How To Address Rapidly Changing Data Representations in an Evolving Scientific Domain Using Aspector

1
How To Address Rapidly Changing Data
Representations in an Evolving Scientific Domain
Using Aspect-oriented Programming Techniques
Overview of Bioinformatics at NEU.

• Karl Lieberherr (lieber_at_ccs.neu.edu)
• College of Computer and Information Science
• Northeastern University
• Boston

2
Motivation

• From Computational Challenges in Structural and
  Functional Genomics by J. Head-Gordon, IBM
  SYSTEMS JOURNAL, VOL 40, NO 2, 2001.

3
Some Quotes From Head-Gordon.

• Although techniques for warehousing techniques
  are as vital in the sciences as in business,
  functional warehouses tailored for specific
  scientific needs are few and far between.
• A key technical reason for this discrepancy is
  that our understanding of the concepts being
  explored in an evolving scientific domain change
  constantly, leading to rapid changes in data
  representation.

4
Some Quotes From Head-Gordon (Refinement).

• evolving scientific domain change constantly,
  leading to rapid changes in data representation.
• Not only changes in data representation but also
  changes in interfaces need protection against
  changes in interfaces.
• Examples additional or modified fields or
  arguments additional or modified types.

5
More Quotes From Head-Gordon.

• When the format of source data changes, the
  warehouse must be updated to read that source or
  it will not function properly. The bulk of these
  modifications involve extremely tedious,
  low-level translation and integration tasks that
  typically require the full attention of both
  database and domain experts. Given the lack of
  the ability to automate this work, warehouse
  maintenance costs are prohibitive, and warehouse
  up-times severely restricted.

6
Protect Against Changes.

• Protection against changes in data representation
  and interfaces. Traditional technique
  information-hiding is good to protect against
  changes in data representation. Does not help
  with changes to interfaces.
• Need more than information hiding to protect
  against interface changes restriction through
  shy programming, called Adaptive Programming
  (AP).

Implementation
Interface
Client
Information Hiding
Shy Programming
7
Problem with Information Hiding

• Shy Programming builds on the observation that
  traditional black-box composition is not
  restricting enough. We use the slogan
  information hiding is not hiding enough. Blackbox
  composition isolates the implementation from the
  interface, but does not decouple the interface
  from its clients.

8
Cover unimportant parts of the interface

• To permit interfaces to evolve, self-discipline
  is required to prevent from programming
  extensively against the interface. Certain parts
  of the interface are best left as if they were
  covered.

Implementation
Interface
Client
Information Hiding
Shy Programming
9
Shy Programming Adaptive Programming

• This disciplined programming is referred to as
  shy programming. Shy programming lets the
  program recover from (or adapt to) interface
  changes. Shy programming is also called Adaptive
  Programming (AP). This is similar to the shyness
  metaphor in the Law of Demeter (LoD) structure
  evolves over time, thus communicate with just a
  subset of the visible objects.

10
Decoupling of Interface

• We summarize the commonalities and differences
  between black-box composition and Shy Programming
  into two principles.
• Black-box Principle the representation of
  objects can be changed without affecting clients.
• Shy-Programming Principle the interface of
  objects can be changed within certain parameters
  without affecting clients.
• It is important to notice that the
  Shy-Programming Principle builds on top of the
  Black-Box principle.

11
Manager Metaphor.
Want to learn about organizing bioinformatics
knowledge.

• A manager M is managing a set of group leaders G,
  each one managing a set of workers W. We consider
  issues related to informing M and requesting
  information from M. We use this example to
  illustrate three points.
• Micromanager no information restriction.
• Shyness helps information restriction.
• Complex requests help information restriction
  and optimization.

12
Manager Metaphor.

• Micromanager no information restriction.
• If the manager is a micromanager (a manager that
  wants to know about and rely on all the details
  of the workers projects), the managing approach
  is brittle because when there is a change in the
  details of one of the workers projects, the
  manager needs to be notified.

13
Manager Metaphor.

• Micromanager no information restriction
  (continued).
• An object-oriented program written in the usual
  way corresponds to the manager that likes to
  micromanage. It is full of detailed knowledge of
  the class graph. An alternative way of
  formulating the same idea is to observe that it
  is good when the workers are shy. A shy worker
  will only share minimal, high-level information
  with the group leader. And this will prevent a
  brittle situation where the group leaders and
  manager rely on too much detail.

