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Last updated on 2024年10月3日

How do you use data acquisition metadata and provenance to improve data storage and retrieval efficiency?

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Data acquisition is the process of collecting and transforming data from various sources for analysis, visualization, or modeling. However, data acquisition is not only about the data itself, but also about the metadata and provenance that describe and track the data. In this article, you will learn how to use data acquisition metadata and provenance to improve data storage and retrieval efficiency.

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MOHAMED KAMEL, CDMP

Data Management Consultant at Entropy | Data governance | Data classification | NDMO compliance
Jeff Sakal

Registered Nurse, Antiques Dealer
Naveen Kanneganti

Global Lead Data Solution Architect-Data & Analytics: Data on Cloud | Big Data Lake Analytics | Data Visualization…

1 What is metadata and provenance?

Metadata is the data about the data, such as the name, format, size, location, quality, or context of the data. Provenance is the data about the data's origin, history, and transformation, such as the source, date, author, or workflow of the data. Metadata and provenance provide valuable information for data management, discovery, and reuse.

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Jeff Sakal

Registered Nurse, Antiques Dealer
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Eidetic Memory Pattern Analysis and a sense of a humorous dynamical labyrinth of logistics linguistics ?? it’s fox in Sox and box on ????????

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2 Why are metadata and provenance important?

Metadata and provenance are important for several reasons. First, they help you organize and catalog your data, making it easier to find, access, and share. Second, they help you understand and trust your data, making it easier to verify, validate, and interpret. Third, they help you optimize and automate your data, making it easier to integrate, process, and update.

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Naveen Kanneganti

Global Lead Data Solution Architect-Data & Analytics: Data on Cloud | Big Data Lake Analytics | Data Visualization |Data Warehouse| BI | Data Modelling | Azure Data | Insights |Real time Data| Data Integration
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Metadata can be used both for Technical as well as Functional purposes. Technical: 1) It helps in understanding the structure of data/table,etc , estimate sizing for the table/database, etc 2) It can also be leveraged to automate things such as meta data driven data extractions 3) Track the changes in the metadata e.g: you can store the meta data and its changes to derive how /when the structure of data got changed - useful esp for auditing/tracebacks/ automation of data extraction Functional: If properly catalogued , may be using tools such as collibra/ Erwin, it helps the users to easily find out which table/database/sources might be having the data that they are looking out for (e.g: by searching in field name)

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3 How to create metadata and provenance?

There are various methods to create metadata and provenance, depending on the type, source, and purpose of your data. For example, standard metadata schemas and formats, such as Dublin Core, JSON-LD, or RDF can be used to ensure consistency and interoperability. Additionally, metadata extraction tools like Apache Tika, MetaDEx, or Meta Mate can be used to generate metadata from data files or web pages. Provenance capture tools like ProvToolbox, YesWorkflow, or DataONE can be used to record the provenance of your data. Lastly, metadata and provenance editors such as MetaEdit+, ProvEdit, or DataTagger can be used to manually create or edit metadata and provenance for your data.

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4 How to store metadata and provenance?

When storing metadata and provenance, the size, complexity, and accessibility of your data should be taken into account. For example, EXIF can be used for images, ID3 for audio, or XML for documents; CSV for tabular data, XML for hierarchical data, or RDF for linked data; CKAN, Dataverse, or Zenodo for open data; and DSpace, EPrints, or Fedora for institutional data. These are just some of the common methods available.

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5 How to retrieve metadata and provenance?

Retrieving metadata and provenance can be done in several ways, depending on the query, format, and output of your data. For instance, you can use search engines or browsers like Google Dataset Search or DataCite Search to find and explore data based on their metadata and provenance. Additionally, you can access and manipulate data through query languages or APIs such as SPARQL, OAI-PMH, or ProvPy. Visualization tools or libraries like ProvVis, ProvViz, or ProvJS can also be used to display and analyze data based on their metadata and provenance.

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6 How to improve data storage and retrieval efficiency?

Utilizing metadata and provenance can significantly improve data storage and retrieval efficiency. This can be done by indexing and classifying data to make it easier to search and filter, compressing and optimizing data for scalability, linking and integrating data for diversity and comprehensiveness, updating and synchronizing data for accuracy, and enriching and annotating data for informativeness.

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MOHAMED KAMEL, CDMP

Data Management Consultant at Entropy | Data governance | Data classification | NDMO compliance
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Metadata enables a comprehensive understanding of the data, which helps in dealing with and improving it by increasing its quality and ease of access and retrieval in accordance with protection considerations.

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Naveen Kanneganti

Global Lead Data Solution Architect-Data & Analytics: Data on Cloud | Big Data Lake Analytics | Data Visualization |Data Warehouse| BI | Data Modelling | Azure Data | Insights |Real time Data| Data Integration
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The metadata, if properly catalogued, can be used to figure out which data is already present in the data storage. This can be used to check, if there is already data present and can reduce the redundant data. While retrieving similar thing happens, instead of just having data/ technical information, having metadata as well - will help in figuring out what the data is about and helps in quicker finding out the relevant data. This is similar to a book, wherein we can directly go to the page of the topic that we are interested, if its properly indexed

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you use data acquisition metadata and provenance to improve data storage and retrieval efficiency?

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1 What is metadata and provenance?

2 Why are metadata and provenance important?

3 How to create metadata and provenance?

4 How to store metadata and provenance?

5 How to retrieve metadata and provenance?

6 How to improve data storage and retrieval efficiency?

7 Here’s what else to consider

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