KNOW-HOW MAPPING WITH ME-MAP
Eric Yu, Arnon Sturm
Daniel Gross, Soroosh Nalchigar, Jian Wang, Sadra Abrishamkar
¨ 
Knowledge Mapping, Goal-Oriented Requirement Engineering, Modeling, Visualization,
Literature Review
Outline
2
1.  Introduction
¤  What
is know-how?
¤  Why it is important to map-out know-how?
¤  Usage of know-how mapping
2.  The landscape of knowledge mapping
¤  Desired
properties from a knowledge mapping approach
¤  Review of potential approach for mapping out know-how
3.  The ME-map approach for know-how mapping
¤  Introduction
of ME-map concepts
¤  Guidelines for developing ME-maps
4. 
5. 
6. 
7. 
Applying the ME-MAP approach
Hands-on with ME-map
Evaluation
Summary and Concluding remarks
1. INTRODUCTION
What is know-how?
1. Introduction
4
Know-how:
knowledge of how to do something well
[Merriam-Webster.com]
We are aiming at making know-how reusable :
a) more accessible
b) of improved quality
c) easier to evolve
d) more cross-discipline
Why it is important to map know-how?
1. Introduction
5
With innovation occurring globally at a fast
pace, researchers and practitioners who are
pushing the boundaries to better deal with new
problems and needs, expend significant efforts
to keep up with the current state of the art.
¨ knowledge needs to be managed and
maintained to better understand tradeoffs
among solutions and identify knowledge
¨ 
Who will use Know-how Maps?
1. Introduction
6
Researchers (e.g., the AOSE case)
¨ 
¤ What
research problems/solutions exist in a domain
(interdisciplinary domains)?
¤ How good are existing solutions?
¤ What are currently outstanding problems?
Practitioners (e.g., the sorting and the big data cases)
¨ 
¤ What
solutions exist for my current problems?
¤ Better utilization of knowledge resources
Examples of Know-how Fragments
1. Introduction
7
¨ 
Sorting Algorithms: Know-how for programmers
¤  A list can be sorted using Bubble sort, with complexity O(n2) and constant
space.
¤  A list can be sorted using Quick sort, with complexity O(n*log(n)) and
(possibly) changing space.
(Cormen et al., Introduction to Algorithms, 2009)
¨ 
Adopting Big Data Tools: Know-how for organizations
¤  Hadoop reduces the cost of ownership and is scalable yet its maturity is
questionable.
¤  Google MapReduce is mature and scalable, but the cost of ownership is
enormous and its support for online queries is limited.
(Eckerson, Big Data and its Impact on Data Warehousing ,2012)
¨ 
Agent-Oriented Software Methodologies: Know-how for researchers
¤  Tropos supports the development lifecycle and provide means for specifying
the social and intentional aspects of MAS applications.
¤  MaSE provides means for specifying social aspects and reactivity, but
neglects the intentional aspect.
(Sturm and Shehory, A Comparative Evaluation of Agent-Oriented Methodologies, 2004)
2. THE LANDSCAPE OF KNOWLEDGE
MAPPING
Desired Properties of Know-how Mapping
2. The landscape of knowledge mapping
9
¨ 
Ease of Use (EOU): the degree to which the approach is easy to
apply, and to the degree to which it is easy to understand the
resulting artifacts.
¨  Expressiveness (EXP): the degree to which the approach represents
and captures all information relevant for mapping out the knowledge
in a domain.
¨  Evolution (EVO): the degree to which the approach supports
extending an existing mapping of a domain while adding new
relevant knowledge .
¨  Reasoning (RES): the degree to which a resulting mapping of
knowledge can be analyzed. This property requires a degree of
formality of the representation used to mapping the knowledge.
¨  Process Support (PRS): the degree to which the approach guides
constructing a knowledge map.
Knowledge Mapping Techniques
10
2. The landscape of knowledge mapping
Literature Review – Usually, a narrative description of the domain.
¨  Tabular (TAB) - Tables present selected features in some table
dimensions.
¨  Classification (CLS) - is similar to the tabular approach in mapping out
knowledge in a domain, but is less detailed.
¨  Hierarchical Value Maps (HVM) - HVMs link through means-ends links
product or service attributes, to perceived benefits or costs, and to
higher level values that represent customer believes.
¨  Cause maps (CM) - CMs are directed graphs which most often take
some form of cause and effect graph.
¨  Conceptual graphs (CG) - CGs are composed of concepts and
conceptual links which were formalized to support inference in a
knowledge base.
