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

With the growing adoption of IoT and sensor technologies, an enormous amount of data is being produced at a very rapid pace and in different application domains. This sensor data consists mostly of live data streams containing sensor observations, generated in a distributed fashion by multiple heterogeneous infrastructures with minimal or no interoperability. RDF streams emerged as a model to represent data streams, and RDF Stream Processing (RSP) refers to a set of technologies to process such data. RSP research has produced several successful results and scientific output, but it can be evidenced that in most of the cases the Web dimension is marginal or missing. It also noticeable the lack of proper infrastructures to enable the exchange of RDF streams over heterogeneous and different types of RSP systems, whose features may vary from data generation to querying, and from reasoning to visualisation. This article defines a set of requirements related to the creation of a web of RDF stream processors. These requirements are then used to analyse the current state of the art, and to build a novel proposal, WeSP, which addresses these concerns.

Fueled by enthusiasm of volunteers, government subsidies, and open data legislation, the Web of Data (WoD) has enjoyed a phenomenal growth. Commercial data, however, has been stuck in proprietary silos, as the monetization strategy for sharing data in the WoD is unclear. This is in contrast to the traditional web where advertisement fueled a lot of the growth. This raises the question how the WoD can (i) maintain its success when government subsidies disappear and (ii) convince commercial entities to share their wealth of data. In this talk based on a paper, we propose a marketplace for decentralized data follow- ing basic WoD principles. Our approach allows a customer to buy data from different, decentralized providers in a transparent way. As such, our marketplace presents a first step towards an economically viable WoD beyond subsidies.

In most existing recommender systems, implicit or explicit interac- tions are treated as positive links and all unknown interactions are treated as negative links. e goal is to suggest new links that will be perceived as positive by users. However, as signed social net- works and newer content services become common, it is important to distinguish between positive and negative preferences. Even in existing applications, the cost of a negative recommendation could be high when people are looking for new jobs, friends, or places to live. In this work, we develop novel probabilistic latent factor mod- els to recommend positive links and compare them with existing methods on ve di erent openly available datasets. Our models are able to produce be er ranking lists and are e ective in the task of ranking positive links at the top, with fewer negative links ( ops). Moreover, we nd that modeling signed social networks and user preferences this way has the advantage of increasing the diversity of recommendations. We also investigate the e ect of regularization on the quality of recommendations, a ma er that has not received enough a ention in the literature. We nd that regularization pa- rameter heavily a ects the quality of recommendations in terms of both accuracy and diversity.

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Appropriate task routing and assignment is an important, but often overlooked, element in crowdsourcing research and practice. In this paper, we explore and evaluate a mechanism that can enable matching crowdsourcing tasks to suitable crowd-workers based on their cognitive abilities. We measure participants’ visual and fluency cognitive abilities with the well-established Kit of Factor- Referenced Cognitive Test, and measure crowdsourcing performance with our own set of developed tasks. Our results indicate that participants’ cognitive abilities correlate well with their crowdsourcing performance. We also built two predictive models (beta and linear regression) for crowdsourcing task performance based on the performance on cognitive tests as explanatory variables. The model results suggest that it is feasible to predict crowdsourcing performance based on cognitive abilities. Finally, we discuss the benefits and challenges of leveraging workers’ cognitive abilities to improve task routing and assignment in crowdsourcing environments.

With the increasing popularity of crowdsourcing and crowd computing, the question of how to select a well-performing crowd process for a problem at hand is growing ever more important. Prior work casted crowd process selection to an optimization problem, whose solution is the crowd process performing best for a user’s problem. However, existing approaches require users to probabilistically model aspects of the problem, which may entail a substantial investment of time and may be error-prone. We propose to use black- box optimization instead, a family of techniques that do not require probabilistic modelling by the end user. Specifically, we adopt Bayesian Optimization to approximate the maximum of a utility function quantifying the user’s (business-) objectives while minimizing search cost. Our approach is validated in a simulation and three real-world experiments. The black-box nature of our approach may enable us to reduce the entry barrier for efficiently building crowdsourcing solutions.

A key issue, whenever people work together to solve a complex problem, is how to divide the problem into parts done by different people and combine the parts into a solution for the whole problem. This paper presents a novel way of doing this with groups of contests called contest webs. Based on the analogy of supply chains for physical products, the method provides incentives for people to (a) reuse work done by themselves and others, (b) simultaneously explore multiple ways of combining interchangeable parts, and (c) work on parts of the problem where they can contribute the most. The paper also describes a field test of this method in an online community of over 50,000 people who are developing proposals for what to do about global climate change. The early results suggest that the method can, indeed, work at scale as intended.

Before building a classifier to make predictions about a target variable, one must decide what input data to use. Most scientific publications about feature selection deal with methods that can be used once training data has been collected. Yet, in the real world, one has to collect, clean and transform data before it can be used to create predictive models. Collecting data is a very expensive and time consuming process. Going through this process for data not relevant to the target variable is very inefficient. A common approach to minimize the effort for feature selection is asking domain experts for their opinion. However, experts have been shown to perform worse at this task than one might expect. In this paper, I present a tool, KrowDD, that is able to identify relevant features among a number of feature ideas before obtaining data. An evaluation using three datasets shows that KrowDD performs significantly better than human experts. KrowDD is the first step on the way to more efficient feature selection: feature selection before obtaining training data.

