A Taxonomy for Describing BI Cloud Services

A Taxonomy for Describing BI Cloud Services Oliver Norkus Jürgen Sauer University of Oldenburg, Department for Computing Science Escherweg 2, 26121 Oldenuburg, Germany oliver.norkus@uni-oldenburg, juergen.sauer@uni-oldenburg.de ABSTRACT Cloud computing is still a driver of many innovations. The cloud comes with features nowadays required in the area of Business Intelligence (BI). BI Cloud offerings are proactive, integrated tools for reporting that are mobile accessible, flexible and scalable. BI in the Cloud is becoming increasingly popular, but a comprehensive reference architecture for describtion, comparison and evaluation of BI Cloud services does not exist yet. A universal model to describe Cloud services in general exists. This, however, is too generic to be applicable to BI Cloud services. Even several conceptual frameworks to describe and characterize explicit BI Cloud services exist. But, as they are developed by different groups and organizations, they differ in their intention, level of description, type of formulation, and in terms of which key issues of BI Cloud service evaluation are addressed. To help overcome the prevalent skepticism of enterprises regarding BI Cloud services, we surveyed different frameworks and approaches for describe and compare BI Cloud services. Based on this survey and the universal model for Cloud service offerings we propose a consolidated taxonomy as an extension of the existing general taxonomy with the aim to uniformly describe, compare and evaluate BI Cloud services. KEYWORDS Cloud service, Business Intelligence in the Cloud, BI as a Service, taxonomy, standardization. 1 INTRODUCTION The term Business Intelligence means the integrated IT-based management support. BI aims to improve the process of decision making. The focus is to provide relevant information to the business users (i.e., decision makers) at the right time at any place [1]. In particular, strategic, intercompany and explorative missions of BI have become the focus of discussion. These involve greater attention to poly-structured data sources, a stronger integration of more sophisticated methods of analysis (advanced, visual and predictive analytics), as well as a stronger orientation towards agility (e.g., via self-service BI). Together with the parallel conducted process integration as well as an addition of new internal and external user groups there is a need to revise established architecture. Especially by regarding that previously used BI systems are mostly rigid, complex and costly [2], cloud computing (CC) is more often used in the field of BI to improve flexibility, scalability and agility [3, 4]. CC is defined as a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e. g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction [5]. The combination of BI and CC is used more intensively. BI in the Cloud yet offers new technology combinations to provide individual and differentiated configurable, scalable and flexible analytical services. BI Cloud services have five main properties [4, 6]: The infrastructure is installed and maintained by the service provider. A BI Cloud service therefore makes available the necessary infrastructure for BI capabilities. So the business users do not have to manage it on their own. The infrastructure is scalable, so it can be expanded dynamically when required. In this way, it can be ensured that the de- ISBN: 978-1-941968-19-2 2015 SDIWC 1

sired analysis can be performed more performant, even with multiple, simultaneous access. The access to the BI Cloud service is done via a browser, and is generally supported by all common mobile and stationary terminals by using standards (e.g., protocols, interfaces). By this, the business users can always access to the BI Cloud service. The use is flexible and dynamic, and can be performed at any time without the service customer to interact with the service provider to agree a time of use. The users pay for their measured consumption on the base of the utilization (pay-per-use). This can be based on the number of visits or based on the usage time. So, cost transparency and predictability is enabled. On the base of the novelty of BI Cloud services, there are not much field reports yet. The absence of standards and transparency especially for description and comparison of BI Cloud services promotes skepticism and incomprehension. There are several approaches to describe, characterize and compare BI Cloud service