{"title":"Appendix Using the Online Environment","authors":"","doi":"10.1017/9781316888773.023","DOIUrl":"https://doi.org/10.1017/9781316888773.023","url":null,"abstract":"","PeriodicalId":186558,"journal":{"name":"Principles of Database Management","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127257099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Physical File Organization and Indexing","authors":"Wilfried Lemahieu, S. V. Broucke, B. Baesens","doi":"10.1017/9781316888773.014","DOIUrl":"https://doi.org/10.1017/9781316888773.014","url":null,"abstract":"","PeriodicalId":186558,"journal":{"name":"Principles of Database Management","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126567130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Data Distribution and Distributed Transaction Management","authors":"Wilfried Lemahieu, S. V. Broucke, B. Baesens","doi":"10.1017/9781316888773.018","DOIUrl":"https://doi.org/10.1017/9781316888773.018","url":null,"abstract":"Chapter Objectives In this chapter, you will learn to: • grasp the basics of distributed systems and distributed databases; • discern key architectural implications of distributed databases; • understand the impact of fragmentation, allocation, and replication; • identify different types of transparency; • understand the steps in distributed query processing; • understand distributed transaction management and concurrency control; • grasp the impact of eventual consistency and BASE transactions. Opening Scenario As Sober envisions growing as part of its long-term strategy, it wants to have a careful understanding of the data implications involved. More specifically, the company wants to know if it would make sense to distribute its data across a network of offices and work with a distributed database. Sober wants to know the impact of data distribution on query processing and optimization, transaction management, and concurrency control. In this chapter, we focus on the specifics of distributed databases (i.e., systems in which the data and DBMS functionality are distributed over different nodes or locations on a network). First, we discuss the general properties of distributed systems and offer an overview of some architectural variants of distributed database systems. Then, we tackle the different ways of distributing data over nodes in a network, including the possibility of data replication. We also focus on the degree to which the data distribution can be made transparent to applications and users. Then, we discuss the complexity of query processing and query optimization in a distributed setting. A next section is dedicated to distributed transaction management and concurrency control, focusing on both tightly coupled and loosely coupled settings. The last section overviews the particularities of transaction management in Big Data and NoSQL databases, which are often distributed in a cluster set-up, presenting BASE transactions as an alternative to the traditional ACID transaction paradigms. Distributed Systems and Distributed Databases Ever since the early days of computing, which were dominated by monolithic mainframes, distributed systems have had their place in the ICT landscape. A distributed computing system consists of several processing units or nodes with a certain level of autonomy, which are interconnected by a network and which cooperatively perform complex tasks. These complex tasks can be divided into subtasks as performed by the individual nodes.","PeriodicalId":186558,"journal":{"name":"Principles of Database Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130350539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Object-Oriented Databases and Object Persistence","authors":"Wilfried Lemahieu, S. V. Broucke, B. Baesens","doi":"10.1017/9781316888773.010","DOIUrl":"https://doi.org/10.1017/9781316888773.010","url":null,"abstract":"","PeriodicalId":186558,"journal":{"name":"Principles of Database Management","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115242807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Data Integration, Data Quality, and Data Governance","authors":"Wilfried Lemahieu, S. V. Broucke, B. Baesens","doi":"10.1017/9781316888773.020","DOIUrl":"https://doi.org/10.1017/9781316888773.020","url":null,"abstract":"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.","PeriodicalId":186558,"journal":{"name":"Principles of Database Management","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124460455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Organizational Aspects of Data Management","authors":"Wilfried Lemahieu, S. V. Broucke, B. Baesens","doi":"10.1017/9781316888773.006","DOIUrl":"https://doi.org/10.1017/9781316888773.006","url":null,"abstract":"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.","PeriodicalId":186558,"journal":{"name":"Principles of Database Management","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117154016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Fundamental Concepts of Database Management","authors":"Wilfried Lemahieu, S. V. Broucke, B. Baesens","doi":"10.1017/9781316888773.003","DOIUrl":"https://doi.org/10.1017/9781316888773.003","url":null,"abstract":"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.","PeriodicalId":186558,"journal":{"name":"Principles of Database Management","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115026632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"NoSQL Databases","authors":"Raj Lalwani","doi":"10.22214/ijraset.2018.6133","DOIUrl":"https://doi.org/10.22214/ijraset.2018.6133","url":null,"abstract":": Databases are needed to store data in a standard format and at a proper location. Earlier Flat File System were used to store data, but there was no standard being implemented in Flat Files. NoSql databases are useful for large sets of distributed data. NoSql databases are effective for big data performance issues that relational databases aren’t built to solve. They are most effective when an organization must analyse large chunks of unstructured data or data that’s stored across multiple virtual servers in the cloud. This research paper suggests What is NoSQL databases? How does it work? And when to use which database according to requirement?","PeriodicalId":186558,"journal":{"name":"Principles of Database Management","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125178449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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