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1911 results in Knowledge Management, Databases and Data Mining

Page 56 of 96

  • Preface

  • Wilfried Lemahieu, KU Leuven, Belgium, Seppe vanden Broucke, KU Leuven, Belgium, Bart Baesens, KU Leuven, Belgium
  • Book:
    Principles of Database Management
    Published online:
    17 August 2019
    Print publication:
    12 July 2018, pp xix-xxiii

  • Summary

    Congratulations! By picking up this book, you have made the first step in your journey through the wonderful world of databases. As you will see in this book, databases come in many different forms – from simple spreadsheets or other file-based attempts and hierarchical structures, to relational, object-oriented, and even graph-oriented ones – and are used across the world throughout a variety of industry sectors to manage, store, and analyze data.

    This book is the result of having taught an undergraduate database management class and a postgraduate advanced database management class for more than ten years. Throughout the years we have found no textbook that covers the material in a comprehensive way without becoming flooded by theoretical detail and losing focus. Hence, after we teamed up together, we decided to start writing a book ourselves. This work aims to offer a complete and practical guide covering all the governing principles of database management, including:

  • • end-to-end coverage, starting with legacy technologies to emerging trends such as Big Data, NoSQL databases, analytics, data governance, etc.;

  • • a unique perspective on how lessons learned from past data management could be relevant in today's technology setting (e.g., navigational access and its perils in CODASYL and XML/OO databases);

  • • a critical reflection and accompanying risk management considerations when implementing the technologies considered, based on our own experiences participating in data and analytics-related projects with industry partners in a variety of sectors, from banking to retail and from government to the cultural sector;

  • • a solid balance between theory and practice, including various exercises, industry examples and case studies originating from diverse and complementary business practices, scientific research, and academic teaching experience.

    • The book also includes an appendix explaining our “online playground” environment, where you can try out many concepts discussed in the book. Additional appendices, including an exam bank containing several cross-chapter questions and references to our YouTube lectures, are provided online as well.

    We hope you enjoy this book and that you, the reader, will find it a useful reference and trusted companion in your work, studies, or research when storing, managing, and analyzing small or Big Data!

    Who This Book is For

    We have tried to make this book complete and useful for both novice and advanced database practitioners and students alike.

  • 9 - Extended Relational Databases

  • from Part II - Types of Database Systems
  • Wilfried Lemahieu, KU Leuven, Belgium, Seppe vanden Broucke, KU Leuven, Belgium, Bart Baesens, KU Leuven, Belgium
  • Book:
    Principles of Database Management
    Published online:
    17 August 2019
    Print publication:
    12 July 2018, pp 231-254

  • Summary

    Chapter Objectives

    In this chapter, you will learn to:

  • • identify the shortcomings of the relational model;

  • • define and use triggers and stored procedures;

  • • understand how RDBMSs can be extended with OO concepts such as user-defined types, user-defined functions, inheritance, behavior, polymorphism, collection types, and large objects;

  • • define and use recursive SQL queries.

    • Opening Scenario

    Now that Sober has decided to continue with its relational model, the company was wondering whether it could enrich it with some smart extensions. It wondered whether it would be possible to make the RDBMS more active so it can autonomously take initiative for action when casespecific situations occur. Although Sober was not entirely convinced about OODBMSs and decided not to continue with them, they appreciate some of the OO concepts introduced. They are wondering whether it would be possible to have a best-of-both-worlds approach, whereby they could enrich their relational model with some of the OO concepts they learned about.

    In this chapter, we revisit RDBMSs, which are very popular in industry. We start by refreshing the key building blocks of the relational model and discuss its limitations. We then look at the ways of extending relational databases, starting with reviewing triggers and stored procedures as two key mechanisms to make RDBMSs more active. Next, we introduce object-relational DBMSs (ORDBMSs). In contrast to the OODBMSs in Chapter 8, ORDBMSs build on a relational engine, but they extend RDBMSs with object-oriented (OO) characteristics. We conclude with recursive queries, which are a powerful extension to the SQL language.

