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References

2019

Zeuch, Steffen; Chaudhary, Ankit; Del Monte, Bonaventura; Gavriilidis, Haralampos; Giouroukis, Dimitrios; Grulich, Philipp M.; Breß, Sebastian; Traub, Jonas; Markl, Volker
The NebulaStream Platform: Data and Application Management for the Internet of Things
Conference on Innovative Data System Research 2020
2019
accepted

Abstract: The Internet of Things (IoT) presents a novel computing architecture for data management: a distributed, highly dynamic, and heterogeneous environment of massive scale. Applications for the IoT introduce new challenges for integrating the concepts of fog and cloud computing in one unified environment. In this paper, we highlight these major challenges and showcase how existing systems handle them. Based on these challenges, we introduce the NebulaStream platform, a general purpose, end-to-end data management system for the IoT. NebulaStream addresses the heterogeneity and distribution of compute and data, supports diverse data and programming models going beyond relational algebra, deals with potentially unreliable communication, and enables constant evolution under continuous operation. In our evaluation, we demonstrate the effectiveness of our approach by providing early results on partial aspects.

Wisiol, Nils; Pirnay, Niklas
XOR Arbiter PUFs have Systematic Response Bias
Proceedings of the 24th International Conference on Financial Cryptography and Data Security
2019
accepted
Behnke, Ilja; Thamsen, Lauritz; Kao, Odej
Héctor: A Framework for Testing IoT Applications Across Heterogeneous Edge and Cloud Testbeds
Proceedings of the 12th {IEEE/ACM} International Conference on Utility and Cloud Computing, {UCC} 2019 , page 15 - 20.
December 2019

Abstract: As a result of the many technical advances in microcomputers and mobile connectivity, the Internet of Things (IoT) has been on the rise in the recent decade. Due to the broad spectrum of applications, networks facilitating IoT scenarios can be of very different scale and complexity. Additionally, connected devices are uncommonly heterogeneous, including micro controllers, smartphones, fog nodes and server infrastructures. Therefore, testing IoT applications is difficult, motivating adequate tool support. In this paper, we present Héctor, a framework for the automatic testing of IoT applications. Héctor allows the automated execution of user-defined experiments on agnostic IoT testbeds. To test applications independently of the availability of required devices, the framework is able to generate virtual testbeds with adjustable network properties. Our evaluations show that simple experiments can be easily automated across a broad spectrum of testbeds. However, the results also indicate that there is considerable interference in experiments, in which many devices are emulated, due to the high resource demand of system emulation.

Thamsen, Lauritz; Verbitskiy, Ilya; Nedelkoski, Sasho; Tran, Vinh Thuy; Meyer, Vinicius; Xavier, Miguel G.; Kao, Odej; De Rose, Cesar A. F.
Hugo: A Cluster Scheduler that Efficiently Learns to Select Complementary Data-Parallel Jobs
Euro-Par 2019: Parallel Processing Workshops,
2019
to be published

Abstract: Distributed data processing systems like MapReduce, Spark, and Flink are popular tools for analysis of large datasets with cluster resources. Yet, users often overprovision resources for their data processing jobs, while the resource usage of these jobs also typically fluctuates considerably. Therefore, multiple jobs usually get scheduled onto the same shared resources to increase the resource utilization and throughput of clusters. However, job runtimes and the utilization of shared resources can vary significantly depending on the specific combinations of co-located jobs. This paper presents Hugo, a cluster scheduler that continuously learns how efficiently jobs share resources, considering metrics for the resource utilization and interference among co-located jobs. The scheduler combines offline grouping of jobs with online reinforcement learning to provide a scheduling mechanism that efficiently generalizes from specific monitored job combinations yet also adapts to changes in workloads. Our evaluation of a prototype shows that the approach can reduce the runtimes of exemplary Spark jobs on a YARN cluster by up to 12.5%, while resource utilization is increased and waiting times can be bounded.

Semmler, Niklas; Smaragdakis, Georgios; Feldmann, Anja
Online Replication Strategies for Distributed Data Stores
OJIOT, 5(1):47-57
August 2019
ISSN: 2364-7108

Abstract: The rate at which data is produced at the network edge, e.g., collected from sensors and Internet of Things (IoT) devices, will soon exceed the storage and processing capabilities of a single system and the capacity of the network. Thus, data will need to be collected and preprocessed in distributed data stores - as part of a distributed database - at the network edge. Yet, even in this setup, the transfer of query results will incur prohibitive costs. To further reduce the data transfers, patterns in the workloads must be exploited. Particularly in IoT scenarios, we expect data access to be highly skewed. Most data will be store-only, while a fraction will be popular. Here, the replication of popular, raw data, as opposed to the shipment of partially redundant query results, can reduce the volume of data transfers over the network. In this paper, we design online strategies to decide between replicating data from data stores or forwarding the queries and retrieving their results. Our insight is that by profiling access patterns of the data we can lower the data transfer cost and the corresponding response times. We evaluate the benefit of our strategies using two real-world datasets.

Schwarzenberg, Robert; Harbecke, David; Macketanz, Vivien; Avramidis, Eleftherios; Möller, Sebastian
Train, Sort, Explain: Learning to Diagnose Translation Models
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, {NAACL-HLT} 2019 , page 29 - 34.
2019

Abstract: Evaluating translation models is a trade-off between effort and detail. On the one end of the spectrum there are automatic count-based methods such as BLEU, on the other end linguistic evaluations by humans, which arguably are more informative but also require a disproportionately high effort. To narrow the spectrum, we propose a general approach on how to automatically expose systematic differences between human and machine translations to human experts. Inspired by adversarial settings, we train a neural text classifier to distinguish human from machine translations. A classifier that performs and generalizes well after training should recognize systematic differences between the two classes, which we uncover with neural explainability methods. Our proof-of-concept implementation, DiaMaT, is open source. Applied to a dataset translated by a state-of-the-art neural Transformer model, DiaMaT achieves a classification accuracy of 75% and exposes meaningful differences between humans and the Transformer, amidst the current discussion about human parity.

Salem, Farouk; Schütt, Thorsten; Schintke, Florian; Reinefeld, Alexander
Scheduling Data Streams for Low Latency and High Throughput on a Cray XC40 Using Libfabric
CUG Conference Proceedings
2019

Abstract: Achieving efficient many-to-many communication on a given network topology is a challenging task when many data streams from different sources have to be scattered concurrently to many destinations with low variance in arrival times. In such scenarios, it is critical to saturate but not to congest the bisectional bandwidth of the network topology in order to achieve a good aggregate throughput. When there are many concurrent point-to-point connections, the communication pattern needs to be dynamically scheduled in a fine-grained manner to avoid network congestion (links, switches), overload in the node's in coming links, and receive buffer overflow. Motivated by the use case of the Compressed Baryonic Matter experiment (CBM), we study the performance and variance of such communication patterns on a Cray XC40 with different routing schemes and scheduling approaches. We present a distributed Data Flow Scheduler (DFS) that reduces the variance of arrival times from all sources at least 30 times and increases the achieved aggregate band width by up to 50%.

