querying business processes under models of uncertainty daniel deutch, tova milo tel-aviv university...

Querying Business Processes Under Models of Uncertainty

Daniel Deutch, Tova MiloTel-Aviv University

ERPERP

HR HR SystemSystem

eComeCommm

CRMCRM

LogisticsLogistics

CustomerCustomer

BankBank

SupplierSupplier

Querying BPs Under Models of Uncertainty2

Outline

Introduction & Motivation

External Events

Partial Tracing

Related and Future work



External Events

Partial Tracing


Outline


Web-based Business Processes are very popular

Querying possible \ likely execution flows of such applications

Allows for optimization, personalized ads, improved business logic,…

Queries over specification structure

Database approach to specification analysis

Querying Business ProcessIntroduction and Motivation


Example BPIntroduction and Motivation


BP specifications are compiled to running code

Modeled as nested DAGs Each DAG corresponds to a function\web-page

Node pairs model activities activation and completion Edges mark flow relation Atomic/compound activities Nesting models implementation relations \ links Guarding formulas (on external events) model choices

Recursion is allowed

BP SpecificationsIntroduction and Motivation


Example SpecificationIntroduction and Motivation

F1

F3F2

chooseTravel

chooseTravel

F4

…

$searchType = “flights only”

Confirm

$searchType = “flights + hotels”

$searchType = “flights+hotels+cars”

…

…

$airline=“BA”

Login

Login

Flights

Flights

Advertise

Advertise

Confirm$airline=“AF”

Confirm

Login

Login

Flights

FlightsAdvertise

Advertise

Confirm

Hotels

Hotels…

$hotel=“Marriott”

$hotel=“CrownePlaza”

…

$choice=“confirm”

F1

$choice=“reset”

…

$airline=“AL”

…


A a specific run is called an EX-flow

Describes activities that occurred in practice + their flow relation

Obtained from a BP by choosing one implementation for each compound activity

Zoom-in edges connect implementations to activation and completion nodes of the corresponding compound activity

Execution trace (Ex-trace) is the log recorded for an EX-flow

Traces size for a given BP may be unbounded

Execution FlowIntroduction and Motivation


Example Execution FlowIntroduction and Motivation

chooseTravel

chooseTravel

Login

Login

Flights

$searchType=“flight only”

$airline = “BA”

Confirm

07:00

07:02

07:04

07:05

07:10

07:30

07:40

07:43

07:41

07:42

Flights

Confirm

Advertise

Advertise...

$choice = “confirm”

...


External Effects, such as user choices, server response time, etc.

Partial Tracing, due to lack of storage, confidentiality, etc.

Thus the past is uncertain, and the future unknown

Sources of UncertaintyIntroduction and Motivation



External Events

Partial Tracing


Outline


Even when traces of past executions are known, future executions are hard to predict

The effect of external events is modeled by logical formulas, guarding implementations

At run-time, formulas’ truth values determines the chosen implementations

UncertaintyExternal Events


Example queries:

What is the possible behavior (flow) for users that do not finalize their reservation?

Which hotels can be reserved by British Airways fliers?

The set of all EX-flows conforming to a query may be large (possibly infinite)

But some are more interesting than others…

TOP-K most likely EX-flowsExternal Events


We define likelihood of EX-flows

Then find the top-k most likely out of these conforming to a query (TOP-K-MATCHES)

What is the typical behavior (flow) for users that do not finalize their reservation?

Which hotels are preferred by British Airways fliers?

TOP-K answers reflect common usage patterns

TOP-K likely EX-flows (cont.)External Events


Execution patterns

Intuitive, similar in structure to execution traces

Seek for occurrences (homomorphism) of the pattern within (any) trace’s sub-graph

May contain transitive nodes and edges

May contain a projection part

Query languageExternal Events


Example QueryExternal Events

chooseTravel

chooseTravel

Any

Any

Start

Start

$Airline = “BA”Flights

Flights

Hotels

Hotels

Confirm

Confirm

chooseTravel

chooseTravel

chooseTravel

chooseTravel


We distinct three classes of distributions, according to their level of dependency

Memory-less (markovian): no dependencies between formulas.

Bounded-memory: dependency in(at most) B last values of each formula.