offerings. To help overcome the prevalent skepticism of enterprises regarding BI Cloud offerings, we surveyed different frameworks for BI Cloud service offerings. However, as they are defined and elaborated by different groups and organizations, they differ in their intention and scope, in their level of abstraction and the type of formalization. In addition, a model for universally describing Cloud services exists [7, 8]. But, this is so generic and universal, that it is not adequate applicable in the field of BI. Based on the survey and also taking into account the existing model for describing Cloud services, we conceptualize a consolidated taxonomy to uniformly describe, characterize and compare BI Cloud service offerings, regardless whether an organizations own offerings or the offerings of external providers are evaluated. In addition, we present the Business Intelligence Cloud Service Navigator (BI-CSN) as a suitable visualization technique to describe visually and compare BI Cloud service offerings. The contribution of this paper is a survey of different frameworks to describe BI Cloud service offerings. With the aim to abolish the skepticism of enterprises regarding BI Cloud services we identified their key factors and designing a defined and elaborated a taxonomy to describe and compare BI Cloud services. The rest of this paper is structured as follows: We present an overview on the surveyed BI Cloud service frameworks and models in section 2, within a description of the survey method. Based on the survey we proposed the consolidated feature models to provide a common foundation for BI Cloud service description and comparison for all roles, even for customers with a lack of basic understanding of the combination of BI and CC. We discuss the feature model and the individual factors of BI Cloud service offerings in section 3. We also present the BI-CSN as an appropriate visualization technique for the visual description and comparison of BI Cloud service offerings and requests in section 4. Within the application, we also have an effect on the evaluation. Finally, in section 5, we conclude with a short summary and remarks on future work. 2 SUBJECT OF RESEARCH This section contains an overview on the surveyed BI Cloud service frameworks, and a description of the surveyed method. We then summarized the differences between the individual frameworks and reference models, which represents the motivation for the consolidated taxonomy for BI Cloud services we propose in section 3. As of this writing, several models and conceptual frameworks to describe and characterize BI Cloud service offerings exist. To help clear away the skepticism of enterprises regarding BI as a service (BIaaS), we conducted a survey of these different frameworks. The surveyed approaches are: ISBN: 978-1-941968-19-2 2015 SDIWC 2

Government and associations: BITKOM [9, 10, 11], Gartner [12], BARC [13, 14]. Industry: SAP [15, 16, 17], Microsoft [18, 19], QlikTech [20], Oracle [21, 22], IBM [23, 24], MicroStrategy [25], TATA [26]. Academia: Adabi [27], Ouf and Nasr [28], Chadha and Iyer [29], Tamer et al. [30], Grivas et al. [31], Demirkan and Delen [32], Baars and Kemper [2], Gurhar and Rathore [33], Gash et al. [34], Thompson and van der Walt [35], Haselmann and Vossen [36], Seufert and Bernhardt [37], Bernhardt and Balluch [38], Weinhardt et al. [39], Torkashvan and Haghighi [40, 41], Leinmeister et al. [42], Schirm et al. [43], Chang [44] and Ereth and Dahl [45]. 2.1 Survey Completeness As shown, the surveyed models represent approaches from different types of organizations; models that were developed by academia, by industry and by governmental structures or other associations. Regarding the industry models, we considered the models provided by SAP, Microsoft, Qlik- Tech, IBM and Oracle. These are representative IT companies and show significant activities in the area of BI in the Cloud [3, 38]. Regarding academia, we surveyed frameworks that represent individual models for BI Cloud and approaches that discuss individual topics such as technical or organizational capabilities. Finally, regarding models by governmental structures of other associations, we considered studies from Gartner, BITKOM, the German Federal Association for Information Technology, Telecommunication and New Media and BARC the German Business Application Research Center. 