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

Nearness expressions such as "near"are context-dependent spatial relations and are subject to the context variability effect. Depending on the provided context, "near"has a different semantic extension. We perform a literature review to identify the effect of context on "near". To integrate the insights from different disciplines, we apply Turney's contextualization framework which distinguishes between two types of features: primary and contextual. Primary features are the qualitative and quantitative distance measures and contextual features are the context factors used to determine a threshold on the nearness measurements. Additionally, we identify the appropriate features for different spatial tasks discussed in the literature. By doing so, we seek to build a foundation for a context-dependent model for "near".

As people increasingly rely on online media and recommender systems to consume information, engage in debates and form their political opinions, the design goals of online media and news recommenders have wide implications for the political and social processes that take place online and offline. Current recommender systems have been observed to promote personalization and more effective forms of informing, but also to narrow the user’s exposure to diverse content. Concerns about echo-chambers and filter bubbles highlight the importance of design metrics that can successfully strike a balance between accurate recommendations that respond to individual information needs and preferences, while at the same time addressing concerns about missing out important information, context and the broader cultural and political diversity in the news, as well as fairness. A broader, more sophisticated vision of the future of personalized recommenders needs to be formed - a vision that can only be developed as the result of a collaborative effort by different areas of academic research (media studies, computer science, law and legal philosophy, communication science, political philosophy, and democratic theory). The proposed workshop will set first steps to develop such a much needed vision on the role of recommender systems on the democratic role of the media and define the guidelines as well as a manifesto for future research and long-term goals for the emerging topic of fairness, diversity, and personalization in recommender systems.

Stream Processing (SP) is an important Big Data technology enabling continuous querying of data streams. The stream setting offers the opportunity to exploit synergies and, theoretically, share the access and processing costs between multiple different collaborators. But what should be the monetary contribution of each consumer when they do not trust each other and have varying valuations of the differing outcomes? In this article, we present Collaborative Stream Processing (CSP), a model where the costs, which are set exogenously by providers, are shared between multiple consumers, the collaborators. For this, we identify three important requirements for CSP to establish trust between the collaborators and propose a CSP al- gorithm, ENCSPA, adhering to these requirements. Based on the collaborators’ outcome valuations and the costs of the raw data streams, ENCSPA computes the payment for each collaborator. At the same time, ENCSPA ensures that no collaborator has an incentive to manipulate the system by providing misinformation about her/his value, budget, or time limit. We show that ENCSPA can calculate payments in a reasonable amount of time for up to one thousand collaborators.

Epidemic spreading occurs among animals, humans, or computers and causes substantial societal, personal, or economic losses if left undetected. Based on known temporal contact networks, we propose an outbreak detection method that identifies a small set of nodes such that the likelihood of detecting recent outbreaks is maximal. The two-step procedure involves i) simulating spreading scenarios from all possible seed configurations and ii) greedily selecting nodes for monitoring in order to maximize the detection likelihood. We find that the detection likelihood is a submodular set function for which it has been proven that greedy optimization attains at least 63% of the optimal (intractable) solution. The results show that the proposed method detects more outbreaks than benchmark methods suggested recently and is robust against badly chosen parameters. In addition, our method can be used for out- break source detection. A limitation of this method is its heavy use of computational resources. However, for large graphs the method could be easily parallelized.

Studying the evolution of knowledge graphs has become an important topic of current research. For that purpose, this thesis provides a foundation by contributing a framework that creates historic snapshots of the Wikidata knowledge graph. In order to save resources, the framework extracts a sample out of the original graph and generates the snapshots based on that sample. The behavior and biases of different traversal-based sampling techniques are analyzed and they agree with previous observations by related work on sampling. This work further describes the types of revisions and how they can be undone in order to create a sequence of versions of the sampled graph in earlier stages of its history. Finally, it demonstrates how the snapshots are returned in a standard RDF format.

Knowledge graphs (KGs) are at the core of numerous applications and their importance is increasing. Yet, knowledge evolves and so do KGs. PubMed, a search engine that primarily provides access to medical publications, adds an estimated 500'000 new records per year - each having the potential to require updates to a medical KG, like the National Cancer Institute Thesaurus. Depending on the applications that use such a medical KG, some of these updates have possibly wide-ranging impact, while others have only local effects. Estimating the impact of a change ex-ante is highly important, as it might make KG-engineers aware of the consequences of their actions during editing or may be used to highlight the importance of a new fragment of knowledge to be added to the KG for some application. This research description proposes a unified methodology for predicting the impact of changes in evolving KGs and introduces an evaluation framework to assess the quality of these predictions.

The updates on knowledge graphs (KGs) affect the services built on top of them. However, changes are not all the same: some updates drastically change the result of operations based on knowledge graph content; others do not lead to any variation. Estimating the impact of a change ex-ante is highly important, as it might make KG engineers aware of the consequences of their action during KG editing or may be used to highlight the importance of a new fragment of knowledge to be added to the KG for some application. The main goal of this contribution is to offer a formalization of the problem. Additionally, it presents some preliminary experiments on three different datasets considering embeddings as operation.Results show that the estimation can reach AUCs of 0.85, suggesting the feasibility of this research.

