LibGuides: TIM-8130 V4: Week 5 Resources

Week 5 Resources

Data Management Body of Knowledge (DAMA-DMBOK)
DAMA International. (2017). Data management body of knowledge (2nd ed.). Technics Publications.
Read Chapter 1: Data Management.
This chapter discusses the importance of data management around the idea that data is an asset that supports the strategic plan of the organization.
Read Chapter 2: Essential Concepts.
This chapter discusses principles, challenges, and strategies used in data management.
Design Guidelines and Process of Metadata Management Based on Data Management Body of Knowledge
Khairunisak, Kusumasari, T. F., & Fauzi, R. (2021). Design guidelines and process of metadata management based on data management body of knowledge. 20217th International Conference on Information Management (ICIM) Information Management (ICIM), 87-91.
This journal article explains the importance of ensuring that data can be easily shared between systems and metadata management best practices used to accomplish this goal.
How to Make a Data Flow Diagram
Lucidchart.com (n.d.). How to make a data flow diagram.
This tutorial describes how to create a data flow diagram to show the movement of data throughout an organization’s business processes.

Data Repository

Tweet IDs Annotated for Enthusiasm and Support Towards Social Causes: CTE, Cyberbullying, and LGBT
Scenario Instructions:

Go to https://doi.org/10.13012/B2IDB-2603648_V1
Review the project information, associated data, and information files.
Use Case: This group of researchers works in the psychology and social sciences department at a local university. The research foci include lexical study surrounding human behavior. In addition to descriptive and inferential analysis, they also engage in natural language processing, sentiment analysis, and text mining. The group needs to be able to conduct descriptive, predictive, diagnostic, and prescriptive analysis sessions.
Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration
Scenario Instructions:

Go to https://doi.org/10.7281/T1/IFSXNV
Review the project information, associated data, and information files.
Use Case: This group of researchers works in the orthopedic and surgical department at a local university. The research foci include human anatomy and physiology for surgical technique enhancement. In addition to descriptive and inferential analysis, they also perform image processing and recognition using fluoroscopy, classification, segmentation, machine learning, and use neural networks for anatomy segmentation and identification. The group must be able to conduct descriptive, predictive, diagnostic, and prescriptive analysis sessions.
Airborne Assessment of Methane Emissions from Offshore Platforms in the U.S. Gulf of Mexico
Scenario Instructions:

Go to https://doi.org/10.7302/v559-6e13
Review the project information, associated data, and information files.
Use Case: This group of researchers works in the pulmonology and respiratory medicine department at a local university. The research foci include interstitial pulmonary disease and environmental factors. In addition to descriptive and inferential analysis, they also engage in image processing and recognition, classification, and segmentation. The group must be able to conduct descriptive, predictive, diagnostic, and prescriptive analysis sessions.

Note: The resources below are suggested, optional readings for use in this course.

Data Management at Scale
Strengholt, P. (2020). Data management at scale. O'Reilly Media, Inc.
Read Chapter 2: Introducing the Scaled Architecture: Organizing Data at Scale.
This chapter discusses different considerations for architecture when developing a data management plan.
Read Chapter 3: Managing Vast Amounts of Data: The Read-Only Data Stores Architecture.
This chapter discusses the Read-Only Data Stores architecture to support intensive data read activity.
Read Chapter 4: Services and API Management: The API Architecture.
This chapter discusses the API architecture to support enterprise-level data collection from a wide variety of sources.
Read Chapter 5: Events and Response Management: The Streaming Architecture.
This chapter discusses an event-driven architecture that is used to support real-time streaming of different data sources using both stateful and stateless throughput systems.
Read Chapter 6: Connecting the Dots.
This chapter discusses data standards related to the use of each of the data management architectures.