2.2 Survey Method The survey method is based on the methodology used by defining and elaborating the approach for description and comparison general Cloud services. Thus, the survey was conducted in several steps. We began by conducting a breathfirst search in order to identify relevant works; with the results described above in subsection 2.1. Next we described the surveyed works using feature models [46, 47, 48]. Feature models provide three benefits for the survey [8]: Accessibility: Like mind maps, feature models are simple and can be clearly arranged, represented, and compared to each other. Formality: Feature models are formal models that can be validated. Furhtermore, they allow modeling exclusions, multiple selections, and cardinality. Structure recognition: Feature models do not impose an organizational structure beforehand and even allow to model incomplete concepts. This is in contrast to tables, for example, wherein columns and rows typically have to be named. Figure 1 shows an example of a feature model. The root element is called Feature Model. The element has two sub features, one is mandatory and the other is optional. Again, both features have subfeatures that illustrate inclusion (and) and exclusion (or). Feature models allow an arbitrary model depth, but for the sake of understandability and readability, the modeling depths of different branches should be as uniform as possible [46, 8]. Legend: Mandatory Optional Or Alternative Abstract Concrete FeatureModel MandatoryFeature OptionalFeature Xor1SubFeature Xor2SubFeature Alt1SubFeature Alt2SubFeature Figure 1. An example of a feature model based on [8]. We then compared the surveyed BI Cloud service frameworks by analyzing the feature models, see subsection 2.3. Finally we synthesized ISBN: 978-1-941968-19-2 2015 SDIWC 3

a consolidated feature model based on the analysis, to uniformly describe and evaluate Cloud service offerings, see section 3. 2.3 Survey Results We compared the surveyed BI Cloud service models by analyzing their feature models. For the sake of brevity, this section represents a short summary of the main results. We first summarize the models and then present an analysis. The analysis represents the motivation for the consolidated BI Cloud service feature model that we propose in section 3. 2.3.1 Models from Government and Associations The models created by governmental structures or other associations are intended to be vendorneutral models. These models describe the degree and intensity of utilisation and capabilities of BIaaS. Thereby a distinction is made between the focus on the deployment model (private, public, and hybrid) and for the architectural level (infrastructure, platform and software). 2.3.2 Industry models The models created by the industry represents their specific BI Cloud service offering. There are descriptions in the fields of architecture, examples of use cases, visualization and configuration functions, service level agreements, data security, deployment model and accounting. For example, SAP [15, 16, 17] settle account on the base of usage hours and Microsoft [18, 19] according the resource using. 2.3.3 Models from academia Adabi [27] and Hasselmann and Vossen [36] deals with data management in the cloud. Ouf and Nars [28] as well as Baars and Kemper [2] present a layer architecture for BI Cloud services. Thompson and van der Walt [35] report from a survey primary in the sector of application of BI Cloud services and offer first statements to consolidation. Demirkan and Delen [32] provide a service-oriented architecture for putting BI in the cloud. Chadha and Iyer [29] show an overview of an architecture for easy implementation first steps to gain quick wins. Tamer et al. [30], Gurhar and Rathore [33] and Gash et al. [34] also show first architectural models, but consider merely aspects. Chadha and Iyer [29] go further and offer a meta model. Seufert and Bernhardt [37] offer a submodel for the aspect of service type. Bernhardt and Balluch [38] use this submodel and so classify some BI Cloud service offerings. Weinhardt et al. [39] present a framework for business models in Clouds. Torkashvan and Haghighi [40] present a framework for cloud service level agreement management and a general service oriented framework for cloud computing [41]. Leinmeister et al. [42] intestigate the stakeholder in the business perspective of CC. Schirm et al. [43] are dealing with sucess factors of BI Cloud solutions. Chang [44] analyse BI in the cloud in the application in the finance industry. Ereth and Dahl [45] present a first approach for a service-based evaluation concept. 