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Stream reasoning studies the application of inference techniques to data characterised by being highly dynamic. It can find application in several settings, from Smart Cities to Industry 4.0, from Internet of Things to Social Media analytics. This year stream reasoning turns ten, and in this article we analyse its growth. In the first part, we trace the main results obtained so far, by presenting the most prominent studies. We start by an overview of the most relevant studies developed in the context of semantic web, and then we extend the analysis to include contributions from adjacent areas, such as database and artificial intelligence. Looking at the past is useful to prepare for the future: in the second part, we present a set of open challenges and issues that stream reasoning will face in the next future.

This paper describes the foundations of smart business process management and serves as an editorial to the corresponding special issue. To this end, we introduce a framework that distinguishes three levels of business process management: multi process management, process model management, and process instance management. For each of these levels we identify major contributions of prior research and describe in how far papers assembled in this special issue extend our understanding of smart business process man- agement.

This paper describes the foundations of smart business process management and serves as an editorial to the corresponding special issue. To this end, we introduce a framework that distinguishes three levels of business process management: multi process management, process model management, and process instance management. For each of these levels we identify major contributions of prior research and describe in how far papers assembled in this special issue extend our understanding of smart business process man- agement.

To this date, all methodologies for detecting plagiarism have focused on investigating the final digital ``outcome'', eg. a document or source code. Our novel approach takes the creation process into account using logged events collected by special software or by macro recorders found in most office applications. We look at interaction logs of an author with the software used for creation of the work. Detection relies on comparing histograms of command usages of multiple logs. A work is classified as plagiarism, if its log deviates too much from logs of ``honestly created'' works or if its log is too similar to another log. The technique supports detecting plagiarism for digital outcomes stemming from \emph{unique} tasks such as thesis as well as \emph{equal} tasks such as assignments where the same problem sets are solved by many students. Evaluation focuses on the latter case using collected logs by an interactive development environment (IDE) from more than 60 students for three programming assignments.

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Decision-making is often supported by decision models. This study suggests that the negative impact of poor data quality (DQ) on decision making is often mediated by biased model estimation. To highlight this perspective, we develop an analytical framework that links three quality levels – data, model, and decision. The general framework is first developed at a high-level, and then extended further toward understanding the effect of incomplete datasets on Linear Discriminant Analysis (LDA) classifiers. The interplay between the three quality levels is evaluated analytically - initially for a one-dimensional case, and then for multiple dimensions. The impact is then further analyzed through several simulative experiments with artificial and real-world datasets. The experiment results support the analytical development and reveal nearly-exponential decline in the decision error as the completeness level increases. To conclude, we discuss the framework and the empirical findings, elaborate on the implications of our model on the data quality management, and the use of data for decision-models estimation.

Recommender systems form the backbone of many interactive systems. They incorporate user feedback to personalize the user experience typically via personalized recommendation lists. As users interact with a system, an increasing amount of data about a user’s preferences becomes available, which can be leveraged for improving the systems’ performance. Incorporating these new data into the underlying recommendation model is, however, not always straightforward. Many models used by recommender systems are computationally expensive and, therefore, have to perform offline computations to compile the recommendation lists. For interactive applications, it is desirable to be able to update the computed values as soon as new user interaction data is available: updating recommendations in interactive time using new feedback data leads to better accuracy and increases the attraction of the system to the users. Additionally, there is a growing consensus that accuracy alone is not enough and user satisfaction is also dependent on diverse recommendations. In this work, we tackle this problem of updating personalized recommendation lists for interactive applications in order to provide both accurate and diverse recommendations. To that end, we explore algorithms that exploit random walks as a sampling technique to obtain diverse recommendations without compromising on efficiency and accuracy. Specifically, we present a novel graph vertex ranking recommendation algorithm called RP3β that reranks items based on three-hop random walk transition probabilities. We show empirically that RP3β provides accurate recommendations with high long-tail item frequency at the top of the recommendation list. We also present approximate versions of RP3β and the two most accurate previously published vertex ranking algorithms based on random walk transition probabilities and show that these approximations converge with an increasing number of samples. To obtain interactively updatable recommendations, we additionally show how our algorithm can be extended for online updates at interactive speeds. The underlying random walk sampling technique makes it possible to perform the updates without having to recompute the values for the entire dataset. In an empirical evaluation with three real-world datasets, we show that RP3β provides highly accurate and diverse recommendations that can easily be updated with newly gathered information at interactive speeds (≪ 100ms).

A critical bottleneck in crowdsourced innovation challenges is the process of reviewing and selecting the best submissions. This bottleneck is especially problematic in settings where submissions are complex intellectual artifacts whose evaluation requires expertise. To help reduce the review load from experts, we offer a computational approach that relies on analyzing sociolinguistic and other characteristics of submission text, as well as activities of the crowd and the submission authors, and scores the submissions. We developed and tested models based on data from contests done in a large citizen-science platform - the Climate CoLab - and find that they are able to accurately predict expert decisions about the submissions, and can lead to substantial reduction of review labor, and acceleration of the review process.

In the Big Data era, data exploration and visualization systems are getting more and more important. In the last few years very large datasets have become a major research challenge and with the development of the Semantic Web an increasing amount of semantic data has been created in form of Resource Description Framework (RDF). With this Design Science thesis, a tool was created to display large amounts of semantic data in serialized N-Triples format as graphs in a web application. The Spectral Coarse Graining method was used in order to be able to produce the representation with very large amounts of data. This Bachelor thesis provides a basic understanding of the topics Semantic Web, Linked Data, RDF and Spectral Coarse Graining. It shows the results of the prototype, explains the architecture of the application, presents the frameworks and databases used, and describes the implemented features and functionalities. For the visualization of the RDF data, seven state-of-the-art, graph-based JavaScript frameworks are analyzed and evaluated using a comprehensive criteria catalog. Finally, the work provides an overview of similar visualization systems for the desktop and the web.