Felix C. Engel

IVIS4BigData: A Reference Model for Advanced Visual Interfaces Supporting Big Data Analysis in Virtual Research Environments

This paper introduces an approach to develop an up-to-date reference model that can support advanced visual user interfaces for distributed Big Data Analysis in virtual labs to be used in e-Science, industrial research, and Data Science education. The paper introduces and motivates the current situation in this application area as a basis for a corresponding problem statement that is utilized to derive goals and objectives of the approach. Furthermore, the relevant state-of-the-art is revisited and remaining challenges are identified. An exemplar set of use cases, corresponding user stereotypes as well as a conceptual design model to address these challenges are introduced. A corresponding architectural system model is suggested as a conceptual reference architecture to support proof-of-concept implementations as well as to support interoperability in distributed infrastructures. Conclusions and an outlook on future work complete the paper.

SenseCare: Using Affective Computing to Manage and Care for the Emotional Wellbeing of Older People

This paper discusses an opportunity for using affective computing modalities to support the monitoring of emotional wellbeing of older people. The ageing population is escalating and is associated with an increase in the number of persons with dementia. It is also reported that older people can suffer from social isolation and that people with dementia can experience a range of negative emotions such as anxiety and depression. We present a model to care for a person’s emotional wellbeing in the home using multiple-modalities such as video, audio, electrodermal activity and photoplethysmography.

Analysis of rumen microbial community in cattle through the integration of metagenomic and network-based approaches

A better understanding of the composition of rumen microbial communities and the association between host genetic and microbial activities has important applications and implication in bioscience. Being capable of revealing the full extent of microbial gene diversity, metagenomics-based approaches hold great promises in this endeavor. This study investigates the rumen microbial community in cattle through the integration of metagenomic and network-based approaches. Based on the relative abundance of 1570 microbial genes identified in a metagenomics analysis, the co-abundance network was constructed and functional modules of microbial genes were identified. One of the main contributions of this study is to develop a random matrix theory-based approach to automatically determine the correlation threshold used to construct the co-abundance network. It has been shown that the network exhibits a highly modular structure with each of the three main modules well separated. The involvement of KEGG pathways in each module was analysed. A close look at the abundance profiles highlights that Module B is strongly associated with methane emissions while Module C is highly enriched with microbial genes associated with feed conversion efficiency.

Towards Enabling Emerging Named Entity Recognition as a Clinical Information and Argumentation Support.

In this paper we discuss the challenges of growing amounts of clinical literature for medical staff. We introduce our concepts emerging Named Entity (eNE) and emerging Named Entity Recognition (eNER) and show the results of an empirical study on the incidence of eNEs in the PubMed document set, which is the main contribution of this article. We discuss how emerging Named Entities can be used for Argumentation Support, Information Retrieval (IR) Support and Trend Analysis in Clinical Virtual Research Environments (VREs) dealing with large amounts of medical literature. Based on the empirical study and the discussion we derive use cases and a data science and user-feedback based architecture for the detection and the use of eNEs for IR and Argumentation Support in clinical VREs, like the related project RecomRatio.

Managing Cultural Assets: Challenges for Implementing Typical Cultural Heritage Archive’s Usage Scenarios

In the domain of cultural heritage, a lot of archives and data collections already exist. Thus, one of the main challenges for curators and archivists in this domain consists of exchanging data with other archives and integrating similar collections. Typical usage scenarios in this area can only be realized by semantically integrating all available data sources. Therefore, the main task of semantic integration consists of bridging the different levels of heterogeneity. Semantic Web technologies might be a solution for these challenges. Ontology Matching, for example, is already successfully applied for bridging some of the heterogeneity types. However, it is not suitable for dissolving heterogeneity conflicts at all levels due to unsatisfactory matching qualities. Still, matching is crucial for the semantic integration success of the distributed data sources. As a result, a main part in semantic integration is still done manually by domain experts. It would be preferable to support their work by at least semi-automatic techniques.

Approach to semi-automatic labeling of video sequences for affective computing-enabling the comprehensive assessment of emotion detection software from mimics

In recent years, most breakthroughs in fields such as image and video processing were based on machine learning technologies that allow computers to recognize objects in images with nearly human precision. In some application domains, computers even surpassed human level performance. These breakthroughs result from an exponential increase of computational resources and digitization of society (massive availability of video material) as well as significant progress in the field of artificial intelligence, namely deep learning. Despite these advances, however, automatically recognizing emotions of humans in video material is still a challenging, but important task in affective computing. Without doubt, the automated detection of social-emotional and psychiatrically relevant information from video material will make a valuable contribution to various domains. This is particularly true for the field of personalized medicine. Major obstacles are high efforts and costs of labeling single video frames manually to create training data for machine learning technologies. To tackle this, we outline and discuss a methodological approach to create labeled video data with a minimum of human involvement.

Recalot.com: Towards a Reusable, Modular, and RESTFul Social Recommender System

Many different recommender system RS frameworks have been developed by the research community. Most of these RS frameworks are designed only for research purposes and offline evaluation of different algorithms. A reuse of such frameworks in a productive environment is only possible with high effort. In this paper, we present a concept of a generic reusable RESTful recommender web service framework, designed to perform directly offline and online analysis for research and to use the recommender algorithms in production.

SenseCare: Towards an Experimental Platform for Home-Based, Visualisation of Emotional States of People with Dementia

Analytics and visualisation of Big Data appearance is still a challenging task. In this paper, the management and visualisation of big data streams will be discussed at hand of data that comes into existence during the care of people with dementia in their own homes. Therein, basic requirements are explored towards the development of a data management, analytics and visualisation platform stemming from application scenarios in which various data streams are created, processed, analysed, visualised and stored for ad hoc or later reuse. The platform will be realised on open ICT standards, implemented within the EC co funded SenseCare project.

Analysing and Representing Data Management Dimensions in Research and Innovation Actions

This article outlines how the Knowledge-based Production Process Planning can be applied to develop data management policy rules (DMPRs). The process supports the creation and evolution of data management plans and their automated execution. The planning is guided by a policy refinement process where a methodological approach derives rules from natural language data management policies. Technologies from the semantic web are used to implement the approach. The article concludes with a cognitive walkthrough of an example to validate the proposed approach.

Advanced Visual Interfaces: Supporting Big Data Applications

This book constitutes the thoroughly refereed post-workshop proceedings of the AVI 2016 Workshop on Road Mapping Infrastructures for Advanced Visual Interfaces Supporting Big Data Applications in Virtual Research Environments, AVI-BDA 2016, held in Bari, Italy, in June 2016. The 10 revised full papers in this volume present the elaborated outcome of the initial position papers capturing the results oft the roadmapping discussions in the workshop at which comments of several external reviewers for these full publications were also integrated.