=Paper=
{{Paper
|id=Vol-1093/paper1
|storemode=property
|title=Towards a Framework for Analytics-driven Domain-specific Mashup Environments
|pdfUrl=https://ceur-ws.org/Vol-1093/paper1.pdf
|volume=Vol-1093
|dblpUrl=https://dblp.org/rec/conf/ectel/Aram13
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==Towards a Framework for Analytics-driven Domain-specific Mashup Environments==
https://ceur-ws.org/Vol-1093/paper1.pdf
Towards a Framework for Analytics-driven
Domain-specific Mashup Environments
Michael Aram and Gustaf Neumann
Vienna University of Economics and Business
Institute for Information Systems and New Media
Welthandelsplatz 1, Building D2, 1020 Vienna, Austria
Abstract. Mashup environments enable end users to directly engage in
the design process of the information system. Traditional mashup tech-
nology offers users generic components and therefore targets technically
skilled people. Recent research investigates domain-specific mashup plat-
forms with the aim to be easier to understand and use. We present a pro-
posal for research towards a generic framework that supports the design
of domain-specific mashup environments through native analytics.
Keywords: Evolutionary Information Systems, Secondary Design, Mashups
1 Evolutionary Educational Information Systems
In this paper, we conceive of an information system as consisting of human be-
ings and/or machines that are interconnected via communication relationships
and that produce and/or use information [1]. Accordingly, we interpret an ed-
ucational information system as a sub-part of a computer-based organizational
information system where educational processes are at the center of attention.
Core processes include learning, coaching, assessment and delivery of learning
content [2]; supporting processes are e.g. authoring of learning material, devel-
opment of learning applications or administration processes.
The traditional mindset considers the technological part of an information
system as being designed by software developers and used by end-users. The
concept of secondary design, however, interprets end-users as “designers in their
own right”, who are actively engaged in the design and modification of the
information system within the context of use [3]. This is particularly desirable
because organizations, and therefore their processes, are necessarily constantly
evolving due to an ever-changing environment. This in turn demands for highly
tailorable technology [4] that can be continuously and substantially adapted by
its stakeholders, particularly by domain experts. In line with [5], we use the term
Evolutionary Educational Information System (EEIS)1 to refer to this class of
educational information systems.
1
“Evolutionary Information Systems” should be understood as an emerging research
field that is currently developed and explored at our institute in a joint research
effort of several researchers (see also [5]). The hereby proposed research into mashup
environments by the author is embedded within this conceptual frame.
1
2 Problem Areas and State of the Art
In [5], we have tried to identify three highly relevant dimensions within the re-
search field around evolutionary systems, thus providing the overall directions for
further investigation – and the broader frame for the hereby proposed research:
D1 – Systematic discovery of improvement potentials within the system.
D2 – Incremental application of corresponding modifications into the system.
D3 – Inclusion of stakeholders into the continuous design process of the system.
With respect to these objectives the emerging mashup paradigm [6] promises a
possible solution space. In line with our notion of EEISs, a mashup environment
comprises a mashup platform plus organizational structures and actors [7]. A
mashup platform is a tailorable technology [4] that allows end users to create,
use, modify and exchange mashups. Mashups are personalized, situational appli-
cations created by end users by dynamically combining web resources to address
the current needs of a person or community [7]. An enterprise mashup stack com-
prises three central technological concepts, i.e. web-based resources, which are
virtualized into widgets and finally combined on demand into mashups [7, 8]. In
general, mashup platforms contribute to the area of D2 by providing a means to
adapt a system’s behavior. In providing a means for end user programming [9],
mashup environments tackle problem area D3. Furthermore, being frequently
used for situational reporting and analytics, mashup platforms can generally
contribute to research direction D1, thus enabling a “business intelligence for
the masses” concept [7].
Recent efforts investigate Domain-specific Mashup Platforms, hence aim to
apply the concept of domain-specific languages [10] to the mashup paradigm (see
e.g. [11]). Here, the main goal remains to make the mashup experience as simple
as possible for the end user. This is attempted by providing easy-to-understand
domain-specific components instead of domain-agnostic generic components. For
example, “ResEval Mash” [11] represents a domain-specific mashup platform for
conducting research evaluation.
