TY - THES A1 - Taleb, Aiham T1 - Self-supervised deep learning methods for medical image analysis T1 - Selbstüberwachte Deep Learning Methoden für die medizinische Bildanalyse N2 - Deep learning has seen widespread application in many domains, mainly for its ability to learn data representations from raw input data. Nevertheless, its success has so far been coupled with the availability of large annotated (labelled) datasets. This is a requirement that is difficult to fulfil in several domains, such as in medical imaging. Annotation costs form a barrier in extending deep learning to clinically-relevant use cases. The labels associated with medical images are scarce, since the generation of expert annotations of multimodal patient data at scale is non-trivial, expensive, and time-consuming. This substantiates the need for algorithms that learn from the increasing amounts of unlabeled data. Self-supervised representation learning algorithms offer a pertinent solution, as they allow solving real-world (downstream) deep learning tasks with fewer annotations. Self-supervised approaches leverage unlabeled samples to acquire generic features about different concepts, enabling annotation-efficient downstream task solving subsequently. Nevertheless, medical images present multiple unique and inherent challenges for existing self-supervised learning approaches, which we seek to address in this thesis: (i) medical images are multimodal, and their multiple modalities are heterogeneous in nature and imbalanced in quantities, e.g. MRI and CT; (ii) medical scans are multi-dimensional, often in 3D instead of 2D; (iii) disease patterns in medical scans are numerous and their incidence exhibits a long-tail distribution, so it is oftentimes essential to fuse knowledge from different data modalities, e.g. genomics or clinical data, to capture disease traits more comprehensively; (iv) Medical scans usually exhibit more uniform color density distributions, e.g. in dental X-Rays, than natural images. Our proposed self-supervised methods meet these challenges, besides significantly reducing the amounts of required annotations. We evaluate our self-supervised methods on a wide array of medical imaging applications and tasks. Our experimental results demonstrate the obtained gains in both annotation-efficiency and performance; our proposed methods outperform many approaches from related literature. Additionally, in case of fusion with genetic modalities, our methods also allow for cross-modal interpretability. In this thesis, not only we show that self-supervised learning is capable of mitigating manual annotation costs, but also our proposed solutions demonstrate how to better utilize it in the medical imaging domain. Progress in self-supervised learning has the potential to extend deep learning algorithms application to clinical scenarios. N2 - Deep Learning findet in vielen Bereichen breite Anwendung, vor allem wegen seiner Fähigkeit, Datenrepräsentationen aus rohen Eingabedaten zu lernen. Dennoch war der Erfolg bisher an die Verfügbarkeit großer annotatierter Datensätze geknüpft. Dies ist eine Anforderung, die in verschiedenen Bereichen, z. B. in der medizinischen Bildgebung, schwer zu erfüllen ist. Die Kosten für die Annotation stellen ein Hindernis für die Ausweitung des Deep Learning auf klinisch relevante Anwendungsfälle dar. Die mit medizinischen Bildern verbundenen Annotationen sind rar, da die Erstellung von Experten Annotationen für multimodale Patientendaten in großem Umfang nicht trivial, teuer und zeitaufwändig ist. Dies unterstreicht den Bedarf an Algorithmen, die aus den wachsenden Mengen an unbeschrifteten Daten lernen. Selbstüberwachte Algorithmen für das Repräsentationslernen bieten eine mögliche Lösung, da sie die Lösung realer (nachgelagerter) Deep-Learning-Aufgaben mit weniger Annotationen ermöglichen. Selbstüberwachte Ansätze nutzen unannotierte Stichproben, um generisches Eigenschaften über verschiedene Konzepte zu erlangen und ermöglichen so eine annotationseffiziente Lösung nachgelagerter Aufgaben. Medizinische Bilder stellen mehrere einzigartige und inhärente Herausforderungen für existierende selbstüberwachte Lernansätze dar, die wir in dieser Arbeit angehen wollen: (i) medizinische Bilder sind multimodal, und ihre verschiedenen Modalitäten sind von Natur aus heterogen und in ihren Mengen unausgewogen, z.B. (ii) medizinische Scans sind mehrdimensional, oft in 3D statt in 