A language for information commerce processes

Automatizing information commerce requires languages to represent the typical information commerce processes. Existing languages and standards cover either only very specific types of business models or are too general to capture in a concise way the specific properties of information commerce processes. We introduce a language that is specifically designed for information commerce. It can be directly used for the implementation of the processes and communication required in information commerce. It allows to cover existing business models that are known either from standard proposals or existing information commerce applications on the Internet. The language has a concise logical semantics. In this paper we present the language concepts and an implementation architecture

Selection Bias in News Coverage: Learning it, Fighting it

News entities must select and filter the coverage they broadcast through their respective channels since the set of world events is too large to be treated exhaustively. The subjective nature of this filtering induces biases due to, among other things, resource constraints, editorial guidelines, ideological affinities, or even the fragmented nature of the information at a journalist's disposal. The magnitude and direction of these biases are, however, widely unknown. The absence of ground truth, the sheer size of the event space, or the lack of an exhaustive set of absolute features to measure make it difficult to observe the bias directly, to characterize the leaning's nature and to factor it out to ensure a neutral coverage of the news. In this work, we introduce a methodology to capture the latent structure of media's decision process on a large scale. Our contribution is multi-fold. First, we show media coverage to be predictable using personalization techniques, and evaluate our approach on a large set of events collected from the GDELT database. We then show that a personalized and parametrized approach not only exhibits higher accuracy in coverage prediction, but also provides an interpretable representation of the selection bias. Last, we propose a method able to select a set of sources by leveraging the latent representation. These selected sources provide a more diverse and egalitarian coverage, all while retaining the most actively covered events

A Dynamic Embedding Model of the Media Landscape

Information about world events is disseminated through a wide variety of news channels, each with specific considerations in the choice of their reporting. Although the multiplicity of these outlets should ensure a variety of viewpoints, recent reports suggest that the rising concentration of media ownership may void this assumption. This observation motivates the study of the impact of ownership on the global media landscape and its influence on the coverage the actual viewer receives. To this end, the selection of reported events has been shown to be informative about the high-level structure of the news ecosystem. However, existing methods only provide a static view into an inherently dynamic system, providing underperforming statistical models and hindering our understanding of the media landscape as a whole. In this work, we present a dynamic embedding method that learns to capture the decision process of individual news sources in their selection of reported events while also enabling the systematic detection of large-scale transformations in the media landscape over prolonged periods of time. In an experiment covering over 580M real-world event mentions, we show our approach to outperform static embedding methods in predictive terms. We demonstrate the potential of the method for news monitoring applications and investigative journalism by shedding light on important changes in programming induced by mergers and acquisitions, policy changes, or network-wide content diffusion. These findings offer evidence of strong content convergence trends inside large broadcasting groups, influencing the news ecosystem in a time of increasing media ownership concentration

Configuration of Distributed Message Converter Systems using Performance Modeling

To find a configuration of a distributed system satisfying performance goals is a complex search problem that involves many design parameters, like hardware selection, job distribution and process configuration. Performance models are a powerful tools to analyse potential system configurations, however, their evaluation is expensive, such that only a limited number of possible configurations can be evaluated. In this paper we present a systematic method to find a satisfactory configuration with feasible effort, based on a two-step approach. First, using performance estimates a hardware configuration is determined and then the software configuration is incrementally optimized evaluating Layered Queueing Network models. We applied this method to the design of performant EDI converter systems in the financial domain, where increasing message volumes need to be handled due to the increasing importance of B2B interaction

webXice: an Infrastructure for Information Commerce on the WWW

Systems for information commerce on the WWW have to support flexible business models if they should be able to cover a wide range of requirements imposed by the different types of information businesses. This leads to non-trivial functional and security requirements both on the provider and consumer side, for which we introduce an architecture and a system implementation, webXice. We focus on the question, how participants with minimal technological requisites, i.e. solely standard Web browsers available, can be technologically enabled to articipate in the information commerce at a system level, while not sacrificing the functionality and security required by an autonomous participant in an information commerce scenario. In particular, we propose an implementation strategy to efficiently support persistent message logging for light-weight clients, that enables clients to collect and manage non-reputiable messages as proofs. We believe that the capability to support minimal system platforms is a necessary precondition for the wide-spread use of any information commerce infrastructure

280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia using Character-level Classification

We propose a simple, yet effective, approach towards inducing multilingual taxonomies from Wikipedia. Given an English taxonomy, our approach leverages the interlanguage links of Wikipedia followed by character-level classifiers to induce high-precision, high-coverage taxonomies in other languages. Through experiments, we demonstrate that our approach significantly outperforms the state-of-the-art, heuristics-heavy approaches for six languages. As a consequence of our work, we release presumably the largest and the most accurate multilingual taxonomic resource spanning over 280 languages

The Curious Case of the PDF Converter that Likes Mozart: Dissecting and Mitigating the Privacy Risk of Personal Cloud Apps

Third party apps that work on top of personal cloud services such as Google Drive and Dropbox, require access to the user's data in order to provide some functionality. Through detailed analysis of a hundred popular Google Drive apps from Google's Chrome store, we discover that the existing permission model is quite often misused: around two thirds of analyzed apps are over-privileged, i.e., they access more data than is needed for them to function. In this work, we analyze three different permission models that aim to discourage users from installing over-privileged apps. In experiments with 210 real users, we discover that the most successful permission model is our novel ensemble method that we call Far-reaching Insights. Far-reaching Insights inform the users about the data-driven insights that apps can make about them (e.g., their topics of interest, collaboration and activity patterns etc.) Thus, they seek to bridge the gap between what third parties can actually know about users and users perception of their privacy leakage. The efficacy of Far-reaching Insights in bridging this gap is demonstrated by our results, as Far-reaching Insights prove to be, on average, twice as effective as the current model in discouraging users from installing over-privileged apps. In an effort for promoting general privacy awareness, we deploy a publicly available privacy oriented app store that uses Far-reaching Insights. Based on the knowledge extracted from data of the store's users (over 115 gigabytes of Google Drive data from 1440 users with 662 installed apps), we also delineate the ecosystem for third-party cloud apps from the standpoint of developers and cloud providers. Finally, we present several general recommendations that can guide other future works in the area of privacy for the cloud

CrossFlow: Cross-Organizational Workflow Management for Service Outsourcing in Dynamic Virtual Enterprises

In this report, we present the approach to cross-organizational workflow management of the CrossFlow project. CrossFlow is a European research project aiming at the support of cross-organizational workflows in dynamic virtual enterprises. The cooperation in these virtual enterprises is based on dynamic service outsourcing specified in electronic contracts. Service enactment is performed by dynamically linking the workflow management infrastructures of the involved organizations. Extended service enactment support is provided in the form of cross-organizational transaction management and process control, advanced quality of service monitoring, and support for high-level flexibility in service enactment. CrossFlow technology is realized on top of a commercial workflow management platform and applied in two real-world scenarios in the contexts of a logistics and an insurance company

Internet-scale storage systems under churn - a study of the steady state using markov models

Markov model