Unstructured Data Analytics
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Tapping Into Unstructured Data
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Author : William H. Inmon
language : en
Publisher: Pearson Education
Release Date : 2007-12-11
Tapping Into Unstructured Data written by William H. Inmon and has been published by Pearson Education this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-12-11 with Business & Economics categories.
The Definitive Guide to Unstructured Data Management and Analysis--From the World’s Leading Information Management Expert A wealth of invaluable information exists in unstructured textual form, but organizations have found it difficult or impossible to access and utilize it. This is changing rapidly: new approaches finally make it possible to glean useful knowledge from virtually any collection of unstructured data. William H. Inmon--the father of data warehousing--and Anthony Nesavich introduce the next data revolution: unstructured data management. Inmon and Nesavich cover all you need to know to make unstructured data work for your organization. You’ll learn how to bring it into your existing structured data environment, leverage existing analytical infrastructure, and implement textual analytic processing technologies to solve new problems and uncover new opportunities. Inmon and Nesavich introduce breakthrough techniques covered in no other book--including the powerful role of textual integration, new ways to integrate textual data into data warehouses, and new SQL techniques for reading and analyzing text. They also present five chapter-length, real-world case studies--demonstrating unstructured data at work in medical research, insurance, chemical manufacturing, contracting, and beyond. This book will be indispensable to every business and technical professional trying to make sense of a large body of unstructured text: managers, database designers, data modelers, DBAs, researchers, and end users alike. Coverage includes What unstructured data is, and how it differs from structured data First generation technology for handling unstructured data, from search engines to ECM--and its limitations Integrating text so it can be analyzed with a common, colloquial vocabulary: integration engines, ontologies, glossaries, and taxonomies Processing semistructured data: uncovering patterns, words, identifiers, and conflicts Novel processing opportunities that arise when text is freed from context Architecture and unstructured data: Data Warehousing 2.0 Building unstructured relational databases and linking them to structured data Visualizations and Self-Organizing Maps (SOMs), including Compudigm and Raptor solutions Capturing knowledge from spreadsheet data and email Implementing and managing metadata: data models, data quality, and more
Unstructured Data Analytics
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Author : Jean Paul Isson
language : en
Publisher: John Wiley & Sons
Release Date : 2018-03-02
Unstructured Data Analytics written by Jean Paul Isson and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-02 with Computers categories.
Turn unstructured data into valuable business insight Unstructured Data Analytics provides an accessible, non-technical introduction to the analysis of unstructured data. Written by global experts in the analytics space, this book presents unstructured data analysis (UDA) concepts in a practical way, highlighting the broad scope of applications across industries, companies, and business functions. The discussion covers key aspects of UDA implementation, beginning with an explanation of the data and the information it provides, then moving into a holistic framework for implementation. Case studies show how real-world companies are leveraging UDA in security and customer management, and provide clear examples of both traditional business applications and newer, more innovative practices. Roughly 80 percent of today's data is unstructured in the form of emails, chats, social media, audio, and video. These data assets contain a wealth of valuable information that can be used to great advantage, but accessing that data in a meaningful way remains a challenge for many companies. This book provides the baseline knowledge and the practical understanding companies need to put this data to work. Supported by research with several industry leaders and packed with frontline stories from leading organizations such as Google, Amazon, Spotify, LinkedIn, Pfizer Manulife, AXA, Monster Worldwide, Under Armour, the Houston Rockets, DELL, IBM, and SAS Institute, this book provide a framework for building and implementing a successful UDA center of excellence. You will learn: How to increase Customer Acquisition and Customer Retention with UDA The Power of UDA for Fraud Detection and Prevention The Power of UDA in Human Capital Management & Human Resource The Power of UDA in Health Care and Medical Research The Power of UDA in National Security The Power of UDA in Legal Services The Power of UDA for product development The Power of UDA in Sports The future of UDA From small businesses to large multinational organizations, unstructured data provides the opportunity to gain consumer information straight from the source. Data is only as valuable as it is useful, and a robust, effective UDA strategy is the first step toward gaining the full advantage. Unstructured Data Analytics lays this space open for examination, and provides a solid framework for beginning meaningful analysis.
Unstructured Data Analytics Using Machine Learning
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Author : M. Sangeetha
language : en
Publisher:
Release Date : 2022-02-02
Unstructured Data Analytics Using Machine Learning written by M. Sangeetha and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-02 with categories.
We can find easily structured data in our database system such as profile records, transaction records, item records. As time goes by, people think about how to handle unstructured like text, image, data satellite, audio, etc. That might give you something useful to make decisions in your business. In this book various unstructured machine learning techniques for data analytics are discussed.
Practical Text Analytics
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Author : Steven Struhl
language : en
Publisher: Kogan Page Publishers
Release Date : 2015-07-03
Practical Text Analytics written by Steven Struhl and has been published by Kogan Page Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-03 with Business & Economics categories.
