Towards a Framework for Analytics-drivenDomain-specific Mashup EnvironmentsMichael Aram and Gustaf NeumannVienna University of Economics and BusinessInstitute for Information Systems and New MediaWelthandelsplatz 1, Building D2, 1020 Vienna, AustriaAbstract. Mashup environments enable end users to directly engage inthe design process of the information system. Traditional mashup technology offers users generic components and therefore targets technicallyskilled people. Recent research investigates domain-specific mashup platforms with the aim to be easier to understand and use. We present a proposal for research towards a generic framework that supports the designof domain-specific mashup environments through native analytics.Keywords: Evolutionary Information Systems, Secondary Design, Mashups1Evolutionary Educational Information SystemsIn this paper, we conceive of an information system as consisting of human beings and/or machines that are interconnected via communication relationshipsand that produce and/or use information [1]. Accordingly, we interpret an educational information system as a sub-part of a computer-based organizationalinformation system where educational processes are at the center of attention.Core processes include learning, coaching, assessment and delivery of learningcontent [2]; supporting processes are e.g. authoring of learning material, development of learning applications or administration processes.The traditional mindset considers the technological part of an informationsystem as being designed by software developers and used by end-users. Theconcept of secondary design, however, interprets end-users as “designers in theirown right”, who are actively engaged in the design and modification of theinformation system within the context of use [3]. This is particularly desirablebecause organizations, and therefore their processes, are necessarily constantlyevolving due to an ever-changing environment. This in turn demands for highlytailorable technology [4] that can be continuously and substantially adapted byits stakeholders, particularly by domain experts. In line with [5], we use the termEvolutionary Educational Information System (EEIS)1 to refer to this class ofeducational information systems.1“Evolutionary Information Systems” should be understood as an emerging researchfield that is currently developed and explored at our institute in a joint researcheffort of several researchers (see also [5]). The hereby proposed research into mashupenvironments by the author is embedded within this conceptual frame.1

2Problem Areas and State of the ArtIn [5], we have tried to identify three highly relevant dimensions within the research field around evolutionary systems, thus providing the overall directions forfurther 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 apossible solution space. In line with our notion of EEISs, a mashup environmentcomprises a mashup platform plus organizational structures and actors [7]. Amashup platform is a tailorable technology [4] that allows end users to create,use, modify and exchange mashups. Mashups are personalized, situational applications created by end users by dynamically combining web resources to addressthe current needs of a person or community [7]. An enterprise mashup stack comprises three central technological concepts, i.e. web-based resources, which arevirtualized into widgets and finally combined on demand into mashups [7, 8]. Ingeneral, mashup platforms contribute to the area of D2 by providing a means toadapt a system’s behavior. In providing a means for end user programming [9],mashup environments tackle problem area D3. Furthermore, being frequentlyused for situational reporting and analytics, mashup platforms can generallycontribute to research direction D1, thus enabling a “business intelligence forthe masses” concept [7].Recent efforts investigate Domain-specific Mashup Platforms, hence aim toapply the concept of domain-specific languages [10] to the mashup paradigm (seee.g. [11]). Here, the main goal remains to make the mashup experience as simpleas possible for the end user. This is attempted by providing easy-to-understanddomain-specific components instead of domain-agnostic generic components. Forexample, “ResEval Mash” [11] represents a domain-specific mashup platform forconducting research evaluation.3Research Questions and ChallengesAs we position this research within the field of Evolutionary Information Systems, we ultimately aim at enabling secondary design within an EEISs in aunified approach. We strive to integrate techniques from the broad fields of analytics [12], domain-specific languages and mashup technologies in a comprehensive framework. Within the mindset we have sketched so far, we formulate botha high-level research question and an incomplete set of derived sub-questions.How should a framework be designed that supports domain experts in their secondary design of Domain-specific Mashup Environments through analytics withinthe context of an Evolutionary Educational Information System?tu2

We are going to search for an answer to this question by investigating the relatedphenomena within the Technology Enhanced Learning (TEL) domain. Therefore, we consider relevant educational stakeholders (e.g. developers of learningresources, e-learning assistants, teachers) as the domain experts, and the systemsthat we have direct access to2 as the EEISs. Within an abductive reflection process, several more concrete subquestions arise. Note, however, that this tentativeset is expected to be extended and amended in the course of our design-orientedresearch effort.– What should be designed in the primary design process, and what should beintentionally left to the secondary design of the domain experts?– How can we enable secondary design of the Domain-specific Mashup Environments (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 asprimary 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 sustainableintegration of a DSMEs within an EEIS?4A Framework for Analytics-driven Domain-specificMashup EnvironmentsWe aim to tackle the identified problems by means of a design-oriented researchapproach [14]. In this section, we sketch a tentative solution suggestion. In a nutshell, the proposed solution shall be manifested in the form of a highly tailorableframework that empowers domain experts to design and maintain DSMEs basedon analytics. Our domain-aware and design-oriented research approach implicitly requires us to construct and deploy working software within an EEIS, whichimplies technological and organizational opportunities and constraints. The actual implementation is particularly important, as its continuous introspectionwithin the EEISs is essential for developing and evaluating it. The intention isto develop software within the technological framework that runs our real worldlearning platforms. In particular, this stack comprises the dotLRN learning management system that is based on the OpenACS community framework, which inturn relies on NaviServer, PostgreSQL and the Next Scripting Framework3 .4.1Objectives of the SolutionThe overall goal of efforts in the field of EEISs is to make progress with respect tothe three research directions (D1–D3). In addition to that, we present here a list23These are [email protected] (see and (see,large learning platforms of the WU Vienna and for Austrian schools, respectively.Please refer to the respective web sites:, et,,

