This chapter discusses data resource management and database management. It explains that data resource management is important for organizations and can be implemented through database administration, data planning, and database management activities. It also outlines the advantages of managing organizational data through database management systems rather than separate files. The chapter objectives are to explain these concepts and provide examples of database types, structures, access methods, and the database development process.
4th industrial revolution fuel by combining big data and deeplearning a qui...Francis Piéraut
Flash presentation on the 4tn industrial revolution fuel by big data and deeplearning did at big data MTL meetup #51 on Feb 13 2017. https://www.meetup.com/Big-Data-Montreal/events/237551533/
4th industrial revolution fuel by combining big data and deeplearning a qui...Francis Piéraut
Flash presentation on the 4tn industrial revolution fuel by big data and deeplearning did at big data MTL meetup #51 on Feb 13 2017. https://www.meetup.com/Big-Data-Montreal/events/237551533/
Explain the importance of implementing data
resource management processes and
technologies in an organization.
• Outline the advantages of a database
management approach to managing the data
resources of a business.
• Explain how database management software
helps business professionals and supports the
operations and management of a business
Comcast is the largest cable and internet provider in the US, reaching more than 30 million customers, and continues to grow its presence in the EU with the acquisition of Sky. Over the last couple years, Comcast has shifted focus to the customer experience.
Understand how the database approach is Understand how the database approach is different and superior to earlier data systems different and superior to earlier data systems
Examine how information demand and Examine how information demand and technology explosion drive database systems technology explosion drive database systems
Trace the evolution of data systems and note Trace the evolution of data systems and note how we have arrive at the database approach how we have arrive at the database approach
Comprehend the benefits of database systems Comprehend the benefits of database systems and perceive the need for them and perceive the need for them
Survey briefly various data models, types of Survey briefly various data models, types of databases, and the database industry
Time Difference: How Tomorrow's Companies Will Outpace Today'sInside Analysis
The Briefing Room with Mark Madsen and WebAction
Live Webcast Feb. 10, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=fa83c6283de99dfb6f38b9d7199cb452
In our increasingly interconnected world, the windows of opportunity for meaningful action are shrinking. Where hours once sufficed, minutes are now the norm. For some transactions, seconds make all the difference, even sub-seconds. Meeting these demands requires a new approach to information architecture, one that embraces the many innovations that are fundamentally changing the data-driven economy.
Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature as he explains how a confluence of advances are changing the nature of data management. He'll be briefed by Sami Akbay of WebAction, who will showcase his company's real-time data platform, designed from the ground up to meet the challenges of leveraging Big Data in concert with all manner of operational enterprise systems.
Visit InsideAnalysis.com for more information.
Sqrrl's Director of Product Marketing, Joe Travaglini, shares some lessons learned about how to approach a "Big Data problem" with his 10 steps to building a Big App, and how to mobilize data-driven thinking into your line of business.
Getting your Records Management program to the next levelNick Inglis
My Closing Keynote for the ARMA Denver Spring Seminar in understanding how downstream processes greatly affect Records Management capabilities leveraging the Hubble telescope as an example.
Secure, Build and Deduplicate Your Data Lake Data with Amazon Lake FormationAmazon Web Services
Securely setting up and managing data lakes today involves complicated and time-consuming tasks. AWS Lake Formation is a new service that makes it easy to set up a secure data lake in just days.This session will focus on the security of the data lake as well as performing deduplication and fuzzy matching of your data. You will be able to ingest, catalog, cleanse, transform, and secure your data. AWS Lake Formation makes it easier to combine analytic tools, like Amazon EMR, Amazon Redshift, Amazon Athena, Amazon Sagemaker, and Amazon QuickSight, on data in your data lake.
Explain the importance of implementing data
resource management processes and
technologies in an organization.
• Outline the advantages of a database
management approach to managing the data
resources of a business.
• Explain how database management software
helps business professionals and supports the
operations and management of a business
Comcast is the largest cable and internet provider in the US, reaching more than 30 million customers, and continues to grow its presence in the EU with the acquisition of Sky. Over the last couple years, Comcast has shifted focus to the customer experience.