¨  Formal Approaches(F) - are approaches that offer sophisticated
logical machinery to represent and analyze knowledge.
¨ 
Scientific Research Ontology
2. The landscape of knowledge mapping
11
ScholOnto
¨ Cito
¨ Annotation for Understanding Research Papers
¨ 
ScholOnto
12
2. The landscape of knowledge mapping
Shum, S.B., Motta, E., Domingue, J.: ScholOnto: An ontology-based digital library server for research documents and
discourse. Int. J. Digit. Libr. 3(3), 237–248 (2000)
CiTO
13
2. The landscape of knowledge mapping
Shotton, D. (2010). CiTO, the Citation Typing Ontology. Journal of Biomedical Semantics, 1(Suppl 1), S6.
Relation Annotation for Understanding Research Papers
14
2. The landscape of knowledge mapping
Tateisi, Y., Shidahara, Y., Miyao, Y., and Aizawa, A.(2013). Relation annotation for understanding research papers. In
Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, 140–148, Sofia, Bulgaria
3. THE ME-MAP APPROACH FOR
KNOW-HOW MAPPING
The ME-Map Approach - Principles
3. The ME-map approach
16
¨ 
Following our observation that in technical domains, the
relationship between a problem and a solution – the
“means-ends” relationship – is a crucial aspect of
knowledge structure of such domains, we focus on:
¤ Explicit and cumulative knowledge in order to make
advancements
¤ Reusable know-how fragments rather than a complete
situated problem-solving knowledge
¤ Means-end relationships, which, we assert, are at the
center of know-how mapping within and across
disciplines
The ME-Map Approach - Principles
17
3. The ME-map approach
Note that a ME-map is an index and not the complete
actual knowledge.
We tried to address the required properties of: Ease of
Use, Expressiveness, Evolution, Reasoning, and Process
Support.
The Illustrative Domain – Business Process Variability
Modeling
3. The ME-map approach
18
The domain deals with ways of modeling variability within business
process models.
¨  Model variability can be achieved in two ways: jointly and separately.
¨  When addressing modeling variability jointly this can be done by
restriction or by extension.
¨  Additionally, there is a tradeoff among the use of the various
approaches.
¨ 
La Rosa, M., W.M.P. van der Aalst, M. Dumas, F.P. Milani. (2013) “Business Process Variability Modeling: A Survey”, QUT ePrints
Reinhartz-Berger, I., P. Soffer, A., Sturm (2010) “Extending the Adaptability of Reference Models”, IEEE Transactions on Systems, Man and
Cybernetics - part A (40) 5, pp. 1045-1056.
La Rosa, M., W.M.P. van der Aalst, M. Dumas , T. Hofstrede. (2009). Questionnaire-based Variability
Modeling for System Configuration. Software and Systems Modeling 8, 2, 251–274.
Modeling Business Process Variability Domain
The ME-map Approach – Meta Model
20
3. The ME-map approach
1..*
Model Element
*
Context
1..*
Reference
specification
1..*
title
link
authors
date
Link
validity
Named Model Element
xor
name
*
1
Task-­‐based Link
source
*
*
Acheived By Link
¨ 
¨ 
Consists Of Link
1
target
Quality
Task
1
1
source
source
1
*
*
*
Association Link
Contribution Link
target
1
source
target
1
*
*
Extended By Link
The approach is inspired by Goal-Oriented Requirements Engineering.
The ME-map builds upon a subset of i*.
Task
21
3. The ME-map approach - Concepts
Definition: A task can be interpreted either as a problem or a
solution. Graphically, it is indicated as a rectangle with rounded
corners.
¨  Formulation: Task should be defined as a simple verb phrase and
avoid conjunction.
¨ 
Task
22
¨ 
3. The ME-map approach - Concepts
Mapping and Action Guidelines:
¤ Any explicated goals, objectives, solutions should be mapped
into tasks.
¤ Artifacts can also be transformed into active tasks.
¤ When identifying a solution consider what the problem it
addresses and vice versa.
Achieved by link
3. The ME-map approach - Concepts
23
¨ 
¨ 
Definition: An achieved by link
represents a means-end relationship. It
indicates that the target task is an
alternative to achieve the source task.
Formulation: Note that all means to
achieve an end are alternatives which
means that each of the alternative fully
addresses the functional task. Thus, this
implies “or” relationships among the
alternatives.
