<p>Entity resolution is an essential tool in processing and analyzing data in order to draw precise conclusions from the information being presented. Further research in entity resolution is necessary to help promote information quality and improved data reporting in multidisciplinary fields requiring accurate data representation.</p> <p><b>Innovative Techniques and Applications of Entity Resolution</b> draws upon interdisciplinary research on tools, techniques, and applications of entity resolution. This research work provides a detailed analysis of entity resolution applied to various types of data as well as appropriate techniques and applications and is appropriately designed for students, researchers, information professionals, and system developers.</p>

Innovative Techniques and Applications of Entity Resolution

Computers & Technology

Wang, Hongzhi

Databases & Big Data

Information Science Reference

Flexible and Wearable Electronics for Smart Clothing

Engineering & Transportation

WANG, GANG

Hou, Chengyi

Engineering

Wiley-VCH

<p><div>This book addresses several knowledge discovery problems on multi-sourced data where the theories, techniques, and methods in data cleaning, data mining, and natural language processing are synthetically used. This book mainly focuses on three data models: the multi-sourced isomorphic data, the multi-sourced heterogeneous data, and the text data. On the basis of three data models, this book studies the knowledge discovery problems including truth discovery and fact discovery on multi-sourced data from four important properties: relevance, inconsistency, sparseness, and heterogeneity, which is useful for specialists as well as graduate students.</div> <div>&nbsp;</div> <div>Data, even describing the same object or event, can come from a variety of sources such as crowd workers and social media users. However, noisy pieces of data or information are unavoidable. Facing the daunting scale of data, it is unrealistic to expect humans to "label" or tell which data source is more reliable. Hence, it is crucial to identify trustworthy information from multiple noisy information sources, referring to the task of knowledge discovery.</div> <div>&nbsp;</div> <div>At present, the knowledge discovery research for multi-sourced data mainly faces two challenges. On the structural level, it is essential to consider the different characteristics of data composition and application scenarios and define the knowledge discovery problem on different occasions. On the algorithm level, the knowledge discovery task needs to consider different levels of information conflicts and design efficient algorithms to mine more valuable information using multiple clues. Existing knowledge discovery methods have defects on both the structural level and the algorithm level, making the knowledge discovery problem far from totally solved.</div></p>

Knowledge Discovery from Multi-Sourced Data (SpringerBriefs in Computer Science)

Ye, Chen

Dai, Guojun

Computer Science

Springer

<p><p>In both the database and machine learning communities, data quality has become a serious issue which cannot be ignored. In this context, we refer to data with quality problems as "dirty data." Clearly, for a given data mining or machine learning task, dirty data in both training and test datasets can affect the accuracy of results. Accordingly, this book analyzes the impacts of dirty data and explores effective methods for dirty data processing.</p> <p>Although existing data cleaning methods improve data quality dramatically, the cleaning costs are still high. If we knew how dirty data affected the accuracy of machine learning models, we could clean data selectively according to the accuracy requirements instead of cleaning all dirty data, which entails substantial costs. However, no book to date has studied the impacts of dirty data on machine learning models in terms of data quality. Filling precisely this gap, the book is intended for a broad audience ranging from researchers in the database and machine learning communities to industry practitioners.</p> <p>Readers will find valuable takeaway suggestions on: model selection and data cleaning; incomplete data classification with view-based decision trees; density-based clustering for incomplete data; the feature selection method, which reduces the time costs and guarantees the accuracy of machine learning models; and cost-sensitive decision tree induction approaches under different scenarios. Further, the book opens many promising avenues for the further study of dirty data processing, such as data cleaning on demand, constructing a model to predict dirty-data impacts, and integrating data quality issues into other machine learning models. Readers will be introduced to state-of-the-art dirty data processing techniques, and the latest research advances, while also finding new inspirations in this field.</p> <br /></p>

Dirty Data Processing for Machine Learning

Qi, Zhixin

Dong, Zejiao

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