2D; (iii) Krankheitsmuster in medizinischen Scans sind zahlreich und ihre Häufigkeit weist eine Long-Tail-Verteilung auf, so dass es oft unerlässlich ist, Wissen aus verschiedenen Datenmodalitäten, z. B. Genomik oder klinische Daten, zu verschmelzen, um Krankheitsmerkmale umfassender zu erfassen; (iv) medizinische Scans weisen in der Regel eine gleichmäßigere Farbdichteverteilung auf, z. B. in zahnmedizinischen Röntgenaufnahmen, als natürliche Bilder. Die von uns vorgeschlagenen selbstüberwachten Methoden adressieren diese Herausforderungen und reduzieren zudem die Menge der erforderlichen Annotationen erheblich. Wir evaluieren unsere selbstüberwachten Methoden in verschiedenen Anwendungen und Aufgaben der medizinischen Bildgebung. Unsere experimentellen Ergebnisse zeigen, dass die von uns vorgeschlagenen Methoden sowohl die Effizienz der Annotation als auch die Leistung steigern und viele Ansätze aus der verwandten Literatur übertreffen. Darüber hinaus ermöglichen unsere Methoden im Falle der Fusion mit genetischen Modalitäten auch eine modalübergreifende Interpretierbarkeit. In dieser Arbeit zeigen wir nicht nur, dass selbstüberwachtes Lernen in der Lage ist, die Kosten für manuelle Annotationen zu senken, sondern auch, wie man es in der medizinischen Bildgebung besser nutzen kann. Fortschritte beim selbstüberwachten Lernen haben das Potenzial, die Anwendung von Deep-Learning-Algorithmen auf klinische Szenarien auszuweiten. KW - Artificial Intelligence KW - machine learning KW - unsupervised learning KW - representation learning KW - Künstliche Intelligenz KW - maschinelles Lernen KW - Representationlernen KW - selbstüberwachtes Lernen Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-644089 ER - TY - JOUR A1 - Taleb, Aiham A1 - Rohrer, Csaba A1 - Bergner, Benjamin A1 - De Leon, Guilherme A1 - Rodrigues, Jonas Almeida A1 - Schwendicke, Falk A1 - Lippert, Christoph A1 - Krois, Joachim T1 - Self-supervised learning methods for label-efficient dental caries classification JF - Diagnostics : open access journal N2 - High annotation costs are a substantial bottleneck in applying deep learning architectures to clinically relevant use cases, substantiating the need for algorithms to learn from unlabeled data. In this work, we propose employing self-supervised methods. To that end, we trained with three self-supervised algorithms on a large corpus of unlabeled dental images, which contained 38K bitewing radiographs (BWRs). We then applied the learned neural network representations on tooth-level dental caries classification, for which we utilized labels extracted from electronic health records (EHRs). Finally, a holdout test-set was established, which consisted of 343 BWRs and was annotated by three dental professionals and approved by a senior dentist. This test-set was used to evaluate the fine-tuned caries classification models. Our experimental results demonstrate the obtained gains by pretraining models using self-supervised algorithms. These include improved caries classification performance (6 p.p. increase in sensitivity) and, most importantly, improved label-efficiency. In other words, the resulting models can be fine-tuned using few labels (annotations). Our results show that using as few as 18 annotations can produce >= 45% sensitivity, which is comparable to human-level diagnostic performance. This study shows that self-supervision can provide gains in medical image analysis, particularly when obtaining labels is costly and expensive. KW - unsupervised methods KW - self-supervised learning KW - representation learning KW - dental caries classification KW - data driven approaches KW - annotation KW - efficient deep learning Y1 - 2022 U6 - https://doi.org/10.3390/diagnostics12051237 SN - 2075-4418 VL - 12 IS - 5 PB - MDPI CY - Basel ER - TY - JOUR A1 - Vitagliano, Gerardo A1 - Hameed, Mazhar A1 - Jiang, Lan A1 - Reisener, Lucas A1 - Wu, Eugene A1 - Naumann, Felix T1 - Pollock: a data loading benchmark JF - Proceedings of the VLDB Endowment N2 - Any system at play in a data-driven project has a fundamental requirement: the ability to load data. The de-facto standard format to distribute and consume raw data is CSV. Yet, the plain text and flexible nature of this format make such files often difficult to parse and correctly load their content, requiring cumbersome data preparation steps. We propose a benchmark to assess the robustness of systems in loading data from non-standard CSV formats