14
Manager Metaphor.

• Shyness helps information restriction
• It is good for the workers to be shy and only
  talk to their group leader and not to the manager
  directly. (Shyness has two facets talk only to a
  few friends AND share minimal information with
  them. Here we use the first facet while in the
  previous point we used the second facet.) The
  group leader will abstract the information from
  the workers and only pass on the abstract
  information to the manager. This will prevent the
  manager from micromanaging. This variant can be
  viewed as an application of the Law of Demeter
  (LoD) which states that an object should talk
  only to closely related objects. The closely
  related object for a worker is the group leader
  and not the manager.

15
Manager Metaphor.

• Shyness helps information restriction
  (continued).
• The motivation is that when things change at the
  worker level, the manager does not have to be
  informed necessarily. The group leader will be
  informed and will decide whether the information
  needs to be passed up.

shielded
16
Manager Metaphor.

• Complex requests help information restriction
  and optimization.
• The manager does not want to be bothered by many
  simple requests from the many workers. Instead
  the manager prefers to get a complex request from
  time to time from a group manager. The complex
  request offers the manager the possibility to see
  all the requests as a whole and to optimize the
  overall result which would not be possible if
  simple requests come one by one and need to be
  satisfied immediately before the totality of all
  simple requests is seen.

17
Manager Metaphor.

• Complex requests help information restriction
  and optimization (continued).
• The same point applies to programming instead of
  sending an object a lot of individual data access
  requests, it is better to send one complex
  request that can be treated as a whole and
  optimized accordingly.

18
Aspect-oriented Programming (AOP).

• AOP is programming with aspects. An aspect is a
  complex request to modify the execution of a
  program. May expose a large interface. This can
  be implemented efficiently by inserting code at
  compile time into the program. An aspect should
  be shy with respect to the program it modifies.

19
AOSD not every concern fits into a component
crosscutting
Goal find new component structures that
encapsulate rich concerns
20
A Reusable Aspect.
abstract public aspect RemoteExceptionLogging
  abstract pointcut logPoint()   after()
throwing (RemoteException e) logPoint()
log.println(Remote call failed in
thisJoinPoint.toString()
( e ).)
abstract
public aspect MyRMILogging extends
RemoteExceptionLogging pointcut logPoint()
call( RegistryServer..(..))
call(private RMIMessageBrokerImpl..(..))
21
Good Aspects Are Shy.
abstract aspect CapabilityChecking pointcut
invocations(Caller c) this(c) call(void
Service.doService(String)) pointcut
workPoints(Worker w) target(w) call(void
Worker.doTask(Task)) pointcut
perCallerWork(Caller c, Worker w)
cflow(invocations(c)) workPoints(w)
before (Caller c, Worker w) perCallerWork(c, w)
w.checkCapabilities(c)
22
Lessons From Manager Metaphor.

• Information hiding does not hide enough.
  Information hiding makes all public interfaces
  available and (Micromanager) makes the point that
  only an abstraction of those interfaces should be
  visible at higher levels.

23
Lessons From Manager Metaphor (Continued).

• In Shy Programming, only high-level information
  about the class or call graph is visible at the
  (shy) programming level and this shields the
  program from many changes to the class or call
  graph in the same way as the manager is shielded
  from many of the changes in the workers
  projects. The role of the group leader is played
  by the glue code that maps high-level information
  to low-level information and vice-versa. Shy
  Programming is graph-shy.

24
Application to Bioinformatics Knowledge

• Need shy programming and shy knowledge
  representation techniques for Bioinformatics.
• Need domain-specific languages to define function
  in a structure-shy way.

25
Another Good Example of AOP.
find all persons waiting at any bus stop on a bus
route
busStops
BusRoute
BusStopList
OO solution one method for each red class
buses
0..
BusStop
BusList
waiting
0..
passengers
Bus
PersonList
Person
0..
26
Traversal Strategy.
find all persons waiting at any bus stop on a bus
route
from BusRoute through BusStop to Person
A complex request
busStops
BusRoute
BusStopList
buses
0..
BusStop
BusList
waiting
0..
passengers
Bus
PersonList
Person
0..
27
Robustness of Strategy.
find all persons waiting at any bus stop on a bus
route
from BusRoute through BusStop to Person
Complex request is class-graph shy
villages
BusRoute
BusStopList
buses
VillageList
busStops
0..
0..
BusStop
BusList
Village
waiting
0..
passengers
Bus
PersonList
Person
0..
28
Writing Aspect-oriented Programs With Strategies.
String WPStrategyfrom BusRoute through BusStop
to Person
class BusRoute int countWaitingPersons()
Integer result (Integer)
Main.cg.traverse(this, WPStrategy,
new Visitor() int r public void
before(Person host) r public
void start() r 0 public
Object getReturnValue() return
new Integer(r) ) return
result.intValue()
A complex request
Complex request plays role of manager
Complex request is class-graph shy
29
Writing Aspect-Oriented Programs With Strategies.
String WPStrategyfrom BusRoute through BusStop
to Person
// Prepare current class graph Main.cg new
ClassGraph() int r aBusRoute.countWaitingPers
ons()
30
ObjectGraph in UML Notation.
BusList
Route1BusRoute
buses
busStops
BusStopList
Bus15Bus
passengers
CentralSquareBusStop
waiting
PersonList
PersonList
JoanPerson
PaulPerson
SeemaPerson
EricPerson
31
ObjectGraphSlice.
BusList
Route1BusRoute
buses
busStops
BusStopList
Bus15Bus
passengers
CentralSquareBusStop
waiting
PersonList
PersonList
JoanPerson
PaulPerson
SeemaPerson
EricPerson
32
Summary So Far.

