Towards an Architecture of BI in the Cloud

Transcription

1 Towards an Architecture of BI in the Cloud Oliver Norkus and Jürgen Sauer University of Oldenburg, Oldenburg, Germany Abstract. Current Business Intelligence Systems fulfill the increased requirements of the business in terms of flexibility and scalability no longer sufficient. Cloud computing offers the potential to overcome these challenges. BI in the Cloud promises more flexibility and agility as well as a higher analysis and query performance. As a reason why the combination of Business Intelligence and Cloud Computing has not achieved yet, a lack of standardization can be identified. So, in this paper we present an architectural approach as a first step of the standardization the field of BI in the Cloud. Keywords: Architecture, BI as a Service, Business Intelligence in the Cloud, Standardization. 1 Introduction In the area of corporate reporting so-called Business Intelligence (BI) systems have been developed with the goal to analyze data from various sources and to gain knowledge in hence of supporting the management in the decision making process. BI is in permanent chance which was particularly noticeable in the recent years [1]. Traditional BI solutions are often rigid, complex and costly [2]. Moreover, a bottleneck is often seen in the context of the development and availability of BI in enterprises in particular due to budget and resources [3]. Predominantly enforced BI usage models, such as on premise or outsourcing models, offer less agility and adaptability [4]. In the huge industrialized and globalized world with the background of the financial and economic crisis, companies must change continuously in order to pass the strong competitive. Companies need to respond quickly to changes and trends and deal with moving market conditions [5]. In addition, the role and functioning of decision-makers vary. A new emerging generation of managers interacts more operational and faster [6]. Accordingly, the decision-making processes and supporting tools have to be agile and flexible [7]. Taken this together, the traditional and often monolithic BI solutions fulfill the increased requirements especially in terms of flexibility and scalability no longer sufficient [4]. Regarding this situation, Cloud Computing (CC) is a promising technological development. The cloud brings several features that meet the

2 2 Towards an Architecture of BI in the Cloud high expectations of analytical applications. BI in the Cloud features flexibility, scalability and self-service issues and promises a new orientation to the respective requirements of the business. In the industry, BI Cloud offers are already available [4], [8]. From an architectural point of view, these offers must be viewed as a black box, because the available information does not go far enough beyond marketing-driven product descriptions. Also there are first scientific statements, we discussed summarized in [4]. As condensate arises, an approach for a reusable architecture is imperceptible. Though, a reason why the combination of BI and CC is not yet common, a lack of standards and a lack of understanding can be identified. To help overcome the prevalent scepticism we present an approch for an architectur for BI in the Cloud. The concern is to build up this architecture as a implementation template to increase the efficiency of the development process, to minimize development risks and to optimize the communication betweet the stakeholders by using this architectural framework as a communication medium. In excess of this, the approach can be the base for a reference architecture. Our aim is to present a proposal for the standardization and generalization of the architecture and environment of BI applications delivered as a cloud service. For conception this architecture, we apply the design science research method. Our research is oriented to the recommendations and principles of the designoriented business computer science based on [9]. Furthermore our research method is based on the design science research process by [10] and [11], in particular the described therein seven research guidelines. Thus, a multi-level, iterative design process as shown in Fig.?? is applied. The various stages of the iterative process model consist of the following steps, which were permanently accompanied by communication of the results in science and practice by publication and project management: 1. Problem identification and motivation includes the description of the scientific problem and the motivation and justification of the research project. Methods used are surveys, literature review and interviews. 2. Design Artefact contains the construction of the IT architecture for BI in the cloud and a guide for implementation. Research methods used are incremental modelling, model-driven-development, prototyping und feedback round with BI consultants and architects. 3. Design evaluation embodies the feasibility demonstration and the application within a case study as well as the assessment and evaluation. Research methods used are action research, metrics and interviews. 4. Research contribution is the reusable IT architecture as a knowledge base created as part of the conceptualization. The rest of this paper is structured as follows: First of all, in section 2 we set up some basic concepts. After that in section 3 we give a disambiguation of BI in the Cloud. In section 4 we present collected requirements and business drivers for BI in the Cloud. Next we present an overview of related work in section 5. After that we present the architectural approch for BI in the Cloud