2.3.4 Analysis There are several models, offerings and approaches of BIaaS. Differences in the offerings exists in the field of architecture model, visualization functions and userfriendliness as well as the billing model. From an architectural point of view, these offerings must be viewed as a black box, because the available information does not go far enough beyond marketingdriven product descriptions. The models from governmental structures and other associations provide primary market observations regarding the use of various offerings. In the academia area, there are already first conceptual considerations and approaches, which mainly focus on aspects and partly only include general statements. Table 1 gives an overview of our findings. As this table show, the surveyed references differ in their level of abstraction, the type of formalization (based on [8]): Level of abstraction: Does the respective reference describe one or more concrete ISBN: 978-1-941968-19-2 2015 SDIWC 4

BI service offerings (instance), does it provide a generic description of BI Cloud (meta model), or does it discuss certain aspects (aspects)? Type of formalization: How is the respective framework described? Typically examples are taxonomies, block diagrams, ontologies, lists and plain text. Table I illustrates that the surveyed references are mostly informal, which hinders comparison and leaves space for unwanted interpretation. The lack of a formal yet comprehensible BI Cloud reference model that represents the curated aggregation of the works of many represents the motivation for the BI Cloud service feature model we propose in the next section. 3 FACTORS OF BI CLOUD SERVICE OFFERINGS As a recent work in the research project Future Business Clouds (FBC) [7], different frameworks and approaches for Cloud service offerings description in general were surveyed and based on this a consolidated reference architecture was proposed [8]. Thus, there is a comprehensive and complete [7, 8], accepted and used [49, 50, 51] approach for universal description of Cloud service offerings. As noted even by publishing this, the approach needs further extension and specification [8]. The existing framework with factors of Cloud service offerings in general represent the base enhanced here. In the following, the identified BI specific factors are presented as an extension of the existing generic framework. For the sake of brevity, in this paper, only the added features are displayed. For a first overview here is referred to Fig. 7, wherein the added features are marked by coloring them green. Based on the analysis in subsubsection 2.3 and as an extension of the existing approach, we designed a consolidated feature model to uniformly describe and evaluate BI Cloud service offerings. In this section we describe the factors of BI Cloud service offerings and in following section 4 we present the BI-CSN as a suitable visualization technique to visually describe and compare BI Cloud services. With this we will have an effect on the application and evaluation. The feature model, as shown in Fig. 2, consists of nine main features for service description: the description of the service type, deployment, pricing, roles, service integration aspects, sourcing options, service level agreements (SLAs), organization specific aspects, and Cloud service characteristics. Additions have resulted in the following sub-models: service type, service level agreement, organizational specific aspects and pricing model. Legend: Mandatory Abstract Concrete BIaaS_Feature_Model ServiceType Deployment Pricing Role Integration Sourcing SLA Characteristics Organization Figure 2. Main features of the taxonomy based on [8] 3.1 Service Type The generic framework considers the service types Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS) and Mashup as a Service with the subfeature Business Process as a Service (BPaaS). To describe BI Cloud service offerings this feature model was extended by the subtypes Visualization as a Service (VaaS), Model as a Service (MaaS), Datawarehouse System (DWS) and Data as a Service (DaaS), see Fig. 3. Firstly, the service types VaaS and MaaS are ordered beneath SaaS because they both describe Software that follows the SaaS approach. VaaS represents all display and presentation aspects whereover analytical functions ISBN: 978-1-941968-19-2 2015 SDIWC 5

