Data matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. Based on research in various domains including applied statistics, health informatics, data mining, machine learning, artificial intelligence, database management, and digital libraries, significant advances have been achieved over the last decade in all aspects of the data matching process, especially on how to improve the accuracy of data matching, and its scalability to large databases. Peter Christen´s book is divided into three parts: Part I, ´´Overview´´, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, ´´Steps of the Data Matching Process´´, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, ´´Further Topics´´, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today. By providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.
Data Scientisten (m/w) sind derzeit auf dem Jobmarkt heißbegehrt. In Amerika sind erfahrene Data Scientisten so beliebt wie eine Getränkebude in der Wüste. Aber auch in Deutschland ist eine steigende Nachfrage nach diesem Skillprofil erkennbar. Immer mehr Unternehmen bauen ´´Analytics´´-Abteilungen auf bzw. aus und suchen entsprechende Mitarbeiter. Nur: was macht eigentlich ein Data Scientist? Irgendetwas mit künstlicher Intelligenz, Machine Learning, Data-Mining, Python-Programmierung und Big Data. So genau weiß es eigentlich niemand ... Das Buch ist eine Einführung und Übersicht über das weitumfassende Themengebiet Data Science. Es werden die Datenquellen (Datenbanken, Data-Warehouse, Hadoop etc.) und die Softwareprodukte für die Datenanalyse vorgestellt (Data-Science-Plattformen, ML Bibliotheken). Die wichtigsten Verfahren des Machine Learnings werden ebenso behandelt wie beispielhafte Anwendungsfälle aus verschiedenen Branchen.
´´Data Mining Techniques for Protein Sequence Analysis´´ describes habitually about various techniques in data mining which are here united in one edifying textbook. These topics recline at the heart of many areas of data mining, machine learning and bioinformatics. This textbook introduces the assessment of the protein sequence in all aspects. A toolbox of inference techniques includes experimentally tested for classification and clustering algorithms. The result is a textbook on information regarding about the protein sequences, and data mining algorithms for a new generation of students, and a consummate entry point into these subjects for professionals in areas related to bioinformatics and data mining.
In Classification, Model Selection is one of the critical issues as different models from different categories are available. To select the best model for any given data set is a challenging task. Meta Learning automates this task by acquiring knowledge from the past experience and stores this knowledge into database called Meta Knowledge Base. When new data set comes, stored knowledge can be used for proving ranking of the candidate algorithms. But one of the problems with Meta Learning is generation of Meta Examples as large number of candidate algorithms and data sets are available. To reduce the generation of Meta Examples into Meta Knowledge Base, Active Meta Learning can be used that reduces generation of Meta Examples and at the same time maintaining the performance of candidate algorithms. In this book, Ranking is provided using Active Meta Learning approach by considering Data set Characteristics.
The booming growth of the World Wide Web has made more and more information available digitally at unprecedented rates and levels of popularity. Also, the Web itself can be considered unprecedented in the almost complete lack of coordination in its creation and in the diversity of backgrounds and motives of its participants. Each of these contributes in making exploratory data analysis hard. In particular, we will focus on one of the steps in exploratory data analysis that is the clustering phase. Clustering is the unsupervised classification of patterns into groups (clusters). In this book, we provide useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We describe three important applications of clustering algorithms in Information Retrieval: (1) Similarity Search for High Dimensional Data Points, with the purpose to find Near Duplicate Images; (2) Measuring Latent Variable in Social Sciences, with the aim to visualize Research Communities; and (3) Generative Model for Content Analysis of Natural Language Documents to detect Events.
With significant growth of bio-molecular sequence data in the last decade the need for algorithms to extract patterns and meaningful information from such data has been felt strongly. Alignment of sequences, in order to determine regions of common descent, has also been an important area of research as it helps scientist discover the evolution of species. Another problem that researchers are putting in a lot of effort into, is document summary. As the lower bound for computation is being met for various algorithms, to further expedite the computing on large data sets, parallelization has become imperative. New multiprocessor architectures like the Cell Broadband Engine have the potential to do extensive calculations and act as mini-supercomputers. Other applications for these include onboard aircraft fault diagnosis and prognosis. We take a peek into some existing algorithms for these problems as well as propose novel algorithms along with their implementations to address these problems in the field of bioinformatics.
This book covers the proceedings from the 2016 International Symposium on Chaos, Complexity and Leadership, and reflects current research results of chaos and complexity studies and their applications in various fields. Included are research papers in the fields of applied nonlinear methods, modeling of data and simulations, as well as theoretical achievements of chaos and complex systems. Also discussed are leadership and management applications of chaos and complexity theory.
Discover the Django web application framework and get started building Python-based web applications. This book takes you from the basics of Django all the way through to cutting-edge topics such as creating RESTful applications. Beginning Django also covers ancillary, but essential, development topics, including configuration settings, static resource management, logging, debugging, and email. Along with material on data access with SQL queries, you´ll have all you need to get up and running with Django 1.11 LTS, which is compatible with Python 2 and Python 3. Once you´ve built your web application, you´ll need to be the admin, so the next part of the book covers how to enforce permission management with users and groups. This technique allows you to restrict access to URLs and content, giving you total control of your data. In addition, you´ll work with and customize the Django admin site, which provides access to a Django project´s data. After reading and using this book, you´ll be able to build a Django application top to bottom and be ready to move on to more advanced or complex Django application development. What You´ll Learn Get started with the Django framework Use Django views, class-based views, URLs, middleware, forms, templates, and Jinja templates Take advantage of Django models, including model relationships, migrations, queries, and forms Leverage the Django admin site to get access to the database used by a Django project Deploy Django REST services to serve as the data backbone for mobile, IoT, and SaaS systems Who This Book Is For Python developers new to the Django web application development framework and web developers new to Python and Django.
During task composition, such as can be found in distributed query processing, workflow systems and AI planning, decisions have to be made by the system and possibly by users with respect to how a given problem should be solved. Although there is often more than one correct way of solving a given problem, these multiple solutions do not necessarily lead to the same result. Some researchers are addressing this problem by providing data provenance information. Others use expert advice encoded in a supporting knowledge-base. However, users do not usually trust complete automation during decision-making for certain domains with natural variation, like biology; they need a way to be able to control and/or intervene with the system´s reasoning to verify parts of the process. This book provides a thorough analysis of the problem and presents a data-centric methodology of measuring decision criticality and describe its potential use. We argue that agent technology is a natural fit for the design of distributed heterogeneous integration systems, particularly in bioinformatics, and we propose a multi-agent system design and architecture as the basis of our framework.
The Manga Guide to Cryptography breaks down how ciphers work, what makes them secure or insecure, and how to decode them. Comic illustrations make it easy to learn about classic substitution, polyalphabetic, and transposition ciphers; symmetric-key algorithms like block and DES (Data Encryption Standard) ciphers; how to use public key encryption technology to generate public/private keys and cryptograms; practical applications of encryption such as digital signatures, identity fraud countermeasures, and ´man in the middle´ attack countermeasures.