1
25-101
What Is the Secret to Preparing Your Data for AI Success?
Sean Hunter
Eclipse Mining Technologies, Tucson, AZ
INTRODUCTION
Artificial Intelligence has a long history but is currently
coming strongly into focus with new advancements. This
leads to questions many people are asking: what exactly is
AI, how can it be used effectively in mining operations, and
what foundation is required to make AI implementations
successful?
In mining, many AI applications exist, from drill pat-
tern optimization to predictive maintenance. However,
many implementations struggle due to data integration
challenges. Mining operations generate large amounts of
data from many sources, such as fleet management sys-
tems, drill sensors, process control systems, and laboratory
results. Unfortunately, this data often exists in silos where
it’s difficult to get it out, making AI applications unable
to leverage it. Worse still, tools that use AI don’t always
provide their data back to the system, creating even further
data silos. All these problems make it hard to take advan-
tage of the data at the mine, slowing down and reducing
the effectiveness of AI.
This paper presents a comprehensive approach to orga-
nizing mining data using an AI-ready knowledge graph
system. We will first explore the evolution of AI in min-
ing, from early expert systems to modern machine learning
applications. Then, we will detail how a knowledge graph
can integrate business context, operational data, and AI
models into a cohesive system that enables more effective
AI implementations across mining operations.
The proposed method has been developed considering
both the unique data challenges faced by the mining indus-
try, while also considering best practices learned from the
AI field at large. By following this approach, it’s possible
to better utilize existing data, getting higher quality and
more complete results faster, and in a way that’s reusable
and composable.
HISTORY OF AI IN MINING
Artificial intelligence in mining is often viewed through the
narrow lens of machine learning and predictive analytics.
However, AI covers a much broader range as it has evolved
over decades. Understanding the full breadth of AI can help
assist in overcoming the data challenges behind successfully
implementing an AI solution.
Current Views on AI and Machine Learning
In mining, there is a strong focus on AI/ML (Machine
Learning.) This is the branch of AI which learns from his-
torical data to build models to make future predictions.
This can cover everything from predictive maintenance to
recovery rate estimation. [1]
Some products are based on this with their own pre-
trained models, drawing on their own data sets. For exam-
ple, a pre-trained classification model could estimate an ore
body bounds underground given photos.
These models can be created from simple techniques
like linear regression all the way up to more complex
approaches using things like neural networks and gradient-
boosted trees. Over the years, a large toolkit of these meth-
ods has been created, which can handle a wide range of
cases and data types.
While this is an important branch of AI, there are
many other important cases to consider.
Early Symbolic AI
Historically, AI was separate from ML and was more
focused on exact answers rather than estimates and predic-
tions. This covers ideas like deductive databases, knowledge
graphs, and expert systems.
All these systems are built on the idea of creating new
information from base data by following a set of predefined,
static rules. A deductive database, for example, is a database
25-101
What Is the Secret to Preparing Your Data for AI Success?
Sean Hunter
Eclipse Mining Technologies, Tucson, AZ
INTRODUCTION
Artificial Intelligence has a long history but is currently
coming strongly into focus with new advancements. This
leads to questions many people are asking: what exactly is
AI, how can it be used effectively in mining operations, and
what foundation is required to make AI implementations
successful?
In mining, many AI applications exist, from drill pat-
tern optimization to predictive maintenance. However,
many implementations struggle due to data integration
challenges. Mining operations generate large amounts of
data from many sources, such as fleet management sys-
tems, drill sensors, process control systems, and laboratory
results. Unfortunately, this data often exists in silos where
it’s difficult to get it out, making AI applications unable
to leverage it. Worse still, tools that use AI don’t always
provide their data back to the system, creating even further
data silos. All these problems make it hard to take advan-
tage of the data at the mine, slowing down and reducing
the effectiveness of AI.
This paper presents a comprehensive approach to orga-
nizing mining data using an AI-ready knowledge graph
system. We will first explore the evolution of AI in min-
ing, from early expert systems to modern machine learning
applications. Then, we will detail how a knowledge graph
can integrate business context, operational data, and AI
models into a cohesive system that enables more effective
AI implementations across mining operations.
The proposed method has been developed considering
both the unique data challenges faced by the mining indus-
try, while also considering best practices learned from the
AI field at large. By following this approach, it’s possible
to better utilize existing data, getting higher quality and
more complete results faster, and in a way that’s reusable
and composable.
HISTORY OF AI IN MINING
Artificial intelligence in mining is often viewed through the
narrow lens of machine learning and predictive analytics.
However, AI covers a much broader range as it has evolved
over decades. Understanding the full breadth of AI can help
assist in overcoming the data challenges behind successfully
implementing an AI solution.
Current Views on AI and Machine Learning
In mining, there is a strong focus on AI/ML (Machine
Learning.) This is the branch of AI which learns from his-
torical data to build models to make future predictions.
This can cover everything from predictive maintenance to
recovery rate estimation. [1]
Some products are based on this with their own pre-
trained models, drawing on their own data sets. For exam-
ple, a pre-trained classification model could estimate an ore
body bounds underground given photos.
These models can be created from simple techniques
like linear regression all the way up to more complex
approaches using things like neural networks and gradient-
boosted trees. Over the years, a large toolkit of these meth-
ods has been created, which can handle a wide range of
cases and data types.
While this is an important branch of AI, there are
many other important cases to consider.
Early Symbolic AI
Historically, AI was separate from ML and was more
focused on exact answers rather than estimates and predic-
tions. This covers ideas like deductive databases, knowledge
graphs, and expert systems.
All these systems are built on the idea of creating new
information from base data by following a set of predefined,
static rules. A deductive database, for example, is a database