Contributions published at Dynamic and Distributed Information Systems (Abraham Bernstein)

Social media has become globally ubiquitous, transforming how people are networked and mobilized. This forum explores research and applications of these new networked publics at individual, organizational, and societal levels. By using a gel mobility assay, we have shown that treatment of HeLa cells with 4-hydroxynonenal, a major product of the peroxidation of membrane lipids and an inducer of heat-shock proteins, has the same effect as heat shock in causing the appearance of a protein which binds to the sequence of DNA specific for the induction of heat-shock genes. Lipoperoxidation and heat exposure seem to share a common mechanism of specific gene activation.

We discuss ambiguities of the two main dimensions of the map proposed by Bentley and colleagues that relate to the degree of self-reflection the observed agents have upon their behavior. This self-reflection is a variant of the "looping effect" which denotes that, in social research, the product of investigation influences the object of investigation. We outline how this can be understood as a dimension of "height" in the map of Bentley et al.

Stream processing engines often need to adhere to QoS contracts during their operation. As they also query external data sources for supplemental information, they might not be able to receive all results in time. This thesis proposes and implements a local cache for the Esper complex event processing engine. This 'inverse cache' stores the results of Esper's background queries that complete after the Esper query timed out and provides this data for subsequent queries. The evaluation of the inverse cache shows that it enables Esper to receive additional external results, leading to a higher recall and faster processing time.

This paper investigates the question whether it is ethically justified to treat Parkinsonian patients with known or suspected pedophilia with deep brain stimulation — given increasing evidence that this treatment might cause impulse control disorders, disinhibition, and hypersexuality. This specific question is not as exotic as it looks at a first glance. First, the same issue is raised for all other types of sexual orientation or behavior which imply a high risk for harming other persons, e.g. sexual sadism. Second, there are also several (psychotropic) drugs as well as legal and illegal leisure drugs which bear severe risks for other persons. We show that Beauchamp and Childress' biomedical ethics fails to derive a veto against medical interventions which produce risks for third persons by making the patients dangerous to others. Therefore, our case discussion reveals a blind spot of the ethics of principles. Although the first intuition might be to forbid the application of deep brain stimulation to pedophilic patients, we argue against such a simple way out, since in some patients the reduction of dopaminergic drugs allowed by deep brain stimulation of the nucleus subthalamicus improves impulsive control disorders, including hypersexuality. Therefore, we propose a strategy consisting of three steps: (1) risk assessment, (2) shared decision-making, and (3) risk management and safeguards.

Our contribution discusses the possibilities and limits of using video games for apprehending and reflecting on the moral actions of their players. We briefly present the results of an extended study that introduces the conceptual idea of a Serious Moral Game (SMG). Then, we outline its possible application in the domain of bioethics for training medical professionals such that they can deal better with moral problems in medical practice. We briefly sketch major components of a SMG Bioethics. The contribution should demonstrate how such an instrument may improve psychological competences that are needed for dealing with various ethical questions within healthcare. The contribution is an intermediate step of a project that aims at actually creating a SMG for training in moral competences that are needed for putting bioethics in practice.

BACKGROUND: The principles of biomedical ethics - autonomy, non-maleficence, beneficence, and justice - are of paradigmatic importance for framing ethical problems in medicine and for teaching ethics to medical students and professionals. In order to underline this significance, Tom L. Beauchamp and James F. Childress base the principles in the common morality, i.e. they claim that the principles represent basic moral values shared by all persons committed to morality and are thus grounded in human moral psychology. We empirically investigated the relationship of the principles to other moral and non-moral values that provide orientations in medicine. By way of comparison, we performed a similar analysis for the business & finance domain. METHODS: We evaluated the perceived degree of "morality" of 14 values relevant to medicine (n1 = 317, students and professionals) and 14 values relevant to business & finance (n2 = 247, students and professionals). Ratings were made along four dimensions intended to characterize different aspects of morality. RESULTS: We found that compared to other values, the principles-related values received lower ratings across several dimensions that characterize morality. By interpreting our finding using a clustering and a network analysis approach, we suggest that the principles can be understood as "bridge values" that are connected both to moral and non-moral aspects of ethical dilemmas in medicine. We also found that the social domain (medicine vs. business & finance) influences the degree of perceived morality of values. CONCLUSIONS: Our results are in conflict with the common morality hypothesis of Beauchamp and Childress, which would imply domain-independent high morality ratings of the principles. Our findings support the suggestions by other scholars that the principles of biomedical ethics serve primarily as instruments in deliberated justifications, but lack grounding in a universal "common morality". We propose that the specific manner in which the principles are taught and discussed in medicine - namely by referring to conflicts requiring a balancing of principles - may partly explain why the degree of perceived "morality" of the principles is lower compared to other moral values.

