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

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Analyzing data streams is a vital task in data science. Often, data comes in different shapes such as triples, tuples, relations, or matrices. Traditional stream processing systems, however, only process data in one of these formats. To enable the processing of streams combining different shapes of data, we developed a system that parses SPARQL queries using the Apache Jena parser and transforms them to Apache Flink topologies. With a custom data type and tailored functions, we enabled the integration of matrices in Jena and therefore allowed to mix graphs, relational, and linear algebra in an RDF graph. This provided a proof of concept that queries may be written for static data and – with the usage of the streaming engine Flink – can easily be run on data streams, even if they contain multiple of the aforementioned types.

The last years have witnessed an increasing shortage of data experts capable of analyzing the omnipresent data and producing meaningful insights. Furthermore, some data scientists mention data preprocessing to take up to 80% of the whole project time. This paper proposes a method for collaborative data analysis that involves a crowd without data analysis expertise. Orchestrated by an expert, the team of novices conducts data analysis through iterative refinement of results up to its successful completion. To evaluate the proposed method, we implemented a tool that supports collaborative data analysis for teams with mixed level of expertise. Our evaluation demonstrates that with proper guidance data analysis tasks, especially preprocessing, can be distributed and successfully accomplished by non-experts. Using the design science approach, iterative development also revealed some important features for the collaboration tool, such as support for dynamic development, code deliberation, and project journal. As such we pave the way for building tools that can leverage the crowd to address the shortage of data analysts.

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Die föderale Schweiz eignet sich hervorragend, um experimentelle Pionierarbeit bei der Digitalisierung der Demokratie zu leisten. Warum sich das lohnt.

Inspired by the World Wide Web, the Web of Data is a network of interlinked data fragments. One of the main advantages of the Web of Data is that all of its content is processable by machines. However, this also has its drawbacks when it comes to monetization of the content: advertisements and donations—two important financial motors in the World Wide Web—do not translate into the Web of Data as they rely on exposing the user to advertisement/call for donations. The remedy this situation, we propose two different monetization strategies for the Web of Data. The first strategy involves a marketplace where users can buy data in an integrated way. The second strategy allows third parties to promote certain data. In return, the sponsors pay money whenever a user follows a link contained in the sponsored data. We identified two different kind of data—commercial and sponsored data—which can benefit from the two respective monetization strategies. With our work, we propose solutions to the problem of financing the creation and maintenance of content in the Web of Data.

The World Wide Web is a massive network of interlinked documents. One of the reasons the World Wide Web is so successful is the fact that most content is available free of any charge. Inspired by the success of the World Wide Web, the Web of Data applies the same strategy of interlinking to data. To this point, most of data in the Web of Data is also free of charge. The fact that the data is freely available raises the question of financing these services, however. As we will discuss in this paper, advertisement and donations cannot easily be applied to this new setting. To create incentives to subsidize data providers, we propose that sponsors should pay the providers to promote sponsored data. In return, sponsored data will be privileged over non-sponsored data. Since it is not possible to enforce a certain ordering on the data the user will receive, we propose to split up the data into different batches and deliver these batches with different delays. In this way, we can privilege sponsored data without withholding any non-sponsored data from the user. In this paper, we introduce a new concept of a delayed-answer auction, where sponsors can pay to prioritize their data. We introduce a new model which captures the particular situation when a user access data in the Web of Data. We show how the weighted Vickrey-Clarke-Groves auction mechanism can be applied to our scenario and we discuss how certain parameters can influence the nature of our auction. With our new concept, we build a first step to a free yet financial sustainable Web of Data.

Today's data is continuously produced by companies, private people and sensors, therefore processing the data should also be in a continuous way. An increasing number of use cases for streaming data require models and systems which can adapt their processing based on changes in the application context and need to be able to integrate various information types such as context, facts and background information to deliver valuable near real time insights. This thesis proposes a model for Context-aware, Facts and Background integrated dynamic Stream Processing (CoFaBidSP). The evaluation results for the implemented prototype show that the metrics run time and events per seconds remain nearly constant, even by including more functionality such as the integration of various information types in dynamic stream processing.

The digitalization of our society creates a large number of data streams, such as stock tickers, tweets, and sensor data. Making use of these streams has tremendous values. In the Semantic Web context, live information is queried from the streams in real-time. Knowledge is discovered by integrating streams with data from heterogeneous sources. Moreover, insights hidden in the streams are inferred and extracted by logical reasoning. Handling large and complex streams in real-time challenges the capabilities of current systems. Therefore, this thesis studies how to improve the efficiency of processing and reasoning over semantic streams. It is composed of three projects that deal with different research problems motivated by real-world use cases. We propose new methods to address these problems and implement systems to test our hypotheses based on real datasets. The first project focuses on the problem that sudden increases in the input stream rate overload the system, causing a reduced or unacceptable performance. We propose an eviction technique that, when a spike in the input data rate happens, discards data from the system to ensure the response latency at the cost of a lower recall. The novelty of our solution lies in a data-aware approach that carefully prioritizes the data and evicts the less important ones to achieve a high result recall. The second project studies complex queries that need to integrate streams with remote and external background data (BGD). Accessing remote BGD is a very expensive process in terms of both latency and financial cost. We propose several methods to minimize the cost by exploiting the query and the data patterns. Our system only needs to retrieve data that are more critical to answer the query and avoids wasting resources on the remaining data in BGD. Lastly, as noise is inevitable in real-world semantic streams, the third project inves- tigates how to use logical reasoning to identify and exclude the noise from high-volume streams. We adopt a distributed stream processing engine (DSPE) to achieve scalability. On top of a DSPE, we optimize the reasoning procedures by balancing the costs of com- putation and communication. Therefore, reasoning tasks are compiled into efficient DSPE workflows that can be deployed across large-scale computing clusters.

Due to the fast increasement of available documents in the Internet, methods are needed which are able to present the content of the data, without the need to read them. This methods already exists, called topic models, but tend to work only for large documents. This work analyses current state-of-the-art topic models as well as presenting some own, context-sensitive approaches on a restricted data set built from abstracts. Then, the best results are visualised to improve the interpretability of the data.

Knowledge bases are becoming a key asset leveraged for various types of applications on the Web, from search engines presenting `entity cards’ as the result of a query, to the use of structured data of knowledge bases to empower virtual personal assistants. Wikidata is an open general-interest knowledge base that is collaboratively developed and maintained by a community of thousands of volunteers. One of the major challenges faced in such a crowdsourcing project is to attain a high level of editor engagement. In order to intervene and encourage editors to be more committed to editing Wikidata, it is important to be able to predict at an early stage, whether an editor will or not become an engaged editor. In this paper, we investigate this problem and study the evolution that editors with different levels of engagement exhibit in their editing behaviour over time. We measure an editor’s engagement in terms of (i) the volume of edits provided by the editor and (ii) their lifespan (i.,e. the length of time for which an editor is present at Wikidata). The large-scale longitudinal data analysis that we perform covers Wikidata edits over almost 4 years. We monitor evolution in a session-by-session- and monthly-basis, observing the way the participation, the volume and the diversity of edits done by Wikidata editors change. Using the findings in our exploratory analysis, we define and implement prediction models that use the multiple evolution indicators.

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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.

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.