Quiring, Erwin; Maier, Alwin; Rieck, Konrad
Misleading Authorship Attribution of Source Code using Adversarial Learning
28th {USENIX} Security Symposium , page 479 - 496.
2019
Derakhshan, Behrouz; Rezaei Mahdiraji, Alireza; Rabl, Tilmann; Markl, Volker
Continuous Deployment of Machine Learning Pipelines
International Conference on Extending Database Technology. International Conference on Extending Database Technology (EDBT-2019), March 25-29, Lisbon, Portugal
Publisher: OpenProceedings,
2019
ISBN: 978-3-89318-081-3
Alt, Christoph; Hübner, Marc; Hennig, Leonhard
Improving Relation Extraction by Pre-trained Language Representations
Proceedings of AKBC 2019. Automated Knowledge Base Construction (AKBC-2019), May 20-22, Amherst, Massachusetts, United States , page 1--18.
Publisher: OpenReview,
2019
Traub, Jonas; Grulich, Philipp; Cuéllar, Alejandro Rodríguez; Breß, Sebastian; Katsifodimos, Asterios; Rabl, Tilmann; Markl, Volker
Efficient Window Aggregation with General Stream Slicing
22th International Conference on Extending Database Technology (EDBT). International Conference on Extending Database Technology (EDBT-2019), 22th, March 26-29, Lisbon, Portugal
Publisher: OpenProceedings,
2019
Zeuch, Steffen; Del Monte, Bonaventura; Karimov, Jeyhun; Lutz, Clemens; Renz, Manuel; Traub, Jonas; Breß, Sebastian; Rabl, Tilmann; Markl, Volker
Analyzing Efficient Stream Processing on Modern Hardware
Proceedings of the VLDB Endowment (PVLDB), 12(5):516--530
2019
Awad, Ahmed; Traub, Jonas; Sakr, Sherif
Adaptive Watermarks: A Concept Drift-based Approach for Predicting Event-Time Progress in Data Streams
21st International Conference on Extending Database Technology (EDBT). International Conference on Extending Database Technology (EDBT-2018), 21st, March 26-29, Vienna, Austria
Publisher: OpenProceedings,
2019
Zhao, Guoguang; Zhao, Jianyu; Li, Yang; Alt, Christoph; Schwarzenberg, Robert; Hennig, Leonhard; Schaffer, Stefan; Schmeier, Sven; Hu, Changjian; Xu, Feiyu
MOLI: Smart Conversation Agent for Mobile Customer Service
Information, 10(2):1--14
February 2019
Ganji, Fatameh; Tajik, Shahin; Sauss, Pascal; Seifert, Jean-Pierre; Forte, Domenic; Tehranipoor, Mark
Rock'n'roll PUFs: Crafting Provably Secure PUFs from Less Secure Ones
In Karine Heydemann, Ulrich Kühne and Letitia Li, editor, Proceedings of 8th International Workshop on Security Proofs for Embedded Systems Volume 11 , page 33 - 48.
September 2019
to be published

Abstract: The era of PUFs has been characterized by the efforts put into research and the devel- opment of PUFs that are resilient against attacks, in particular, machine learning (ML) attacks. Due to the lack of systematic and provable methods for this purpose, we have witnessed the ever-continuing competition between PUF designers/ manufacturers, crypt- analysts, and of course, adversaries that maliciously break the security of PUFs. This is despite a series of acknowledged principles developed in cryptography and complexity theory, under the umbrella term “hardness amplification”. This paper aims at narrowing the gap between these studies and hardware security, specifically for applications in the domain of PUFs. To this end, this paper provides an example of somewhat hard PUFs and demonstrates how to build a strongly secure construction out of these considerably weaker primitives. Our theoretical findings are discussed in an exhaustive manner and supported by the silicon results captured from real-world PUFs.

Poularakis, Konstantinos; Iosifidis, George; Smaragdakis, Georgios; Tassiulas, Leandros
Optimizing Gradual SDN Upgrades in ISP Networks
IEEE/ACM Transactions on Networking, 27(1):288 - 301
September 2019

Abstract: Nowadays, there is a fast-paced shift from legacy telecommunication systems to novel software-defined network (SDN) architectures that can support on-the-fly network reconfiguration, therefore, empowering advanced traffic engineering mechanisms. Despite this momentum, migration to SDN cannot be realized at once especially in high-end networks of Internet service providers (ISPs). It is expected that ISPs will gradually upgrade their networks to SDN over a period that spans several years. In this paper, we study the SDN upgrading problem in an ISP network: which nodes to upgrade and when we consider a general model that captures different migration costs and network topologies, and two plausible ISP objectives: 1) the maximization of the traffic that traverses at least one SDN node, and 2) the maximization of the number of dynamically selectable routing paths enabled by SDN nodes. We leverage the theory of submodular and supermodular functions to devise algorithms with provable approximation ratios for each objective. Using real-world network topologies and traffic matrices, we evaluate the performance of our algorithms and show up to 54% gains over state-of-the-art methods. Moreover, we describe the interplay between the two objectives; maximizing one may cause a factor of 2 loss to the other. We also study the dual upgrading problem, i.e., minimizing the upgrading cost for the ISP while ensuring specific performance goals. Our analysis shows that our proposed algorithm can achieve up to 2.5 times lower cost to ensure performance goals over state-of-the-art methods.

Skrzypczak, Jan; Schintke, Florian; Schütt, Thorsten
Linearizable State Machine Replication of State-Based CRDTs without Logs.
In Peter Robinson and Faith Ellen, editor, Proceedings of the 2019 (ACM) Symposium on Principles of Distributed Computing, (PODC) , page 455 - 457.
2019
ISBN: 978-1-4503-6217-7

Abstract: General solutions of state machine replication have to ensure that all replicas apply the same commands in the same order, even in the presence of failures. Such strict ordering incurs high synchronization costs due to the use of distributed consensus or a leader. This paper presents a protocol for linearizable state machine replication of conflict-free replicated data types (CRDTs) that neither requires consensus nor a leader. By leveraging the properties of state-based CRDTs in particular the monotonic growth of a join semilattice synchronization overhead is greatly reduced. In addition, updates just need a single round trip and modify the state 'in-place' without the need for a log. Furthermore, the message size overhead for coordination consists of a single counter per message. While reads in the presence of concurrent updates are not wait-free without a coordinator, we show that more than 97\,% of reads can be handled in one or two round trips under highly concurrent accesses. Our protocol achieves high throughput without auxiliary processes such as command log management or leader election. It is well suited for all practical scenarios that need linearizable access on CRDT data on a fine-granular scale.