Sketch-based query formulation is very common in image and video retrieval as these techniques often complement textual retrieval methods that are based on either manual or machine generated annotations. In this paper, we present a retrieval approach that allows to query visual media collections by sketching concept maps, thereby merging sketch-based retrieval with the search for semantic labels. Users can draw a spatial distribution of different concept labels, such as "sky", "sea" or "person" and then use these sketches to find images or video scenes that exhibit a similar distribution of these concepts. Hence, this approach does not only take the semantic concepts themselves into account, but also their semantic relations as well as their spatial context. The efficient vector representation enables efficient retrieval even in large multimedia collections. We have integrated the semantic sketch query mode into our retrieval engine vitrivr and demonstrated its effectiveness.

Over the last few years, natural language interfaces (NLI) for databases have gained significant traction both in academia and industry. These systems use very different approaches as described in recent survey papers. However, these systems have not been systematically compared against a set of benchmark questions in order to rigorously evaluate their functionalities and expressive power. In this paper, we give an overview over 24 recently developed NLIs for databases. Each of the systems is evaluated using a curated list of ten sample questions to show their strengths and weaknesses. We categorize the NLIs into four groups based on the methodology they are using: keyword-, pattern-, parsing- and grammar-based NLI. Overall, we learned that keyword-based systems are enough to answer simple questions. To solve more complex questions involving subqueries, the system needs to apply some sort of parsing to identify structural dependencies. Grammar-based systems are overall the most powerful ones, but are highly dependent on their manually designed rules. In addition to providing a systematic analysis of the major systems, we derive lessons learned that are vital for designing NLIs that can answer a wide range of user questions.

The propagation of animal diseases has been shown to be strongly affected by animal transportation networks, and therefore livestock movement databases have been created worldwide that can now be analysed. To analyse such large datasets, they can be modelled and visualised as networks. A visualisation of such a large multivariate and temporal network can be challenging and is the focus of this bachelor thesis. We explore the possibility of generating a focused subgraph of the network that helps users to understand, explore and analyse possible disease spreading paths within the network. Furthermore, we create an interactive visualisation that can be explored by the users and help them understand how the animal transports connect the different vertices/farms and highlight interactively the subsections that are interesting for them.

Many people nowadays read news on the Internet. The selection of available articles is often personalized and matches the interests of their respective readers. So-called recommender systems are used for this. When primarily focusing on the interests of their readers, however, these systems can lead to people receiving only one-sided news about recent events. Filter bubbles are a possible consequence of this. An algorithm for a recommender system is developed in this thesis, one that optimizes for diversity, in order to counteract this development. The focus lies on creating recommendation lists, which focus on political diversity of news articles.

The number of data sources continuously producing fast-changing data streams and needing tailor-made solutions to detect unexpected events increases rapidly. Outlier detection in univariate data streams already receives considerable attention, mainly in financial data, while multivariate anomaly detection, especially without ground truth, is less explored. We present state of the art in anomaly detection in general, its adoption for data streams and techniques for evaluation without ground truth. We implement a density-based clustering algorithm that summarizes multivariate data streams with micro clusters, and we evaluate it on synthetic and real-world data sets. We propose an extension of the algorithm to incorporate data drift to distinguish between pioneers and outliers correctly. The performed experiments show a performance improvement caused by the proposed drift-influence hyperparameters and revealed a correlation between an intrinsic data property and the anomaly detection performance, which allows hyperparameter tuning without ground truth.

With the rise of AI, smart technology is taking over many aspects of our lives. We rely on it increasingly more often for simple and also for complex tasks. But do people really trust these smart systems or do they still prefer the old-fashioned human? To answer this question, this work explores trust in AI. We used a neural network as a representative and image classification as an example task that can be performed by a smart system. Is a user's trust in an answer influenced by knowing whether it was given by another human or by an AI? To check for a possible bias, we conducted an experiment in the form of a survey with 900 participants on the crowd-sourcing platform Amazon Mechanical Turk. It pitted labels for images and their visually represented explanations obtained from the neural network against those produced by humans. Using a multi-dimensional scale to measure trust, we gained insights for different settings. They varied regarding the available information: giving the origin of label and explanation versus withholding or disguising sources, e.g. a human-generated label and explanation is presented as coming from AI. We compared the results and found few statistically significant differences between the various setups. This led us to conclude that no clear bias exists toward AI- or human-produced results and that knowledge about the source and the availability thereof does not exhibit a distinct influence on trust of humans in AI.

We present a tutorial focusing on video retrieval tasks, where state-of-the-art deep learning approaches still benefit from interactive decisions of users. The tutorial covers general introduction to the interactive video retrieval research area, state-of-the-art video retrieval systems, evaluation campaigns and recently observed results. Moreover, a significant part of the tutorial is dedicated to a practical exercise with three selected state-of-the-art systems in the form of an interactive video retrieval competition. Participants of this tutorial will gain a practical experience and also a general insight of the interactive video retrieval topic, which is a good start to focus their research on unsolved challenges in this area.