Deep brain stimulation (DBS) is a standard therapy for several movement disorders, and the list of further indications that are investigated is growing rapidly. We performed two surveys among DBS experts (n1D 113) and centers (n2D 135) to identify ethical focal points in the current global practice of DBS. The data indicate a mismatch between the patients’ fears and the frequencies of the suspected side effects, a significant “satisfaction gap,” signs of improvements of outcome, habituation effects in terms of involved disciplines, a growing spectrum of novel indications that partly conflicts with the experts’ success probability ratings, and differences in the density of supply between countries that might affect the future development of DBS. We formulate ethical recommendations with regard both to patient-related practices (e.g., recruitment, assurance of alternatives) and to institutional development (e.g., measures for quality assurance and for the development of novel DBS indications).

Semantic technologies provide flexible and scalable solutions to master and make sense of an increasingly vast and complex data landscape. However, while this potential has been acknowledged for various application scenarios and domains, and a number of success stories exist, it is equally clear that the development and deployment of semantic technologies will always remain reliant of human input and intervention. This is due to the very nature of some of the tasks associated with the semantic data management life cycle, which are famous for their knowledge-intensive and/or context-specific character; examples range from conceptual modeling in almost any flavor, to labeling resources (in different languages), describing their content in terms of ontological terms, or recognizing similar concepts and entities. For this reason, the Semantic Web community has always looked into applying the latest theories, methods and tools from CSCW (Computer Supported Cooperative Work), participatory design, Web 2.0, social computing, and, more recently crowdsourcing to find ways to engage with users and encourage their involvement in the execution of technical tasks. Existing approaches include the usage of wikis as semantic content authoring environments, leveraging folksonomies to create formal ontologies, but also human computation approaches such as games with a purpose or micro-tasks. This document provides a summary of the Dagstuhl Seminar 14282: Crowdsourcing and the Semantic Web, which in July 2014 brought together researchers of the emerging scientific community at the intersection of crowdsourcing and Semantic Web technologies. We collect the position statements written by the participants of seminar, which played a central role in the discussions about the evolution of our research field.

In this paper, we present a design and implementation of FD-Buffer, a cost-based adaptive buffer manager for flash memory devices. Due to flash memory’s unique hardware features, it has an inherent read-write asymmetry: writes involve expensive erase operations, which usually makes them much slower than reads. To address this read-write asymmetry, we revisit buffer management and consider the average I/O cost per page access as the main cost metric, as opposed to the traditional miss rate. While there have been a number of buffer management algorithms that take the read-write asymmetry into consideration, most algorithms fail to effectively adapt to the runtime workload or different degrees of asymmetry. In this paper, we develop a new replacement algorithm in which we separate clean and dirty pages into two pools. The size ratio of the two pools is automatically adapted based on the read-write asymmetry and the runtime workload. We evaluate the FD-Buffer with trace-driven experiments on real flash memory devices. Our trace-driven evaluation results show that our algorithm achieves 4.0-33.4 percent improvement of I/O performance on flash memory, compared to state-of-the-art flash-aware replacement policies.

The Semantic Web is an extremely diverse research area. Unlike scientists in other research fields, we investigate a diverse set questions using a plethora of methods. The goal of this primer is to provide context for scientists in the Semantic Web and Linked Data domain about the purpose of research questions and their associated hypotheses, the tension between rigor and relevance thereof, possible evaluation approaches typically used, and pitfalls in terms of reliability and validity. For example, where is the scientific problem in developing a system or a tool? How do we frame the discussion of generating linked data from a given corpus such that others will actually care about our work? When is it a good idea to use our (Semantic Web) technology when the problem has already been successfully attacked by other means? We strive to make this primer as practical as possible. Hence, after a short more theoretical introduction we will pick up a series of examples from our research domain and use them to exemplify the implications of our introductory theoretical treatment. We hope that this text will help the reader to explore the scientific basis of their research more systematically.

Recommender systems play an important role in helping people finding items they like. One type of recommender system is collaborative filtering that considers feedback of like-minded people. The fundamental assumption of collaborative filtering is that people who previously shared similar preferences behave similarly later on. This paper introduces several novel, classification-based similarity metrics that are used to compare user preferences. Furthermore, the concept of partial preference similarity based on a machine learning model is presented. For evaluation the cold-start behavior of the presented classification-based similarity metrics is evaluated in a large-scale experiment. It is shown that classification-based similarity metrics with machine learning significantly outperforms other similarity approaches in different cold-start situations under different degrees of data-sparseness.

The detection of fraudulent patterns in large sets of financial transaction data is a crucial task in forensic investigations of money laundering, employee fraud and various other illegal activities. Scalable and flexible tools are needed to be able to analyze these large amounts of data and express the complex structures of the patterns that should be detected. This thesis presents a novel approach of locally identifying associations between incoming and outgoing transactions for each participant of the transaction network and then aggregating these associations to larger patterns. The identified patters can be pruned and visualized in a graphical user interface to conduct further investigations. The evaluation of our approach shows that it allows a stream-processing of real-world financial transactions with a throughput of more than one million transactions per minute. Furthermore we demonstrate the capability of our approach to express six sophisticated money laundering patterns, as reported by the Egmont group, and successfully retrieve components that correspond to these patterns. To the best of our knowledge, this approach is the first to scalably identify dependent financial transactions based on local transaction matching, while providing a flexible query language to cover a broad range of financial fraud cases.