Recording the Context of Action for Process

Documentation

Ian Wootten

Cardiff University, UK

I.M.Wootten@cs.cf.ac.uk

Context Definitions:

Circumstances forming the setting for an event, statement or idea [Oxford English Dictionary, 2008]

User environment elements a computer knows about [Brown, 1995]

Characterisation of the situation of entities [Dey and Abowd, 2000]

Properties which can support/dispute evidence of actions More than component interaction More informed judgements can be made Subjective in nature

Ad-hoc documentation between applications Records of data with unknown relationships could be useful

May help out at a later date

Process Distinction Provenance is about processes

“The process which led to some data” [Groth et al. 2006]

Sequences of actions How did this come to be the way it is?

Achieved by: Documenting relationships, component interaction Evidence

If actions in a process are the same, locating distinct traces becomes more difficult e.g. I invoke this workflow multiple times, are any records

unique? Were they performed in different situations?

Context Uses

Automatic assertion in legacy actors E.g. Long running, data mining services

Prediction of future actor properties Record context and actions Probabilistic model constructed

Similarity of past process traces Context recorded and compared for two

provenance traces And others….

Documenting Process Cannot answer all provenance queries

with documentation of interaction alone Eg. What was happening to cause such

behaviour? Why does execution of the same workflow result in different execution times? How do we know an action is subject to the same conditions?

We know nothing of the context under which assertions are made Answers can be given by entities

themselves (e.g using PReServ) Particular focus on deriving context from

measurable values

Invoke

Result

f1()

f2()

Host

arg

Result

Actor

f1(arg)

f2(f1(arg))

Time Series Knowledge Representation (TSKR) Properties and States for an

actor are represented using the TSKR [Moerchen 2006] Series extracted from several

numerical variables Segmentation, Shape-based

Coincidence intervals found

Resultant series shows time intervals when multiple conditions occur (states) Monitored variables specified

by service administrator

States represented in transition table

S1 S1S2 S2S6S3 S4 S5 S7

D F E D

A B C B A

F D

AD BD CD BDC

FBF

BE ADAF

11 12 13 33 32 22 12 31 11

Chords/States

Patterns

Tones

S1 S1S2 S2S6S3 S4 S5 S7

Documenting Context Provide a mechanism to specify and

automatically record environmental context for any application Capture using process documentation as

assertions of actor state, using PReServ Operate according to a particular owner

defined policy Triggered on service execution through

service wrappers Reuse existing monitoring resources

(Ganglia, Nagios) through plug-ins

Our policy configuration Gathers monitoring data and mines

states using TSKR

I1

I2

I3

StAR Monitoring Sources

Slicer

Atlas Image

Atlas Header

Atlas Slice

PAssertion

M

Registry

Monitoring Policy

Observer

Observer

Host System

Client

Experimentation

TSKR series patterns can be used for comparison of states Where series is segmented

Where vast collections of data need to be explored

Based upon Context component distances Maximum distance possible

State q

1

State r

2

1

1

Pa

tte

rn E

lem

ent

s

1 3

TSKR Transition history mapped to a transition table Used as a predictive tool

Ran two services from provenance challenge 1000 times Context recorded as actor state assertions Action recorded as interaction assertions

Prediction Results

Three approaches: State prediction

(TSKR) Random (with

history) Random

Similarity Results

Indicates small subsets of documentation

Conclusions

Context helps to understand evidence For processes realised using SOA, understanding records

of action Actions may be the same but performed in different

circumstances

TSKR is a good fit for context measurement Registry approach assists in context capture Automates the collection of actor state Demonstrated as:

Good predictor of state Useful identification of state properties