This paper discusses how usage patterns and preferences of inhabitants can be learned efficiently to allow smart homes to autonomously achieve energy savings. We propose a frequent sequential pattern mining algorithm suitable for real-life smart home event data. The performance of the proposed algorithm is compared to existing algorithms regarding completeness/correctness of the results, run times as well as memory consumption and elaborates on the shortcomings of the different solutions. We also propose a recommender system based on the developed algorithm. This recommender provides recommendations to the users to reduce their energy consumption. The recommender system was deployed to a set of test homes. The test participants rated the impact of the recommendations on their comfort. We used this feedback to adjust the system parameters and make it more accurate during a second test phase. The historical dataset provided by digitalSTROM contained 33 homes with 3521 devices and over 4 million events. The system produced 160 recommendations on the first phase and 120 on the second phase. The ratio of useful recommendations was close to 10%.

Large scale telecommunications networks need to be continuously monitored to detect problems and react accordingly to ensure the networks stability. Current monitoring systems are well capable to monitor such large scale installations for simple situations like failing routers, links or abnormal link utilization. However, current systems fail to provide near real-time in-depth root cause analysis for nation scale networks. This is particularly important in cases where the monitoring dashboard is flooded with notifications caused by side-effects of one bigger failure. In our project we develop a processing system capable of providing this root cause analysis in a timely fashion even for large telecommunications networks. To cope with these challenges we defined a specialized processing model for the detection and analysis of complex error situations which is largely based on event stream processing mechanisms. In this paper we discuss the first stage of this process and its initial implementation.

Software-as-a-Service (SaaS) is a delivery model whose basic idea is to provide applications to the customer on demand over the Internet. In contrast to similar but older approaches, SaaS promotes multi-tenancy as a tool to exploit economies of scale. This means that a single application instance serves multiple customers. However, a major throwback of SaaS is the customers' hesitation of sharing infrastructure, application code, or data with other tenants. This is due to the fact that one of the mayor threads of mixed-tenancy is information disclosure due to a system malfunction, system error, or aggressive actions by individual users. So far the only approach in research to counteract on this hesitation has been to enhance the isolation between tenants using the same instance. Our approach (presented in earlier work) tackles this hesitation differently. It allows customers to choose if or even with whom they want to share the application. The approach enables the customer to make that choice not just for the entire application but specifically for individual application components (AC). One of the challenges towards the realization of a mixed-tenancy platform is the capturing of customer requirements. This is why the focus of this paper is to present an approach that enables tenants to express their unique deployment needs. This approach is realized using Semantic Web technologies.

In this paper we describe an approach to graphically externalize the cloud potential of a company, considering its architectural description. For this purpose it is shown how current architectural description can be extended, in terms of knowledge and graphical representation. The goal is to focus on the most important features and aspects to consider during the evaluation of shifting into a cloud environment. Even if each company has different strategies and approaches to its business activities, there are some domains related to the shift in a cloud environment that should be considered in any case. This paper shows how these main areas can be taken into account in order to extend the architectural representation of a company and express its cloud readiness.

Cloud computing has emerged as a strong factor driving companies to remarkable business success. Far from just being an IT level support solution cloud computing is triggering changes in their core business models by making them more efficient and cost-effective. This has generated an interest for a lot of companies to try and adopt cloud computing for their existing and new business process. In this research we present an approach which a company can use to analyze if its operations can be positively impacted by moving to the cloud. Further we describe our approach using which the company can make that transition to the cloud.

Handling changes of business processes, and making sure systems are up and running after a change in the business process with minimum downtime is something which has been of interest to researchers for long and there have been several approaches proposed for it. With Cloud computing becoming increasingly popular businesses require a strong system for cloud based implementations which can handle change management of processes. In this paper we propose a change management approach for cloud backed business process models.

The extensive work on Knowledge Engineering in the 1990s has resulted in a systematic analysis of task-types, and the corresponding problem solving methods that can be deployed for different types of tasks. That analysis was the basis for a sound and widely accepted methodology for building knowledge-based systems, and has made it is possible to build libraries of reusable models, methods and code.In this paper, we make a first attempt at a similar analysis for Semantic Web applications. We will show that it is possible to identify a relatively small number of task-types, and that, somewhat surprisingly, a large set of Semantic Web applications can be described in this typology. Secondly, we show that it is possible to decompose these task-types into a small number of primitive (atomic) inference steps. We give semi-formal definitions for both the task-types and the primitive inference steps that we identify. We substantiate our claim that our task-types are sufficient to cover the vast majority of Semantic Web applications by showing that all entries of the Semantic Web Challenges of the last 3 years can be classified in these task-types.