Contributions in Merlin

The state-of-the-art in mining software repositories mirrors software artifacts from various sources into monolithic relational databases. This puts a lot of querying power in the hands of the software miners, however it comes at the cost of enclosing the data and hamper cross-application reuse. In this paper we discuss four problem scenarios to illustrate that Semantic Web technology is able to overcome these limitations. However, it requires that the software engineering research community agrees on two prerequisites: (a) a common vocabulary to talk about software repositories -- an ontology; (b) a strategy for generating unique and stable references to all software artifacts inside such a repository - a Universal Resource Identifier (URI).

We analyze the attractiveness of investment strategies over a variety of investment horizons from the viewpoint of an investor with preferences described by Cumulative Prospect Theory (CPT), currently the most prominent descriptive theory for decision making under uncertainty. A bootstrap technique is applied using historical return data of 1926–2008. To allow for variety in investors’ preferences, we conduct several sensitivity analyses and further provide robustness checks for the results. In addition, we analyze the attractiveness of the investment strategies based on a set of experimentally elicited preference parameters. Our study reveals that strategy attractiveness substantially depends on the investment horizon. While for almost every preference parameter combination a bond strategy is preferred for the short run, stocks show an outperformance for longer horizons. Portfolio insurance turns out to be attractive for almost every investment horizon. Interestingly, we find probability weighting to be a driving factor for insurance strategies’ attractiveness.

Publication and citation rankings have become major indicators of the scientific worth of universities and determine to a large extent the career of individual scholars. Such rankings do not effectively measure research quality, which should be the essence of any evaluation. These quantity rankings are not objective; two citation rankings, based on different samples, produce entirely different results. For that reason, an alternative ranking is developed as a quality indicator, based on membership on academic editorial boards of professional journals. It turns out that the ranking of individual scholars based on that measure is far from objective. Furthermore, the results differ markedly, depending on whether research quantity or quality is considered. Thus, career decisions based on rankings are dominated by chance and do not reflect research quality. We suggest that evaluations should rely on multiple criteria. Public management should return to approved methods such as engaging independent experts who in turn provide measurements of research quality for their research communities.

Functional neuroimaging techniques are used widely in cognitive neuroscience to investigate aspects of functional specialization and functional integration in the human brain. Functional integration can be characterized in two ways, functional connectivity and effective connectivity. While functional connectivity describes statistical dependencies between data, effective connectivity rests on a mechanistic model of the causal effects that generated the data. This review addresses the conceptual and methodological basis of established techniques for characterizing effective connectivity using functional magnetic resonance imaging (fMRI) data. In particular, we focus on dynamic causal modeling (DCM) of fMRI data and emphasize the importance of model selection procedures and nonlinear mechanisms for context-dependent changes in connection strengths.

The advent of social markup languages and lightweight public data access methods has created an opportunity to share the social, documentary and system information locked in most servers as a mashup. Whereas solutions already exists for creating and managing mashups from network sources, we propose here a mashup framework whose primary information sources are the applications and user files of a server. This enables us to use server legacy data sources that are already maintained as part of basic administration to semantically link user documents and accounts using social web constructs.

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Resource allocations determine an important management task for operational Grids and networks, especially under the constraint of commercially offered resources. Therefore, the need for an optimal allocation of this task arises, and this paper proposes a trust-incentive-based combinatorial double auction algorithm for these resource allocations in Grids. The key and new contribution is the design of a trust-incentive mechanism, which is integrated into an existing combinatorial double auction algorithm (a) to improve the performance of Grid resource allocation and (b) ensure that trust values of participating bidders (typically Grid users, termed peers) are considered. In the newly developed trust-incentive-based algorithm, each peers' trust value is adopted to adjust their bids in the process of the combinatorial double auction. After each transaction, peers participating in the transaction rate each other to setup and update the bilateral trust relationship. Those simulation results obtained demonstrate that the algorithm proposed can improve the efficiency of resource sharing greatly by providing applicable incentives to trustworthy peers to contribute more resources. Moreover, this algorithm can identify and eliminate malicious peers in the system to enhance the Grid security level in that respect.

Analysis of IP traffic is highly important, since it determines the starting point of many network management operations, such as intrusion detection, network planning, network monitoring, or accounting and billing. One of the most utilized metering data formats in analysis applications are IP (Internet Protocol) flow records. With the increase of IP traffic, such traffic analysis applications need to cope with a constantly increasing number of flow records. Typically, centralized approaches to IP traffic analysis have scalability problems, which are addressed by replacing existing hardware with more powerful CPUs and faster memory. In contrast, this paper developed and implemented SCRIPT (Scalable Real-time IP Flow Record Analysis), which defines a scalable analysis framework that can be used to distribute flow records to multiple nodes performing traffic analysis in order to balance the overall workload among those nodes. Due to its generic design, the framework developed can be extended and used to distribute other metering data, such as packet headers, payloads, or accounting records.

In the recent years, several technical solutions have been developed that allow Bandwidth-on-Demand services to be offered by service providers. In the context of the European academic networks, the technical feasibility of such services has been demonstrated on several occasions. The move from a research activity to a production service presents further requirements in addition to the overcoming of technical challenges. Most notably, apart from organization of day-to-day operations, financial issues have to be solved. These relate to sharing the costs of a service that is delivered jointly by independent organizations. The main cost factors are operations staff as well as network resources that are purchased for on-demand use. Moreover, the financial issues also include pricing models for use of the service use as well as the distribution of the revenues among the participating organizations. The present paper considers this fundamental BoD scenario together with key challenges in detail, before discussing the general approach for specifying a cost/pricing model.