Nedelkoski, Sasho; Thamsen, Lauritz; Verbitskiy, Ilya; Kao, Odej
Multilayer Active Learning for Efficient Learning and Resource Usage in Distributed IoT Architectures
2019 IEEE International Conference on Edge Computing (EDGE) , page 8 - 12.
2019

Abstract: The use of machine learning modeling techniques enables smart IoT applications in geo-distributed infrastructures such as in the areas of Industry 4.0, smart cities, autonomous driving, and telemedicine. The data for these models is continuously emitted by sensor-equipped devices. It is usually unlabeled and commonly has dynamically-changing data distribution, which impedes the learning process. However, many critical applications such as telemedicine require highly accurate models and human supervision. Therefore, online supervised learning is often utilized, but its application remains challenging as it requires continuous labeling by experts, which is expensive. To reduce the cost, active learning (AL) strategies are used for efficient data selection and labeling. In this paper we propose a novel AL framework for IoT applications, which employs data selection strategies throughout the multiple layers of distributed IoT architectures. This enables an improved utilization of the available resources and reduces costs. The results from the evaluation using classification and regression tasks and synthetic as well as real-world datasets in multiple settings show that the use of multilayer AL can significantly reduce communication, expert costs, and energy, without a loss in model performance. We believe that this study motivates the development of new techniques that employ selective sampling strategies on data streams to optimize the resource usage in IoT architectures.

Mattes, Dirk
Ein System zur deterministischen Wiedergabe von verteilten Algorithmen auf Anwendungsebene.
Humboldt-Universität zu Berlin,
2019
Mohammad Mahdavi, Ziawasch Abedjan, Raul Castro Fernandez, Samuel Madden, Mourad Ouzzani, Michael Stonebraker,; Tang, Nan
Raha: A Configuration-Free Error Detection System
SIGMOD
2019
Iskender, Neslihan; Gabryszak, Aleksandra; Polzehl, Tim; Hennig, Leonhard; Möller, Sebastian
A Crowdsourcing Approach to Evaluate the Quality of Query-based Extractive Text Summaries
11th International Conference on Quality of Multimedia Experience QoMEX 2019 , page 1 - 3.
2019

Abstract: High cost and time consumption are concurrent barriers for research and application of automated summarization. In order to explore options to overcome this barrier, we analyze the feasibility and appropriateness of micro-task crowdsourcing for evaluation of different summary quality characteristics and report an ongoing work on the crowdsourced evaluation of query-based extractive text summaries. To do so, we assess and evaluate a number of linguistic quality factors such as grammaticality, non-redundancy, referential clarity, focus and structure & coherence. Our first results imply that referential clarity, focus and structure & coherence are the main factors effecting the perceived summary quality by crowdworkers. Further, we compare these results using an initial set of expert annotations that is currently being collected, as well as an initial set of automatic quality score ROUGE for summary evaluation. Preliminary results show that ROUGE does not correlate with linguistic quality factors, regardless if assessed by crowd or experts.Further, crowd and expert ratings show highest degree of correlation when assessing low quality summaries. Assessments increasingly divert when attributing high quality judgments.

Semmler, Niklas; Smaragdakis, Georgios; Feldmann, Anja
Distributed Mega-Datasets: The Need for Novel Computing Primitives
39th {IEEE} International Conference on Distributed Computing Systems
November 2019

Abstract: With the ongoing digitalization, an increasing number of sensors is becoming part of our digital infrastructure. These sensors produce highly, even globally, distributed data streams. The aggregate data rate of these streams far exceeds local storage and computing capabilities. Yet, for radical new services (e.g., predictive maintenance and autonomous driving), which depend on various control loops, this data needs to be analyzed in a timely fashion. In this position paper, we outline a system architecture that can effectively handle distributed mega-datasets using data aggregation. Hereby, we point out two research challenges: The need for (1) novel computing primitives that allow us to aggregate data at scale across multiple hierarchies (i.e., time and location) while answering a multitude of a priori unknown queries, and (2) transfer optimizations that enable rapid local and global decision making.

Wisiol, Nils; T. Becker, Georg; Margraf, Marian; Soroceanu, Tudor A. A.; Tobisch, Johannes; Zengin, Benjamin
Breaking the Lightweight Secure {PUF:} Understanding the Relation of Input Transformations and Machine Learning Resistance.
{IACR} Cryptology ePrint Archive,
2019

Abstract: Physical Unclonable Functions (PUFs) and, in particular, XOR Arbiter PUFs have gained much research interest as an authentication mechanism for embedded systems. One of the biggest problems of (strong) PUFs is their vulnerability to so called machine learning attacks. In this paper we take a closer look at one aspect of machine learning attacks that has not yet gained the needed attention: the generation of the sub-challenges in XOR Arbiter PUFs fed to the individual Arbiter PUFs. Specifically, we look at one of the most popular ways to generate sub-challenges based on a combination of permutations and XORs as it has been described for the "Lightweight Secure PUF". Previous research suggested that using such a sub-challenge generation increases the machine learning resistance significantly. Our contribution in the field of sub-challenge generation is three-fold: First, drastically improving attack results by Rührmair et al., we describe a novel attack that can break the Lightweight Secure PUF in time roughly equivalent to an XOR Arbiter PUF without transformation of the challenge input. Second, we give a mathematical model that gives insight into the weakness of the Lightweight Secure PUF and provides a way to study generation of sub-challenges in general. Third, we propose a new, efficient, and cost-effective way for sub-challenge generation that mitigates the attack strategy we used and outperforms the Lightweight Secure PUF in both machine learning resistance and resource overhead.

Hartung, Marc; Schintke, Florian; Schütt, Thorsten
Pinpoint Data Races via Testing and Classification
2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW); 3rd International Workshop on Software Faults (IWSF 2019)
2019
Gholami, Masoud; Schintke, Florian
Multilevel Checkpoint/Restart for Large Computational Jobs on Distributed Computing Resources
2019 IEEE 38th Symposium on Reliable Distributed Systems (SRDS)
2019
accepted
Geldenhuys, Morgan K.; Thamsen, Lauritz; Gontarska, Kain Kordian; Lorenz, Felix; Kao, Odej
Effectively Testing System Configurations of Critical IoT Analytics Pipelines
2019 IEEE International Conference on Big Data
Publisher: IEEE,
December 2019
to be published
Abedjan, Ziawasch
Data Profiling
Encyclopedia of Big Data Technologies.
2019
Çakal, Öykü Özlem; Mahdavi, Mohammad; Abedjan, Ziawasch
CLRL: Feature Engineering for Cross-Language Record Linkage
EDBT , page 678--681.
2019
Esmailoghli, Mahdi; Redyuk, Sergey; Martinez, Ricardo; Abedjan, Ziawasch; Rabl, Tilmann; Markl, Volker
Explanation of Air Pollution Using External Data Sources
BTW , page 297--300.
2019
Abedjan, Ziawasch; Boujemaa, Nozha; Campbell, Stuart; Casla, Patricia; Chatterjea, Supriyo; Consoli, Sergio; Costa Soria, Cristobal; Czech, Paul; Despenic, Marija; Garattini, Chiara; Hamelinck, Dirk; Heinrich, Adrienne; Kraaij, Wessel; Kustra, Jacek; Lojo, Aizea; Martin Sanchez, Marga; Angel Mayer, Miguel; Melideo, Matteo; Menasalvas, Ernestina; Moller Aarestrup, Frank; Narro Artigot, Elvira; Petkovic, Milan; Reforgiato Recupero, Diego; Rodriguez Gonzalez, Alejandro; Roesems Kerremans, Gisele; Roller, Roland; Romao, Mario; Ruping, Stefan; Sasaki, Felix; Spek, Wouter; Stojanovic, Nenad; Thoms, Jack; Vasiljevs, Andrejs; Verachtert, Wilfried; Wuyts, Roel
Data Science in Healthcare: Benefits, Challenges and Opportunities
Data Science for Healthcare - Methodologies and Applications
page 3--38.
2019
Warnecke, Alexander; Arp, Daniel; Wressnegger, Christian; Rieck, Konrad
Don't Paint It Black: White-Box Explanations for Deep Learning in Computer Security
CoRR, abs/1906.02108
2019

