Data warehouses consolidate various activities of a business and often form the backbone for generating reports that support important business decisions. Errors in data tend to creep in for a variety of reasons. Some of these reasons include errors during input data collection and errors while merging data collected independently across different databases. These errors in data warehouses often result in erroneous upstream reports, and could impact business decisions negatively. Therefore, one of the critical challenges while maintaining large data warehouses is that of ensuring the quality of data in the data warehouse remains high. The process of maintaining high data quality is commonly referred to as data cleaning. In this book, we first discuss the goals of data cleaning. Often, the goals of data cleaning are not well defined and could mean different solutions in different scenarios. Toward clarifying these goals, we abstract out a common set of data cleaning tasks that often need to be addressed. This abstraction allows us to develop solutions for these common data cleaning tasks. We then discuss a few popular approaches for developing such solutions. In particular, we focus on an operator-centric approach for developing a data cleaning platform. The operator-centric approach involves the development of customizable operators that could be used as building blocks for developing common solutions. This is similar to the approach of relational algebra for query processing. The basic set of operators can be put together to build complex queries. Finally, we discuss the development of custom scripts which leverage the basic data cleaning operators along with relational operators to implement effective solutions for data cleaning tasks.

The book outlines selected projects conducted under the supervision of the author. Moreover, it discusses significant relations between Interactive Granular Computing (IGrC) and numerous dynamically developing scientific domains worldwide, along with features characteristic of the author’s approach to IGrC. The results presented are a continuation and elaboration of various aspects of Wisdom Technology, initiated and developed in cooperation with Professor Andrzej Skowron. Based on the empirical findings from these projects, the author explores the following areas: (a) understanding the causes of the theory and practice gap problem (TPGP) in complex systems engineering (CSE); (b) generalizing computing models of complex adaptive systems (CAS) (in particular, natural computing models) by constructing an interactive granular computing (IGrC) model of networks of interrelated interacting complex granules (c-granules), belonging to a single agent and/or to a group of agents; (c) developing methodologies based on the IGrC model to minimize the negative consequences of the TPGP. The book introduces approaches to the above issues, using the proposed IGrC model. In particular, the IGrC model refers to the key mechanisms used to control the processes related to the implementation of CSE projects. One of the main aims was to develop a mechanism of IGrC control over computations that model a project’s implementation processes to maximize the chances of its success, while at the same time minimizing the emerging risks. In this regard, the IGrC control is usually performed by means of properly selected and enforced (among project participants) project principles. These principles constitute examples of c-granules, expressed by complex vague concepts (represented by c-granules too). The c-granules evolve with time (in particular, the meaning of the concepts is also subject of change). This methodology is illustrated using project principles applied by the author during the implementation of the POLTAX, AlgoTradix, Merix, and Excavio projects outlined in the book.

Lectures on Perception: An Ecological Perspective addresses the generic principles by which each and every kind of life form—from single celled organisms (e.g., difflugia) to multi-celled organisms (e.g., primates)—perceives the circumstances of their living so that they can behave adaptively. It focuses on the fundamental ability that relates each and every organism to its surroundings, namely, the ability to perceive things in the sense of how to get about among them and what to do, or not to do, with them. The book’s core thesis breaks from the conventional interpretation of perception as a form of abduction based on innate hypotheses and acquired knowledge, and from the historical scientific focus on the perceptual abilities of animals, most especially those abilities ascribed to humankind. Specifically, it advances the thesis of perception as a matter of laws and principles at nature’s ecological scale, and gives equal theoretical consideration to the perceptual achievements of all of the classically defined ‘kingdoms’ of organisms—Archaea, Bacteria, Protoctista, Fungi, Plantae, and Animalia.

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

In the everyday world, much of what we do as social beings is dictated by how we perceive and manage our interpersonal space. This is called proxemics. At its simplest, people naturally correlate physical distance to social distance. We believe that people’s expectations of proxemics can be exploited in interaction design to mediate their interactions with devices (phones, tablets, computers, appliances, large displays) contained within a small ubiquitous computing ecology. Just as people expect increasing engagement and intimacy as they approach others, so should they naturally expect increasing connectivity and interaction possibilities as they bring themselves and their devices in close proximity to one another. This is called Proxemic Interactions. This book concerns the design of proxemic interactions within such future proxemic-aware ecologies. It imagines a world of devices that have fine-grained knowledge of nearby people and other devices—how they move into range, their precise distance, their identity, and even their orientation—and how such knowledge can be exploited to design interaction techniques. The first part of this book concerns theory. After introducing proxemics, we operationalize proxemics for ubicomp interaction via the Proxemic Interactions framework that designers can use to mediate people’s interactions with digital devices. The framework, in part, identifies five key dimensions of proxemic measures (distance, orientation, movement, identity, and location) to consider when designing proxemic-aware ubicomp systems. The second part of this book applies this theory to practice via three case studies of proxemic-aware systems that react continuously to people’s and devices’ proxemic relationships. The case studies explore the application of proxemics in small-space ubicomp ecologies by considering first person-to-device, then device-to-device, and finally person-to-person and device-to-device proxemic relationships. We also offer a critical perspective on proxemic interactions in the form of “dark patterns,” where knowledge of proxemics may (and likely will) be easily exploited to the detriment of the user. Table of Contents: Acknowledgments / Videos / Figure Credits / Introduction / Part I / Ubicomp in Brief / Proxemic Interactions Theory / Operationalizing Proxemics for Ubicomp Interaction / Exploiting Proxemics to Address Challenges in Ubicomp Ecologies / Part II: Exploiting Proxemics in Ubicomp Ecologies / Person/Person-to-Device Proxemic Interactions / Device-to-Device Proxemic Interactions / Considering Person-to-Person and Device-to-Device Proxemics / Dark Patterns / Conclusion / References / Author Biographies