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38536 results in Cambridge Textbooks

Page 1175 of 1927

  • 2 - Architecture and Categorization of DBMSs

  • 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 20-37

  • Summary

    Chapter Objectives

    In this chapter, you will learn to:

  • • identify the key components of a DBMS architecture;

  • • understand how these components work together for data storage, processing, and management;

  • • categorize DBMSs based upon data model, degree of simultaneous access, architecture, and usage.

    • Opening Scenario

    To kick-start its business, Sober purchased the customer database of Mellow Cab, a firm that recently stepped out of the taxi business. Unfortunately, the database has been handed over in a legacy CODASYL format that Sober is not familiar with. Sober also needs a new database to store transaction details whenever passengers book either a ride-hailing or ride-sharing service. Other data (e.g., multimedia) are an option they are interested in. Sober wants to continuously store the location of its taxis and periodically review hot-spot pick-up and drop-off locations. Sober is looking at ways to manage all these data sources in the optimal way.

    As discussed in Chapter 1, a DBMS supports the creation, usage, and maintenance of a database. It consists of several modules, each with their specific functionality, that work together according to a predefined architecture. In this chapter, we zoom into this internal architecture and provide a categorization of DBMSs along various dimensions. The overview of the chapter is straightforward. We start by discussing the components that together make up a DBMS. Next, we provide a classification of DBMSs in terms of data model, degree of simultaneous access, architecture, and usage.

    Architecture of a DBMS

    As discussed before, a DBMS needs to support various types of data management-related activities, such as querying and storage. It also must provide interfaces to its environment. To achieve both of these goals, a DBMS is composed of various interacting modules that together make up the database management system architecture. Figure 2.1 shows an overview of the key components of a DBMS architecture. We review each component in more detail in what follows.

    Figure 2.1 is by no means exhaustive. Depending upon the vendor and implementation, some components may be left out and others added. On the left, you can see various ways of interacting with the DBMS. DDL statements create data definitions that are stored in the catalog. Interactive queries are typically executed from a front-end tool, such as a command-line interface, simple graphical user interface, or forms-based interface. Applications interact with the DBMS using embedded DML statements.

  • 4 - Organizational Aspects of Data Management

  • 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 79-90

  • Summary

    Chapter Objectives

    In this chapter, you will learn to:

  • • identify the basic concepts of data management;

  • • understand the role and importance of catalogs, metadata, data quality, and data governance;

  • • identify key roles in database modeling and management;

  • • understand the differences between an information architect, database designer, data owner, data steward, database administrator, and data scientist.

    • Opening Scenario

    Sober realizes that the success of its entire business model depends on data, and it wants to make sure the data are managed in an optimal way. The company is looking at how to organize proper data management and wondering about the corresponding job profiles to hire. The challenge is twofold. On the one hand, Sober wants to have the right data management team to ensure optimal data quality. On the other hand, Sober only has a limited budget to build that team.

    In this chapter, we zoom into the organizational aspects of data management. We begin by elaborating on data management and review the essential role of catalogs and metadata, both of which were introduced in Chapter 1. We then discuss metadata modeling, which essentially follows a similar database design process to that which we described in Chapter 3. Data quality is also extensively covered in terms of both its importance and its underlying dimensions. Next, we introduce data governance as a corporate culture to safeguard data quality. We conclude by reviewing various roles in data modeling and management, such as information architect, database designer, data owner, data steward, database administrator, and data scientist.

    Data Management

    Data management entails the proper management of data and the corresponding data definitions or metadata. It aims at ensuring that (meta-)data are of good quality and thus a key resource for effective and efficient managerial decision-making. In the following subsections we first review catalogs, the role of metadata, and the modeling thereof. This is followed by a discussion on data quality and data governance.

    Catalogs and the Role of Metadata

    The importance of good metadata management cannot be understated. In the past this was often neglected, resulting in significant problems when applications needed to be updated or maintained. In the file-based approach to data management, the metadata were stored in each application separately, creating the issues discussed in Chapter 1.