3 Research Questions and Challenges
As we position this research within the field of Evolutionary Information Sys-
tems, we ultimately aim at enabling secondary design within an EEISs in a
unified approach. We strive to integrate techniques from the broad fields of ana-
lytics [12], domain-specific languages and mashup technologies in a comprehen-
sive framework. Within the mindset we have sketched so far, we formulate both
a high-level research question and an incomplete set of derived sub-questions.
How should a framework be designed that supports domain experts in their sec-
ondary design of Domain-specific Mashup Environments through analytics within
the context of an Evolutionary Educational Information System? t
u
2
We are going to search for an answer to this question by investigating the related
phenomena within the Technology Enhanced Learning (TEL) domain. There-
fore, we consider relevant educational stakeholders (e.g. developers of learning
resources, e-learning assistants, teachers) as the domain experts, and the systems
that we have direct access to2 as the EEISs. Within an abductive reflection pro-
cess, several more concrete subquestions arise. Note, however, that this tentative
set is expected to be extended and amended in the course of our design-oriented
research effort.
– What should be designed in the primary design process, and what should be
intentionally left to the secondary design of the domain experts?
– How can we enable secondary design of the Domain-specific Mashup Envi-
ronments (DSMEs) and still preserve control of the system functions?
– How should one account for and deal with “transitive secondary design”,
i.e. when the secondary design process of domain experts actually acts as
primary design for the secondary design of other end-user groups?
– What is a suitable “gentle slope” [13] deployment process for the incremental,
evolutionary establishment of such mashup environments within an EEIS?
– How can we incorporate analytics to facilitate the emergence and sustainable
integration of a DSMEs within an EEIS?
4 A Framework for Analytics-driven Domain-specific
Mashup Environments
We aim to tackle the identified problems by means of a design-oriented research
approach [14]. In this section, we sketch a tentative solution suggestion. In a nut-
shell, the proposed solution shall be manifested in the form of a highly tailorable
framework that empowers domain experts to design and maintain DSMEs based
on analytics. Our domain-aware and design-oriented research approach implic-
itly requires us to construct and deploy working software within an EEIS, which
implies technological and organizational opportunities and constraints. The ac-
tual implementation is particularly important, as its continuous introspection
within the EEISs is essential for developing and evaluating it. The intention is
to develop software within the technological framework that runs our real world
learning platforms. In particular, this stack comprises the dotLRN learning man-
agement system that is based on the OpenACS community framework, which in
turn relies on NaviServer, PostgreSQL and the Next Scripting Framework3 .
4.1 Objectives of the Solution
The overall goal of efforts in the field of EEISs is to make progress with respect to
the three research directions (D1–D3). In addition to that, we present here a list
2
These are Learn@WU (see https://learn.wu.ac.at) and LMS.at (see https://lms.at),
large learning platforms of the WU Vienna and for Austrian schools, respectively.
3
Please refer to the respective web sites: http://dotlrn.org, http://openacs.org,
http://naviserver.sourceforge.net, http://postgresql.org, http://next-scripting.org.
3
of relevant high-level objectives of this concrete research effort. For brevity, typi-
cal requirements known from the field of software engineering (e.g. performance,
scalability, usability, etc.) and from research projects (e.g. rigorous architectural
design decisions, openness, etc.) are not mentioned explicitly.
High Tailorability for Enabling Secondary Design of DSMEs. Our main objective
is to design, develop, and deploy a framework that allows diverse stakeholders
within an EEIS to collaboratively construct mashup environments specifically
tailored to their respective domains and needs. Thus, the design decisions made
in the course of the construction of the framework shall account for the concept
of secondary design. Firstly, this includes a generic infrastructure that allows
technical experts to develop and integrate internal or external resources and
make them available for the domain experts in the form of “virtualized com-
ponents” (widgets) [7]. Secondly, it requires means for the domain experts to
derive or introduce domain-specific components. Thirdly, end users within the
organization should be enabled to efficiently find, evaluate, and use these tailored
domain-specific components (widgets, mashups, and resources).
