Authors :
Ramla Suhra
Volume/Issue :
Volume 9 - 2024, Issue 10 - October
Google Scholar :
https://tinyurl.com/mr2epjpm
Scribd :
https://tinyurl.com/yh3evatf
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24OCT1676
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Organizations these days increasingly rely on
fast growing data for their critical business decision
making. To leverage full potential of data, petabytes of
data are being ingested into central data lakes mostly
powered by cloud. They also realize that it is not enough
to just collect huge amounts of data. To derive value from
this data, it must be cleansed, interconnected and
translated from its complex technicalities into an easily
interpretable and more familiar business terminology.
Building a semantic view of the data enriched with
business metrics enable users to query, analyze and
visualize information as quickly as the business demands.
While the semantic layer is perceived as the cornerstone
in modern data architecture, there are different
perspective towards where or how this should be
implemented. Additionally, understanding the evolution
of semantic layer over the years can help choose the right
architecture when attempting to build one in an
organization. With the advent of Artificial Intelligence
(AI), it is imperative that we discuss the impact of AI on
this topic. This research delves into the evolution of the
need and significance of semantic layer, exploring their
architecture, benefits. It also analyzes the challenges
faced during semantic layer adoption and the outlook.
Keywords :
Data Analytics; Artificial Intelligenc;, Data Architecture; Semantic Layer.
References :
- J.-M. Cambot, B. Liautaud, and S. F. Sa, “US5555403A - Relational database access system using semantically dynamic objects - Google Patents.” https://patents.google.com/patent/US5555403
- “Microstrategy, Inc. v. Business Objects, S.A., 661 F. Supp. 2d 548 | Casetext Search + Citator.” https://casetext.com/case/microstrategy-inc-v-business-objects-3
- Wikipedia contributors, “Online analytical processing,” Wikipedia, Oct. 08, 2024. https://en.wikipedia.org/wiki/Online_analytical_processing
- M. 2023 7 M. Read, “What is a data cube?,” 365 Data Science, May 02, 2023. https://365datascience.com/trending/data-cube/
- Emilien, “Business Intelligence Trends 2020,” Wiiisdom | Analytics Governance Solutions, Feb. 09, 2023. https://wiiisdom.com/ebook/business-intelligence-trends-2020/
- A. Kumar, “The semantic layer movement: the rise & current state,” Modern Data 101, May 02, 2024. https://moderndata101.substack.com/p/the-semantic-movement-the-story-of
- A. Keydunov, “Real-time AI experiences can’t advance without a universal semantic layer,” RTInsights, Mar. 03, 2024. https://www.rtinsights.com/real-time-ai-experiences-cant-advance-without-a-universal-semantic-layer/
- A. Schwanke, “Semantic layer — one layer to serve them all - Axel Schwanke - medium,” Medium, Aug. 29, 2024. [Online]. Available: https://medium.com/@axel.schwanke/semantic-layer-one-layer-to-serve-them-all-d0ef7eff1ffa
- “The Journey towards metric Standardization | Uber Blog,” Uber Blog, Jan. 12, 2021. https://www.uber.com/blog/umetric/
Organizations these days increasingly rely on
fast growing data for their critical business decision
making. To leverage full potential of data, petabytes of
data are being ingested into central data lakes mostly
powered by cloud. They also realize that it is not enough
to just collect huge amounts of data. To derive value from
this data, it must be cleansed, interconnected and
translated from its complex technicalities into an easily
interpretable and more familiar business terminology.
Building a semantic view of the data enriched with
business metrics enable users to query, analyze and
visualize information as quickly as the business demands.
While the semantic layer is perceived as the cornerstone
in modern data architecture, there are different
perspective towards where or how this should be
implemented. Additionally, understanding the evolution
of semantic layer over the years can help choose the right
architecture when attempting to build one in an
organization. With the advent of Artificial Intelligence
(AI), it is imperative that we discuss the impact of AI on
this topic. This research delves into the evolution of the
need and significance of semantic layer, exploring their
architecture, benefits. It also analyzes the challenges
faced during semantic layer adoption and the outlook.
Keywords :
Data Analytics; Artificial Intelligenc;, Data Architecture; Semantic Layer.