    Limitations of the Relational Model

    The relational data model, and the RDBMS implementations thereof, have been very successful for managing well-structured numerical and alphanumerical data. One of the major reasons for this is the mathematical simplicity and soundness of the relational model. As discussed in Chapter 6, its two key building blocks are tuples and relations. A tuple is a composition of values of attribute types that describe an entity. It can be created by using a tuple constructor. A relation is a mathematical set of tuples describing similar entities. It can be created by using a set constructor. As also mentioned in Chapter 6, the relational model requires all relations to be normalized. Data about entities can be fragmented across multiple relations, which are connected using primary–foreign key relationships.

  • 15 - Accessing Databases and Database APIs

  • from Part III - Physical Data Storage, Transaction Management, and Database Access
  • Wilfried Lemahieu, KU Leuven, Belgium, Seppe vanden Broucke, KU Leuven, Belgium, Bart Baesens, KU Leuven, Belgium
  • Book:
    Principles of Database Management
    Published online:
    17 August 2019
    Print publication:
    12 July 2018, pp 458-515

  • Summary

    Chapter Objectives

    In this chapter, you will learn:

  • • how database systems can be accessed from the outside world;

  • • what is meant by a database application programming interface (API);

  • • to understand the differences between proprietary versus universal APIs, between embedded versus call-level APIs, and early binding versus late binding;

  • • which universal database application programming interfaces are available to interact with database systems;

  • • how DBMSs play their role within the World Wide Web and the internet.

    • Opening Scenario

    Sober has decided on a relational DBMS vendor, has drawn up the relational schema to implement, and has verified that the database is working as planned by importing sample data and testing some SQL queries. However, a DBMS system does not live in isolation. Sober is planning to develop several applications that will have to connect to its DBMS. For instance, Sober is thinking about a website where customers can book cabs and retrieve their order history, and a mobile app that will have to fetch information from the DBMS. Finally, Sober is also planning to develop a desktop application for the customer support team that will be used internally, but also has to be able to access the same DBMS. Sober is still thinking about different options in terms of programming languages to develop these applications, but wonders how easy it will be for these applications to access the database in order to store and retrieve information from it.

    In this chapter, we take a closer look at the forms of accessing database systems. Naturally, the manner of how a DBMS is interfaced with heavily depends on the system architecture it applies. Accessing a DBMS in a legacy mainframe set-up is different than interfacing with a DBMS in a client–server-based architecture, so we open this chapter with an overview of different database system architectures. Next, we turn our attention toward the different database application programming interfaces, or APIs for short. As the name suggests, a database API exposes an interface through which clients, third-party, and end-user applications can access, query, and manage the DBMS. Finally, we look at DBMSs’ role and place within the World Wide Web, and how recent trends in this landscape are influencing and shaping the requirements imposed on DBMSs.

  • 19 - Big Data

  • from Part IV - Data Warehousing, Data Governance, and (Big) Data Analytics
  • Wilfried Lemahieu, KU Leuven, Belgium, Seppe vanden Broucke, KU Leuven, Belgium, Bart Baesens, KU Leuven, Belgium
  • Book:
    Principles of Database Management
    Published online:
    17 August 2019
    Print publication:
    12 July 2018, pp 626-663

  • Summary

    Chapter Objectives

    In this chapter, you will learn:

  • • what is meant by “Big Data” and its “5 Vs”;

  • • to understand the differences between traditional database management systems and Big Data technologies such as Hadoop;

  • • to understand the differences between traditional data warehousing approaches and Big Data technologies;

  • • to identify the tradeoffs when opting to adopt a Big Data stack;

  • • what the links are between Big Data and NoSQL databases.

    • Opening Scenario

    Sober has made its first steps in setting up a data warehouse on top of its existing DBMS stack to kick-start its business intelligence activities, with the main goal of reporting purposes. However, Sober's management is hearing a lot lately about “Big Data” and “Hadoop” with regards to storing and analyzing huge amounts of data. Sober is wondering whether these technologies would offer an added benefit. So far, Sober is happy with its relational DBMS, which integrates nicely with its business intelligence tooling. The mobile development team, meanwhile, has been happy to work with MongoDB, a NoSQL database, to handle the increased workload from mobile users. Hence, the question for Sober is: what would a Big Data stack offer on top of this?

