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

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In Web stream processing, there are queries that integrate Web data of various velocity, categorized broadly as streaming (i.e., fast changing) and background (i.e., slow changing) data. The introduction of local views on the background data speeds up the query answering process, but requires maintenance processes to keep the replicated data up-to-date. In this work, we study the problem of maintaining local views in a Web setting, where background data are usually stored remotely, are exposed through services with constraints on the data access (e.g., invocation rate limits and data access patterns) and, contrary to the database setting, do not provide streams with changes over their content. Then, we propose an initial solution: WBM, a method to maintain the content of the view with regards to query and user-defined constraints on accuracy and responsiveness.

Most Semantic Web applications rely on querying graphs, typically by using SPARQL with a triple store. Increasingly, applications also analyze properties of the graph structure to compute statistical inferences. The current Semantic Web infrastructure, however, does not efficiently support such operations. Hence, developers have to painstakingly retrieve the relevant data for statistical post-processing. In this paper we propose to rethink query execution in a triple store as a highly parallelized asynchronous graph ex- ploration on an active index data structure. This approach also allows to integrate SPARQL-querying with the sam- pling of graph properties. To evaluate this architecture we implemented Random Walk TripleRush, which is built on a distributed graph processing system and operates by routing query and path descriptions through a novel active index data structure. In experiments we find that our architecture can be used to build a competitive distributed graph store. It can often return first results quickly, thanks to its asynchronous architecture. We show that our architecture supports the execution of various types of random walks with restarts that sample interesting graph properties. We also evaluate the scalability and show that the architecture supports fast answer times even on a dataset with more than a billion triples.

This thesis introduces two new algorithms that can be used to find approximate solutions to Distributed Constraint Optimization Problems (DCOPs). One of the new algorithms is based on Ranked DSA (RDSA), a modification of the classical Distributed Stochastic Algorithm (DSA). The other new algorithm is based on Distributed Simulated Annealing (DSAN). Both new algorithms performed well with graph colouring problems, surpassing all other tested algorithms in the longer term. However, RDSA and the new algorithms had problems with randomized DCOPs, which have more gradual constraints than graph colouring problems.

An evident shift from static web pages to online collaboration platforms can be observed in the World Wide Web. Wikipedia and CrossValidated are two examples of such platforms. They are entirely dependent on the user's contributions and content generation presents itself as an iterative process. That makes the mentioned platforms a reliable source that is always up-to-date. This thesis discusses information extraction from such platforms that contain statistical knowledge and tries to make a first step towards representing statistical knowledge entirely in structured graphs, which would make it possible to execute data analysis as a hierarchical process. It is shown that valuable data may be extracted successfully, but the need to further assure their quality still exists.

This thesis aims to offer a new kind of approach to solve the statistical flaws in research by helping people to efficiently extract information from research publications. The statistical flaws is a problem which afflicts many research fields by creating wrong discoveries. This issue has also an impact on daily life since the discoveries carried out from scientific publications are used everyday in different occasions, e.g., in the medical field. The solution proposed to reduce this problem is to create a new crowdsourcing platform, CrowdSA, which outsources the complex work of the reviewers to the crowd. This system is able to extract from any kind of publications several statistical methods and validate them. The extraction as well as the validation are performed by distributing different questions to the crowd and collecting their answers.

In this paper we present foxPSL, an extended and scalable implementation of Probabilistic Soft Logic (PSL) based on the distributed graph processing framework SIGNAL/COLLECT. PSL is a template language for hinge-loss Markov Random Fields, in which MAP inference is formulated as a constrained convex minimization problem. A key feature of PSL is the capability to represent soft truth values, allowing the expression of complex domain knowledge. To the best of our knowledge, foxPSL is the first end-to-end distributed PSL implementation, supporting the full PSL pipeline from problem definition to a distributed solver that implements the Alternating Direction Method of Multipliers (ADMM) consensus optimization. foxPSL provides a Domain Specific Language that extends standard PSL with a type system and existential quantifiers, allowing for efficient grounding. We compare the performance of foxPSL to a state-of-the-art implementation of ADMM consensus optimization in GraphLab, and show that foxPSL improves both inference time and solution quality.

Open innovation systems have provided organizations with unprecedented access to the “wisdom of the crowd,” allowing them to collect candidate solutions for problems they care about, from potentially thousands of individuals, at very low cost. These systems, however, face important challenges deriving, ironically, from their very success: they can elicit such high levels of participation that it becomes very challenging to guide the crowd in productive ways, and pick out the best of what they have created. This article reviews the key challenges facing open innovation systems and proposes some ways the research community can move forward on this important topic.

