Pattern-based Features

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Do you need to process sequential and structure data (e.g. structured logs)? Use off-the-shelf pattern mining techniques to mine patterns from data and use the mined patterns as features (in combination with classic data mining / machine learning techniques). http://research.microsoft.com/apps/pubs/default.aspx?id=1883 http://research.microsoft.com/en-us/events/dapse2013/

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Pattern-based Features
A Data Transformation Pattern
http://research.microsoft.com/en-us/projects/tark/
Venkatesh-Prasad Ranganath
Jithin Thomas
Microsoft Research, India
http://research.microsoft.com/ICSE2013

Context / Scenarios
• Compatibility Testing
• Test Prioritization / Test Suite Minimization
• Representative Identification
• Similar Case Recommendation
• Anomaly Detection

Constraints
Input Characteristics
– Data is sequential
– Data is structured
– Fields may be irrelevant
– Values may be irrelevant
– Value flow may be
relevant
Output Constraints
– Usable with existing
DM/ML algorithms
– Amenable to simple
reasoning
– Accessible
– Possess explanatory
power

A Data Transformation Pattern
• Use off-the-shelf techniques to mine patterns
– Item-set mining
– Temporal pattern mining
– Association rule mining*
– Graph mining*
• Use patterns as features
– Binary/Categorical features: Presence of patterns
– Numeric features: Properties of patterns
* We have not tried these pattern mining techniques.

Example
Win8
USB 3.0
Driver Stack
XHCI
Driver1
Win7
USB 2.0
Driver Stack
Driver1
EHCI OHCI UHCI
Driver2
USB 2.0
device
USB 2.0
device
When a USB 2.0 device is plugged into a USB 3.0 port on
Win8, will the USB 3.0 driver in Win8 exhibit the same behavior
as the USB 2.0 driver?

Example
USB2
Log
Tark
USB2 Patterns
USB3
Log
Tark
USB3 Patterns
Structural and Temporal
Pattern Diffing
USB2
Patterns
USB3
Patterns
DispatchIrp forward alternates with IrpCompletion && PreIoCompleteRequest
when
IOCTLType=IRP_MJ_PNP(0x1B),IRP_MN_START_DEVICE(0x00), irpID=SAME, and
IrpSubmitDetails.irp.ioStackLocation.control=SAME
IOCTLType=URB_FUNCTION_BULK_OR_INTERRUPT_T
RANSFER(0x09)
&& IoCallDriverReturn && IoCallDriverReturn.irql=2
&& IoCallDriverReturn.status=0xC000000E

Pattern-based Features
Input Characteristics
– Data is sequential
– Data is structured
– Fields may be irrelevant
– Values may be irrelevant
– Value flow may be relevant
Output Constraints
– Usable with existing
DM/ML algorithms
– Amenable to simple
reasoning
– Accessible
– Possess explanatory power
Pattern
– Use off-the-shelf
techniques to mine
patterns
• Item-set mining
• Temporal pattern mining
– Use patterns as features
• Binary/Categorical features
• Numeric features
http://research.microsoft.com/en-us/projects/tark/

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