    Data are everywhere. To give some staggering examples, IBM projects that every day we generate 2.5 quintillion bytes of data. Every minute, more than 300,000 tweets are created, Netflix subscribers are streaming more than 70,000 hours of video, Apple users download 30,000 apps, and Instagram users like almost two million photos. In relative terms, 90% of the data in the world have been created in the last two years. These massive amounts of data yield an unprecedented treasure of internal customer knowledge, ready to be analyzed using state-of-the-art analytical techniques to better understand and exploit customer behavior by identifying new business opportunities together with new strategies. In this chapter, we zoom into this concept of “Big Data” and explain how these huge data troves are changing the world of DBMSs. We kick-off by reviewing the 5 Vs of Big Data. Next, we zoom in on the common Big Data technologies in use today. We discuss Hadoop, SQL on Hadoop, and Apache Spark.

  • 3 - Conceptual Data Modeling Using the (E)ER Model and UML Class Diagram

  • from Part I - Databases and Database Design
  • Wilfried Lemahieu, KU Leuven, Belgium, Seppe vanden Broucke, KU Leuven, Belgium, Bart Baesens, KU Leuven, Belgium
  • Book:
    Principles of Database Management
    Published online:
    17 August 2019
    Print publication:
    12 July 2018, pp 38-78

  • Summary

    Chapter Objectives

    In this chapter, you will learn to:

  • • understand the different phases of database design: conceptual design, logical design, and physical design;

  • • build a conceptual data model using the ER model and understand the limitations thereof;

  • • build a conceptual data model using the EER model and understand the limitations thereof;

  • • build a conceptual data model using the UML class diagram and understand the limitations thereof.

    • Opening Scenario

    Sober has decided to invest in a new database and begin a database design process. As a first step, it wants to formalize the data requirements in a conceptual data model. Sober asks you to build both an EER and a UML data model for its business setting. It also wants you to extensively comment on both models and properly indicate their shortcomings.

    In this chapter we start by zooming out and reviewing the database design process. We elaborate on conceptual, logical, and physical database design. We continue the chapter with conceptual design, which aims at elucidating the data requirements of a business process in a formal way. We discuss three types of conceptual data models: the ER model; the EER model; and the UML class diagram. Each model is first defined in terms of its fundamental building blocks. Various examples are included for clarification. We also discuss the limitations of the three conceptual data models and contrast them in terms of their expressive power and modeling semantics. Subsequent chapters continue from the conceptual data models of this chapter and map them to logical and internal data models.

    Phases of Database Design

    Designing a database is a multi-step process, as illustrated in Figure 3.1. It starts from a business process. As an example, think about a B2B procurement application, invoice handling process, logistics process, or salary administration. A first step is requirement collection and analysis, where the aim is to carefully understand the different steps and data needs of the process. The information architect (see Chapter 4) will collaborate with the business user to elucidate the database requirements. Various techniques can be used, such as interviews or surveys with end-users, inspections of the documents used in the current process, etc. During the conceptual design, both parties try to formalize the data requirements in a conceptual data model.

  • 12 - Physical File Organization and Indexing

  • from Part III - Physical Data Storage, Transaction Management, and Database Access
  • Wilfried Lemahieu, KU Leuven, Belgium, Seppe vanden Broucke, KU Leuven, Belgium, Bart Baesens, KU Leuven, Belgium
  • Book:
    Principles of Database Management
    Published online:
    17 August 2019
    Print publication:
    12 July 2018, pp 351-394

  • Summary

    Chapter Objectives

    In this chapter, you will learn to:

  • • grasp the basic principles of storage hardware and physical database design;

  • • understand how data items can be organized into stored records;

  • • identify various methods for primary and secondary file organization;

    • Opening Scenario

    Now that Sober has its relational logical data model from Chapter 6 ready, it wants to understand how it can be physically implemented. The company is also wondering whether there exist any physical means to speed up the response times of frequently used queries.

    This chapter focuses on the most important principles pertaining to the physical organization of records and files. As such, it can be considered as the prerequisite to Chapter 13, which applies these principles in the context of physical database organization. In this way Chapters 12 and 13 are complementary in covering all facets of physical database design – the translation of a logical data model into an internal data model, including the design of indexes to speed up data access.

    First, we present some overall properties of storage devices and the impact of the mechanicals of hard disk drives on the performance aspects of data retrieval. After that, we overview the mapping of logical modeling constructs onto physical concepts, and thus of the logical data model onto the internal model. Then, we briefly discuss record organization, covering the different alternatives to organize physical data records, consisting of individual data fields. After that comes the main body of this chapter, focusing on file organization and covering methods to organize records into physical files, as well as techniques to efficiently search for records with certain characteristics. We discuss several approaches, such as sequential files, hashing and the use of different index types, including B+-trees. Chapter 13 follows on from this chapter, applying the insights gained from the file organization section to the particular context of physical database organization.

