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Discovering occurrences of user-defined patterns in historical data representing
collaborative activities in virtual user environment
Jozef Wagner
František Babič
Ján Paralič
KKUI, FEI
TU Kosice
Outline
Background Source data Analysis Patterns Conclusion
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Background (1)
Virtual user environment as working or learning platform
Collaborative activities within various groups of users.– Lead to new learning and knowledge practices
All activities within virtual user environment are logged
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Background (2)
Complex processes, Ill defined and not easily formulated
Not all activities are captured– F2F meetings, field trials, minutes, interviews
Researchers and Teachers wants to evaluate user behavior during these activities, identify and understand knowledge practices
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Source data
Dedicated server accepts event logs from various end user tools participating in the learning process
In each event– Identify user, object, working group– Type of action, Time– Custom properties
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Analyses
Quantitative– Summarized information– Aggregation, filters
Social network analyses Historical retrospective
– Based on timeline views
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Quantitative analyses
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Timeline view (1)
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Timeline view (2)
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Timeline view (3)
Visualization of performed events on the timeline in chronological order
Interactive tool Filtering, zooming Possiblity to add external events and
annotations Posibility to create and use patterns
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Patterns (1)
Process discovery techniques are not applicable Our processes are not rigid, well defined Patterns help to identify critical points and best
practices Attempt to formally and explicitly define parts of
the process Interactive and iterative
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Patterns (2)
Patterns is a sequence of pattern elements, each representing one generalized event
Include logical operations, unification Beyond simple sequence, branching Define weight, multiplicity Searching generates tree of matches
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Clojure(.org)
LISP on JVM Better Java than Java Dynamic functional homoiconic language Lazy evaluation for sequences Concurrent programming made easy
– STM– Immutable data
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Open Questions
Formal description for patterns Filter out similar results
– But provide means to display them, if requested
Return also partial matches
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Database for logs
Mysql MonetDB (column oriented) Key/Value storage (CouchDB) Graph storage
– Neo4j (JSON)– Allegro graph (SPARQL)
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Thank you for your attention
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