In an age where customer opinion and feedback can have an immediate, major effect upon the success of a business or organization, marketers must have the ability to analyze unstructured data in everything from social media and internet reviews to customer surveys and phone logs. Practical Text Analytics is an essential daily reference resource, providing real-world guidance on the effective application of text analytics. The book presents the analysis process so that it is immediately understood by the marketing professionals who must use it, so they can apply proven concepts and methods correctly and with confidence. By decoding industry terminology and demonstrating practical application of data models once reserved for experts, Practical Text Analytics shows marketers how to frame the right questions, identify key themes and find hidden meaning from unstructured data. Readers will learn to develop powerful new marketing strategies to elevate customer experience, solidify brand value and elevate reputation. Online resources include self-test questions, chapter review Q&A and an Instructor's Manual with text sources and instructions.
Unstructured Data Analysis
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Author : Matthew Windham
language : en
Publisher:
Release Date : 2019-07-31
Unstructured Data Analysis written by Matthew Windham and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-31 with Computers categories.
Unlock the power of regular expressions and entity resolution to transform your analytics projects Unstructured data is the most voluminous form of data in the world, and analysts rarely receive it in perfect condition for processing. In other words, textual data needs to be cleaned, transformed, and enhanced before value can be derived from it. Unstructured Data Analysis: Entity Resolution and Regular Expressions in SAS shows SAS programmers of virtually all skill levels how to harness the robust power of regular expressions and entity resolution within the SAS programming language for a wide array of everyday applications of unstructured data analyses. This book uses a practical, examples-based approach to present techniques for unstructured data processing and provides the foundational information needed to perform advanced applications. Beginning with regular expressions in SAS, readers will progress to learning the building blocks of Entity Resolution Analytics including entity extraction, ETL, entity resolution, network mapping and analysis, and management concepts. Filled with motivational examples and helpful guidelines, this book is a critical reference for every analytics professional who works with unstructured data.
Building The Unstructured Data Warehouse
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Author : William H. Inmon
language : en
Publisher:
Release Date : 2011
Building The Unstructured Data Warehouse written by William H. Inmon and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.
Learn essential techniques from data warehouse legend Bill Inmon on how to build the reporting environment your business needs now Answers for many valuable business questions hide in text. How well can your existing reporting environment extract the necessary text from email, spreadsheets, and documents, and put it in a useful format for analytics and reporting? Transforming the traditional data warehouse into an efficient unstructured data warehouse requires additional skills from the analyst, architect, designer, and developer. This book will prepare you to successfully implement an unstructured data warehouse and, through clear explanations, examples, and case studies, you will learn new techniques and tips to successfully obtain and analyze text. Master these ten objectives: Build an unstructured data warehouse using the 11-step approach Integrate text and describe it in terms of homogeneity, relevance, medium, volume, and structure Overcome challenges including blather, the Tower of Babel, and lack of natural relationships Avoid the Data Junkyard and combat the "Spider's Web" Reuse techniques perfected in the traditional data warehouse and Data Warehouse 2.0, including iterative development Apply essential techniques for textual Extract, Transform, and Load (ETL) such as phrase recognition, stop word filtering, and synonym replacement Design the Document Inventory system and link unstructured text to structured data Leverage indexes for efficient text analysis and taxonomies for useful external categorization Manage large volumes of data using advanced techniques such as backward pointers Evaluate technology choices suitable for unstructured data processing, such as data warehouse appliances
How Big Data Analytics Can Shape Corporate Strategy
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Author : Gabriel Socha
language : en
Publisher: GRIN Verlag
Release Date : 2018-08-16
How Big Data Analytics Can Shape Corporate Strategy written by Gabriel Socha and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-16 with Business & Economics categories.
Essay from the year 2018 in the subject Business economics - Business Management, Corporate Governance, , language: English, abstract: As one of the most trending business topics, Big Data Analytics is having an enormous influence on today’s executives. Big Data Analytics is improving business efficiency and productivity. Besides, big data has become a whole industry, growing at a fast pace. Additionally, innovative business models based on big data are disrupting traditional markets. This essay provides an overview of the topic Big Data Analytics in a business context and explores the dimensions how and under which circumstances companies can build their strategy around those capabilities and what challenges have to be tackled. Furthermore, a case study illustrates how a company, not even 25 years old, could grow into one of the largest corporations worldwide – due to big data.
Analytical Queries For Unstructured Data
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Author : Daniel Kang
language : en
Publisher:
Release Date : 2025-10-27
Analytical Queries For Unstructured Data written by Daniel Kang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-27 with Computers categories.