can be used. This includes for example Dashboards, Scorecards and Reports. MaaS covers the aspects of flexibility in the form of selfservice BI (e.g. modifying reports, redefining scores) and offers several analytical functions by providing On-Line Analytical Process (OLAP) and Data Mining functionalities. Secondly, the PaaS feature was extended by the subfeature Datawarehouse System as a platform service. DWS covers the management of the Cloud BI system. This includes e. g. development of analytical functions, the configuration of the extract, transform and load (ETL) process, the control of data flows, the user management and the creation of multidimensional data models for the use in reports. Thirdly, the service type DaaS is ordered beneath IaaS, cause the focus is on storage. IaaS is devided in the infrastructure features storage (DaaS) and computing. DaaS includes the provision of infrastructure in the form of storage explicitly for Data Warehouses, data marts, BI repositories and staging areas for the ETL process. indicators (KPIs), as well as agreed change processes. Both, the functional and nonfunctional got extended by service qualities that are important to describe BI Cloud service offerings. The functional qualities for example now contain the feature Availability, Backups and Data Recovery. The non-functional on the other hand got extended by the qualities Data Protection, Access Authorization and Laws. Legend: SLA Mandatory Optional Abstract Concrete Qualities Processes Functional NonFunctional DataIO Usability ResponseTime ErrorBehavior Availability Backups DataRecovery Locality Performance UsagePrice Integrability Safety DataProtection AccessAuthorization Laws Legend: Mandatory Optional Abstract Concrete SaaS VaaS MaaS Dashboard Scorecard Report OLAP DataMining SelfServiceBI Measures Modalities Termination Incentives Penalties KPIs PaaS DWS Development Configuration Figure 4. Service Level Agreements ServiceType StagingArea IaaS MashupAsAService DaaS Computing BPaaS Figure 3. Service Type 3.2 Service Level Agreement Repository DataMarts DataWareHouse Service Level Agreements (SLAs) are of central importance for the successful cooperation between the participants in a BI Cloud environment. Figure 4 shows the extended SLA feature model. The model considers functional and non-functional service qualities, agreed governance aspects such as key performance 3.3 Organizational specific aspects To establish trust among the participants in a BI Cloud environment, organizations must be able to state their reputation and to assess their capabilities. The model considers the organizations reputation as well as the organizations capabilities, see Fig. 5. Regarding the reputation the model consider certificates, reference projects, KPIs, benchmarks and reports, and further information such as websites. On the other hand, an organizations capabilities depend on the given resources, employee knowledge, technical skills and business skill. Further the capabilities or trust were extended by the feature Support. This feature describes if ISBN: 978-1-941968-19-2 2015 SDIWC 6

the cloud service provider offers support services inclusive or if the customer has to pay for it separately. Company Trust BusinessSkills TechnicalSkills KnowledgeBase Resources Support Information ExpertAssesments Legend: Mandatory Optional Abstract Concrete 4 BUSINESS INTELLIGENCE CLOUD SERVICE NAVIGATOR Regarding the description of a concrete BI Cloud service, a feature of the BI Cloud feature model presented in section III can either be said to be present or not. This presence or absence can be represented visually by coloring these two states of each BI Cloud service description feature in a given graphical representation of the feature hierarchy. Reputation Benchmarks KPIs References Certificates Figure 5. Organizational specific aspects 3.4 Pricing model As Fig. 6 shows, the pricing model is distinguished between Free, Pay per Usage and Abonnement. Free means free use, for example, to attract new customers. Pay per Usage covers payment based on measured usage. Abonnement stands for periodic payments for a recurring use. Filed under Pay per Usage are pay-per-time-ofuse, pay-per-usage and pay-per-unit. Within pay-per-time-of-use is mentioned a payment for certain period of use. Pay-per-usage covers the payment for the number of consideration respective views of a reports (e. g. pay-perreport). A resource-based billing, e.g. payment for calculation power units or storage requirements, so used hardware resources, is summarized with pay-per-unit. Pricing Free PayperUse Abonnement