and cover recent developments in event mining such as techniques for temporal pattern mining, temporal data classification and clustering. We also introduce EventMiner as a comprehensive knowledge-based event mining framework for analyzing heterogeneous big data.
The book will be useful for both practitioners and researchers working in different computer science fields. Data miners/scientists and data analysts can benefit from high-performance event mining techniques introduced in this book. Also, The book is accessible to many readers and not necessarily just those with strong backgrounds in computer science. Public health professionals, epidemiologists, physicians, and social scientists can benefit from the new perspective of this book in harnessing the value of heterogeneous big data for building diverse real-life applications.
ACM Books is pleased to announce the signing of a new book in our Turing Award series, Foundations of Computation and Machine Learning: The Work of Leslie Valiant, edited by Rocco Servedio of Columbia University.
Valiant received the prestigious ACM Turing Award in 2010 for 2010 "For transformative contributions to the theory of computation, including the theory of probably approximately correct (PAC) learning, the complexity of enumeration and of algebraic computation, and the theory of parallel and distributed computing."
The book will feature a short biography of Valiant, as well as analysis of his seminal works by today's leading computer scientists.
Intelligent Computing for Interactive System Design provides a comprehensive resource on what has become the dominant paradigm in designing novel interaction methods, involving gestures, speech, text, touch and brain-controlled interaction, embedded in innovative and emerging human-computer interfaces. These interfaces support ubiquitous interaction with applications and services running on smartphones, wearables, in-vehicle systems, virtual and augmented reality, robotic systems, the Internet of Things (IoT), and many other domains that are now highly competitive, both in commercial and in research contexts.
This book presents the crucial theoretical foundations needed by any student, researcher or practitioner working on novel interface design, with chapters on statistical methods, digital signal processing (DSP) and machine learning (ML). These foundations are followed by chapters that discuss case studies on smart cities, brain computer interfaces, probabilistic mobile text entry, secure gestures, personal context from mobile phones, adaptive touch interfaces and automotive user interfaces. The case studies chapters also highlight an in-depth look at the practical application of DSP and ML methods used for the processing of touch, gesture, biometric or embedded sensor inputs. A common theme throughout the case studies is ubiquitous support for humans in their daily professional or personal activities.
In addition, the book provides walk-through examples of different DSP and ML techniques and their use in interactive systems. Common terms are defined, and information on practical resources is provided (e.g., software tools, data resources) for hands-on project work to develop and evaluate multimodal and multi-sensor systems. In a series of in-chapter commentary boxes, an expert on the legal and ethical issues explores the emergent deep concerns of the professional community, on how DSP and ML should be adopted and used in a socially appropriate ways, to most effectively advance human performance during ubiquitous interaction with omnipresent computers.
This carefully edited collection is written by international experts and pioneers in the field of DSP and ML. It provides a textbook for students, and a reference and technology roadmap for developers and professionals working in interaction design on emerging platforms.
Online advertising has grown from almost nothing at the end of last century to an annual spend of over 200B dollars globally. Today, online advertising garners the most advertising dollars of any advertising channel including TV. Online advertising is computational advertising since most the decisions of which ads to show to a user in a given context are determined by algorithms. Indeed, computational advertising was one of the first big data applications. For this reason, the problems behind computational advertising have driven research into large-scale machine learning and algorithmic game theory and is responsible for many advances in those areas as well as in parallel computing architectures. This book covers the current state of the art of computational advertising. That includes the economics of online advertising, understanding and modeling consumer behavior, matching ads and consumers, user response prediction and measurement of ad effectiveness. We also cover ad allocation, campaign management and optimization, as well as fraud and privacy issues. Today, computational advertising intersects computer science, economics marketing and psychology. Hence, after 20 years of advances in this field we hope this book fills the needs of researchers, practitioners and graduate students who want to understand the state of the art in this multidisciplinary area.
Stephen A. Cook was awarded the ACM Turing Award in 1982, in recognition of "his advancement of our understanding of the complexity of computation in a significant and profound way." Cook's theory of NP-completeness is one of the most fundamental and enduring contributions in computer science, and has a singificant impact outside the field. This volume will present works on NP-completeness and other contributions which, while perhaps not as well known, has also had a significant impact on computing theory and practice, as well as mathematical logic. With additional material, including a biographical chapter, Professor Cook's Turing Award address, and a full bibliography of his work, the volume will provide an excellent resource for anyone wishing to understand the foundations of Cook's work as well as its ongoing significance and relevance to current research problems in computing and beyond.
Pointer analysis provides information to disambiguate indirect reads and writes of data through pointers and indirect control flow through function pointers or virtual functions. Thus it enables application of other program analyses to programs containing pointers. There is a large body of literature on pointer analysis. However, there is no material that brings out a uniform coherent theme by separating fundamental concepts from advanced techniques and tricks. This book fills this void.
The book focuses on fundamental concepts instead of trying to cover the entire breadth of the literature on pointer analysis. Bibliographic notes point the reader to relevant literature for more details.
