What is a Metadata Visualizer?
Metadata defined in DAMA DMBOK, a visualizer that could relate to our universal set
Introduction
An algebraic representation of systematic and compositional elements in a diagrammatic format is a metadata visualizer.
The act of visualizing qualitative spatial relations algebra such as TPCC (Ternary Point Configuration Calculus), RCC (Region Connection Calculus) and AIA (Allen’s Interval Algebra) in a diagrammatic format falls under metadata visualizer.
Metadata is describing data about the data. There are 5 types of metadata as per DAMA DMBOK UK. They are:
(1) Technical
(2) Operational
(3) Business
(4) Process
(5) Stewardship
The IBM data value chain and the Digest cycle are examples containing Process Metadata. Business rules and ETL information are operational and business in nature.
Visualizing Neural Networks
In visualizing neural networks, the code is transformed into diagrammatic nodes that are used for processing information such as model cutting, post processing, adding custom layers or transformation.
It is useful to analyse visually the performance of the graph between for example a keras based neural network and openvino based neural network graph. Tensorflow has Tensorboard and its graph visualization, which are used to perform analysis on operations, data such as images and log files.
Visualizing Distributions
In data science, a metadata extractor is attached with the regression technique or a support vector machine learning technique. It is usually in the lines of analysing algorithms and may use combinatorics.
Let’s take performance as the attribute of analysis: there is time, cost and quality split as analysis contexts. Conducting statistical tests on tabular data with examples such as null hypothesis, t-tests, skewness tests are good examples for visualizing metadata of a data distribution.
Elements of Data Transfer
Metadata is context-driven and transforms the types of metadata in several contexts of analysis.
Operational metadata of the analysis of speech data for evaluating the speech information may be transformed to technical metadata semantically representing the chords of the speech for the entire project, as well as for stewardship data that is describing a text may get transformed to business data for the text analysis project as an entirety.
This is with regards to the Data and the Project taken as Contexts of analysis.
There is another scenario where Solutions and Project are considered for Data Analysis.
In a client-facing task, the business metadata from a solutions perspective may get transformed to process metadata for a Project which is billed. This is because the client-facing task can involve taking up phone calls, submitting a web-based form or using existing client preferences recorded for the delivery of the task.
Another scenario is when stage based relationship is taken into perspective. In this stage based relationship where there is a final delivery to the client, the initial technical metadata such as drawings, configurations etc. in an architectural scenario gets transformed into business metadata by providing that outcome of delivery.
It is quite important to note that cost is involved in such elements of data transfer.
Consider a third scenario where the process metadata in training a neural network cascades to technical metadata. This is predominantly seen at intermediary layers of the neural network where it learns and transforms the input into a visualization.
Treating Qualitative Spatial Relations
It is quite interesting to note that QSRs form the basis in the entirety of metadata visualization. Region Connection Calculus visualizes the locations, sizing and bounds of a figurative method.
Allen’s Interval Algebra analyses temporal relationships in a way that it understands when two distributions closes in and calculates the degree of separation. It evaluates at what points the two distinct distributions show the same behaviour.
In ternary Point configuration calculus, if the distributions show concordance in any of their representations, then it is bound to display that as a TPCC based visualization.
Conclusion
Metadata describing a measurement for a requirement ensures the creation of fit criterion for that requirement. Rationale in Volere Shell is explanatory due to the integration of process metadata. Metadata is investigative in nature as its journalistic attributes: 5WH (What, How, When, Why, Who) come into play and that is the reason for its transformation in The Elements of Data Transfer.
The methods of visualization is always contextual in nature just as the metadata itself. What makes metadata visualization different is because it is a figurative explanation that can be automated.
Some of the standardized visualizations have been in discussion but those that are relevant to metadata are well known to us. The logical and physical data models associated to metadata repository is used for visualizations. In the cases where metadata architecture is defined, a good understanding of data lineage is essential.