PayperUsage PayperUnit PayperTimeofUse Figure 6. Pricing model Legend: Alternative Abstract Concrete Figure 7. BI-CSN: CSN extended by BI specific featured (colored: green). Several visual representations to describe and characterize BI Cloud services exist. For example, Grivas et al. [31] and Seufert and Bernhardt [37] uses informal boxes and lines diagrams, Chadha and Iyer [29] uses tables and other like Thompson and van der Walt [35] just use text. We developed BI Cloud Service Navigators (BI-CSN) as enlargement of the existing Cloud Service Navigator [8] for describing generic Cloud services. Thereby, BI-CSNs are based on Sunburst diagrams. The goal of this visualization is to support the fast perception of service description. In this section, we first present our BI-CSN in subsection 4.1. Next to that, we discuss the ISBN: 978-1-941968-19-2 2015 SDIWC 7

application in section 4.2 and the evaluation in subsection 4.3. 4.1 BI-CSN The CSN was extended by the mentioned BI specifics to be able to describe BI Cloud service offerings. The new BI specifics were arranged in the BI-CSN shown in Fig. 7. The root of the feature hierarchy is placed at the center of the BI-CSN. The child features are then concentric placed in surrounding rings. Features on the same level of hierarchy are placed on the same ring as siblings, while sub features are placed in the outer sections that their ancestors cut from the pie. Present service descriptions are colored and absent features would be transparent. The green colored features in Fig. 7 are the new BI specifics, which are added based on factors of BI Cloud service offerings as result of the survey and the consolidation. By using the BI-CSN to describe existing BI Cloud service the coloring represents the service properties. Present features will be colored and absent feature will be transparent. The application of the taxonomy by using the BI-CSN is following in the next section. analyzed data can be visualized through different types of diagrams. Thereby, Power BI follows the MaaS and VaaS approach. Power BI is based on the Microsoft Platform Windows Azure which is hosted in a Virtual-Private Cloud in Microsofts own data centers. The scale of the demand can be performed in self management and the accounting is based on the resources used. Microsoft offers detailed SLA with assurances e.g. in security, maintenance, response times. Support has to be aditionally orded and payed. 4.2 Application In total, the BI-CSN was applied to eight BI Cloud service offerings. Here, to not over extend this paper, are shown two cases the BI Cloud service Power BI from Microsoft and the BI Cloud service SAP HANA Cloud from SAP. In the BI-CSN (Power BI in Fig. 8 and SAP HANA Cloud in Fig. 9) the blue colored features means that these features are present and the transparent (white) features are absent. Figure 8 shows the specified BI-CSN for the BI Cloud offer Microsoft Power BI. Microsoft Office 365 with Microsoft Excel and supplemented with Microsoft Sharepoint Online represents the main components of Power BI. Power BI follows the SaaS approach and offers the possibilities to visualize, to model and to analyze data for example by using OLAP functions and Data Mining. Furthermore, the Figure 8. BI-CSN for Microsoft Power BI An other BI Cloud service offering that was used for the evaluation was the SAP HANA Cloud Platform, see Fig. 9. SAP HANA Cloud is offered as a Platform as a Service in the sense of a Data Warehouse System as platform service (DWSaaS). On this plattform the VaaS and MaaS offerings SAP BI OnDemand and Lumira Cloud are deployed. Further considered here, is the platform offering. The SAP HANA Cloud is hosted by Amazon, so that a third party is involved. Just as Microsoft, SAP offers detailed SLA aspects. ISBN: 978-1-941968-19-2 2015 SDIWC 8

Within expert dialogues on this subject, we presented the taxonomy and the application to several offerings and discussed it. The completeness and feasibility has been confirmed there. By visualizing the consideration deals with the BI-CSN, the comparability showed the visually simple as possible. A comparison of existing factors of a BI Cloud services is easily possible by visually obervation of the specific BI-CSN. Also, with the BI-CSN the requirements of customers can be described and these can simply be compared with existing BI Cloud services to find the most appropriate service. 