Native Analytics. The need for analytics is especially true for Evolutionary In-
formation Systems (EISs), which strive to enable secondary design and there-
fore require means for investigating and interpreting the system’s behavior in a
quantifiable manner. Firstly, as a basis, data generated by (instantiations of) the
framework shall follow clear semantics (e.g. via collaboratively defined ontolo-
gies) in order to facilitate data mining techniques [15]. Secondly, the framework
shall facilitate domain-relevant analytics, e.g. via collaboratively-annotated sit-
uational analytics mashups. This is intended to support a continuous evaluation
of the mashup environment and its underlying processes for all its stakeholders,
ultimately contributing to D1.
Incremental Applicability. We state the applicability and deployability of the
framework within an existing EEIS as an important objective (see D2), both from
a technological and an organizational perspective. The latter demands a “gentle
slope” system [13] that can be incrementally learned by the domain experts.
Therefore, means for the inclusion or transformation of (legacy) components
and artifacts from the existing system shall be considered.
4.2 Contributions
The hereby proposed research effort is intended to contribute to the field of in-
formation systems research and to relevant reference disciplines and sub-areas
such as TEL, domain-specific software engineering and learning analytics. The
contributions, i.e. additions to the knowledge base, are going to be artifacts [14,
16], and in particular open source software. The artifacts, which embody the
new knowledge, are going to comprise (i) design principles for the construction
of DSMEs within EEISs; (ii) concrete models describing the constructed envi-
ronments and abstract models that generalize from these; (iii) experiences from
and methods for constructing such environments.
4
5 A Pluralistic Design-oriented Research Configuration
In general, research is “an activity that contributes to the understanding of a
phenomenon” [14, 17]. In design-oriented research, these phenomena are partly
created by the researcher [14]. The construction process is supposed to reveal
design principles that can be applied to a class of similar systems [18]. Numerous
process models can be found in the literature, from “micro-scale” cognitive mod-
els [19] over “project-scale” [20, 21] to “macro-scale” aggregate models applica-
ble to efforts of multiple research communities [14]. However, a common scheme
among these process models remains. After some form of problem awareness or
trigger, the researcher iteratively switches between constructive and evaluative
activities. The construction allows for creativity, nevertheless design decisions
must be grounded in the knowledge base and be made explicit [22].
A range of different qualitative and quantitative evaluation approaches [17]
seems appropriate for the various parts of this research. Ultimately, the created
artifacts are going to be evaluated against the defined goals and objectives.
For example, while the architecture of the system will be evaluated by means
of expert reviews, interviews or confirmatory focus groups [17] with domain
experts will be considered for evaluating the appropriateness of the system for
the business environment. As our intention is to natively incorporate analytics,
we expect to be able to directly gain useful quantitative usage data.
6 Conclusion and Outlook
We have presented our intention to succeed with research into DSMEs. We be-
lieve that incorporating analytics into these systems has the potential to make
them even more useful and effective and can help to tackle some of the problems
that arise within EEISs. We conclude this paper with a research agenda.
M1 – Tentative Design. A crucial first step is to further elaborate the solution
suggestion to result in a tentative design [14]. We expect this to include de-
sign principles, high level architectural models, and user interface mockups.
M2 – Mashup Platform Prototype. As a basis for further developments, we
plan to implement a prototype of a flexible mashup platform based on and
integrated with our existing EEISs.
M3 – DSMEs Prototype. This iteration of the prototype development shall ac-
tually enable domain experts to design DSMEs.
M4 – Analytics-based DSMEs Prototype. In this phase we are going to enhance
the process of designing DSMEs by using analytics.
M5 – Evaluation. While various evaluation activities will have already hap-
pened at this stage, we will finally evaluate the prototype within its environ-
ment for its appropriateness to tackle the overall problem areas and defined
objectives (see Sections 2 and 4). To this end, a framework for continuous
evaluation is intended to be designed and included from the beginning.
The activities preceding each milestone are expected to take us about two
months, and we plan at least one publication as a result.
5
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