Understand how the database approach is Understand how the database approach is different and superior to earlier data systems different and superior to earlier data systems
Examine how information demand and Examine how information demand and technology explosion drive database systems technology explosion drive database systems
Trace the evolution of data systems and note Trace the evolution of data systems and note how we have arrive at the database approach how we have arrive at the database approach
Comprehend the benefits of database systems Comprehend the benefits of database systems and perceive the need for them and perceive the need for them
Survey briefly various data models, types of Survey briefly various data models, types of databases, and the database industry
Time Difference: How Tomorrow's Companies Will Outpace Today'sInside Analysis
The Briefing Room with Mark Madsen and WebAction
Live Webcast Feb. 10, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=fa83c6283de99dfb6f38b9d7199cb452
In our increasingly interconnected world, the windows of opportunity for meaningful action are shrinking. Where hours once sufficed, minutes are now the norm. For some transactions, seconds make all the difference, even sub-seconds. Meeting these demands requires a new approach to information architecture, one that embraces the many innovations that are fundamentally changing the data-driven economy.
Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature as he explains how a confluence of advances are changing the nature of data management. He'll be briefed by Sami Akbay of WebAction, who will showcase his company's real-time data platform, designed from the ground up to meet the challenges of leveraging Big Data in concert with all manner of operational enterprise systems.
Visit InsideAnalysis.com for more information.
Sqrrl's Director of Product Marketing, Joe Travaglini, shares some lessons learned about how to approach a "Big Data problem" with his 10 steps to building a Big App, and how to mobilize data-driven thinking into your line of business.
Getting your Records Management program to the next levelNick Inglis
My Closing Keynote for the ARMA Denver Spring Seminar in understanding how downstream processes greatly affect Records Management capabilities leveraging the Hubble telescope as an example.
Secure, Build and Deduplicate Your Data Lake Data with Amazon Lake FormationAmazon Web Services
Securely setting up and managing data lakes today involves complicated and time-consuming tasks. AWS Lake Formation is a new service that makes it easy to set up a secure data lake in just days.This session will focus on the security of the data lake as well as performing deduplication and fuzzy matching of your data. You will be able to ingest, catalog, cleanse, transform, and secure your data. AWS Lake Formation makes it easier to combine analytic tools, like Amazon EMR, Amazon Redshift, Amazon Athena, Amazon Sagemaker, and Amazon QuickSight, on data in your data lake.
Similar to Chap005 MIS (Management Information System) (20)
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
To understand databases, it is useful to remember that the elements of data that make up the database are divided into hierarchical levels. These logical data elements make up the foundation data concepts upon which a database is built.
Character. The most basic logical element is the character, which consists of a single alphabetic, numeric, or other symbol. While it may take several bits or bytes to represent a character digitally, remember that these refer to physical storage, not the logical concept of the character itself.
Field. A field is a grouping of characters that represent a characteristic of a person, place, thing, or event. A person's name is typically placed in a field. A field is a data item. A data field represents an attribute or some entity.
Record. A record is a collection of interrelated fields. For example, an employee's payroll record usually contains several fields, such as their name, social security number, department, and salary. Records may be fixed-length or variable-length.
File. A file is a collection of interrelated records. For example, a payroll file might contain all of the payroll files for all the employees of a firm. Files are usually classified by the application for which they are used.
Database. A database is an integrated collection of logically interrelated records or files. For example, the personnel database of a firm might contain payroll, personnel action, and employee skills files. The data stored in a database is independent of the application programs using it and of the type of secondary storage devices on which it is stored.
Teaching Tips
This slide corresponds to Figure 5.2 on p. 174 and relates to material on pp. 174-175.
Under the database management approach, data records are consolidated into databases that can be accessed by many different application programs. A database management system (DBMS) is a set of computer programs that control the creation, maintenance, and use of the databases of an organization and its end users. Four major DBMS facilities include:
Database Development. A DBMS allows control of development to be placed with database administrators. The administrator uses a data definition language (DDL) to develop and specify the data contents, relationships, and structure of each database, and to modify these specifications when necessary. This approach improves integrity and security for the organizational databases. The information is stored in a data dictionary, which uses data definitions to specify what all the records and files are, can be, and, if desired, to automatically enforce data element definitions when fields, records, or files are modified.