• Aspect-oriented software development helps to
  create software that is
• More flexible supports easy adaptation to
  rapidly changing interfaces.
• Easier to understand and also shorter.
• Supports the Shy Programming Principle.

33
Institute for Complex Scientific Software

• Institute Home Page
• http//www.icss.neu.edu/

34
What?

• Problem driving institute
• Complexity of building software systems to enable
  scientific research

• Objective
• Develop general methodologies for
  building complex scientific software using latest
  computer science research

35
Goals.
Applications
Scientific Software Solutions
The Institute
New Methodologies
Computer Science
36
Applicable Computer Science Research.

• Aspect-Oriented Software Development
• Software Components
• Parallelism
• Domain Specific Languages
• Visualization
• Knowledge-Based Support Systems

37
Three Testbeds.

• THEMATICS (M. Ondrechen protein function from
  structure high external visibility)
• Proc. Nat. Academy of Science publication
• Featured in popular scientific magazines
  Nature, American Chemical Society, Science Daily
• Subsurface Sensing and Imaging (many Institute
  participants from this area)
• Parallel Geant4 (CERN Cooperman, Reucroft and
  Swain particle matter interaction -- million
  line program)

38
Some Other Faculty Highlights.

• Valentin Ilyin.
• Protein structure analysis novel structural
  alignment method which produces high quality
  alignments.
• visual analytical bioinformatics interface
  (Friend).
• Roger Giese.
• The long term goal is to learn whether the
  measurement of DNA adducts in people can help to
  individualize cancer prevention, analogous to the
  measurement of cholesterol as a biomarker for
  risk of a heart attack.

39
Some Other Faculty Highlights.

• Bob Futrelle.
• I'm particularly interested in the relations
  between bio-ontologies and text and diagrams.

40
Conclusions

• Northeastern University and the Institute for
  Complex Scientific Software create knowledge of
  significant interest to bioinformatics.
• Aspect-Oriented Software Development is a useful
  technology for the rapidly evolving area of
  bioinformatics.

41
The End
42
PathSet Algorithm

• We have developed an efficient graph search
  algorithm that solves the following problem
• Input
• Graph G1 (V1, E1) with source s and target t.
• Graph G2 (V2, E2) where V1 is a subset of V2.
• Question Does G2 contain a path that is an
  expansion of a path in G1 from s to t (the
  algorithm works even if s and t are sets of
  nodes.)

43
Explanation.

• Given a path p, a path p' is called an expansion,
  if p' can be obtained by inserting one or more
  elements between elements of p.
• More generally, we can find a third graph that
  succinctly represents all possible such paths in
  G2.
• Do you see applications of such an algorithm in
  biology?

44
Motivation.

• G1 is a small graph that lists important
  nodes.
• G2 is a large graph in which we want to
  recognize paths that are expansions of paths in
  the the small graph.
• Expansions of paths may contain additional nodes
  that are noise nodes.

45
Notes

• There is a path in G2 iff the traversal graph of
  G1 and G2 is not empty.
• G1 may have exponentially many paths from s to t.

46
Topic Switch.
47
Lessons From Manager Metaphor (Continued).

• AOP is related to (Micromanager) through the
  observation that aspects should be loosely
  coupled to the base programs they modify. The
  aspect should not be brittle with respect to the
  detailed calling structure of the base program in
  the same way as the manager should not rely on
  the details of the workers project. There is an
  intermediary, called glue code, that maps the
  aspect to the detailed usage context. AOP is
  call-graph shy.