    Storage Hardware and Physical Database Design

    Physical database design pertains to the design of an internal data model and to the way in which the logical database concepts discussed in the previous chapters are realized physically as stored records, physical files, and, ultimately, a physical database.

  • 5 - Legacy Databases

  • from Part II - Types of Database Systems
  • Wilfried Lemahieu, KU Leuven, Belgium, Seppe vanden Broucke, KU Leuven, Belgium, Bart Baesens, KU Leuven, Belgium
  • Book:
    Principles of Database Management
    Published online:
    17 August 2019
    Print publication:
    12 July 2018, pp 93-103

  • Summary

    Chapter Objectives

    In this chapter, you will learn to:

  • • understand why it is important to know the basic concepts of legacy databases;

  • • identify the basic building blocks and limitations of the hierarchical model;

  • • identify the basic building blocks and limitations of the CODASYL model.

    • Opening Scenario

    Sober purchased the customer database of Mellow Cab, who recently stepped out of the taxi business after many years at the top. Since Mellow Cab started in the mid-1970s, it was still using outdated database technology. It used CODASYL to store and manage its customer data. Sober is not familiar with this technology and wants to understand its limitations before deciding to either continue with it or invest in a modern DBMS.

    In this chapter we briefly zoom into legacy database technologies and their logical data models. Although these are outdated and semantically inferior to modern database technologies, there are three reasons to review them. Firstly, many firms still struggle with legacy databases due to historical implementations and limited IT budgets – hence, knowing the basic characteristics of these models is essential to the maintenance of the corresponding database applications and the potential migration to modern DBMSs. Secondly, understanding the basics of these legacy models will contribute to better understanding the semantical richness of newer database technologies. Finally, the concept of procedural DML and navigational access originally introduced by these legacy models has also been adopted by more recent databases such as OODBMSs.

    In this chapter we cover both the hierarchical and CODASYL data models. Throughout the discussion, we extensively discuss the expressive power and limitations of both models.

    The Hierarchical Model

    The hierarchical model is one of the first data models developed. The model originated during the Apollo moon missions program conducted by NASA. To manage the huge amount of data collected, IBMdeveloped the Information Management System or IMS DBMS (1966–1968). There is no formal description available and it has lots of structural limitations. Therefore, it is considered legacy.

    The two key building blocks of the hierarchical model are record types and relationship types. A record type is a set of records describing similar entities. Think about a product record type or a supplier record type. A record type has zero, one, or more records. It consists of fields or data items.

  • 18 - Data Integration, Data Quality, and Data Governance

  • from Part IV - Data Warehousing, Data Governance, and (Big) Data Analytics
  • Wilfried Lemahieu, KU Leuven, Belgium, Seppe vanden Broucke, KU Leuven, Belgium, Bart Baesens, KU Leuven, Belgium
  • Book:
    Principles of Database Management
    Published online:
    17 August 2019
    Print publication:
    12 July 2018, pp 590-625

  • Summary

    Chapter Objectives

    In this chapter, you will learn to:

  • • identify the key challenges and approaches for data and process integration;

  • • understand the basic mechanisms of searching unstructured data within an organization and across the World Wide Web;

  • • define data quality as a multidimensional concept and understand how master data management (MDM) can contribute to it;

  • • understand different frameworks and standards for data governance;

  • • highlight more recent approaches in data warehousing, data integration, and governance.

    • Opening Scenario

    Things are going well at Sober. The company has set up a solid data environment based on a solid relational database management system used to support the bulk of its operations. Sober's mobile app development team has been using MongoDB as a scalable NoSQL DBMS to handle the increased workload coming from mobile app users and to provide back-end support for experimental features the team wants to test in new versions of their mobile app. Sober's development and database team is already paying attention to various data quality and governance aspects: the RDBMS is the central source of truth, strongly focusing on solid schema design and regular quality checks being performed on the data. The NoSQL database is an additional support system to handle large query volumes from mobile users in real-time in a scalable manner, but where all data changes are still being propagated to the central RDBMS. This is done in a manual manner, which sometimes leads to the two data sources not being in agreement with each other. Sober's team therefore wants to consider better data quality approaches to implement more robust quality checks on data and make sure that changes to the NoSQL database are propagated to the RDBMS system in a timely and correct manner. Sober also wants to understand how their data flows can be better integrated with their business processes.