and with structural inconsistencies. First, we formalize a model to describe the issues that affect real-world files and use it to derive a systematic lpollutionz process to generate dialects for any given grammar. Our benchmark leverages the pollution framework for the csv format. To guide pollution, we have surveyed thousands of real-world, publicly available csv files, recording the problems we encountered. We demonstrate the applicability of our benchmark by testing and scoring 16 different systems: popular csv parsing frameworks, relational database tools, spreadsheet systems, and a data visualization tool. Y1 - 2023 U6 - https://doi.org/10.14778/3594512.3594518 SN - 2150-8097 VL - 16 IS - 8 SP - 1870 EP - 1882 PB - Association for Computing Machinery CY - New York ER - TY - CHAP A1 - Marx, Julian A1 - Brünker, Felix A1 - Mirbabaie, Milad A1 - Stieglitz, Stefan ED - Bui, Tung X. T1 - Digital activism on social media BT - the role of brand ambassadors and corporate reputation management T2 - Proceedings of the 57th Annual Hawaii International Conference on System Sciences N2 - Social media constitute an important arena for public debates and steady interchange of issues relevant to society. To boost their reputation, commercial organizations also engage in political, social, or environmental debates on social media. To engage in this type of digital activism, organizations increasingly utilize the social media profiles of executive employees and other brand ambassadors. However, the relationship between brand ambassadors’ digital activism and corporate reputation is only vaguely understood. The results of a qualitative inquiry suggest that digital activism via brand ambassadors can be risky (e.g., creating additional surface for firestorms, financial loss) and rewarding (e.g., emitting authenticity, employing ‘megaphones’ for industry change) at the same time. The paper informs both scholarship and practitioners about strategic trade-offs that need to be considered when employing brand ambassadors for digital activism. KW - the bright and dark side of social media in the marginalized contexts KW - brand ambassadors KW - digital activism KW - reputation management KW - social media Y1 - 2024 UR - https://hdl.handle.net/10125/107250 SN - 978-0-99813-317-1 SP - 7205 EP - 7214 PB - Department of IT Management Shidler College of Business University of Hawaii CY - Honolulu, HI ER - TY - CHAP A1 - Mirbabaie, Milad A1 - Rieskamp, Jonas A1 - Hofeditz, Lennart A1 - Stieglitz, Stefan ED - Bui, Tung X. T1 - Breaking down barriers BT - how conversational agents facilitate open science and data sharing T2 - Proceedings of the 57th Annual Hawaii International Conference on System Sciences N2 - Many researchers hesitate to provide full access to their datasets due to a lack of knowledge about research data management (RDM) tools and perceived fears, such as losing the value of one's own data. Existing tools and approaches often do not take into account these fears and missing knowledge. In this study, we examined how conversational agents (CAs) can provide a natural way of guidance through RDM processes and nudge researchers towards more data sharing. This work offers an online experiment in which researchers interacted with a CA on a self-developed RDM platform and a survey on participants’ data sharing behavior. Our findings indicate that the presence of a guiding and enlightening CA on an RDM platform has a constructive influence on both the intention to share data and the actual behavior of data sharing. Notably, individual factors do not appear to impede or hinder this effect. KW - open science practices in information systems research KW - conversational agents KW - data sharing KW - digital nudging KW - open science KW - research data management Y1 - 2024 UR - https://hdl.handle.net/10125/106457 SN - 978-0-99813-317-1 SP - 672 EP - 681 PB - Department of IT Management Shidler College of Business University of Hawaii CY - Honolulu, HI ER - TY - JOUR A1 - Nguyen, Dong Hai Phuong A1 - Georgie, Yasmin Kim A1 - Kayhan, Ezgi A1 - Eppe, Manfred A1 - Hafner, Verena Vanessa A1 - Wermter, Stefan T1 - Sensorimotor representation learning for an "active self" in robots BT - a model survey JF - Künstliche Intelligenz : KI ; Forschung, Entwicklung, Erfahrungen ; Organ des Fachbereichs 1 Künstliche Intelligenz der Gesellschaft für Informatik e.V., GI / Fachbereich 1 der Gesellschaft für Informatik e.V N2 - Safe human-robot interactions require robots to be able to learn how to behave appropriately in spaces populated by people and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations. In humans, these capabilities are thought to be related to our ability to perceive our body in space, sensing the location of our limbs during movement, being aware of other objects and agents, and controlling our body parts to interact with them intentionally. Toward the next generation of robots with bio-inspired capacities, in this paper, we first review the developmental processes of underlying mechanisms of these abilities: The sensory representations of body schema, peripersonal space, and the active self in humans. Second, we provide a survey of robotics models of these sensory representations and robotics models of the self; and we compare these models with the human counterparts. Finally, we analyze what is missing from these robotics models and propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents by developing sensory representations through self-exploration. KW - Developmental robotics KW - Body schema KW - Peripersonal space KW - Agency KW - Robot learning Y1 - 2021 U6 - https://doi.org/10.1007/s13218-021-00703-z SN - 0933-1875 SN - 1610-1987 VL - 35 IS - 1 SP - 9 EP - 35 PB - Springer CY - Berlin ER - TY - THES A1 - Huegle, Johannes T1 - Causal discovery in practice: Non-parametric conditional independence testing and tooling for causal discovery T1 - Kausale Entdeckung in der Praxis: Nichtparametrische bedingte Unabhängigkeitstests und Werkzeuge für die Kausalentdeckung N2 - Knowledge about causal structures is crucial for decision support in various domains. For example, in discrete manufacturing, identifying the root causes of failures and quality deviations that interrupt the highly automated production process requires causal structural knowledge. However, in practice, root cause analysis is usually built upon individual expert knowledge about associative relationships. But, "correlation does not imply causation", and misinterpreting associations often leads to incorrect conclusions. Recent developments in methods for causal discovery from observational data have opened the opportunity for a data-driven examination. Despite its potential for data-driven decision support, omnipresent challenges impede causal discovery in real-world scenarios. In this thesis, we make a threefold contribution to improving causal discovery in practice. (1) The growing interest in causal discovery has led to a broad spectrum of methods with specific assumptions on the data and various implementations. Hence, application in practice requires careful consideration of existing methods, which becomes laborious when dealing with various parameters, assumptions, and implementations in different programming languages. Additionally, evaluation is challenging due to the lack of ground truth in practice and limited benchmark data that reflect real-world data characteristics. To address these issues, we present a platform-independent modular pipeline for causal discovery and a ground truth framework for synthetic data generation that provides comprehensive evaluation opportunities, e.g., to examine the accuracy of causal discovery methods in case of inappropriate assumptions. (2) Applying constraint-based methods for causal discovery requires selecting a conditional independence (CI) test, which is particularly challenging in mixed discrete-continuous data omnipresent in many real-world scenarios. In this context, inappropriate assumptions on the data or the commonly applied discretization of continuous variables reduce the accuracy of CI decisions, leading to incorrect causal structures. Therefore, we contribute a non-parametric CI test leveraging k-nearest neighbors methods and prove its statistical validity and power in mixed discrete-continuous data, as well as the asymptotic consistency when used in constraint-based causal discovery. An extensive evaluation of synthetic and real-world data shows that the proposed CI test outperforms state-of-the-art approaches in the accuracy of CI testing and causal discovery, particularly in settings with low sample sizes. (3) To show the applicability and opportunities of causal discovery in practice, we examine our contributions in real-world discrete manufacturing use cases. For example, we showcase how causal structural knowledge helps to understand unforeseen