AI Platform
Research Areas
Intro
Artificial intelligence has already penetrated deep into daily life. When shopping, we receive
product recommendations via AI. When driving, we ask our voice recognition secretary things that
we want to know. Advanced services like autonomous driving, robo-advisor, and real-time
translation are already in wide use, with the scope of such services growing by the day. Many users
are captivated by the performance of these and other revolutionary AI services.
But what if all AI-embedded products and technologies were each based on a different standard
and format?
Big Data & AI Platform is what links now-familiar AI services together and enables new ones to
function in the same environment. It not only creates the systems and data needed for AI but also
standardizes big data analysis procedures while providing optimized algorithms.
In an era in which the platform is the primary competitive agent, the importance of Big Data & AI
Platform is continuing to grow in every field.
What is
AI Platform?
HIT studies four areas in Big Data & AI Platform research.
The first is Data Governance, which is about data management—the key ingredient of AI and big
data. This area aims to both satisfy the high security requirements of financial data and integrate
big data analytical methods while making them more convenient to use.
The second area is Data Engineering, which enables close communication between data and the
user. This area focuses on studying platforms that standardize AI algorithms and patterns and feeds
standardized logic into the system to enable substantive and efficient analysis.
The third area is DataOps, which aims to provide an environment and culture for software
development that is grounded in collaboration and integration. This area is becoming increasingly
important for AI, which is growing both in scale and complexity.
The fourth area is Data Analytics Process, which involves standardizing big data analysis processes
and provides standardized elements and data flows through platforms.
This area aims to improve the efficiency and quality of outcomes of data analysis conducted for
business understanding, data acquisition and understanding, modeling, distribution, and customer
acceptance.
Big Data
&
AI Platform
Data Analytics
Process
- Data Analytics Process
-
· Data analysis modeling
methodology -
· Data analysis project
management methodology -
· Data analysis modeling
cooperation system
Data
Governance
- Data Governance
- · Data integration
-
· Data security/
authentication -
· Data quality
management - · Business advancements
DataOps
- DataOps
- · Data pipeline
-
· Deployment/operating system
for analysis models -
· Lightweight modeling
algorithms
Data
Engineering
- Data Engineering
-
· Modeling algorithm
optimization -
· Automated modeling
technologies -
· Graph-based analytical
algorithms -
· Modeling algorithms for
distributed environments
Future Research
HIT is currently working on applying a standardized Big Data & AI Platform to all HFG
affiliates. The ultimate goal is to provide a platform that every employee can utilize to
perform their tasks. In other words, it should be a platform that analysts can use to
develop algorithms and analyze big data through standardized processes and that those in
the field can use to check diverse data-based statistics and from which to develop insights.
Also, depending on the situation, non-AI experts should be able to conduct basic data
analysis with an easy interface or an analytical model that has already been created by
analysts.
In the financial sector, AI is now indispensable. Given the times we live in today, HIT looks
to apply advanced AI technologies to the financial sector through Big Data & AI Platform
while at the same time simplifying AI for easy use in finance-related collaborations.
Through these two goals, HIT aims at ultimately taking a dominant position in the future of
finance AI.