3 Towards an Architecture of BI in the Cloud 3 Design Process Problem relevance and motivation Design Artefakt Design Evaluation Research Contribution Research method Fig. 1. Research process in section 6. To demonstrate the feasibility we implement a prototype base on the architecture to proof the concept. For testing and evaluation we used this prototype in a case study, presented in section 7. Last, we will give a summary and name further work in section 8. 2 Foundations BI refers to procedures and processes for the systematic analysis. BI is introduced in section 2.1. Technologies covering the ability to use commoditized hardware access (Cloud Computing) is covered in section 2.2. The merging of these both concepts to BI in the Cloud is covered in section Business Intelligence Business Intelligence is a broad category of technologies and applications for gathering, storing, analyzing and visualizing information. BI is associated with supporting IT resource management in order to optimize the process of decisionmaking, with a focus in providing decision makers with necessary information at just the right time. Here, the aim is to support all decision-makers with in-depth knowledge to come to an elaborated decision. By the initially technological driven development, BI turned increasingly to an IT-based, process- and content-driven analytical business management tool [12]. The understanding of the term BI ranges from multidimensional data structures on individual information systems to complex system landscapes analyzing large quantities of data for management. Analytical information systems focus on the provisioning of information and functional support for analyses in support of decision-makers and managers [13]. 2.2 Cloud computing Cloud Computing (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

4 4 Towards an Architecture of BI in the Cloud be rapidly provisioned and released with minimal management effort or service provider interaction [14]. The central aspect of CC is the notion of a Cloud service. It can be definied as the provision of virtual IT resources (i.e. logical resources that are mapped to physical hardware) demonstrating the following characteristics [14], [15]: Resource pooling: Cloud services enable the shared utilization of physical resources by means of virtualization (virtual resources instead of physical hardware) and multitenancy (multitenant data management). Rapid elasticity: Services can be immediately provisioned and released as demanded and the resources available for provisioning appear often to be virtually unlimited. Measured service: Service utilization is measured and ideally monetized with a pay-per-use accounting and pricing model. Broad network access: Cloud services are supported by technical standardization and ubiquitous network access. On-demand self-service: Consumers can unilaterally provide themselves with services as they need, due to extensive service automation on the side of the provider. 3 Business Intelligence in the Cloud BI in the Cloud is not a fundamentally new technology. There are precursor and roots in both domains BI and CC. BI in the Cloud yet offers new technology combinations to provide individual and differentiated configurable, scalable and flexible analytical services. An early definition made in 2010 describes Business Intelligence Cloud (BI Cloud) as an IT architecture with the purpose of providing analytical capabilities as a service. A distinction therein is made between the focus on the application (private, public, and hybrid) and for the architectural level (infrastructure, platform, and software) [8]. More specifically in 2014, a definition states that analytical applications can be deployed as a cloud service, and that the outcome is called Business Intelligence as a Service (BIaaS). Such BI Cloud services reflects on the characteristics of typical cloud services features described below [4], [16]. BIaaS enables organizations to flexibly respond to moving requirements and business demands. Through flexible allocation, resources can be distributed depending upon project needs, particular to make complex queries in an efficient manner possible. The average employee (without specialized training) becomes able to automatically allocate and utilize resources around the particular services they are interested in through the system, without having to specifically invoke requests through the IT department. Analytical applications in the cloud promote availability and reliability. They are accessible via an Internet connection from anywhere with only a web-enabled device. Reports and analyses can be adapted to directives and the needs of stakeholders, so BIaaS provides a high level of agility. BIaaS also provides flexibility at the technical level, e.g., modifying cubes, ETL processes, or multidimensional databases, which can all be

5 Towards an Architecture of BI in the Cloud 5 processed in a simple and flexible manner. Analytical applications in the cloud promise very strong performance levels for queries and analyses, especially rapid deployments of complex reports and a short turnaround time when changing parameters. BIaaS can be billed on a per-use basis. Payment can be calculated for some aspect of the report, for example, the number of invocations, the hardware resources used (e.g. CPU time), or access to proprietary data sets [4], [16]. 4 Requirements and Business Drivers As a recent work [16], [17] we performed face-to-face interviews with BI architects and BI developers as well as decision-makers of various hierarchical levels for the discovery of requirements and business drivers. In accordance with agile software development process, we performed requirements engineering in multiple iterations. We evaluated each interview to identify requirements and business drivers. In addition to the interviews, we have searched requirements in the relevant literature. Here, no new requirements were identified, an intersection of the sets of requirements was existing. During the requirements engineering we were able to derive following summarized requirements for BI in the Cloud: Availability: System redundancy is required to guarantee high availability, e.g. data backups and redundant infrastructure. Accessibility: Thus, the service should be available on a wide range of devices, (not only on traditional computers, but also on mobile devices), and must be available over the Internet. Accounting: Given that BI systems are expensive to set up and maintain, it is necessary for clients to pay for their usage. The charges can be calculated either with respect to the complexity of the calculations or according to the volume of reports generated. User empowerment: managers and decision-makers should not need to write complex database queries or analyze raw data. The user interface needs be usable without special training, and allow, the user to intuitively understand the inter-face and the analysis result. This is a key to user acceptance. Web standards: Nearly every mobile device or computer supports web technologies. The best way to bring the service to a wide variety of devices is to use the accepted standards. Performance: The duration of calculation and preparation as well as and data transport to the user should be minimized. Poor performance leads to decreased user acceptance of BI systems. While the quality of mobile Internet connections an unavoidable factor, keeping transmitted data small and presenting overview before detail (like loading a graphic file over a dialup connection) helps to mitigate this. Modularity and expandability: It should be possible to subsequently add complementary functionality to the prototype. Privacy / data security: Data should only be visible to authorized persons and protected against unauthorized access.