5 CONCLUSION Figure 9. BI-CSN for SAP HANA Cloud 4.3 Evaluation As part of a comprehensive literature review (see section 2), many approaches in the area of BI Cloud have been discovered. We identified and removed dublicates, unrelevant references and reviews and editorials. We identifies approaches of different groups - that s why our approach is based on a certain wide opinion. On the base of the survey and their analysis, the conceptualization of the taxonomy was performed as part of an interative design science process with repetetive incremental steps. The individual factors of the references were successively compared, considered and consolidated. This ensures that our architecture is on the current state of the status of scientific, industry and governmental and other associations. The application is importance to demonstrate the feseability. So we took eight BI Cloud offerings and used our taxonomy to describe them (due to the brevity, the application is displayed on two BI Cloud services, see section 4.2). In the application it was found out, that our architecture is completely inasmuch as all features of the observed offerings were covered. In this article, a taxonomy for the description and comparison of BI Cloud services was introduced. This is a tool for orientation and understanding of the subject area BI in the cloud. The taxonomy can also be used to identify and evaluate business needs and to support the selection of suitable offerings. To help overcome the prevalent skepticism of enterprises regarding BI Cloud services, we surveyed different frameworks for BI Cloud service description. Based on this survey, we proposed a consolidated taxonomy in the form of a feature model to uniformly describe, compare and evaluate BI Cloud services, regardless of whether an organizations own offers or the offers of external providers are evaluated. In addition, we present the BI-CSN as a suitable visualization technique to describe visually and compare BI Cloud service offerings and requests. Based on the application of the BI-CSN on existing services we evaluated and demonstrated the feasibility and completeness. However, there are several open issues that we consider as future work: The proposed framework should be continuously adapted to the developments in order not to lose validity and soundness by the appearance of new BI cloud service facets. An automatic selection of BI Cloud offerings can be realized by using the taxonomy for describing business requirements and to compare these with service descriptions to find a suitable service for the ISBN: 978-1-941968-19-2 2015 SDIWC 9

considered scenario. Here in particular, existing approaches from the service-oriented field (e. g. service oriented architecture (SOA), unified service description language (USDL) etc.) could be used. For the future development of BI Cloud systems and services this new domain should be standardized. A standardized architecture could provide transparency, reduce uncertainty and help to raise the potentials. Some questions of enterprise, system and software architecture are not still clear and transparent clarified. Therefore many offerings are in an early maturity and heterogeneous. With this contribution is a first step in the direction of standardization is done. Regarding the overall goal of the contribution, we consider our work as an important step towards reducing the skepticism in BI Cloud services, since it provides a consolidated view on their features. We delivered a taxonomy that can be used for the description, comparison, selection of BI Cloud services. REFERENCES [1] H. Chen, R. Chiang and V. C. Storey, Business Intelligence and Analytics: From Big Data to Big Impact, MIS quarterly, vol. 36(4), pp. 11651188, 2012. [2] H. Baars and H. G. Kemper, Business intelligence in the cloud?, PACIS 2010 Proceedings, Paper 145, 2010. [3] O. Norkus and H.-J. Appelrath, Towards a Business Intelligence Cloud, Proceedings of the Third International Conference on Informatics Engineering and Information Science (ICIEIS2014) Lodz University of Technology, Lodz, Poland, pp 5566, September 2014. [4] O. Norkus et al., An Approach for a Cloud-based Contribution Margin Dashboard in the Field of Electricity Trading, In: Douglas Cunningham, Petra Hofstedt, Klaus Meer, Ingo Schmitt (Hrsg.): INFORMATIK 2015, Lecture Notes in Informatics (LNI), Gesellschaft fr Informatik, Bonn 2015, Bonner Kllen Verlag, 9/2015. [5] P. Mell and T. Grance, The NIST Denition of Cloud Computing, National Institute of Standards and Technology, Information Technology Laboratory, Gaithersburg, NIST, Special Publication 800-145, September 2011. [6] O. Norkus, B. Friedrich, F. Merkel, I. Schweer, Margin Control as a Cloud Service (Deckungsbeitragsrechnung als Cloud-Dienst), In: Norbert Gronau (Ed.): ERP Management, 15(2), GITO Verlag, pp. 27-29, vol 5, 2015. [7] H.