Intro
Preface
Organization
Contents
Life Science Workflow Services (LifeSWS): Motivations and Architecture
1 Introduction
1.1 Use Cases
1.2 The Centrality of Workflows
1.3 Paper Outline
2 Motivating Examples
2.1 High-Throughput Phenotyping in the Context of Climate Change
2.2 Epidemic Modeling
3 Architecture of LifeSWS
3.1 Functional Architecture
3.2 Presentation and Directory Services
3.3 Workflow Services
3.4 Data Management Services
4 LifeSWS Platforms
4.1 Presentation and Directory
4.2 Model Management
4.3 Data Management

5 Use Cases with LifeSWS
5.1 High-Throughput Phenotyping
5.2 Epidemic Modeling
6 Related Work
7 Conclusion
References
The Power of Weak Signals: A Twitter Analysis on Game of Thrones' Final Season
1 Introduction and Motivations
2 Weak Signal: A Notion with Many Facets
3 Weak Signals Identification Techniques
3.1 Techniques Based on Automatic Language Processing
3.2 Techniques Based on Graph Theory
4 BEAM: A Framework for the Detection and Interpretation of Weak Signals
5 Use Case: Game of Thrones' Final Season
5.1 Data Preparation

5.2 Detection of Weak Signals
5.3 Interpretation of Weak Signals
6 Validation of BEAM
6.1 Studying the Results Replication
6.2 Studying the Robustness of BEAM
7 Discovering Latent Variables that Participate the Most in the Signal
8 Conclusions and Future Works
References
Engineering Runtime Root Cause Analysis of Detected Anomalies
1 Introduction
2 Related Work
3 Background: ThirdEye's AD and RCA Approach
4 Infrastructure Overview and Data Representation
4.1 Infrastructure Overview
4.2 Data Representation
5 Implementation Details

5.1 Metric Aggregator
5.2 Baseline Aggregator
5.3 Anomaly Detection Module
5.4 Root Cause Analysis Module
6 Evaluation
7 Conclusions
References
Characterization of the IPFS Public Network from DHT Requests
1 Introduction
2 Interplanetary File System
2.1 Writing Operation
2.2 Read Operation
2.3 Main Uses of IPFS
3 Motivations and Objectives
4 Material and Methods
4.1 Difference with Previous Studies
4.2 Ethical Question on Data Collection
5 Results
5.1 Type of the Requested Files
5.2 Geolocalisation of the Nodes

5.3 Popularity of the Files
5.4 Activity in the Network
5.5 Overhead of the DHT
5.6 Storage
5.7 Replication of Results on Amazon Web Services Platform (AWS)
6 Discussion
7 Conclusion
References
Customised Concept Weighting: A Neural Network Approach
1 Introduction
2 Problem Statement
3 Related Works
4 Concept Weighting
4.1 Neural Network Layers
4.2 Normalising the Neural Network Structure
4.3 Weight Learning
5 Experiments
5.1 Data
5.2 Concept Weighting
6 Conclusion
References
Author Index