Abstract: Deep learning is increasingly used as a basic building block of security systems. Unfortunately, deep neural networks are hard to interpret, and their decision process is opaque to the practitioner. Recent work has started to address this problem by considering black-box explanations for deep learning in computer security (CCS'18). The underlying explanation methods, however, ignore the structure of neural networks and thus omit crucial information for analyzing the decision process. In this paper, we investigate white-box explanations and systematically compare them with current black-box approaches. In an extensive evaluation with learning-based systems for malware detection and vulnerability discovery, we demonstrate that white-box explanations are more concise, sparse, complete and efficient than black-box approaches. As a consequence, we generally recommend the use of white-box explanations if access to the employed neural network is available, which usually is the case for stand-alone systems for malware detection, binary analysis, and vulnerability discovery.

Alonso, Gustavo; Binnig, Carsten; Pandis, Ippokratis; Salem, Kenneth; Skrzypczak, Jan; Stutsman, Ryan; Thostrup, Lasse; Wang, Tianzheng; Wang, Zeke; Ziegler, Tobias
DPI: The Data Processing Interface for Modern Networks
{CIDR} 2019, 9th Biennial Conference on Innovative Data Systems Research 2019, Online Proceedings
2019

Abstract: As data processing evolves towards large scale, distributed plat-forms, the network will necessarily play a substantial role in achieving efficiency and performance. Increasingly, switches, network cards, and protocols are becoming more flexible while programmability at all levels (aka, software defined networks) opens up many possibilities to tailor the network to data processing applications and to push processing down to the network elements. In this paper, we propose DPI, an interface providing a set of simple yet powerful abstractions flexible enough to exploit features of modern networks (e.g., RDMA or in-network processing) suit-able for data processing. Mirroring the concept behind the Message Passing Interface (MPI) used extensively in high-performance computing, DPI is an interface definition rather than an implementation so as to be able to bridge different networking technologies and to evolve with them. In the paper we motivate and discuss key primitives of the interface and present a number of use cases that show the potential of DPI for data-intensive applications, such as analytic engines and distributed database systems

Chmiela, S; Sauceda, HE; Poltavsky, I; Müller, KR; Tkatchenko, A
sGDML: Constructing accurate and data efficient molecular force fields using machine learning
Computer Physics Communications, 240:38--45
2019

Abstract: We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global potential energy surfaces (PES) for molecules with a few dozen atoms from a limited number of user-provided reference molecular conformations and the associated atomic forces. Here, we introduce a Python software package to reconstruct and evaluate custom sGDML force fields (FFs), without requiring in-depth knowledge about the details of the model. A user-friendly command-line interface offers assistance through the complete process of model creation, in an effort to make this novel machine learning approach accessible to broad practitioners. Our paper serves as a documentation, but also includes a practical application example of how to reconstruct and use a PBE0+MBD FF for paracetamol. Finally, we show how to interface sGDML with the FF simulation engines ASE (Larsen et al., 2017) and i-PI (Kapil et al., 2019) to run numerical experiments, including structure optimization, classical and path integral molecular dynamics and nudged elastic band calculations.

Salem, Farouk; Schintke, Florian; Schütt, Thorsten; Reinefeld, Alexander
Scheduling data streams for low latency and high throughput on a Cray XC40 using Libfabric
Concurrency and Computation Practice and Experience, :1 - 14
2019

Abstract: Achieving efficient many-to-many communication on a given network topology is a challenging task when many data streams from different sources have to be scattered concurrently to many destinations with low variance in arrival times. In such scenarios, it is critical to saturate but not to congest the bisectional bandwidth of the network topology in order to achieve a good aggregate throughput. When there are many concurrent point-to-point connections, the communication pattern needs to be dynamically scheduled in a fine-grained manner to avoid network congestion (links, switches), overload in the node's in coming links, and receive buffer overflow. Motivated by the use case of the Compressed Baryonic Matter experiment (CBM), we study the performance and variance of such communication patterns on a Cray XC40 with different routing schemes and scheduling approaches. We present a distributed Data Flow Scheduler (DFS) that reduces the variance of arrival times from all sources at least 30 times and increases the achieved aggregate band width by up to 50%.

Arras, A; Osman, A; Müller, Klaus-Robert; Samek, Wojciech
Evaluating Recurrent Neural Network Explanations
CoRR, abs/1904.11829
2019

Abstract: Recently, several methods have been proposed to explain the predictions of recurrent neural networks (RNNs), in particular of LSTMs. The goal of these methods is to understand the network's decisions by assigning to each input variable, e.g., a word, a relevance indicating to which extent it contributed to a particular prediction. In previous works, some of these methods were not yet compared to one another, or were evaluated only qualitatively. We close this gap by systematically and quantitatively comparing these methods in different settings, namely (1) a toy arithmetic task which we use as a sanity check, (2) a five-class sentiment prediction of movie reviews, and besides (3) we explore the usefulness of word relevances to build sentence-level representations. Lastly, using the method that performed best in our experiments, we show how specific linguistic phenomena such as the negation in sentiment analysis reflect in terms of relevance patterns, and how the relevance visualization can help to understand the misclassification of individual samples.

Alt, Christoph; Hübner, Marc; Hennig, Leonhard
Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics Volume 1 , page 1388--1398.
Publisher: Association for Computational Linguistics,
2019

Abstract: Distantly supervised relation extraction is widely used to extract relational facts from text, but suffers from noisy labels. Current relation extraction methods try to alleviate the noise by multi-instance learning and by providing supporting linguistic and contextual information to more efficiently guide the relation classification. While achieving state-of-the-art results, we observed these models to be biased towards recognizing a limited set of relations with high precision, while ignoring those in the long tail. To address this gap, we utilize a pre-trained language model, the OpenAI Generative Pre-trained Transformer (GPT) (Radford et al., 2018). The GPT and similar models have been shown to capture semantic and syntactic features, and also a notable amount of “common-sense” knowledge, which we hypothesize are important features for recognizing a more diverse set of relations. By extending the GPT to the distantly supervised setting, and fine-tuning it on the NYT10 dataset, we show that it predicts a larger set of distinct relation types with high confidence. Manual and automated evaluation of our model shows that it achieves a state-of-the-art AUC score of 0.422 on the NYT10 dataset, and performs especially well at higher recall levels.