In this paper we study the differences in historical worldviews between Western and Eastern cultures, represented through the English, Chinese and Japanese Wikipedia. In particular, we analyze the historical networks of the world’s leaders since the beginning of written history, comparing them in the three different language versions of Wikipedia.

This work contributes an approach on graphical notation for crowd processes and a proof-of-concept implementation of an IDE to dynamically compose processes and run them with graphical feedback. Two sample processes, Find-Fix-Verify and an Image-labeling algorithm, are implemented to show the capabilities of CrowdProcessDesigner. Among the basic capabilities such as divide-and-conquer it additionally supports iteration through loop constructs. Further, the tool supports parameter recombination in order to facilitate evaluation of several process prototype variants. The tool is extensible via OSGI modules and initially supports processes provided by PPLib with Mechanical Turk and CrowdFlower as available portals. A preliminary evaluation of the tool has been done with 6 software engineers.

Distributed constraint optimization allows to solve problems in domains like scheduling, traffic flow management or sensor network management. It is a well-researched field and various algorithms have been proposed. However, the dynamic nature of some of these problems in the real world have been overlooked by researchers and problems are often assumed to be static during the course of the computation. The benchmarking of distributed constraint optimization algorithms (DCOP) with changing problem definitions currently lacks a solid theoretical foundation and standardized protocols. This thesis aimed to measure the performance of different types of DCOP algorithms on dynamic problems with a focus on local-iterative algorithms and especially on the MaxSum algorithm and possibly contribute to the field. A complete, a local-iterative message-passing and a local-iterative approximate best-response algorithm for distributed constraint optimization have been implemented for comparison. In the implementation of the MaxSum algorithm, a variation of the usual graph structure has been attempted. As a real-world use case for benchmarking, the meeting scheduling problem has been mapped as distributed constraint optimization problem. A framework has been designed that allows dynamic changes to constraints, variables and the problem domain during run-time. The algorithms have been benchmarked in a static, as well as in a dynamic environment with various parameters and with a focus on solution quality over time. This thesis further proposes a solution to store, further process and monitor the results of the computation in real-time without affecting the performance of the algorithms.

Anorexia nervosa (AN) is one of several neuropsychiatric disorders that are increasingly tackled experimentally using stereotactic neurosurgery (deep brain stimulation and ablative procedures). We analyze all 27 such cases published between 1990 and 2014. The majority of the patients benefitted significantly from neurosurgical treatments, in terms of both weight restoration and psychiatric morbidity. A remission of AN was reported in 61% of patients treated with DBS and 100% of patients treated with ablative surgery. Unfortunately, information on side effects is insufficient, and after DBS, severe side effects occurred in some cases. Altogether, the risk–benefit evaluation is positive, particularly for ablative stereotactic procedures. However, fundamental ethical issues are raised. We discuss whether neurosurgery can be justified for treating psychiatric disorders of the will that are seemingly self-inflicted, such as addiction or AN, and where cultural factors contribute significantly to their development. We suggest that although psychosocial factors determine the onset of AN, this is not a legitimate argument for banning neurosurgical treatments, since in AN, a vicious circle develops that deeply affects the brain, undermines the will, and prevents ceasing the self-destructive behavior. Three confounding issues provide ethical challenges for research in neurosurgery for AN: first, a scarce information base regarding risks and benefits of the intervention; second, doubtful capabilities for autonomous decision making; and third, the minor age of many patients. We recommend protective measures to ensure that stereotactic neurosurgery research can proceed with respect for the patients' autonomy and orientation to the beneficence principle.

Die Gesellschaft für Informatik e.V. (GI) vergibt gemeinsam mit der Schweizer Informatik Gesellschaft (SI), der Österreichischen Computergesellschaft (OCG) und dem German Chapter of the ACM (GChACM) jährlich einen Preis für eine hervorragende Dissertation im Bereich der Informatik. Hierzu zählen nicht nur Arbeiten, die einen Fortschritt in der Informatik bedeuten, sondern auch Arbeiten aus dem Bereich der Anwendungen in anderen Disziplinen und Arbeiten, die die Wechselwirkungen zwischen Informatik und Gesellschaft untersuchen. Die Auswahl dieser Dissertationen stützt sich auf die von den Universitäten und Hochschulen für diesen Preis vorgeschlagenen Dissertationen. Jede dieser Hochschulen kann jedes Jahr nur eine Dissertation vorschlagen. Somit sind die im Auswahlverfahren vorgeschlagenen Kandidatinnen und Kandidaten bereits „Preisträger“ ihrer Hochschule.