Database Interrogation. A DBMS allows end users without programming skills to ask for information from a database using a query language or report generator. Queries are usually made one of two ways:
SQL (Structured Query Language). This uses the basic form of SELECT ...FROM...WHERE. After SELECT the user lists the data fields to be retrieved. After FROM the user lists the files or tables from which the data must be retrieved. After WHERE the user specifies conditions that limit the search.
QBE (Query by Example). This method allows users to point and click on display boxes for each of the data fields in one or more files to specify the rules of the search
Database Maintenance. Updating the databases and other maintenance are conducted by transaction processing programs.
Application Development. A DBMS makes application development much easier and quicker by allowing developers to include data manipulation language (DML) statements in their programs that let the DBMS perform necessary data-handling activities.
Teaching Tips
This slide corresponds to Figure 5.5 on p. 177 and relates to material on pp. 177-180.
Six major types of databases are illustrated on the slide and used by computer-based organizations:
Operational Databases. These databases store detailed data needed to support the operations of the entire organization. They are also called subject area databases (SADB), transaction databases, and production databases. These also include databases of Internet and electronic commerce activity, such as click stream data or data describing online behavior of visitors at a company’s website.
Data Warehouse Databases. These store data from current and previous years that has been extracted from the various operational and management databases of the organization. As a standardized and integrated central source of data, warehouses can be used by managers for pattern processing, where key factors and trends about operations can be identified from the historical record.
Data Marts. Are subsets of the data included in a Data Warehouse which focus on specific aspects of a company, e.g. department, business process, etc.
Distributed Databases. These are the databases of local workgroups and departments at regional offices, branch offices, and other work sites needed to complete the task at hand. They include relevant information from other organizational databases combined with data and information generated only at the particular site. These databases can reside on network servers, on the World Wide Web, or on Intranets and Extranets.
End User Databases. These consist of a variety of data files developed by end users at their workstations. For example, an end user in sales might combine information on a customer’s order history with her own notes and impressions from face-to-face meetings to improve follow-up.
External Databases. Many organizations make use of privately generated and owned online databases or data banks that specialize in a particular area of interest. Access is usually through a subscription for continuing links or a one-time fee for a specific piece of information (like the results of a single search). Other sources like those found on the Web are free.
Teaching Tips
This slide corresponds to Figure 5.10 on p. 181 and relates to material on pp. 180-182.
A data warehouse stores data that has been extracted from various operational, external, and other databases within the organization.
To create a data warehouse, data from various databases are captured, cleaned, e.g. sorted, filtered, converted, and transformed into data that can be better used for analysis. The data is then stored in the enterprise data warehouse, from where it can be moved into data marts or to an analytical data store that holds data to support certain types of analysis.
Metadata, that defines the data in the data warehouse, is stored in a Metadata Directory that is used to support data administration. A variety of analytical software tools can then be used to query, report, and analyze data.
One such means for analyzing data in a data warehouse is called data mining.
In data mining, the data in a data warehouse are analyzed to reveal hidden patterns and trends in historical business activity. This can help managers make decisions about strategic changes in business operations.
Teaching Tips
This slide corresponds to Figure 5.11 on p.182 and relates to material on pp. 182-183.
The rapid growth of websites on the Internet and corporate intranets and extranets has dramatically increased the use of databases of hypertext and hypermedia documents.
Hypermedia Database: A website stores information in a hypermedia database consisting of a home page and other hyperlinked pages of multimedia or mixed media (text, graphics and photographic images, video clips, audio segments, and so on).
Browser: A web browser on your client PC is used to connect with a web network server. This server runs web software to access and transfer the web pages you request.
Web Site: A website uses a hypermedia database consisting of HTML (Hypertext Markup Language) pages, GIF (graphics image files) files, and video files.
Web Server Software: Acts as a database management system to manage the use of the interrelated hypermedia pages of the website.
Teaching Tips
This slide corresponds to Figure 5.13 on p.184 and relates to material on pp. 183-184.