Alber, Maximilian; Lapuschkin, Sebastian; Seegerer, Philipp; Hägele, Miriam; Schütt, Kristof T.; Montavon, Grégoire; Samek, Wojciech; Müller, Klaus-Robert; Dähne, Sven; Kindermans, Pieter-Jan
iNNvestigate neural networks!
J. Mach. Learn. Res., 20:93:1--93:8
2019

Abstract: In recent years, deep neural networks have revolutionized many application domains of machine learning and are key components of many critical decision or predictive processes. Therefore, it is crucial that domain specialists can understand and analyze actions and pre- dictions, even of the most complex neural network architectures. Despite these arguments neural networks are often treated as black boxes. In the attempt to alleviate this short- coming many analysis methods were proposed, yet the lack of reference implementations often makes a systematic comparison between the methods a major effort. The presented library iNNvestigate addresses this by providing a common interface and out-of-the- box implementation for many analysis methods, including the reference implementation for PatternNet and PatternAttribution as well as for LRP-methods. To demonstrate the versatility of iNNvestigate, we provide an analysis of image classifications for variety of state-of-the-art neural network architectures.

Ganji, Fatemeh; Forte, Domenic; Seifert, Jean-Pierre
PUFmeter a Property Testing Tool for Assessing the Robustness of Physically Unclonable Functions to Machine Learning Attacks
IEEE Access, 7:122513 - 122521
August 2019
ISSN: 2169-3536

Abstract: As PUFs become ubiquitous for commercial products (e.g., FPGAs from Xilinx, Altera, and Microsemi), attacks against these primitives are evolving toward more omnipresent and even advanced techniques. Machine learning (ML) attacks, among other non-invasive attacks, are proven to be feasible and cost-effective in the real-world. However, for PUF designers, it still remains an open question whether their countermeasures, or even new designs, are resistant to these types of attacks. Although standard metrics for estimating PUF quality exist, the most common approaches for measuring resistance to ML attacks are empirical. This paper introduces PUFmeter, a new publicly available toolbox consisting of in-house developed algorithms, to provide a firm basis for the robustness assessment of PUFs against ML attacks. To this end, new metrics and notions are reintroduced by PUFmeter to PUF designers and manufacturers. Furthermore, to prepare the PUF input-output pairs adequately before conducting any analysis, PUFmeter involves modules that output the minimum number of measurement repetitions and the upper bound on the noise level affecting the PUF responses.

2018

Chen Xu, Rudi Poepsel Lemaitre, Juan Soto, Volker Markl
Fault-Tolerance for Distributed Iterative Dataflows in Action
PVLDB 11(12), :1990-1993
2018

Abstract: Distributed dataflow systems (DDS) are widely employed in graph processing and machine learning (ML), where many of these algorithms are iterative in nature. Typically, DDS achieve fault-tolerance using checkpointing mechanisms or they exploit algorithmic properties to enable fault-tolerance without the need for checkpoints. Recently, for graph processing, we proposed utilizing unblocking checkpointing, to parallelize the execution pipeline and checkpoint writing, as well as confined recovery, to enable fast recovery upon partial node failures. Furthermore, for ML algorithms implemented using broadcast variables, we proposed utilizing replica recovery, to leverage broadcast variable replicas and facilitate failure recovery checkpointing-free. In this demonstration, we showcase these fault-tolerance techniques using Apache Flink. Attendees will be able to: (i) run representative iterative algorithms including PageRank, Connected Components, and K-Means, (ii) explore the internal behavior of DDS under the influence of unblocking checkpointing, and (iii) trigger failures, to observe the effects of confined recovery and replica recovery.

Traub, Jonas; Grulich, Philipp; Rodríıguez Cuéllar, Alejandro; Breß, Sebastian; Katsifodimos, Asterios; Rabl, Tilmann; Markl, Volker
Scotty: Efficient Window Aggregation for out-of-order Stream Processing
, page 1300-1303.
2018

Abstract: Computing aggregates over windows is at the core of virtually every stream processing job. Typical stream processing applications involve overlapping windows and, therefore, cause redundant computations. Several techniques prevent this redundancy by sharing partial aggregates among windows. However, these techniques do not support out-of-order processing and session windows. Out-of-order processing is a key requirement to deal with delayed tuples in case of source failures such as temporary sensor outages. Session windows are widely used to separate different periods of user activity from each other. In this paper, we present Scotty, a high throughput operator for window discretization and aggregation. Scotty splits streams into non-overlapping slices and computes partial aggregates per slice. These partial aggregates are shared among all concurrent queries with arbitrary combinations of tumbling, sliding, and session windows. Scotty introduces the first slicing technique which (1) enables stream slicing for session windows in addition to umbling and sliding windows and (2) processes out-of-order tuples efficiently. Our technique is generally applicable to a broad group of dataflow systems which use a unified batch and stream processing model. Our experiments show that we achieve a throughput an order of magnitude higher than alternative stateof-the-art solutions.

Quoc-Cuong To, Juan Soto, Volker Markl
A survey of state management in big data processing systems
VLDB J. 27(6), :847-872
2018

Abstract: The concept of state and its applications vary widely across big data processing systems. This is evident in both the research literature and existing systems, such as Apache Flink, Apache Heron, Apache Samza, Apache Spark, and Apache Storm. Given the pivotal role that state management plays, particularly, for iterative batch and stream processing, in this survey, we present examples of state as an enabler, discuss the alternative approaches used to handle and implement state, capture the many facets of state management, and highlight new research directions. Our aim is to provide insight into disparate state management techniques, motivate others to pursue research in this area, and draw attention to open problems.

Niklas Stoehr, Johannes Meyer, Volker Markl, Qiushi Bai, Taewoo Kim, De-Yu Chen, Chen Li
Heatflip: Temporal-Spatial Sampling for Progressive Heat Maps on Social Media Data
, page 3723-3732.
2018

Abstract: Keyword-based heat maps are a natural way to explore and analyze the spatial properties of social media data. Dealing with large datasets, there may be many different keywords, making offline pre-computations very hard. Interactive frameworks that exploit database sampling can address this challenge. We present a novel middleware technique called Heatflip, which issues diametrically opposed samples into the temporal and spatial dimensions of the data stored in an external database. Spatial samples provide insights into the temporal distribution and vice versa. The progressive exploration approach benefits from adaptive indexing and combines the retrieval and visualization of the data in a middleware layer. Without any a priori knowledge of the underlying data, the middleware can generate accurate heat maps in 85% shorter processing times than conventional systems. In this paper, we discuss the analytical background of Heatflip, showcase its scalability, and validate its performance when visualizing large amounts of social media data.

Seibert, Felix; Peters, Mathias; Schintke, Florian
mproving I/O Performance Through Colocating Interrelated Input Data and Near-Optimal Load Balancing.
Proceedings of the IPDPSW; Fourth IEEE International Workshop on High Performance Big Data, Deep Learning and Cloud Computing (HPBDC), Volume 2018 , page 448-457.
2018

Note: Best paper award

Arp, Daniel; Quiring, Erwin; Krueger, Tammo; Dragiev, Stanimir; Rieck, Konrad
Privacy-Enhanced Fraud Detection with Bloom Filters
Security and Privacy in Communication Networks - 14th International Conference, Proceedings Part I , page 396--415.
2018

Abstract: The online shopping sector is continuously growing, generating a turnover of billions of dollars each year. Unfortunately, this growth in popularity is not limited to regular customers: Organized crime targeting online shops has considerably evolved in the past years, causing significant financial losses to the merchants. As criminals often use similar strategies among different merchants, sharing information about fraud patterns could help mitigate the success of these malicious activities. In practice, however, the sharing of data is difficult, since shops are often competitors or have to follow strict privacy laws. In this paper, we propose a novel method for fraud detection that allows merchants to exchange information on recent fraud incidents without exposing customer data. To this end, our method pseudonymizes orders on the client-side before sending them to a central service for analysis. Although the service cannot access individual features of these orders, it is able to infer fraudulent patterns using machine learning techniques. We examine the capabilities of this approach and measure its impact on the overall detection performance on a dataset of more than 1.5 million orders from a large European online fashion retailer.