Achieved by link
3. The ME-map approach - Concepts
24
¨ 
Mapping and Action Guidelines:
¤ Look for alternatives
¤ Look for sentences consisting of the terms like:
Means end, Solves, Addresses, ….
Consists Of Link
25
¨ 
3. The ME-map approach - Concepts
Definition: A consists of link indicates that the target task is part of
the source task, and that all connected target tasks should be
accomplished in order to fully satisfy the source task. That means,
that it is actually a problem or solution decomposition and all parts
are required to fulfil the source task.
Consists Of Link
3. The ME-map approach - Concepts
26
Formulation: Note that all parts of the whole task are required
to be achieved. Thus, this implies “and” relationships among
the parts.
¨  Mapping and Action Guidelines:
¤ Decompose the tasks
¤ Look for phrases consisting of the following: Part of, Consists
of, Comprises, Include(s/ing)
¨ 
Quality
27
Definition: A quality is a
characterization of a task. Graphically,
it is indicated as an ellipse.
¨  Formulation:
¤ A quality should be defined as a
simple adverbial phrase. Can use
both adverb (-ility) and adjective
(e.g. scalable) depend on what is
common in the domain.
¨  Mapping and Action Guidelines:
¤ Look for criteria for assessing a task.
¨ 
3. The ME-map approach - Concepts
Association Link
28
The ME-map approach - Concepts
Definition: An association links is used to connect qualities with tasks.
It is specified by an unlabeled and non-directional line between a
task and a quality. Its semantics implies that the qualities associated
should be taken into account when evaluating alternatives for that
task.
¨  Formulation: Note that qualities associated with a task affect also
other tasks that further refined it via the achieved by links chain.
¨ 
Contribution Link
3. The ME-map approach - Concepts
29
Definition: A contribution link indicates that the
source task (or quality) has a contribution to a
quality.
¨ 
¤ The
contribution could be either positive or negative. Yet, in case
quantitative metrics are provided, this can be incorporated as well.
¤ Optionally, a contribution link may hold its validity in terms of how
the contribution was determined. These include various levels such
as self-subjective evaluation, self-claim based evaluation, empirical
evaluation, and external evaluation.
Contribution Link
30
3. The ME-map approach - Concepts
Extended by Link
3. The ME-map approach - Concepts
31
Definition: An extended by link indicates that the target task is an
extension of the source task.
¨  Formulation:
¨ 
¤  Note
that the qualities associated to the extended task apply also to its
extensions.
n  The
default of the contribution links from the extended task are the same for its extensions.
n  Yet, these could be overwritten by specifying new contribution links from the extensions to
the related qualities.
¤  Essentially,
an extended by link is a shortcut to have another alternative with
repeating multiple contribution links.
¨ 
Mapping and Action Guidelines:
¤  When
introducing a task check whether it uses as a base another task.
Extended by Link
32
3. The ME-map approach - Concepts
References and Contexts
33
3. The ME-map approach - Concepts
Reference
¨  Definition: A reference is an information source that provides
justification for the elements within the map.
¨  Formulation: Each element within the map should be associated with
at least one reference.
Context
¨  Definition: A context is a descriptive information which states the
conditions and settings in which the element was determined. A
context may be an experimental setting, a data set, assumption etc.
¨  Formulation: Should be concise.
Evaluation: Procedures
3. The ME-map approach
34
We conducted evaluations in various forms
¨ 
¤ Apply
the approach to various domains.
¤ Iteratively revisit the approach in light of the
desired properties.
¤ Check the way people understand ME-map.
¤ Check the way people develop such maps
and their perception over such maps.
Evaluation: Results
3. The ME-map approach
35
Comprehension of a domain seems to be better than
“regular” means it term of increased quality and
decreased time.
¨  While constructing ME-maps, it is easy to follow the
approach concepts and it was found to be useful in
terms of understanding domains.
¨ 
¤ Nevertheless,
more (concrete) guidelines are required.