Rather than being driven completely by pointer analysis’s practical effectiveness, the book evolves the concepts from the first principles based on the language features, brings out the interactions of different abstractions at the level of ideas, and finally, relates them to practical observations and the nature of practical programs.
Principles of Graph Data Management and Analytics is the first textbook on the subject for upper-level undergraduates, graduate students and data management professionals who are interested in the new and exciting world of graph data management and computation. The book blends together the two thinly connected disciplines – a database-minded approach to managing and querying graphs, and an analytics-driven approach to perform scalable computation on large graphs. It presents a detailed treatment of the underlying theory and algorithms, and prevalent techniques and systems; it also presents textbook use cases and real-world problems that can be solved by combining database-centric and analysis-centric approaches. The book will enable students to understand the state of the art in graph data management, to effectively program currently available graph databases and graph analytics products, and to design their own graph data analysis systems.To help this process, the book supplements its textual material with several data sets, small and large, that will be made available through the book’s website. Several free and contributed software will also be provided for readers for practice.
This book discusses the capabilities of Linked-Data and the Semantic Web modeling languages, such as RDFS (Resource Description Framework Schema) and OWL (Web Ontology Language) as well as more recent standards based on these. The book provides examples to illustrate the use of Semantic Web technologies in solving common modeling problems with many exercises and examples of the use of the techniques.
The book provides an overview of the Semantic Web and aspects of the Web and its architecture relevant to Linked Data. It then discusses semantic modeling and how it can support the development from chaotic information gathering to one characterized by information sharing, cooperation, and collaboration. It also explains the use of RDF and linked-data to implement the Semantic Web by allowing information to be distributed over the Web or over intranets, along with the use of SPARQL to access RDF data.
Moreover, the reader is introduced to components that make up a Semantic Web deployment and how they fit together, the concept of inferencing in the Semantic Web, and how RDFS differs from other schema languages. In addition, the 2015 “Linked Data Platform” standard is also explored. The book also considers the use of SKOS (Simple Knowledge Organization System) to manage vocabularies by taking advantage of the inferencing structure of RDFS-Plus. It also presents SHACL, a language for checking graph constraints in linked data systems, and a number of useful ontologies includingschema.org, the most successfully deployed Semantic Web technology to date.
This book is intended for the linked data and semantic Web practitioner looking for clues on how to add more expressivity to allow better linking and use both on the web and in the enterprise, and for the working ontologist who is trying to create a domain model on the Semantic Web.
Software history has a deep impact on current software designers, computer scientists and technologists. Decisions and design constraints made in past are often unknown or poorly understood by current students, yet modern software systems use software based on those earlier decisions and design constraints. This work looks at software history through specific software areas and extracts student-consumable practices, learnings, and trends that are useful in current and future software design. It also exposes key areas that are highly used in modern software, yet no longer taught in most computing programs. Written as a textbook, this book uses past and current specific cases to explore the impact of specific software evolution trends and impacts.
Static program analysis studies the behavior of programs under all possible inputs. It is an area with a wealth of applications, in virtually any tool that processes programs. A compiler needs static analysis in order to detect errors (e.g., undefined variables) or to optimize code (e.g., eliminate casts or devirtualize calls). A refactoring or a program understanding tool need global program analysis in order to answer useful questions such as “where could this program variable have been set?” or “which parts of the program can influence this value?'' A security analyzer needs program analysis to determine “can the arguments of this private operation ever be affected by untrusted user input?” A concurrency bug detector needs program analysis in order to tell whether a program can ever deadlock or have races. Static program analysis is practically valuable, but it is also hard. It is telling that the quintessential undecidable computing problem, the “halting problem”, is typically phrased as a program analysis question: “can there be a program that accepts another program as input and determines whether the latter always terminates?” Other program analysis problems have given rise to some of the best known techniques and algorithms in computer science (e.g., data-flow frameworks). This book offers a comprehensive treatment of the principles, concepts, techniques and applications of static program analysis, illustrated and explained. The emphasis is on understanding the tradeoffs of different kinds of static program analysis algorithms and on appreciating the main factors for critically evaluating a static analysis and its suitability for practical tasks.
User interfaces for our increasingly varied computational devices have long been oriented toward graphical screens and virtual interactors. Since the advent of mass market graphical interfaces in the mid-1980s, most human-computer interaction has been mediated by graphical buttons, sliders, text fields, and their virtual kin.
And yet, humans are profoundly physical creatures. Throughout our history (and prehistory), our bodies have profoundly shaped our activities and engagement with our world, and each other. Despite -- and perhaps also, because of -- the many successes of keyboard, pointer, touch screen, and (increasingly) speech modalities of computational interaction, many have sought alternate prospects for interaction that more deeply respect, engage, and celebrate our embodied physicality.
For several decades, tangible and embodied interaction (TEI) has been the topic of intense technological, scientific, artistic, humanistic, and mass-market research and practice. In this book, we elaborate on many dimensions of this diverse, transdisciplinary, blossoming topic.