Open Data Use Cases
Over the recent years, more and more data has become openly available on the internet. However, currently this valuable base of information remains largely unused by companies. In order to fill this gap, we develop the Data App Store, an online platform that supports businesses in the discovery, integration and use of open data.
- Conduct a case study on the usage of open data in one of partner companies of the Data App Store project, namely Nestlé, Swisscom, and SBB.
Contact: Andreas Lang or Christine Legner
Requirements and Notation for Information Supply Chains
The concept of an information supply chain consists of all activities and work associated with the transformation of raw data to the delivery of information products to the end consumer and involves the participation of several actors. It functions as an analogy to product supply chain. The goal is to understand how data circulates throughout various corporate systems and functions.
- Review existing literature on the topic
- Identify requirements for Information Supply Chains
- Propose a notation scheme
- Otto, B., & Ofner, M. (2010). Towards a Process Reference Model for Information Supply Chain Management. ECIS.
- Sun, S., & Yen, J. (2005). Information Supply Chain: A Unified Framework for Information-Sharing. ISI.
Contact: Clément Labadie or Christine Legner
A Data Management Perspective on Information Security Frameworks
Information Security is covered by a variety of general purpose frameworks (relating to governance and auditing, among others). Data management is a subset of these topics that falls under the umbrella of these frameworks, and may be either explicitely or implicitely addressed.
- Identify security-related data management design areas (e.g. access rights, privacy compliance)
- Select and review information security frameworks
- Provide a mapping of data management design areas and information security requirements
- National Institute of Standards and Technology (NIST), & United States of America. (2014). Framework for Improving Critical Infrastructure Cybersecurity.
- De Haes, S., Van Grembergen, W., & Debreceny, R. S. (2013). COBIT 5 and enterprise governance of information technology: Building blocks and research opportunities. Journal of Information Systems, 27(1), 307-324.
Contact: Clément Labadie or Christine Legner
Approaches for Big Data Management
Big Data is a relatively new technological trend – as such, the way it should be used and manage in corporate environments still needs further definition. Big data management is the organization, administration and governance of large volumes of both structured and unstructured data.
- Review Big Data-related litterature
- Identify design areas and requirements for Big Data Management
- Suggest an approach for Big Data Management in corporate environments
- Chen, J., Chen, Y., Du, X., Li, C., Lu, J., Zhao, S., & Zhou, X. (2013). Big data challenge: a data management perspective. Frontiers of Computer Science, 7, 157-164.
- Cohen, E., Hirama, K., & Rossi, R. (2015). Characterizing Big Data Management.
Contact: Clément Labadie or Christine Legner
Analytics as a Service: Self-Service Analytics
Analytical solutions are mainly adopted by large enterprises, however cloud services provide a cost-effective approach to support its adoption by a wider range of organizations. In fact, the global analytics as a service (AaaS) market is expected to grow from $5.9 billion in 2015 to $22.24 billion in 2020 (ResearchandMarkets, 2016). Besides the reduced costs for implementation, several other factors favor cloud services for business analytics, particularly increased agility owing to the scalability of cloud.
- Review of literature on cloud analytics to investigate the different features describing AaaS.
- Review case studies on self-service analytics and its adoption in organizations.
- Analyze requirements for self-service analytics for well-targeted offerings
- Baars, H., Kemper, H.-G., 2010. Business intelligence in the cloud? In PACIS, pp. 145.
- Demirkan, H., Delen, D., 2013. Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud. Decis. Support Syst. 55, 412–421.
- Ereth, J. and Baars, H. Cloud-Based Business Intelligence and Analytics Applications – Business Value and Feasibility. In PACIS 2015 Proceedings. 2015.
- Sun, X., Gao, B., Fan, L., An, W., 2012. A Cost-Effective Approach to Delivering Analytics as a Service, in: 2012 IEEE 19th International Conference on Web Services (ICWS). pp. 512–519.
Contact: Dana Naous or Christine Legner
User Preference Models for Cloud Services
By 2020, “more than $1 trillion in IT spending will be directly or indirectly affected by the shift to cloud” (Gartner, 2016). Cloud services aremainly delivered in three fundamental service models including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). Given the rapid increase of cloud service offerings, users are confronted with multiple options. They find it difficult to evaluate cloud services with the various levels of performance and different economic models.
- Review of literature on approaches for cloud service selection (SaaS or IaaS).
- Analysis of cloud comparison websites to provide practical insights into the must-have features and appreciated attributes by users for selected types of services.
- Gathering criteria that fit users’ needs respectively functional, non-functional, operational and economic requirements.
- Develop a model or framework combining general criteria for cloud service selection.
- Garg, S.K., Versteeg, S., Buyya, R., 2013. A Framework for Ranking of Cloud Computing Services. Future Gener. Comput. Syst. 29, 1012–1023.
- Koehler, P., Anandasivam, A., Dan, M.A., 2010. Cloud Services from a Consumer Perspective, in: AMCIS 2010 Proceedings. Lima, Peru, p. 329.
- Ma, D., Kauffman, R.J., 2014. Competition between software-as-a-service vendors. IEEE Trans. Eng. Manag. 61, 717–729.
- Qu, L., Wang, Y., Orgun, M.A., Liu, L., Liu, H., Bouguettaya, A., 2015. CCCloud: Context-Aware and Credible Cloud Service Selection Based on Subjective Assessment and Objective Assessment. IEEE Trans. Serv. Comput. 8, 369–383.
Contact: Dana Naous or Christine Legner