    In this chapter we will look at some managerial and technical aspects of data integration. We will zoom in on data integration techniques, data quality, and data governance. As companies often end up with many information systems and databases over time, the concept of data integration becomes increasingly important to consolidate a company's data to provide one, unified view to applications and users.

  • 10 - XML Databases

  • from Part II - Types of Database Systems
  • Wilfried Lemahieu, KU Leuven, Belgium, Seppe vanden Broucke, KU Leuven, Belgium, Bart Baesens, KU Leuven, Belgium
  • Book:
    Principles of Database Management
    Published online:
    17 August 2019
    Print publication:
    12 July 2018, pp 255-299

  • Summary

    Chapter Objectives

    In this chapter, you will learn to:

  • • understand the basic concepts of XML, Document Type Definition, XML Schema Definition, Extensible Stylesheet Language, namespaces, and XPath;

  • • process XML documents using the DOM and SAX APIs;

  • • store XML documents using a document-oriented, data-oriented, or hybrid approach;

  • • grasp the key differences between XML and relational data;

  • • map between XML documents and (object-)relational data using table-based mapping, schema-oblivious mapping, schema-aware mapping, and SQL/XML;

  • • search XML data using full-text search, keyword-based search, structured search using XQuery, and semantic search using RDF and SPARQL;

  • • use XML for information exchange in combination with message-oriented middleware (MOM) and web services;

  • • understand other data representation formats such as JSON and YAML.

    • Opening Scenario

    For regulatory and insurance purposes, Sober needs to store a report for each accident. The report should include the date, the location (including GPS coordinates), a summary of what happened and the individuals involved. Furthermore, for each individual Sober needs to know:

  • • the name;

  • • whether he/she is a driver driving a Sober car or not, a pedestrian, or a cyclist;

  • • whether he/she was injured.

    • The report should also include information about aid provided, such as police or ambulance assistance. Sober would like to know the best way to store this report.

    In this chapter, we discuss how to store, process, search, and visualize XML documents, and how DBMSs can support this. We start by looking at the XML data representation standard and discuss related concepts such as DTDs and XSDs for defining XML documents, XSL for visualizing or transforming XML documents, and namespaces to provide for a unique naming convention. This is followed by introducing XPath, which uses path expressions to navigate through XML documents. We review the DOM and SAX API to process XML documents. Next, we cover both the documentand data-oriented approach for storing XML documents. We extensively highlight the key differences between the XML and relational data model. Various mapping methods between XML and (object-)relational data are discussed: table-based mapping, schema-oblivious mapping, schema-aware mapping, and the SQL/XML extension. We also present various ways to search XML data: full-text search, keyword-based search, structured search, XQuery, and semantic search. We then illustrate how XML can be used for information exchange, both at the company level using RPC and message-oriented middleware and between companies using SOAP or REST-based web services.

  • 7 - Relational Databases: Structured Query Language (SQL)

  • from Part II - Types of Database Systems
  • Wilfried Lemahieu, KU Leuven, Belgium, Seppe vanden Broucke, KU Leuven, Belgium, Bart Baesens, KU Leuven, Belgium
  • Book:
    Principles of Database Management
    Published online:
    17 August 2019
    Print publication:
    12 July 2018, pp 146-206

  • Summary

    Chapter Objectives

    In this chapter, you will learn to:

  • • understand the importance of SQL in an RDBMS environment;

  • • use SQL as a data definition language (DDL);

  • • use SQL as a data manipulation language (DML) for retrieving, inserting, deleting, and updating data;

  • • define and use views in SQL;

  • • define indexes in SQL;

  • • use SQL for granting and revoking privileges;

  • • understand how SQL can be used to manage metadata.