production downtimes or adds decision support in case of failures and quality deviations in automotive body shop assembly lines. N2 - Kenntnisse über die Strukturen zugrundeliegender kausaler Mechanismen sind eine Voraussetzung für die Entscheidungsunterstützung in verschiedenen Bereichen. In der Fertigungsindustrie beispielsweise erfordert die Fehler-Ursachen-Analyse von Störungen und Qualitätsabweichungen, die den hochautomatisierten Produktionsprozess unterbrechen, kausales Strukturwissen. In Praxis stützt sich die Fehler-Ursachen-Analyse in der Regel jedoch auf individuellem Expertenwissen über assoziative Zusammenhänge. Aber "Korrelation impliziert nicht Kausalität", und die Fehlinterpretation assoziativer Zusammenhänge führt häufig zu falschen Schlussfolgerungen. Neueste Entwicklungen von Methoden des kausalen Strukturlernens haben die Möglichkeit einer datenbasierten Betrachtung eröffnet. Trotz seines Potenzials zur datenbasierten Entscheidungsunterstützung wird das kausale Strukturlernen in der Praxis jedoch durch allgegenwärtige Herausforderungen erschwert. In dieser Dissertation leisten wir einen dreifachen Beitrag zur Verbesserung des kausalen Strukturlernens in der Praxis. (1) Das wachsende Interesse an kausalem Strukturlernen hat zu einer Vielzahl von Methoden mit spezifischen statistischen Annahmen über die Daten und verschiedenen Implementierungen geführt. Daher erfordert die Anwendung in der Praxis eine sorgfältige Prüfung der vorhandenen Methoden, was eine Herausforderung darstellt, wenn verschiedene Parameter, Annahmen und Implementierungen in unterschiedlichen Programmiersprachen betrachtet werden. Hierbei wird die Evaluierung von Methoden des kausalen Strukturlernens zusätzlich durch das Fehlen von "Ground Truth" in der Praxis und begrenzten Benchmark-Daten, welche die Eigenschaften realer Datencharakteristiken widerspiegeln, erschwert. Um diese Probleme zu adressieren, stellen wir eine plattformunabhängige modulare Pipeline für kausales Strukturlernen und ein Tool zur Generierung synthetischer Daten vor, die umfassende Evaluierungsmöglichkeiten bieten, z.B. um Ungenauigkeiten von Methoden des Lernens kausaler Strukturen bei falschen Annahmen an die Daten aufzuzeigen. (2) Die Anwendung von constraint-basierten Methoden des kausalen Strukturlernens erfordert die Wahl eines bedingten Unabhängigkeitstests (CI-Test), was insbesondere bei gemischten diskreten und kontinuierlichen Daten, die in vielen realen Szenarien allgegenwärtig sind, die Anwendung erschwert. Beispielsweise führen falsche Annahmen der CI-Tests oder die Diskretisierung kontinuierlicher Variablen zu einer Verschlechterung der Korrektheit der Testentscheidungen, was in fehlerhaften kausalen Strukturen resultiert. Um diese Probleme zu adressieren, stellen wir einen nicht-parametrischen CI-Test vor, der auf Nächste-Nachbar-Methoden basiert, und beweisen dessen statistische Validität und Trennschärfe bei gemischten diskreten und kontinuierlichen Daten, sowie dessen asymptotische Konsistenz in constraint-basiertem kausalem Strukturlernen. Eine umfangreiche Evaluation auf synthetischen und realen Daten zeigt, dass der vorgeschlagene CI-Test bestehende Verfahren hinsichtlich der Korrektheit der Testentscheidung und gelernter kausaler Strukturen übertrifft, insbesondere bei geringen Stichprobengrößen. (3) Um die Anwendbarkeit und Möglichkeiten kausalen Strukturlernens in der Praxis aufzuzeigen, untersuchen wir unsere Beiträge in realen Anwendungsfällen aus der Fertigungsindustrie. Wir zeigen an mehreren Beispielen aus der automobilen Karosseriefertigungen wie kausales Strukturwissen helfen kann, unvorhergesehene Produktionsausfälle zu verstehen oder eine Entscheidungsunterstützung bei Störungen und Qualitätsabweichungen zu geben. KW - causal discovery KW - causal structure learning KW - causal AI KW - non-parametric conditional independence testing KW - manufacturing KW - causal reasoning KW - mixed data KW - kausale KI KW - kausale Entdeckung KW - kausale Schlussfolgerung KW - kausales Strukturlernen KW - Fertigung KW - gemischte Daten KW - nicht-parametrische bedingte Unabhängigkeitstests Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-635820 ER - TY - JOUR A1 - Wiemker, Veronika A1 - Bunova, Anna A1 - Neufeld, Maria A1 - Gornyi, Boris A1 - Yurasova, Elena A1 - Konigorski, Stefan A1 - Kalinina, Anna A1 - Kontsevaya, Anna A1 - Ferreira-Borges, Carina A1 - Probst, Charlotte T1 - Pilot study to evaluate usability