Find related topics. | Discover entities. | See connections. | Build a topical map.
Building topical map: Starting…
Process mining: History, Art, Science & Products
Process mining is a family of techniques for analyzing event data to understand and improve operational processes. Part of the fields of data science and process management, process mining is generally built on logs that contain case id, a unique identifier for a particular process instance; an activity, a description of the event that is occurring; a…
Use the mouse wheel or two fingers (on touchscreens) to zoom in and out of the map.
More settings
Process mining topic overview
The analysis highlights History, Art, Science and Products as prominent areas in the source structure around Process mining.
Related topics
39
Source areas
4
Connected nodes
45
Extracted relationships
248
Concept neighborhoods
29
Bridge connections
45
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Overview · 18 topics
History and place in data science · 11 topics
Categories · 8 topics
Process mining software · 2 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How Process mining connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around Process mining shows recurring relationship patterns in the source. For example, Process mining → Aalst, Action, Alex, AMIA Annu Symp Proc, Analyze, Apers, Application, Applications, Approaches, ARIS PPM, ARIS Process Performance Manager, Atzeni, Auerbach PublicationsKirchmer, Berlin, Berti, BPM, Business Intelligence, Business Process Cockpit, Business Process Intelligence, Business Process Management Another extracted example is Process mining → Aalst, An, Another, As, BPMN, Conformance, EPCs, For, Helps, It, Many, Nowadays, One, Performance, Petri, Process, Recently, Reijers, Rozinat, Song. Use these groups to spot repeated connection types before inspecting the individual relationships.
Process mining
Top relations
related to Further reading· 162
Process mining → Aalst, Action, Alex, AMIA Annu Symp Proc, Analyze, Apers, Application, Applications, Approaches, ARIS PPM, ARIS Process Performance Manager, Atzeni, Auerbach PublicationsKirchmer, Berlin, Berti, BPM, Business Intelligence, Business Process Cockpit, Business Process Intelligence, Business Process Management
related to Categories· 26
Process mining → Aalst, An, Another, As, BPMN, Conformance, EPCs, For, Helps, It, Many, Nowadays, One, Performance, Petri, Process, Recently, Reijers, Rozinat, Song
Process mining relationships Subject–Predicate–Object triples
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 248 structured relationships around Process mining. Examples in this analysis include Process mining → is a → family of techniques for analyzing event data to understand and improve operational processes and resources → instance of → and sometimes other information. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
Process mining
is a
family of techniques for analyzing event data to understand and improve operational processes
0.90
text
resources
instance of
and sometimes other information
0.80
text
costs
instance of
and sometimes other information
0.80
text
and so on.There are three main classes of process mining techniques
instance of
and sometimes other information
0.80
text
BAM
instance of
and timestamps.Contemporary management trends
0.80
text
inductive miner were developed for process discovery
instance of
More powerful algorithms
0.80
text
alpha algorithm
instance of
using techniques
0.80
text
processing times
instance of
The model is extended with additional performance information
0.80
text
cycle times
instance of
The model is extended with additional performance information
0.80
text
waiting times
instance of
The model is extended with additional performance information
0.80
text
costs
instance of
The model is extended with additional performance information
0.80
text
etc.
instance of
The model is extended with additional performance information
0.80
text
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around Process mining bring nearby vocabulary together. In this analysis, examples include Process, Data and Business. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
Process mining
Process
Data
Business
Model
Techniques
Discovery
Event
Example
Management
Research
Used
Conformance
process mining
Process
Data
Techniques
Business
Model
Discovery
Event
Research
Workflow
Example
Used
Management
data science
Mining
Event
Process
Techniques
Science
Conference
International
Used
Learning
Logs
Research
Pp
process management
Workflow
Information
Business
Data
Model
Techniques
Discovery
Logs
Systems
Science
Event
Example
workflow management system
Management
Workflow
Information
Business
Logs
Systems
Science
Used
Discovery
Mining
Process
Application
business activity monitoring
Management
Process
Intelligence
Workflow
Discovery
Mining
Processes
Systems
Der
Van
Aalst
Conformance
operations management
Workflow
Information
Business
Logs
Systems
Science
Process
Mining
Application
Learning
Models
Processes
business process intelligence
Learning
Management
Data
Business
Process
Model
Techniques
Discovery
Intelligence
Workflow
Event
Mining
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For Process mining, one of the stronger structural bridges in this analysis connects Process mining with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Min side: 3
Process mining — Overview· splits 25 ⟂ 21Process mining — History and place in data science· splits 34 ⟂ 12Process mining — Categories· splits 37 ⟂ 9Process mining — Process mining software· splits 43 ⟂ 3
Map overview Semantic statistics
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
Process mining
Nodes46
Edges45
Triples248
Avg. degree1.96
Density0.043478
Components1
Source & methodology
TTTA analyzes the structure around Process mining to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Art, Science & Products, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