Renner, T.; Müller, J.; Kao, O.
"Endolith: A Blockchain-Based Framework to Enhance Data Retention in Cloud Storages,"
26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), Cambridge , page 627-634..
2018

Abstract: Blockchains like Bitcoin and Ethereum have seen significant adoption in the past few years and show promise to design applications without any centralized reliance on third parties. In this paper, we present Endolith, an auditing framework for verifying file integrity and tracking file history without third party reliance using a smart contract-based blockchain. Annotated files are continuously monitored and metadata about changes including file hashes are stored tamper-proof on the blockchain. Based on this, Endolith can prove that a file stored a long time ago has not been changed without authorization or, if it did, track when it has changed, by whom. Endolith implementation is based on Ethereum and Hadoop Distributed File System (HDFS). Our evaluation on a public blockchain network shows that Endolith is efficient for files that are infrequently modified but often accessed, which are common characteristics of data archives.

Clemens Lutz, Sebastian Breß, Tilmann Rabl, Steffen Zeuch, Volker Markl
Efficient and Scalable k‑Means on GPUs
Datenbank-Spektrum 18(3), :157-169
2018

Abstract: k-Means is a versatile clustering algorithm widely used in practice. To cluster large data sets, state-of-the-art implementations use GPUs to shorten the data to knowledge time. These implementations commonly assign points on a GPU and update centroids on a CPU. We identify two main shortcomings of this approach. First, it requires expensive data exchange between processors when switching between the two processing steps point assignment and centroid update. Second, even when processing both steps of k-means on the same processor, points still need to be read two times within an iteration, leading to inefficient use of memory bandwidth. In this paper, we present a novel approach for centroid update that allows us to efficiently process both phases of k-means on GPUs. We fuse point assignment and centroid update to execute one iteration with a single pass over the points. Our evaluation shows that our k-means approach scales to very large data sets. Overall, we achieve up to 20 × higher throughput compared to the state-of-the-art approach.

Jeyhun Karimov, Tilmann Rabl, Volker Markl
PolyBench: The First Benchmark for Polystores
, page 24-41.
2018

Abstract: Modern business intelligence requires data processing not only across a huge variety of domains but also across different paradigms, such as relational, stream, and graph models. This variety is a challenge for existing systems that typically only support a single or few different data models. Polystores were proposed as a solution for this challenge and received wide attention both in academia and in industry. These are systems that integrate different specialized data processing engines to enable fast processing of a large variety of data models. Yet, there is no standard to assess the performance of polystores. The goal of this work is to develop the first benchmark for polystores. To capture the flexibility of polystores, we focus on high level features in order to enable an execution of our benchmark suite on a large set of polystore solutions.

Thamsen, Lauritz; Verbitskiy, Ilya ,; Rabier, Benjamin; Kao, Odej
Learning Efficient Co-locations for Scheduling Distributed Dataflows in Shared Clusters
In Services Transactions on Big Data (Vol. 4, No. 1). Services Society.
2018

Abstract: Resource management systems like YARN or Mesos allow sharing cluster resources by running data-parallel processing jobs in temporarily reserved containers. Containers, in this context, are logicalleases of resources as, for instance, a number of cores and main memory, allocated on a particularnode. Typically, containers are used without resource isolation to achieve high degrees of overallresource utilization despite the often fluctuating resource usage of single analytic jobs. However, somecombinations of jobs utilize the resources better and interfere less with each other when running on thesame nodes than others. This paper presents an approach for improving the resource utilization and job throughput whenscheduling recurring distributed data-parallel processing jobs in shared cluster environments. Usinga reinforcement learning algorithm, the scheduler continuously learns which jobs are best executedsimultaneously on the cluster. We evaluated a prototype implementation of our approach with HadoopYARN, exemplary Flink jobs from different application domains, and a cluster of commodity nodes.Even though the measure we use to assess the goodness of schedules can still be improved, the resultsof our evaluation show that our approach increases resource utilization and job throughput.

Janßen, Gerrit; Verbitskiy, Ilya; Renner, Thomas; Thamsen, Lauritz
Scheduling Stream Processing Tasks on Geo-Distributed Heterogeneous Resources.
2018 IEEE International Conference on Big Data (IEEE BigData). Presented at the First International Workshop on the Internet of Things Data Analytics (IoTDA)
2018

Abstract: Low-latency processing of data streams from distributed sensors is becoming increasingly important for a growing number of IoT applications. In these environments sensor data collected at the edge of the network is typically transmitted in a number of hops: from devices to intermediate resources to clusters of cloud resources. Scheduling processing tasks of dataflow jobs on all the resources of these environments can significantly reduce application latencies and network congestion. However, for this schedulers need to take the heterogeneity of processing resources and network topologies into account.This paper examines multiple methods for scheduling distributed dataflow tasks on geo-distributed, heterogeneous resources. For this, we developed an optimization function that incorporates the latencies, bandwidths, and computational resources of heterogeneous topologies. We evaluated the different placement methods in a virtual geo-distributed and heterogeneous environment with an IoT application. Our results show that metaheuristic methods that take service quality metrics into account can find significantly better placements than methods that only take topologies into account, with latencies reduced by almost 50%.

Quiring, Erwin; Arp, Daniel; Rieck, Konrad
Forgotten Siblings: Unifying Attacks on Machine Learning and Digital Watermarking
2018 {IEEE} European Symposium on Security and Privacy , page 488 - 502.
2018

Abstract: Machine learning is increasingly used in securitycritical applications, such as autonomous driving, face recognition, and malware detection. Most learning methods, however, have not been designed with security in mind and thus are vulnerable to different types of attacks. This problem has motivated the research field of adversarial machine learning that is concerned with attacking and defending learning methods. Concurrently, a separate line of research has tackled a very similar problem: In digital watermarking, a pattern is embedded in a signal in the presence of an adversary. As a consequence, this research field has also extensively studied techniques for attacking and defending watermarking methods. The two research communities have worked in parallel so far, unnoticeably developing similar attack and defense strategies. This paper is a first effort to bring these communities together. To this end, we present a unified notation of blackbox attacks against machine learning and watermarking. To demonstrate its efficacy, we apply concepts from watermarking to machine learning and vice versa. We show that countermeasures from watermarking can mitigate recent model-extraction attacks and, similarly, that techniques for hardening machine learning can fend off oracle attacks against watermarks. We further demonstrate a novel threat for watermarking schemes based on recent deep learning attacks from adversarial learning. Our work provides a conceptual link between two research fields and thereby opens novel directions for improving the security of both, machine learning and digital watermarking.