4. APPLYING THE ME-MAP
APPROACH
Domains Explored To-Date
4. Applying the ME-Map approach
37
¨ 
Explored know-how mapping in several domains and at
different levels of abstraction
¤  Geo-Engineering
¤  Web
Data Mining
¤  Goal-Oriented Software Architecture
¤  Big Data
¤  Agent-Oriented Software Engineering
¤  Knowledge Mapping
¤  Data Mining
¤  Architecture Description Languages
¤  Variability in Business Processes
¨ 
Minimal set of concepts was so-far sufficient
¤  to
identify problems and associated qualities
¤  solutions to these problems and their evaluation
¤  opportunities for innovation
The Domain of ADL
38
4. Applying the ME-Map approach
Connecting the Maps
4. Applying the ME-Map approach
39
The data mining domain
The marketing domain
40
40
Tools
4. Applying the ME-Map approach
41
¨ 
At this stage we are using a concept map tools called CMAPTools:
http://cmap.ihmc.us/
¨ 
We are currently developing a mapping framework that would better
allow us to accomplish the goals of using ME-maps
KNOW-HOW EVOLUTION
Solution Domain-Classification in DM
43
43
Top Data mining application domain is CRM
44
Source: http://www.kdnuggets.com/polls/2012/where-applied-analytics-data-mining.html
44
Problem Domain- Customer Relation Management
45
linked to the previous map
45
5. HANDS-ON
An Example….
5. Hands-On
47
¨ 
A Model-Driven Approach to Enterprise Data Migration
Raghavendra Reddy Yeddula, Prasenjit Das, and Sreedhar Reddy
In a typical data migration project, analysts identify the mappings
between source and target data models at a conceptual level using
informal textual descriptions. An implementation team translates these
mappings into programs that migrate the data. While doing so, the
programmers have to understand how the conceptual models and
business rules map to physical databases. We propose a modeling
mechanism where we can specify conceptual models, physical models
and mappings between them in a formal manner. We can also specify
rules on conceptual models. From these models and mappings, we can
automatically generate a program to migrate data from source to
target. We can also generate a program to migrate data access
queries from source to target. The overall approach results in a
significant improvement in productivity and also a significant reduction
in migration errors.
48
5. Hands-On
And now…Briefing
5. Hands-On
49
You got 3 abstracts from the conference proceedings.
¨  Map out these abstracts using the concepts of the ME-map.
¨  One can start with allocating the important sentences or adopt a more
holistic approach and abstract the main concepts to be mapped.
¨ 
Start with the first abstract and will monitor the progress as we go.
¨  We will then initiate a reflection session over your experience.
¨ 
At this stage for developing the maps we are using concept maps –
and their associated tool – CMAPTOOL: http://cmap.ihmc.us/
¨  In the exercise we will develop the map by hand.
¨ 
Reflection on the ME-map Construction
5. Hands-On
50
The ME-map is easy to use when developing a new map.
¨  The ME-map is useful and helpful for mapping out research
outcomes.
¨  Using the ME-map it is easy to construct a map of a single paper. ¨  Using the ME-map it is easy to construct a map of multiple papers. ¨  The ME-map facilitates better organization of the knowledge.
¨  The ME-map explicates differences among studies.
¨  The ME-map facilitates grouping or clustering studies.
¨  The ME-map helps in identifying important properties and
features.
¨  The ME-map encourages critical thinking towards a research.
¨ 
6. EVALUATION
Evaluation
6. Evaluation
52
We conducted evaluations in various forms
¨ 
¤ Apply
the approach to various domains.
¤ Iteratively revisit the approach in light of the
desired properties.
¤ Check the way people understand ME-map.
¤ Check the way people develop such maps
and their perception over such maps.
Comprehension Evaluation - Objectives
6. Evaluation
53
¤ Evaluate
the effectiveness of understanding a
knowledge domain (maps vs. written review)
¤ Compare the two approaches in terms of level of
understanding of the domain, and relative time to
find answers.
¤ Evaluate the effects of participant’s familiarity with
goal-oriented concepts and modeling.
¤ Analyze the extent to which participants understand
a map generated by others.
Comprehension Evaluation – Hypotheses
6. Evaluation
54
¤ ME-Map
is an effective way of knowledge transfer in
terms of:
n Domain
comprehension
n Time efficiency
¤ ME-Map
is able to capture and transfer knowledge in
aspects of:
n Finding
problems, finding alternatives, evaluating alternative
solutions, evaluating solutions’ effects, and finding knowledge
gaps.
¤ Participant’s
familiarity with goal-oriented concepts can
have positive affects on the performance over time.