  • 5 - Derivation without Affixation

  • Ingo Plag, Heinrich-Heine-Universität Düsseldorf
  • Book:
    Word-Formation in English
    Published online:
    30 April 2019
    Print publication:
    12 July 2018, pp 105-130

  • Summary

    This chapter deals with non-affixational word-formation processes. First, three major problems of conversion are discussed. This is followed by an introduction to prosodic morphology with a detailed analysis of some morphological categories that are expressed by chiefly prosodic means, such as truncated names, -y diminutives, clippings, and blends. Finally, abbreviations and acronyms are investigated.

  • 1 - Fundamental Concepts of Database Management

  • 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 3-19

  • Summary

    Chapter Objectives

    In this chapter, you will learn to:

  • • understand the differences between the file versus database approach to data management;

  • • discern the key elements of a database system;

  • • identify the advantages of database systems and database management.

    • Opening Scenario

    Since Sober is a startup company, it must carefully decide how it will manage all its data. The company is thinking about storing all its data in Word documents, Excel files, and maybe some other files (e.g., Notepad) as well.

    In this chapter, we discuss the fundamental concepts of database management. Many ideas presented here are elaborated in later chapters. We kick off by reviewing popular applications of database technology, and follow this by defining key concepts such as a database and a database management system, or DBMS. Next, we step back in time and discuss the file-based approach and contrast it with the database approach to data management. We then zoom into the elements of a database system. We conclude by discussing the advantages of database design.

    Applications of Database Technology

    Data are everywhere and come in different shapes and volumes. These data need to be stored and managed using appropriate data management or database technologies. Think about the storage and retrieval of traditional numeric and alphanumeric data in an application developed to keep track of the number of products in stock. For each product, the product number, product name, and available quantity needs to be stored. Replenishment orders need to be issued as soon as the quantity drops below the safety limit. Every replenishment order has an order number, order date, supplier number, supplier name, and a set of product numbers, names, and quantities.

    Database technology is not just for traditional numeric and alphanumeric data. It can also store multimedia data such as pictures, audio, or video – YouTube and Spotify support the querying of music based upon artist, album, genre, playlist, or record label. Biometric data, including fingerprints and retina scans, are often used for security, such as border control as you enter a country. Information is also gathered by wearables, such as a Fitbit or an Apple Watch, which continuously monitor and analyze your health and fitness. Geographical information systems (GIS) applications, such as Google Maps, store and retrieve all types of spatial or geographical data.

  • 11 - NoSQL 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 300-348

  • Summary

    Chapter Objectives

    In this chapter, you will learn:

  • • what is meant by “NoSQL” and how this movement differs from earlier approaches;

  • • to understand the differences between key–value-, tuple-, document-, column-, and graph-based databases;

  • • about the defining characteristics of NoSQL databases, such as their capability to scale horizontally, their approach toward data replication and how this relates to eventual consistency, their APIs, and how they are queried and interacted with;

  • • how to query NoSQL databases, using MapReduce, Cypher, and other approaches.

    • Sober has been happily using its relational database system for quite a while now, but is experiencing some limits of its existing system. In particular, due to the growth of its mobile applications (now serving many users simultaneously), some queries are performing slower, causing users to wait a few seconds when working with the app. Sober's database administrators have identified that this is mainly due to the normalized approach of its relational database model, causing many queries to involve joining tables, which results in bottlenecks when thousands of users are hailing cabs simultaneously.

    Another issue is that the mobile developers regularly want to experiment with new features (to do so, a “beta” version of the app is rolled out to a subset of users), though given the emphasis on data consistency put forward by RDBMSs, changing existing data schemas so they can handle new data fields is a time-intensive effort. The app developers are not very familiar with this, causing back-and-forth between the development team and the database administrators.

    Given these new issues, Sober is thinking of different scenarios to handle its growth. One option would be to roll out additional servers to handle the increased usage, but keep using the RDBMS system to do so. Another possibility would be to explore a NoSQL database solution. Sober's database administrator has heard about newer approaches such as MongoDB, Cassandra, and others, which offer strong scaling opportunities and a schema-free approach to data management.

    Relational database management systems (RDBMSs) put a lot of emphasis on keeping data consistent. They require a formal database schema, and new data or modifications to existing data are not accepted unless they comply with this schema in terms of data type, referential integrity, etc. Sometimes this focus on consistency may become a burden, because it induces (in some cases unnecessarily) overhead and hampers scalability and flexibility.

Page 1175 of 1927