The security and integrity of an organization's databases are the major concerns of the data resource management efforts. Key activities of data resource management include:
Database Administration. This area is responsible for developing and maintaining the organization's data dictionary, as well as designing and monitoring the performance of databases, and enforcing the standards for database use and security.
Data Planning. Data planning is a corporate planning and analysis function responsible for the overall data architecture for the firm. This role ensures that data resources are developed to support the firm's strategic mission and plans.
Data Administration. This area involves the establishment and enforcement of policies and procedures for managing data as a strategic corporate resource. This means standardizing data so that it is available to all end users from whatever database they are working.
Teaching Tips
This slide corresponds to Figure 5.14 on p.185 and relates to material on pp. 184-186.
The relationships among the records stored in databases are based upon one of several logical database structures or models. These fundamental database structures are described below.
Hierarchical Structure. Under this tree-like structure, each data element is related only to one element above it, a so-called one-to-many relationship. All records are dependent and arranged in multilevel structures.
Network Structure. This structure features a many-to-many arrangement whereby the DBMS can access a data element by following one of several paths.
Relational Structure. This model has become the most popular structure and is used by most microcomputers. All data elements within the database are viewed as being stored in the form of simple tables. The DBMS can link data elements from various tables to provide information to end users.
Teaching Tips
This slide corresponds to Figure 5.16 on p. 189 and relates to material on pp. 189-190.
Object-Oriented Structure. Objects consist of data values describing the attributes of an entity and the operations that can be performed on the data. This is called encapsulation and allows object-oriented database structures to better handle complex types of data such as video and audio. The object-oriented model also supports inheritance, allowing new objects to replicate some or all the characteristics of one or more parent objects, as shown in the slide. Such capabilities allow developers to copy and combine objects, allowing for very rapid development of new database solutions.
Multidimensional Structure. This structure uses cells within a multidimensional framework to aggregate data related to elements within a given dimension. Each cell combines with similar cells to form a coherent "cube" of information and data, which in turn is combined with other cubes to form dimensions. As a result they are both compact and easy to understand. Multidimensional structures have fast become the most popular database structure for analytical databases that support online analytical processing (OLAP) applications.
Teaching Tips
This slide corresponds to Figure 5.17 on p.191 and Figure 5.18 on p. 192 and relates to material on pp. 190-192.
Efficient access to data is the critical necessity of an effective database system. Key concepts and terms associated with file access include:
Key Fields. This is a unique identifier of the data record.
URLs. The files and databases on the Internet and corporate intranets and extranets use URLs (Uniform Resource Locators) for data access. Thus, pages of hyperlinked text and multimedia documents on the Web and intranet/extranet websites are accessed by URLs.
Sequential Organization. This refers to a structure in which the records are physically stored in a specified order according to a key field in each record.
Sequential Access. This refers to the predetermined order of processing data. Each record is accessed according to the same set of commands. Access begins at the start of the file or record and proceeds, in order, to the end. This is a fast and efficient method for processing large volumes of data.
Direct Access. Under this method, records do not have to be arranged into any particular sequence on storage media, however the computer must keep track of the storage location of each record.
Key Transformation. This common direct access technique performs an arithmetic computation on a key field or record and uses the number that results as an address to store and access that record.
Indexed Sequential Access Method. This approach combines features of both sequential and direct access. Sequential storage allows for large volume processing while indexed addressing allows direct access of smaller amounts of data from individual records.
Teaching Tips
This slide relates to material on pp. 193-196.
Database planning, beyond that of a personal or small business end user database created by a database management package, typically requires use of a top-down data planning process based upon the systems development model covered earlier:
1. Data Planning. At this stage, planners develop a model of business processes. This results in an enterprise model of business processes with documentation.
2. Requirements Specification. This stage defines the information needs of end users in a business process. Description of needs may be provided in natural language or using the tools of a particular design methodology.
3. Conceptual Design. This stage expresses all information requirements in the form of a high-level model.
4. Logical Design. This stage translates the conceptual model into the data model of a DBMS.
5. Physical Design. This stage determines the data storage structures and access methods.
Teaching Tips
This slide corresponds to Figure 5.22 on p.197 and relates to material on pp. 196-198.