6 6 Towards an Architecture of BI in the Cloud Next we present an overview of related work and mirror these requirements on existing approaches. Afther that, to fill the exposed gap, we present our architectural approach for BI in the Cloud in section 6. 5 Related work In a survey, we searched Mendeley and Google Scholar for the terms business intelligence, cloud computing, BI, Cloud, BIaaS, framework and architecture in various combinations. We identified more than 3,000 references. After further filtering, we were able to reduce this set by 2,920. Afterwards we identified and removed 17 duplicates, 8 unrelated references and 14 reviews and editorials. Finally we turned left with 41 full text references from which 16 turned out to be unrelated work and 11 were identified as reviews or editorials. Subsequent to this, only 14 full text references, actually dealing with BI in the Cloud remained. For the sack of brevity, this section represents a short summary of the main results. Adabi [18] and Hasselmann and Vossen [19] deals with data management in the cloud. Ouf and Nars [20] as well as Baars and Kemper [2] present a layer architecture for BI Cloud services. Thompson and van der Walt [21] report from a survey primary in the sector of application of BI Cloud services and offer first statements to consolidation. Demirkan and Delen [22] provide a serviceoriented architecture for putting BI in the cloud. Chadha and Iyer [23] show an overview of an architecture for easy implement first steps to gain quick wins. Tamer et al. [24], Gurjar and Rathore [25] and Gash et al. [26] also show first architectural models, but consider merely aspects. Here, Chadha and Iyer [23] go further and offer a meta model. Seufert and Bernhardt [8] offer a submodel for the aspect of service type. Bernhardt and Balluch [27] take this submodel and so assign considered market offerings. Mircea et al. [5] propose an approach for the combination of BI and CC for delivery agility in economy. Table 1 gives an overview of our findings. As this table show, the surveyed references differ in their level of abstraction and in the type of formalization: Level of abstraction: Does the respective reference describe one or more concrete BI Cloud service realizations (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 1 illustrates that the surveyed references are mostly informal, which hinders comparison and leaves space for unwanted interpretation. We discovered different aspects and approaches and some notes on instances. Likewise, some meta models which are not suitable as implementation template because of the lack of formality. On the set of our findings, we found no reference which show or hint a comprehensively described, consistent, clear architecture for BI in the Cloud.

7 Towards an Architecture of BI in the Cloud 7 Table 1. Results of Literature Analysis Reference Level of Formality Abstraction meta model aspect instance informal in part formal formal Adabi [18] X X Ouf and Nasr [20] X X Chadha and Iyer [23] X X Tamer et al. [24] X X Baars and Kemper [2] X X Gurjar and Rathore [25] X X Gash et al. [26] X X Thompson and van der Walt [21] X X Haselmann and Vossen [19] X X Seufert and Bernhardt [8] X X Bernhardt and Balluch [27] X X Demirkan and Delen [22] X X Mircea et al. [5] X X The lack of a formal yet comprehensible architecture for BI in the Cloud represents the motivation for the architecture model we propose in the following section. 6 Architecture for BI in the Cloud Using the cloud technology for BI effects a lot of benefits. This also is recognized in the industry, where first early products are on the market, as well as in the research, where BI in the Cloud is a relevant topic. However, there still exists a lack of consolidation and standardization regarding the architecture. Our approach is to fill this gap by presenting a reusable architecture for BI in the Cloud. For delivery of BI cloud services a specific system architecture is needed. Our approach, as a first step for standardization the architecture, in the form of a component diagramm modelled with the Unified Modeling Language (UML) is shown in Figure 2 and described below. The user of the system are manager and decision-makers. They interact directly with the Local Client. The Local Client represents the user interface and control logic as well as display data to the user. For managing the complexity it separates the view, the control and the data by using the model-view-control design pattern. The Local Client is performed locally on the terminal device of