and acceptability of the 'Animated Alcohol Assessment Tool' in Russian primary healthcare JF - Digital health N2 - Background and aims: Accurate and user-friendly assessment tools quantifying alcohol consumption are a prerequisite to effective prevention and treatment programmes, including Screening and Brief Intervention. Digital tools offer new potential in this field. We developed the ‘Animated Alcohol Assessment Tool’ (AAA-Tool), a mobile app providing an interactive version of the World Health Organization's Alcohol Use Disorders Identification Test (AUDIT) that facilitates the description of individual alcohol consumption via culturally informed animation features. This pilot study evaluated the Russia-specific version of the Animated Alcohol Assessment Tool with regard to (1) its usability and acceptability in a primary healthcare setting, (2) the plausibility of its alcohol consumption assessment results and (3) the adequacy of its Russia-specific vessel and beverage selection. Methods: Convenience samples of 55 patients (47% female) and 15 healthcare practitioners (80% female) in 2 Russian primary healthcare facilities self-administered the Animated Alcohol Assessment Tool and rated their experience on the Mobile Application Rating Scale – User Version. Usage data was automatically collected during app usage, and additional feedback on regional content was elicited in semi-structured interviews. Results: On average, patients completed the Animated Alcohol Assessment Tool in 6:38 min (SD = 2.49, range = 3.00–17.16). User satisfaction was good, with all subscale Mobile Application Rating Scale – User Version scores averaging >3 out of 5 points. A majority of patients (53%) and practitioners (93%) would recommend the tool to ‘many people’ or ‘everyone’. Assessed alcohol consumption was plausible, with a low number (14%) of logically impossible entries. Most patients reported the Animated Alcohol Assessment Tool to reflect all vessels (78%) and all beverages (71%) they typically used. Conclusion: High acceptability ratings by patients and healthcare practitioners, acceptable completion time, plausible alcohol usage assessment results and perceived adequacy of region-specific content underline the Animated Alcohol Assessment Tool's potential to provide a novel approach to alcohol assessment in primary healthcare. After its validation, the Animated Alcohol Assessment Tool might contribute to reducing alcohol-related harm by facilitating Screening and Brief Intervention implementation in Russia and beyond. KW - Alcohol use assessment KW - Alcohol Use Disorders Identification Test KW - screening tools KW - digital health KW - mobile applications KW - Russia KW - primary healthcare KW - usability KW - acceptability Y1 - 2022 U6 - https://doi.org/10.1177/20552076211074491 SN - 2055-2076 VL - 8 PB - Sage Publications CY - London ER - TY - JOUR A1 - Omranian, Sara A1 - Angeleska, Angela A1 - Nikoloski, Zoran T1 - PC2P BT - parameter-free network-based prediction of protein complexes JF - Bioinformatics N2 - Motivation: Prediction of protein complexes from protein-protein interaction (PPI) networks is an important problem in systems biology, as they control different cellular functions. The existing solutions employ algorithms for network community detection that identify dense subgraphs in PPI networks. However, gold standards in yeast and human indicate that protein complexes can also induce sparse subgraphs, introducing further challenges in protein complex prediction. Results: To address this issue, we formalize protein complexes as biclique spanned subgraphs, which include both sparse and dense subgraphs. We then cast the problem of protein complex prediction as a network partitioning into biclique spanned subgraphs with removal of minimum number of edges, called coherent partition. Since finding a coherent partition is a computationally intractable problem, we devise a parameter-free greedy approximation algorithm, termed Protein Complexes from Coherent Partition (PC2P), based on key properties of biclique spanned subgraphs. Through comparison with nine contenders, we demonstrate that PC2P: (i) successfully identifies modular structure in networks, as a prerequisite for protein complex prediction, (ii) outperforms the existing solutions with respect to a composite score of five performance measures on 75% and 100% of the analyzed PPI networks and gold standards in yeast and