Comprehension Evaluation – Method
6. Evaluation
55
Questionnaire
¨  Subjects
¨ 
Participant
Scholarly
Search
Goal
Oriented
Modelling
Concepts
Literature
Reviews
Web Mining
Domain
Knowledge
Mapping
G0 1
Usually
Unfamiliar
N/A
Unfamiliar
Somewhat familiar
G0 2
Usually
Unfamiliar
N/A
Unfamiliar
Unfamiliar
G0 3
Usually
Unfamiliar
N/A
Unfamiliar
Unfamiliar
G0 4
Every day
Unfamiliar
N/A
Unfamiliar
Unfamiliar
G1 1
Usually
Very familiar
N/A
Very familiar
Somewhat familiar
G1 2
Usually
Very familiar
N/A
Unfamiliar
Unfamiliar
G1 3
Usually
Very familiar
N/A
Very familiar
Very familiar
G1 4
Usually
Very familiar
N/A
Very familiar
Very familiar
W 1
Occasionally
N/A
Unfamiliar
Unfamiliar
Unfamiliar
W 2
Occasionally
N/A
Very familiar
Somewhat familiar
Very familiar
W 3
Usually
N/A
Very familiar
Somewhat familiar
Somewhat familiar
W 4
Occasionally
N/A
Somewhat familiar
Somewhat familiar
Unfamiliar
Comprehension Evaluation – Results
6. Evaluation
56
00:36:00
100%
90%
00:28:48
80%
70%
00:21:36
60%
50%
00:14:24
40%
30%
00:07:12
20%
10%
00:00:00
0%
Average Time
Average mark
G0
G1
W
Average performance score across the groups
G0
G1
W
Average time spent across the groups
56
Comprehension Evaluation – Results
6. Evaluation
57
100% Overall feature performance results
90% 80% 70% 60% G0 50% 40% G1 30% W 20% 10% 0% Finding Alterna8ves Finding Problems Evalua8ng Alterna8ve Evalua8ng Solu8on Effects Finding Knowledge Gap Solu8ons 00:04:54 00:04:11 Overall feature time results
00:03:27 00:02:44 G0 00:02:01 G1 W 00:01:18 00:00:35 Finding Alterna8ves Finding Problems Evalua8ng Alterna8ve Evalua8ng Solu8on Effects Finding Knowledge Gap Solu8ons 57
Comprehension Evaluation – Back to the Hypotheses
6. Evaluation
58
¨ 
Domain comprehension and scores:
¤ ME-Map
approach has some advantage on domain
understandability and comprehension.
¨ 
Time:
¤ The
ME-Map approach facilitates and speeds up information
finding and domain understanding and learnability.
58
Comprehension Evaluation – Threats to Validity
6. Evaluation
59
Limited number of participants
¨  A single domain
¨ 
Construction Evaluation – Objectives
60
6. Evaluation
(G1) Validate whether ME-map provide sufficient
expressiveness for mapping a literature review (i.e., a
domain).
(G2) Examine the easiness of mapping a literature review
using ME-map.
(G3) Check whether literature review mapping using MEmap provides additional insights for the understanding and
analysis of the domain.
(G4) Explore whether ME-map facilitates the positioning of
a research agenda.
Construction Evaluation – Procedure
6. Evaluation
61
Participants: 4 graduate students
Duration: a whole day
Preparation: The students were asked to gather literature regarding
their own research domain.
Training: 1hr
Tasks:
n 
n 
n 
Mapping:
n map out the literature they had gathered.
n position their own research on that map.
n search for additional alternatives that were triggered by examining the
previous map.
Questionnaire
Discussion
Construction Evaluation - Results
6. Evaluation
62
¨ 
The quality of the maps increases as the evaluation progress.
¤  In
¨ 
the beginning the participants neglect qualities.
The characteristics of the domain also affected the way the map were
constructed:
¤  Software
Ecosystems – not clear what the qualities are, as the domain is in
its incubation stage.
¤  Organizational Flexibility – the focus here was on qualities as the meansend chain was less visible.
¤  Data Mining for Business Application – the means-end chain was much
clearer.
Construction Evaluation – Results
63
Issue/Participant
The ME-map is useful and helpful for mapping out literature review in terms of research
objectives.
The ME-map is useful and helpful in facilitating gap identification within the literature
review.
The ME-map is useful and helpful in facilitating identification for future research related
to the literature review.
The ME-map is useful and helpful for identifying other domains that can potentially
contribute to addressing problems in the domain that you are focusing on.
Using the ME-map it is easy to construct a map of the literature review. The ME-map is easy to use when developing a new map.
The ME-map is easy to use when maintaining or modifying an existing map
The ME-map facilitates new insights regarding the literature review.
The ME-map facilitates better organization of the knowledge of the literature review.
The ME-map explicates differences among studies within the literature review.
The ME-map facilitates grouping or clustering studies within the literature review.
The ME-map helps in identifying important properties and features within the literature
review.
The map helps in better positioning your own work.