-J. Appelrath, H. Kagermann and H. Krcmar, Future Business Clouds: A contribution to the project of the future Internet-based services for the economy (Future Business Clouds: Ein Beitrag zum Zukunftsprojekt Internetbasierte Dienste fr die Wirtschaft), Herbert Utz Verlag, 2014 [8] S. Gudenkauf, M. Josefiok, A. Göring and O. Norkus, A Reference Architecture for Cloud Service Offers, Software Engineering for Business Information Systems, 2013. [9] M. Weber et al. Cloud Computing Monitor 2015, BITKOM KPMG, 2015. [10] M. Weber et al. Cloud Computing Monitor 2013, BITKOMKPMG, 2013. [11] M. Weber et al., Cloud Computing - Evoluton in der Technik, Revolution im Business, BITKOM- Leitfaden, BITKOM, 2009. [12] R. Sallam et al., Magic Quadrant for Business Intelligence and Analytics Platforms, Gartner, 2015. [13] C. Bange, T. Grosser, N. Janoschek, Big Data Use Cases - Getting real on data monetization, BARC, 2015. [14] C. Bange, S. Roggers, Cloud Business Intelligence and Data Management as a Service - A Global Survey on Adoption, Challenes and Outlook, BARC, 2011. [15] SAP http://www.sap.com/germany/pc/ analytics/business-intelligence/ software/data-visualization/ cloud.html, last visit: 09.05.2015. [16] SAP, http://go.sap.com/product/ analytics/lumira/cloud.html,last visit: 09.05.2015. [17] SAP, https://websmp207. sap-ag.de/ sapidp/ 011000358700001269622010E.pdf, last visit: 09.05.2015. ISBN: 978-1-941968-19-2 2015 SDIWC 10

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[42] S. Leinmeister, M. Böhm, C. Riedl, Christoph, H. Krcmar, The business perspective of cloud computing: actors, roles and value networks, ECIS 2010 Proceedings, Paper 56, 2010. [43] N. Schirm, T. Frank, M. Henkel, F. Bensberg, Success factors for cloud-based cusiness intelligence solutions (Erfolgsfaktoren cloudbasierter Business Intelligence Lösungen), Proceedings der 12. Internationalen Tagung Wirtschaftsinformatik (WI2015), 2015. [44] V. Chang, The business intelligence as a service in the cloud, Future Generation Computer Systems, Volume 37, pp. 512-534, 2014. [45] J. Ereth, D. Dahl, Business intelligence in the cloud: fundamentals for a service-based evaluation concept, Tagungsband des 5. Workshops Business Intelligence der GI-Fachgruppe Business Intelligence, Paper No. 1, 2013. [46] D. Batory, Feature Models, Grammars and Propositional Formulas, Proceedings of the 9th international conference on Software Product Lines, ser. SPLC05. Berlin, Heidelberg: Springer-Verlag, 2005, pp. 720. [47] K. Czarnecki and U. W. Eisenecker, Generative Programming: Methods, Tool, and Applications, 6th ed, Addison Wesley, April 2005. [48] K. Czarnecki and S. Helsen, Feature-based survey of model transformation approaches, IBM Systems Journal, vol. 45, no. 3, pp. 621646, 2006. [49] S. Kolb and G. Wirtz, Towards application portability in platform as a service, Service Oriented System Engineering (SOSE), 2014 IEEE 8th International Symposium on. IEEE, 2014. [50] A. Göring, S. Gudenkauf, M. Josefiok and O. Norkus, A taxonomy for describing cloud service offers (Eine Taxonomie zur Beschreibung von Cloud-Dienstangeboten), VDE-Kongress 2014, VDE VERLAG, 2014. [51] V. Andrikopoulos, A. Darsow, D. Karastoyanova, F. Leymann, CloudDSFThe Cloud Decision Support Framework for Application Migration, Service-Oriented and Cloud Computing, pp. 1-16, Springer, Berlin Heidelberg 2014. Table 1. Results of Literature Analysis Reference Level of Formality Abstraction meta model aspect instance informal in part formal formal BITKOM [9, 10, 11] X X Gartner [12] X X BARC [13, 14] X X SAP [15, 16, 17] X X Microsoft [18, 19] X X QlikTech [20] X X Oracle [21] X X IBM [23, 24] X X MicroStrategy [25] X X TATA [26] X X Adabi [27] X X Ouf and Nasr [28] X X Chadha and Iyer [29] X X Tamer et al. [30] X X Grivas et al. [31] X X Demirkan and Delen [32] X X Baars and Kemper [2] X X Gurhar and Rathore [33] X X Gash et al. [34] X X Thompson and van der Walt [35] X X Haselmann and Vossen [36] X X Seufert and Bernhardt [37] X X Bernhardt and Balluch [38] X X Weinhardt et al. [39] X X Torkashvan and X X Haghighi [40, 41] Leinmeister X X et al. [42] Schirm et al. [43] X X Chang [44] X X Ereth and Dahl [45] X X ISBN: 978-1-941968-19-2 2015 SDIWC 12