human, respectively, and (iii,iv) does not compromise GO semantic similarity and enrichment score of the predicted protein complexes. Therefore, our study demonstrates that clustering of networks in terms of biclique spanned subgraphs is a promising framework for detection of complexes in PPI networks. Y1 - 2021 U6 - https://doi.org/10.1093/bioinformatics/btaa1089 SN - 1367-4811 VL - 37 IS - 1 SP - 73 EP - 81 PB - Oxford Univ. Press CY - Oxford ER - TY - BOOK A1 - Zhang, Shuhao A1 - Plauth, Max A1 - Eberhardt, Felix A1 - Polze, Andreas A1 - Lehmann, Jens A1 - Sejdiu, Gezim A1 - Jabeen, Hajira A1 - Servadei, Lorenzo A1 - Möstl, Christian A1 - Bär, Florian A1 - Netzeband, André A1 - Schmidt, Rainer A1 - Knigge, Marlene A1 - Hecht, Sonja A1 - Prifti, Loina A1 - Krcmar, Helmut A1 - Sapegin, Andrey A1 - Jaeger, David A1 - Cheng, Feng A1 - Meinel, Christoph A1 - Friedrich, Tobias A1 - Rothenberger, Ralf A1 - Sutton, Andrew M. A1 - Sidorova, Julia A. A1 - Lundberg, Lars A1 - Rosander, Oliver A1 - Sköld, Lars A1 - Di Varano, Igor A1 - van der Walt, Estée A1 - Eloff, Jan H. P. A1 - Fabian, Benjamin A1 - Baumann, Annika A1 - Ermakova, Tatiana A1 - Kelkel, Stefan A1 - Choudhary, Yash A1 - Cooray, Thilini A1 - Rodríguez, Jorge A1 - Medina-Pérez, Miguel Angel A1 - Trejo, Luis A. A1 - Barrera-Animas, Ari Yair A1 - Monroy-Borja, Raúl A1 - López-Cuevas, Armando A1 - Ramírez-Márquez, José Emmanuel A1 - Grohmann, Maria A1 - Niederleithinger, Ernst A1 - Podapati, Sasidhar A1 - Schmidt, Christopher A1 - Huegle, Johannes A1 - de Oliveira, Roberto C. L. A1 - Soares, Fábio Mendes A1 - van Hoorn, André A1 - Neumer, Tamas A1 - Willnecker, Felix A1 - Wilhelm, Mathias A1 - Kuster, Bernhard ED - Meinel, Christoph ED - Polze, Andreas ED - Beins, Karsten ED - Strotmann, Rolf ED - Seibold, Ulrich ED - Rödszus, Kurt ED - Müller, Jürgen T1 - HPI Future SOC Lab – Proceedings 2017 T1 - HPI Future SOC Lab – Proceedings 2017 N2 - The “HPI Future SOC Lab” is a cooperation of the Hasso Plattner Institute (HPI) and industry partners. Its mission is to enable and promote exchange and interaction between the research community and the industry partners. The HPI Future SOC Lab provides researchers with free of charge access to a complete infrastructure of state of the art hard and software. This infrastructure includes components, which might be too expensive for an ordinary research environment, such as servers with up to 64 cores and 2 TB main memory. The offerings address researchers particularly from but not limited to the areas of computer science and business information systems. Main areas of research include cloud computing, parallelization, and In-Memory technologies. This technical report presents results of research projects executed in 2017. Selected projects have presented their results on April 25th and November 15th 2017 at the Future SOC Lab Day events. N2 - Das Future SOC Lab am HPI ist eine Kooperation des Hasso-Plattner-Instituts mit verschiedenen Industriepartnern. Seine Aufgabe ist die Ermöglichung und Förderung des Austausches zwischen Forschungsgemeinschaft und Industrie. Am Lab wird interessierten Wissenschaftlern eine Infrastruktur von neuester Hard- und Software kostenfrei für Forschungszwecke zur Verfügung gestellt. Dazu zählen teilweise noch nicht am Markt verfügbare Technologien, die im normalen Hochschulbereich in der Regel nicht zu finanzieren wären, bspw. Server mit bis zu 64 Cores und 2 TB Hauptspeicher. Diese Angebote richten sich insbesondere an Wissenschaftler in den Gebieten Informatik und Wirtschaftsinformatik. Einige der Schwerpunkte sind Cloud Computing, Parallelisierung und In-Memory Technologien. In diesem Technischen Bericht werden die Ergebnisse der Forschungsprojekte des Jahres 2017 vorgestellt. Ausgewählte Projekte stellten ihre Ergebnisse am 25. April und 15. November 2017 im Rahmen der Future SOC Lab Tag Veranstaltungen vor. T3 - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 130 KW - Future SOC Lab KW - research projects KW - multicore architectures KW - In-Memory technology KW - cloud computing KW - machine learning KW - artifical intelligence KW - Future SOC Lab KW - Forschungsprojekte KW - Multicore Architekturen KW - In-Memory Technologie KW - Cloud Computing KW - maschinelles Lernen KW - Künstliche Intelligenz Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-433100 SN - 978-3-86956-475-3 SN - 1613-5652 SN - 2191-1665 IS - 130 PB - Universitätsverlag Potsdam CY - Potsdam ER -