8 8 Towards an Architecture of BI in the Cloud the users. This component sends the requests to the Load and Resource Manager component. The Local Client consist of three subcomponents Report Display, Report Configurator and Client Business Logic. The Report Display is responsible for visualization the graphical user interface and for displaying the reports. With data binding this component is connected to the Client Business Logic component. The Report Configurator is used to configure the interface, e. g. select the visualization and chart types and thus constitutes a basis for achieving the flexibility. The Client Business Logic is the core component of the Web Client and contains the control logic and functionality of the client and communicates with the server. Local Client «UI» Load and Resource Manager «Component» [1...*] Accounting «Component» [1...*] Dispatcher «Component» Tenancy and User Manager «Component» ETL & Source Monitor «Component» Data Retrieval «Component» [1...*] Source Data «API» Data Warehouse «Database» [1...*] Fig. 2. Basic Structure of the Architecture The Load and Resource Manager is a proxy server that forwards the request to an available Dispatcher component running inside a virtual machine. The Load and Resource Manager consists of three subcomponents Load Balancer, Resource Monitor and Resource Manager. The Load Balancer is responsible for the distribution of the client requests to the Dispatcher component based on the utilization. A Resource Monitor measures and reports the load on each VM, so that the Resource Management component can start up or shut down VM instances as needed. This response to the incoming load maintains Quality of Service for availability, accessibility, and query performance.

9 Towards an Architecture of BI in the Cloud 9 Depending on the type of the request, the Dispatcher communicates with the various other server components in order to answer the user request. The Tenancy and User Manager consists functions for create, delete and manage tenancies and users. In addition, there are subcomponents Account Manager, Authentication and Session Manager and Authorization and Rights Manager. Usage based billing is made possible by tracking the usage of individual users through the Accounting component. The component Account Manager is recording the use intensity by a user and therefore for each client. This results in a settlement in such a way based on the usage time, resource respective hardware utilization, or number of report views per customer or per client. For encapsulating and optimizing database access for the user requests, all analysis requests go through the Data Retrieval component. This runs on separate virtual machines (VM), so that resource intensive operations do not affect the overall responsiveness. To avoid unnecessary database queries and to enforce security, the Data Retrieval component verifies that the user who initiated a request has sufficient permissions to execute it. The Data Retrieval comprises three subcomponents. The Query Manager component receives the user requests, refine them in database queries in cooperation with the component Query Optimizer which improves the query plan and translate queries if needed in hardware-specific expressions. The Storage Manager deal with the different data warehouses. The data of the BI Cloud system is stored depending on the properties of the data and the storage. Taking into account the sensitivity and the structure of the data and the price, the location and the performance the Storage Manager selects during the extract, transform and load (ETL) process the best location for the relevant data from the source systems. This is done rule-based on an abstraction layer over all storage services. The Data Warehouse is the database and stands in the architecture for all storage services. The used storage services can be private or public cloud storage services, located in different areas with different security and performance properties. They are managed by the Storage Manager. The component ETL and Source Monitor is responsible for observing the source systems, starting the ETL process, which results in storing homogenous data in the Data Warehouse. The subcomponent Source Monitor keeps a watch on the source databases and initializes a load by exceeding of a specified delta, periodical or on demand. The subcomponent ETL Processor communicates to the source systems and provides the physical transmission of data. The Source Data represents the source systems which can be accessed via an application programming interface (API). Scalability and Flexibility are main characteristics of this architecture. The main features that cause are summarized as follows: The modular design of the components, the spread of the incoming requests based on the utilization of the physical and virtual components, the start and end of further components based on the load and use,