Schmidtke, Robert; Schintke, Florian; Schütt, Thorsten
From Application to Disk: Tracing I/O Through the Big Data Stack.
High Performance Computing ISC High Performance 2018 International Workshops
2018
Sebastian Breß, Bastian Köcher, Henning Funke, Steffen Zeuch, Tilmann Rabl, Volker Markl
Generating custom code for efficient query execution on heterogeneous processors
VLDB J. 27(6), :797-822
2018

Abstract: Processor manufacturers build increasingly specialized processors to mitigate the effects of the power wall in order to deliver improved performance. Currently, database engines have to be manually optimized for each processor which is a costly and error- prone process. In this paper, we propose concepts to adapt to and to exploit the performance enhancements of modern processors automatically. Our core idea is to create processor-specific code variants and to learn a well-performing code variant for each processor. These code variants leverage various parallelization strategies and apply both generic- and processor-specific code transformations. Our experimental results show that the performance of code variants may diverge up to two orders of magnitude. In order to achieve peak performance, we generate custom code for each processor. We show that our approach finds an efficient custom code variant for multi-core CPUs, GPUs, and MICs.

Behrens, Tobias; Rosenfeld, Viktor; Traub, Jonas; Breß, Sebastian; Markl, Volker
Efficient SIMD Vectorization for Hashing in OpenCL
, page 489-492.
2018

Abstract: Hashing is at the core of many efficient database operators such as hash-based joins and aggregations. Vectorization is a technique that uses Single Instruction Multiple Data (SIMD) instructions to process multiple data elements at once. Applying vectorization to hash tables results in promising speedups for build and probe operations. However, vectorization typically requires intrinsics – low-level APIs in which functions map to processorspecific SIMD instructions. Intrinsics are specific to a processor architecture and result in complex and difficult to maintain code. OpenCL is a parallel programming framework which provides a higher abstraction level than intrinsics and is portable to different processors. Thus, OpenCL avoids processor dependencies, which results in improved code maintainability. In this paper, we add efficient, vectorized hashing primitives to OpenCL. Our results show that OpenCL-based vectorization is competitive to intrinsics on CPUs but not on Xeon Phi coprocessors.

Verbitskiy, Ilya; Thamsen, Lauritz; Renner, Thomas; Kao, Odej
CoBell: Runtime Prediction for Distributed Dataflow Jobs in Shared Clusters
10th IEEE International Conference on Cloud Computing Technology and Science (CloudCom)
2018

Abstract: Low-latency processing of data streams from distributed sensors is becoming increasingly important for a growing number of IoT applications. In these environments sensor data collected at the edge of the network is typically transmitted in a number of hops: from devices to intermediate resources to clusters of cloud resources. Scheduling processing tasks of dataflow jobs on all the resources of these environments can significantly reduce application latencies and network congestion. However, for this schedulers need to take the heterogeneity of processing resources and network topologies into account.This paper examines multiple methods for scheduling distributed dataflow tasks on geo-distributed, heterogeneous resources. For this, we developed an optimization function that incorporates the latencies, bandwidths, and computational resources of heterogeneous topologies. We evaluated the different placement methods in a virtual geo-distributed and heterogeneous environment with an IoT application. Our results show that metaheuristic methods that take service quality metrics into account can find significantly better placements than methods that only take topologies into account, with latencies reduced by almost 50%.

Thamsen, Lauritz; Renner, Thomas; Verbitskiy, Ilya; Kao, Odej
Adaptive Resource Management for Distributed Data Analytics
In Lucio Grandinetti, Seyedeh Leili Mirtaheri, Reza Shahbazian, Thomas Sterling, Vladimir Voevodin (eds.), Advances in Parallel Computing – Big Data and HPC: Ecosystem and Convergence. IOS Press
2018

Abstract: Increasingly large datasets make scalable and distributed data analytics necessary. Frameworks such as Spark and Flink help users in efficiently utilizing cluster resources for their data analytics jobs. It is, however, usually difficult to anticipate the runtime behavior and resource demands of these distributed data analytics jobs. Yet, many resource management decisions would benefit from such information. Addressing this general problem, this chapter presents our vision of adaptive resource management and reviews recent work in this area. The key idea is that workloads should be monitored for trends, patterns, and recurring jobs. These monitoring statistics should be analyzed and used for a cluster resource management calibrated to the actual workload. In this chapter, we motivate and present the idea of adaptive resource management. We also introduce a general system architecture and we review specific adaptive techniques for data placement, resource allocation, and job scheduling in the context of our architecture.