The ME-map encourages critical thinking regarding the literature review.
6. Evaluation
1 2 3 4
1 6 5 6
5 3 5 5
1 5 5 5
3 4 6 4
6
7
1
4
6
6
4
6
7
7
5
6
7
5
7
6
7
7
7
5
5
7
7
2
5
5
4
4
5
7
5
3
6 7 7 6
6 5 7 2
Construction Evaluation – Results
6. Evaluation
64
¨ 
¨ 
¨ 
¨ 
¨ 
¨ 
Modeling notation vs. modeling process: although the approach facilitates the critical thinking and
the identification of important aspect, it lacks the proper notations to indicate some other aspects, such
as: marking your research contribution, indicating important references, and differentiating between
problems and solutions.
Usability: all participants stated that the approach is usable and easy to learn as it consists of a
minimal set of mapping constructs.
Usefulness: the approach provides a clear visualization of the state-of-the-art and the model can
facilitate various analysis. In addition, it further helps in explicating the contribution of your own
research. Respondents felt that the maps could foster collaboration among researchers.
Expressiveness: with respect to expressiveness a concern was raised as to how to specify
dependencies among qualities and tasks. Sometimes, different combination of qualities values can
affect differently on other qualities. Another issue was the ability to determine the progression over
time.
Mapping: sometimes literature does not follow the means-ends structure thus making the mapping
challenging. Another issue is how to determine the degree of contribution, since it may be subjective. It
was suggested that a binary distinction between positive or negative contribution may be sufficient for
the purpose of distinguishing between solution contribution to problem qualities
Scalability: it is not clear, what are the available means for managing complex maps.
Construction Evaluation – Back to the research
objectives
6. Evaluation
65
It seems that although the domains were mapped
correctly, some of the ME-map expressiveness features
(G1) require further exploration (e.g., complex
contribution and contribution level).
¨  The ME-map approach was found to be easy to use
(G2).
¨  The ME-map approach offers additional insights for
the understanding and analysis of existing literature
reviews (G3).
¨  The ME-map approach facilitates the positioning of a
research agenda (G4).
¨ 
Construction Evaluation – Threats to Validity
66
6. Evaluation
Although the evaluation provided us with important insights,
the results should be taken with caution due to the
following reasons:
¨  the number of participants was low, nevertheless
generated a large number of comments.
¨  the participants (being students) are somewhat
depended on the researchers, though they were
encouraged to provide critical remarks to help improve
the approach, which they did.
¨  the selection of the domains and sources affects the
results; we therefore referred to the characterizations of
the domains and sources in our evaluation.
SUMMARY AND CONCLUDING
REMARKS
The Way Forward
68
Provide additional guidelines for map developers.
¨  Improve scaleability of managing such maps.
¨  Implementing a framework to support the ME-map approach.
¨  Examine the notion of social know-how modeling
¨ 
¤  What
is the required expressiveness for crowd modeling of know-how?
¤  Trust management
¨ 
Develop tools for knowledge acquisition
¤  E.g.
semi-automated extraction and integration of know-how structures from
knowledge sources
Devise reasoning mechanisms
¤ Identify gaps
¤ Tailor context-specific solutions
¨  Evaluate, Evaluate, Evaluate
¨ 
References
69
Arnon Sturm, Daniel Gross, Jian Wang, Soroosh Nalchigar, Eric S. K.
Yu: Mapping and Usage of Know-How Contributions, CAiSE Forum 2014
post proceedings.
¨  Arnon Sturm, Daniel Gross, Jian Wang, Eric S. K. Yu: Analyzing Engineering Contributions using a Specialized Concept Map. CAiSE (Forum/Doctoral Consortium) 2014: 89-96
¨  Jian Wang, Arnon Sturm, Daniel Gross, Eric S. K. Yu: Know-How Mapping: From i* to ME-maps. iStar 2014
¨  Daniel Gross, Arnon Sturm, Eric S. K. Yu: Towards Know-how Mapping Using Goal Modeling. iStar 2013:
115-120
¨ 
Questions???
70
Eric Yu, Faculty of Information, University of Toronto, Canada
[email protected]
Arnon Sturm - Information Systems Engineering, Ben-Gurion University of the Negev,
Israel
[email protected]
Daniel Gross, Faculty of Information, University of Toronto, Canada
[email protected]
Soroosh Nalchigar, Computer Science Department, University of Toronto, Canada
[email protected]
Jian Wang, State Key Lab of Software Engineering, Wuhan University, China
[email protected]