of relevant high-level objectives of this concrete research effort. For brevity, typical requirements known from the field of software engineering (e.g. performance,scalability, usability, etc.) and from research projects (e.g. rigorous architecturaldesign decisions, openness, etc.) are not mentioned explicitly.High Tailorability for Enabling Secondary Design of DSMEs. Our main objectiveis to design, develop, and deploy a framework that allows diverse stakeholderswithin an EEIS to collaboratively construct mashup environments specificallytailored to their respective domains and needs. Thus, the design decisions madein the course of the construction of the framework shall account for the conceptof secondary design. Firstly, this includes a generic infrastructure that allowstechnical experts to develop and integrate internal or external resources andmake them available for the domain experts in the form of “virtualized components” (widgets) [7]. Secondly, it requires means for the domain experts toderive or introduce domain-specific components. Thirdly, end users within theorganization should be enabled to efficiently find, evaluate, and use these tailoreddomain-specific components (widgets, mashups, and resources).Native Analytics. The need for analytics is especially true for Evolutionary Information Systems (EISs), which strive to enable secondary design and therefore require means for investigating and interpreting the system’s behavior in aquantifiable manner. Firstly, as a basis, data generated by (instantiations of) theframework shall follow clear semantics (e.g. via collaboratively defined ontologies) in order to facilitate data mining techniques [15]. Secondly, the frameworkshall facilitate domain-relevant analytics, e.g. via collaboratively-annotated situational analytics mashups. This is intended to support a continuous evaluationof the mashup environment and its underlying processes for all its stakeholders,ultimately contributing to D1.Incremental Applicability. We state the applicability and deployability of theframework within an existing EEIS as an important objective (see D2), both froma technological and an organizational perspective. The latter demands a “gentleslope” system [13] that can be incrementally learned by the domain experts.Therefore, means for the inclusion or transformation of (legacy) componentsand artifacts from the existing system shall be considered.4.2ContributionsThe hereby proposed research effort is intended to contribute to the field of information systems research and to relevant reference disciplines and sub-areassuch as TEL, domain-specific software engineering and learning analytics. Thecontributions, i.e. additions to the knowledge base, are going to be artifacts [14,16], and in particular open source software. The artifacts, which embody thenew knowledge, are going to comprise (i) design principles for the constructionof DSMEs within EEISs; (ii) concrete models describing the constructed environments and abstract models that generalize from these; (iii) experiences fromand methods for constructing such environments.4

5A Pluralistic Design-oriented Research ConfigurationIn general, research is “an activity that contributes to the understanding of aphenomenon” [14, 17]. In design-oriented research, these phenomena are partlycreated by the researcher [14]. The construction process is supposed to revealdesign principles that can be applied to a class of similar systems [18]. Numerousprocess models can be found in the literature, from “micro-scale” cognitive models [19] over “project-scale” [20, 21] to “macro-scale” aggregate models applicable to efforts of multiple research communities [14]. However, a common schemeamong these process models remains. After some form of problem awareness ortrigger, the researcher iteratively switches between constructive and evaluativeactivities. The construction allows for creativity, nevertheless design decisionsmust 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 createdartifacts are going to be evaluated against the defined goals and objectives.For example, while the architecture of the system will be evaluated by meansof expert reviews, interviews or confirmatory focus groups [17] with domainexperts will be considered for evaluating the appropriateness of the system forthe business environment. As our intention is to natively incorporate analytics,we expect to be able to directly gain useful quantitative usage data.6Conclusion and OutlookWe have presented our intention to succeed with research into DSMEs. We believe that incorporating analytics into these systems has the potential to makethem even more useful and effective and can help to tackle some of the problemsthat arise within EEISs. We conclude this paper with a research agenda.M1 – Tentative Design. A crucial first step is to further elaborate the solutionsuggestion to result in a tentative design [14]. We expect this to include design principles, high level architectural models, and user interface mockups.M2 – Mashup Platform Prototype. As a basis for further developments, weplan to implement a prototype of a flexible mashup platform based on andintegrated with our existing EEISs.M3 – DSMEs Prototype. This iteration of the prototype development shall actually enable domain experts to design DSMEs.M4 – Analytics-based DSMEs Prototype. In this phase we are going to enhancethe process of designing DSMEs by using analytics.M5 – Evaluation. While var

Towards a Framework for Analytics-driven Domain-speci c 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 .