10 10 Towards an Architecture of BI in the Cloud the transfer of control logic in the Local Client. On this way, systems based on this architecture can deliver agile BI capabilities. That the development of such a system is possible, we show in the next section by presenting our prototype within the proof of concept. 7 Proof of concept We implement a prototype based on our architecture so we show the feasibility. For evaluation and testing, we have realized a scenario with the prototype so we applied the prototype in a case study [16]. The prototype and the case study are described below. The implementation of the prototype is based on state of the art technologies to assess the feasibility of the conceptualization. Partly the technology used is provided by the Hasso-Plattner-Institute (HPI). The virtual machines required for running the components are managed using HP Converged Cloud, which adheres to the OpenStack architecture. As database system a shared SAP HANA instance is used. The abstraction layer above the theoretically different data storage based on Apache JClouds. Through this implementation is shown that our architecture can be used as realization template for BI cloud systems. Based on the architecture specification was examined whether a fulfillment of functional and qualitative requirements is possible. The feasibility study has been completed successfully. We have performed a scenario-based architecture analysis method with a case study from the German energy industry [16]. As a case study we set up the contribution margin control in the field of electricity trading. With our prototype we realized different dashboard as a kind of a BI application. Within this case study we evaluated the prototype and the architecture by applying and testing by experts from the energy domain. As another evaluation technique, already during the design, reviews and feedback loops with BI architects and developers were done. The results showed, that the architecture is well suited to be the base to build up a BI Cloud systems. 8 Conclusion and further work In this paper, we presented our approach for the architecture for BI in the Cloud. Therefor we gave our motivation, set up the foundations and report the surveyed requirements and business drivers. The architecture was developed in an iterative incremental process describes in this contribution before the actual architecture was discussed. Once the feasibility study has been presented, we introduce the prototype and the case study. Our architectural approach can be used as an implement template, as shown in the feasibility study. By using this architecture as a implementation template it helps to increase the efficiency of the development process, to minimize development risks and to optimize the communication betweet the stakeholders by using this architectural framework as a communication medium.

11 Towards an Architecture of BI in the Cloud 11 The feasibility of our approach was shown in the form of a prototype. With a scenario we have tested this prototype in a simulated real environment. According to feedback from early and final interviews for the qualitative evaluation with BI experts like architects and developers, our approach is very promising and provides a valuable contribution to increase the standardization and to produce the transparency of this new field. Regarding the overall goal of the contribution, we consider our work as an important step towards the standardization of the field BI in the Cloud and towards the reducing skepticism and uncertainties. Our approach can thus form the basis of a reference architecture. However, there are several open issues that we consider as future work: The architectural approach should be continously improved. In addition to the standardization of components, also the API, the data and exchange formats and the delivery concept are to standardize. Besides the standardization of the software architecture for implementing a system, our approach should also be build up as a comparison and evaluation tool. Another important research area will be the safety and security, which played only a subordinate role in focusing the feasibility and answering basic architectural questions. In addition to the further evaluation and continuing improvement of the architecture it is planned to expand it to an evaluation and comparison model for BI cloud systems. In the long term, we aim to provide a proposal of a reference architecture for BI in the Cloud. References 1. Chen, H., Chiang, R., Storey, V.C.: Business Intelligence and Analytics: From Big Data to Big Impact, MIS quarterly, vol. 36(4), pp (2012) 2. Baars, H., Kemper, H.G.: Business intelligence in the cloud?, In: Pacific Asia Conference on Information Systems (PACIS), pp (2010) 3. Imhoff, C., White, C.: Self-service business intelligence empowering users to generate insights, TDWI best practices report (2011) 4. Norkus, O., Appelrath, H.-J.: Towards a Business Intelligence Cloud, In: Proceedings of the Third International Conference on Informatics Engineering and Information Science (ICIEIS2014), pp 5566, Lodz (2014) 5. Mircea, M., Ghilic-Micu, B., Stoica, M.: Combining business intelligence with cloud computing to delivery agility in actual economy, In: Journal of Economic Computation and Economic Cybernetics Studies, pp , vol (2011) 6. Power, D.J.: Challenges of real-time decision support, In. Burstein, F, Brezillon, P, Zaslavsky, A. (eds.) Supporting Real Time Decision-Making. LLC, vol. 13, pp. 311, Springer, Heidelberg (2011) 7. Rosca, I., Moldoveanu, G.: Management in turbulent conditions, In: Journal of Economic Computation and Economic Cybernetics Studies and Research, pp. 512, vol. 2 (2009). 8. Seufert, A., Bernhardt, N.: Business Intelligence und Cloud Computing, HMD Praxis der Wirtschaftsinformatik, pp. 3441, vol (2010) 9. Österle, H., Becker, J., Frank, U., Hess, T., Karagiannis, D., Krcmar, H., Loos, P., Mertens, P., Oberweis A., Sinz, E.J.: Memorandum on Design-Oriented Business computer science: A plea for Rigor and Relevance (Memorandum zur gestaltungsorientierten Wirtschaftsinformatik: Ein Plädoyer für Rigor und Relevanz (2010).

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