    • Opening Scenario

    The relational model adopted by Sober needs to serve various information needs. For example, the insurance firm of the company requests an overview of all accidents together with the number of Sober cars involved and the average damage amount per accident. As part of its customer relationship management (CRM) program, the company wants to retrieve the customer with the maximum wait time and offer him or her a free ride-hailing service. Furthermore, Sober also would like to find out which customers have never booked any of its services and target them with a promotion campaign. To implement its eco-friendly tree-planting program, Sober needs to know which customers booked more than 20 Sober ride-sharing services. Finally, the company remembers that one of the limitations of the EER conceptual data model was that it is perfectly possible that customers book either a ride-hailing or ride-sharing service with their own car. To safeguard its data quality, Sober wants to periodically check that this is not the case in its relational database.

    In the previous chapter, we dealt with the modeling aspects of relational databases. In this chapter, we elaborate on SQL, which is the lingua franca of RDBMSs and one of the most popular data definition and manipulation languages in use in the industry nowadays. Its core functionality is implemented, with only some minor variations, throughout all RDBMS products. We discuss the SQL language constructs to express a relational model and to formulate queries that retrieve and modify the data in a relational database.

    First, we provide an overview of the SQL data definition language (SQL DDL) to define a relational data model. We also discuss how the SQL data manipulation language (SQL DML) can be used for data manipulation such as retrieving data, updating data, inserting new data and deleting existing data. We then elaborate on SQL views and SQL indexes, which are part of the external and internal data model, respectively.

Information Literacy in the Workplace
  • Edited by Marc Forster
  • Published by:
    Facet
    Published online:
    08 June 2018
    Print publication:
    30 April 2017
  • This book explains how information literacy (IL) is essential to the contemporary workplace and is fundamental to competent, ethical and evidence-based practice. In today’s information-driven workplace, information professionals must know when research evidence or relevant legal, business, personal or other information is required, how to find it, how to critique it and how to integrate it into one’s knowledge base. To fail to do so may result in defective and unethical practice which could have devastating consequences for clients or employers. There is an ethical requirement for information professionals to meet best practice standards to achieve the best outcome possible for the client. This demands highly focused and complex information searching, assessment and critiquing skills.

Innovations in Information Retrieval
  • Perspectives for Theory and Practice
  • Edited by Allen Foster, Pauline Rafferty
  • Published by:
    Facet
    Published online:
    08 June 2018
    Print publication:
    31 July 2011
  • The advent of new information retrieval (IR) technologies and approaches to storage and retrieval provide communities with previously unheard of opportunities for mass documentation, digitization, and the recording of information in all its forms. This book introduces and contextualizes these developments and looks at supporting research in IR, the debates, theories and issues. Contributed by an international team of experts, each authored chapter provides a snapshot of changes in the field, as well as the importance of developing innovation, creativity and thinking in IR practice and research. Key discussion areas include: browsing in new information environments classification revisited: a web of knowledge approaches to fiction retrieval research music information retrieval research folksonomies, social tagging and information retrieval digital information interaction as semantic navigation assessing web search machines: a webometric approach. The questions raised are of significance to the whole international library and information science community, and this is essential reading for LIS professionals , researchers and students, and for all those interested in the future of IR.

Interactive Information Seeking, Behaviour and Retrieval
  • Edited by Ian Ruthven, Diane Kelly
  • Published by:
    Facet
    Published online:
    08 June 2018
    Print publication:
    31 August 2011
  • Information retrieval (IR) is a complex human activity supported by sophisticated systems. Information science has contributed much to the design and evaluation of previous generations of IR system development and to our general understanding of how such systems should be designed and yet, due to the increasing success and diversity of IR systems, many recent textbooks concentrate on IR systems themselves and ignore the human side of searching for information. This book is the first text to provide an information science perspective on IR. Unique in its scope, the book covers the whole spectrum of information retrieval, including: history and background information; behaviour and seeking task-based information; searching and retrieval approaches to investigating information; interaction and behaviour information; representation access models; evaluation interfaces for IR; interactive techniques; web retrieval, ranking and personalization; and, recommendation, collaboration and social search multimedia: interfaces and access. A key text for senior undergraduates and masters' level students of all information and library studies courses, this book is also useful for practising LIS professionals who need to better appreciate how IR systems are designed, implemented and evaluated.

Data Mining and Analysis
  • Fundamental Concepts and Algorithms
  • Mohammed J. Zaki, Wagner Meira, Jr
  • Published online:
    28 May 2018
    Print publication:
    12 May 2014
  • The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike.

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