Unstructured data, in the form of text, images, video, and audio, is produced at exponentially higher rates. In tandem, machine learning (ML) methods have become increasingly powerful at analyzing unstructured data. Modern ML methods can now detect objects in images, understand actions in videos, and even classify complex legal texts based on legal intent. Combined, these trends make it increasingly feasible for analysts and researchers to automatically understand the "real world." However, there are major challenges in deploying these techniques: 1) executing queries efficiently given the expense of ML methods, 2) expressing queries over bespoke forms of data, and 3) handling errors in ML methods.In this monograph, challenges and advances in data management systems for unstructured data using ML are discussed, with a particular focus on video analytics. Using ML to answer queries introduces new challenges. First, even turning user intent into queries can be challenging: it is not obvious how to express a query of the form "select instances of cars turning left." Second, ML models can be orders of magnitude more expensive compared processing traditional structured data. Third, ML models and the methods to accelerate analytics with ML models can be error-prone.Recent work in the data management community has aimed to address all of these challenges. Users can now express queries via user-defined functions, opaquely through standard structured schemas, and even by providing examples. Given a query, recent work focuses on optimizing queries by approximating expensive "gold" methods with varying levels of guarantees. Finally, to handle errors in ML models, recent work has focused on applying outlier and drift detection to data analytics with ML.
Efficient And Accurate Systems For Querying Unstructured Data
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Author : Daniel Kang
language : en
Publisher:
Release Date : 2022
Efficient And Accurate Systems For Querying Unstructured Data written by Daniel Kang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Volumes of unstructured, non-tabular data (e.g., videos, audio, and text) have been increasing exponentially. This data is exciting to scientific researchers, business analysts, and data scientists for downstream analyses. For example, video can be used by urban planners to analyze traffic, ecologists to understand hummingbird-bacteria microcosms, and data scientists to analyze customer behavior in stores. However, this is impossible to do manually at scale: exabytes of data are generated per day, outstripping manual processing capacity. In recent years, automatic analysis over this unstructured data has become possible via machine learning (ML). Analysts can use ML to extract structured information from these unstructured sources, such as object types and location from a video. The structured information can subsequently be used in downstream analysis, e.g., the urban planner can count the number of cars that passed by an intersection. Unfortunately, using ML for these analyses is challenging. Deploying ML is prohibitively expensive for many organizations: naively analyzing a year of video from a small town can cost millions in cloud compute credits. ML methods are also unreliable, returning incorrect results, which can lead to downstream errors. Finally, deploying ML for analytics requires knowledge of deep learning, data systems, programming, and other technical skills. In light of these challenges, we make two observations: many applications can tolerate approximations, if there are guarantees on accuracy, and methods for answering unstructured data queries range by up to 10 orders of magnitude in cost. In this dissertation, we develop systems and algorithms for efficient and reliable unstructured data analytics, leveraging the two observations. Instead of returning exact answers, we return approximate answers generated by cheap approximations to expensive ML methods. Our systems can return statistically valid answers on a wide range of query types, including selection, aggregation, and limit queries. Furthermore, our systems can be up to orders of magnitude cheaper than standard methods of answering queries. We further develop systems for monitoring and quality assurance over ML pipelines. In addition to being deployed for analytics, ML is increasingly being deployed in mission-critical settings, such as in autonomous vehicles. Despite being deployed in these settings, models are often unmonitored and the training data is often not vetted. To address this, we propose abstractions for monitoring and quality assurance of ML deployments: model assertions and learned observation assertions. These assertions allow domain experts to specify errors, both at deployment time and over the data used to train these models. Assertions can find errors with both high recall (75%) and high precision (100%) in real-world autonomous vehicle, video analytics, and medical datasets. The systems and abstractions in this dissertation have been deployed in a variety of real-world settings, including for autonomous vehicles and ecological analysis.
Unstructured Data Analysis
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Author : Matthew Windham
language : en
Publisher:
Release Date : 2018-09
Unstructured Data Analysis written by Matthew Windham and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09 with Computers categories.
Unlock the power of regular expressions and entity resolution to transform your analytics projects Unstructured data is the most voluminous form of data in the world, and analysts rarely receive it in perfect condition for processing. In other words, textual data needs to be cleaned, transformed, and enhanced before value can be derived from it. Unstructured Data Analysis: Entity Resolution and Regular Expressions in SAS® shows SAS programmers of virtually all skill levels how to harness the robust power of regular expressions and entity resolution within the SAS programming language for a wide array of everyday applications of unstructured data analyses. This book uses a practical, examples-based approach to present techniques for unstructured data processing and provides the foundational information needed to perform advanced applications. Beginning with regular expressions in SAS, readers will progress to learning the building blocks of Entity Resolution Analytics including entity extraction, ETL, entity resolution, network mapping and analysis, and management concepts. Filled with motivational examples and helpful guidelines, this book is a critical reference for every analytics professional who works with unstructured data.