General

Distribution ClassesExternal Events


Our Example SpecificationExternal events

F1

F3F2

chooseTravel

chooseTravel

F4

…

$searchType = “flights only”

Confirm

$searchType = “flights + hotels”

$searchType = “flights+hotels+cars”

…

…

$airline=“BA”

Login

Login

Flights

Flights

Advertise

Advertise

Confirm$airline=“AF”

Confirm

Login

Login

Flights

FlightsAdvertise

Advertise

Confirm

Hotels

Hotels…

$hotel=“Marriott”

$hotel=“CrownePlaza”

…

$choice=“confirm”

F1

$choice=“reset”

…

$airline=“AL”

…


DistributionExternal Events


For memory-less distribution, we find the TOP-K matches in PTIME (data complexity)

[compute a compact representation of output]

For bounded-memory distributions, NP-completeness in the data size, but we give powerful heuristics

In all settings, NP-completeness in the query size

For general distributions, we show undecidability

ResultsExternal Events


For memory-less distributions, Dynamic Programming algorithm

Gradually computes a table holding the i’th most probable trace rooted at any activity

Implemented with satisfactory performance

Algorithms (intuition)External Events


For bounded-memory, we build a memory-less BP where activity is a “state”, holding all relevant information for future computations.

This “explodes” exponentially the number of compound activity names.

Optimizations utilize conditional independencies between formulas.

Compute approximation of “actual memory”, and maintain only that.

Algorithms (intuition, cont.)External Events



External Events

Partial Tracing


Outline


Partial Tracing, due to lack of storage, confidentiality,…

• Naïve tracing records all activities accurately

• Semi-Naïve tracing contains only partial information on the names of some activities

• Selective tracing may omit some activities occurrences

Tracing systems (called types) are represented by a renaming function and a deletion set

Types of Partial TracesPartial Tracing


Trip

Trip

Search

Search

Hotel Flight

Flight

Print

Print

Credit1

Hotel Credit1

Luxury

Luxury

Search

Search

LuxHotel LuxFlight

LuxFlight

Print

Print

Credit2

LuxHotel Credit2

Credit2

Credit2

Credit1

Credit1

Example BPPartial Tracing


Trip

Trip

Search

Search

Hotel

Hotel

Flight

Flight

Print

Print

Credit1

Credit1

Trip

Trip

Search

Search

LuxHotel

LuxHotel

Credit2

Credit2

LuxFlight

LuxFlight

Print

Print

Credit2

Credit2

Luxury

Luxury

Credit1

Credit1

Naïve TracesPartial Tracing


Trip

Trip

Search

Search

Hotel

Hotel

Flight

Flight

Print

Print

Credit

Credit

Trip

Trip

Search

Search

Hotel

Hotel

Credit

Credit

Flight

Flight

Print

Print

Credit

Credit

Luxury

Luxury

Credit

Credit

Semi-Naïve TracesPartial Tracing


Trip

Trip

Search

Search

Hotel

Hotel

Flight

Flight

Print

Print

Credit

Credit

Trip

Trip

Search

Search

Hotel

Hotel

Credit

Credit

Flight

Flight

Print

Print

Credit

Credit

Credit

Credit

Selective TracesPartial Tracing


VLDB sneak preview…

Type inference: Given an input type and a query over its traces, infer a type representing exactly the qualifying traces

Type checking: Given also an output type, verify that the qualifying traces conform to it

Practical motivation stems from optimization of queries over execution traces repositories

Type Inference & CheckingPartial Tracing


Type Inference• Impossible with only naïve tracing• Possible, but with exponential blowup for semi-naïve tracing

systems• In PTIME for selective tracing systems

Type Checking• NP-hard and solvable in EXPTIME for naïve and semi-naïve

tracing systems• Undecidable for selective tracing systems

ComplexityPartial Tracing


Selective tracing is “ideal” for type inference

Other methods should be considered for type checking

Practical ImplicationPartial Tracing


Given a partial trace, what is its most likely origin?

Or, more generally, given a pattern (query) of partial traces, what are the

most likely origins of partial traces of this pattern ?

Good news: All query evaluation algorithms extend to this context (even without knowing the tracing system…)

Lets talk about (top-k) queriesPartial Tracing



External Events

Partial Tracing


Outline


(Probabilistic) Recursive State Machines with temporal logic as query language

Probabilistic Relational DBs

Probabilistic XML

Graph grammars with MSO (or FO) as query language

BP and Web applications mining

Related workRelated & Future Work


Practical applications: • Web-sites design• On-line advertisements• Improved business logic

Enriched Query Language• Joins• Data values• Projection queries with further aggregation functions

Distribution

Optimization

Efficient Type checking under some restrictions

Inference of specifications/probability distributions

Future workRelated & Future Work

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( = Thanks )

querying business processes under models of uncertainty daniel deutch, tova milo tel-aviv university...

Documents

models of uncertaintyeven

models of uncertaintyweb

models of uncertaintya

future workquerying

future executions

exflowdescribes activities

querying business processes

possible behavior flow