UNIT-V FMM.HYDRAULIC TURBINE - Construction and working
IOE_Individual.pptx
1. Linked Analytics Data Sets
Name:- Shivamkumar Prasad
Roll no:- 44
Guided By :- Prof. Reena Kothari
2. INTRODUCTION
• Data Organization is the practice of categorizing and classifying data to make it more usable Similar to a file
folder, where we keep important documents, you'll need to arrange your data in the most logical and orderly
fashion, so you and anyone else who accesses it can easily find what they are looking for.
• Good data organization strategy are important because your data contains the keys to managing your company's
most valuable assets.
• An analytics strategy is part of your comprehensive strategic vision to specify how data is collected and used to
inform business decision. it is meant to provide clarity on key reporting metrics by: specifying the sources and
the types of data that are collected and used for reporting.
Strategies to organize Data for Analytics:-
1. Linked Analytics DataSets
2. Analytical DataSets
3. Building Analytical Datasets
3. Linked Analytics DataSets
Linked Data is a set of design principles for sharing machine - readable interlinked data on the web. when
combined with open data(data that can be freely used and distributed), it is called linked open data(LOD)
An RDF database(resource Description Framework) such as Ontotext,s GraphDB is an Example of LOD.
It is able to handle huge datasets coming from disparate soucres and link them to Open data, which boots
knowledge discovery and efficient data driven analytics
4. Linked Data is one of the core pillars of the Semantic Web, also known as the Web of Data. The
Semantic Web is about making links between datasets that are understandable not only to humans,
but also to machines, and Linked Data provides the best practices for making these links possible. In
other words, Linked Data is a set of design principles for sharing machine-readable interlinked data
on the Web
In computing, linked data (often capitalized as Linked Data) is structured data which is interlinked
with other data so it becomes more useful through semantic queries. It builds upon
standard Web technologies such as HTTP, RDF and URIs, but rather than using them to serve web
pages only for human readers, it extends them to share information in a way that can be read
automatically by computers. Part of the vision of linked data is for the Internet to become a
global database.[1]
5. The IoT Analytics Dataset is a materialized view defined in SQL over a Datastore, multiple Datasets can be created
over a single Datastore
Analytical DataSets
Building Analytical Datasets
• Analytical datasets are semi-denormalized tables. By semi-denormalized, this means including not just the ID
code of a field but the description for it as well. You may also decide to create categories based on value ranges
and include these as separate features.
• The goal is to make life easy for your analysts more than a focus on efficiently storing values, as it would be with
purely relational database design. You are, essentially, prebuilding the transformed datasets that an analyst
would be building using SQL to preprocess a dataset in preparation to train an ML model anyway.
6. Conclusion
The data generated from IoT devices turns out to be of value only if it gets subjected to analysis,
which brings data analytics into the picture. Data Analytics (DA) is defined as a process, which is
used to examine big and small data sets with varying data properties to extract meaningful
conclusions and actionable insights. These conclusions are usually in the form of trends,
patterns, and statistics that aid business organizations in proactively engaging with data to
implement effective decision-making processes.