2017

Traub, Jonas; Steenbergen, Nikolaas; Grulich, Philipp; Rabl, Tilmann; Markl, Volker
I²: Interactive Real-Time Visualization for Streaming Data
in Proc. 20th International Conference on Extending Database Technology (EDBT), March 21-24, 2017.
March 2017
Schütt, Kristof T.; Kindermans, Pieter-Jan,; Sauceda, Huziel E.; Chiemela, Stefan; Tkatchenko, Alexandre; Müller, Klaus-Robert
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Neural Information Processing Systems (NIPS)
2017
Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan; Müller, Klaus R.; Tkatchenko, Alexandre
Quantum-chemical insights from deep tensor neural networks
In: Nature Communications 8,
January 2017
Rohrmann, Till; Schelter, Sebastian; Rabl, Tilmann; Markl, Volker
Gilbert: Declarative Sparse Linear Algebra on Massively Parallel Dataflow Systems
in BTW 2017 (pp. 269-288)
March 2017
Renner, Thomas; Müller, Johannes; Thamsen, Lauritz; Kao, Odej
Addressing Hadoop's Small File Problem With an Appendable Archive File Format.
In the Proceedings of the Computing Frontiers Conference (CF).
2017
Montavon, Grégoire; Samek, Wojciech; Müller, Klaus-Robert
Methods for interpreting and understanding deep neural networks
Digital Signal Processing February 2018, Vol. 73,:1-15
2017
Kunft, Andreas; Katsifodimos, Asterios; Schelter, Sebastian; Rabl, Tilmann; Mark, Volker
BlockJoin: Efficient Matrix Partitioning Through Joins
Proceedings of the VLDB Endowment (PVLDB) Volume 10
2017
Kiefer, Martin; Heimel, Max; Breß, Sebastian; Markl, Volker
Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models
Proceedings of the VLDB Endowment (PVLDB) Volume 10
2017
Gupta, P.; Gramatke, A.; Einspanier, R.; Schütte, M.; von Kleist, M.; Sharbati, J.
In silico cytotoxicity assessment on cultured rat intestinal cells deduced from cellular impedance measurement.
Accepted for: Toxicology in Vitro,
2017
Gornitz, N.; Lima, L. A.; Müller, K. R.; Kloft, M.; Nakajima, S.
Support Vector Data Descriptions and K-means Clustering: One Class?
IEEE Transactions on Neural Networks and Learning Systems, Volume: PP, Issue: 99,:1 - 13
2017
Goldsmith, B. R.; Boley, M.; Vreeken, J.; Scheffler, M.; Ghiringhelli, L. M.
Uncovering structure-property relationships of materials by subgroup discovery.
In: New J. Phys. 19: 013031,
2017
Giotsas, V.; Smaragdakis, G.; Feldmann, A.; Berger, A.; Aben, E.
Detecting Peering Infrastructure Outages in the Wild.
In: ACM SIGCOMM
2017
Ghiringhelli, L. M.; Vybiral, J.; Ahmetcik, E.; Ouyang, R.; Levchenko,, S. V.; Draxl, C.; Scheffler, M.
Learning physical descriptors for materials science by compressed sensing.
In: New J. Phys. 19: 023017,
2017
Gelß, P.; Klus, S.; Matera, S.; Schütte, Ch.
Nearest-Neighbor Interaction Systems in the Tensor Train Format.
Accepted for: J. Comp. Physics,
2017
Feldmann, Anja; Hauswirth, M.; Markl, V.
Enabling Wide Area Data Analytics with CDPPs (Collaborative Distributed Processing Pipelines.
In: IEEE Int. Conference on Distributed Computing Systems (IEEE ICDCS), Blue-Sky Ideas / Vison Track,
2017
Deng, D.; Fernandez, R.; Abedjan, Z.; Wang, S.; Stonebraker, S.; Elmagarmid, A.; Ilyas, I.; Madden, S.; Ouzzani, M.; Tang, N.
The Data Civilizer System.
In: CIDR
2017
Conrad, T.; Genzel, M.; Cvetkovic, N.; Wulkow, N.; Leichtle, A.; Vybiral, J.; Kutyniok, G.; Schütte, Ch.
Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data.
In: BMC Bioinformatics, 18(160),
2017
Chmiela, S.; Tkatchenko, A.; Sauceda, H. E.; Poltavsky, I.; Schütt, K. T.; Müller, K.-R.
Machine learning of accurate energy-conserving molecular force fields.
In: Science Advances, 3(5),
2017
Brockherde, F.; Vogt, L.; Li, L.; Tuckerman, M. E.; Burke, K.; Müller, K. R.
Bypassing the Kohn-Sham equations with machine learning
Nature Communications, 8(1), 872.,
2017
Bosse, S.; Maniry, D.; Müller, K. R.; Wiegand, Th.; Samek, w.
Deep neural networks for no-reference and full-reference image quality assessment
IEEE Transactions on Image Processing.
2017
Boley, M.; Goldsmith, B.; Ghiringhelli, L.; Vreeken, J.
Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery.
In: Data Mining and Knowledge Discovery 5(2017),
2017
Boden, Ch.; Spina, A.; Rabl, T.; Markl, V.
Benchmarking Data Flow Systems for Scalable Machine Learning.
In: BeyondMR@SIGMOD
2017
Bergen, E.; Edlich, St.
Post-Debugging in Large Scale Analytic Systems.
In: Datenbanksysteme für Business, Technologie und Web (BTW), Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS) , page 65-72.
2017
Alber, Maximilian; Zimmert, Julian; Dogan, Urun; Kloft, Marius
Distributed optimization of multi-class SVMs
PLOS ONE 12(6): e0178161,
2017
Alber, Maximilian; Kindermans, Pieter-Jan; Schütt, Kristof T.; Müller, Klaus-Robert; Sha, Fei
An Empirical Study on The Properties of Random Bases for Kernel Methods
Advances in Neural Information Processing Systems 30 (NIPS)
2017

2016

Yukawa, Masahiro; Müller, Klaus-Robert
Why Does a Hilbertian Metric Work Efficiently in Online Learning With Kernels?
In: IEEE SIGNAL PROCESSING LETTERS, 2 3(10):1424 - 1428
2016
Xu, Chen; Holzmer, Marcus; Kaul, Manohar; Markl, Volker
Efficient Fault-tolerance for Iterative Graph Processing on Distributed Dataflow Systems
In: 32nd IEEE International Conference on Data Engineering (ICDE)
2016
Vidovic, Marina M.-C.; Görnitz, Nico; Müller, Klaus R; Kloft, Marius
Feature Importance Measure for Non-linear Learning Algorithms
Presented at NIPS 2016 Workshop on Interpretable Machine Learning in Complex Systems,
November 2016
Verbitskiy, Ilya; Thamsen, Lauritz; Kao, Odej
When to Use a Distributed Dataflow Engine: Evaluating the Performance of Apache Flink.
International IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), IEEE
2016
Treder, Matthias S.; Porbadnigk, Anne K.; Forooz, Shahbazi; Müller, Klaus-Robert; Blankertz, Benjamin
The LDA beamformer: Optimal estimation of ERP source time series using linear discriminant analysis
In: NeuroImage, 1 2 9:279 - 291
2016
Thamsen, Lauritz; Verbitskiy, Ilya; Schmdt, Florian; Renner, Thomas; Kao, Odej
Selecting Resources for Distributed Dataflow Systems According to Runtime Targets.
In the Proceddings of IEEE 35th International Performance Computing and Communications Conference (IPCCC).
2016
Thamsen, Lauritz; Renner, Thomas; Kao, Odej
Continuously Improving the Resource Utilization of Iterative Parallel Dataflows.
In the Proceedings of the IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW). Presented at the International Workshop on Big Data and Cloud Performance (DCPerf). IEEE.
2016
Thamsen, Lauritz; Renner, Thomas; Byfeld, Marvin; Paeschke, Markus; Schröder, Daniel; Böhm, Felix
Visually Programming Dataflows for Distributed Data Analytics.
In the Proceddings of IEEE International Conference on Big Data (Big Data).
2016
Schmidtke, Robert; Laubender, Guido; Steinke, Thomas
Big Data Analytics on Cray XC Series DataWarp using Hadoop, Spark and Flink
In: Cray User Group (CUG) 2016 Proceedings
2016
Sannelli, C.; Vidaurre, C.; Müller, K. R.; Blankertz, B.
Ensembles of adaptive spatial filters increase BCI performance: an online evaluation.
In: Journal of neural engineering, 13(4), 046003
2016
Samek, Wojciech; Blythe, Duncan A. J.; Curio, Gabriel; Müller, Klaus-Robert; Blankertz, Benjamin; Nikulin, Vadim V.
Multiscale temporal neural dynamics predict performance in a complex sensorimotor task
In: NeuroImage, 141:291 - 303
2016
Renner, Thomas; Thamsen, Lauritz; Kao, Odej
CoLoc: Distributed Data and Container Colocation for Data-Intensive Applications.
. In the Proceddings of IEEE International Conference on Big Data (Big Data).
2016
Pronobis, W.; Panknin, D.; Kirschnik, J.; Srinivasan, V.; Samek, w.; Markl, V.; Kaul, M.; Müller, K.-R.; Nakajima, S
Sharing Hash Codes for Multiple Purpose.
Pronobis et al., arXiv:1609.03219,
2016
Nakajima, S.; Tomioka, R.; Sugiyama, M.; Babacan, S. D.
Condition for Perfect Dimensionality Recovery by Variational Bayesian PCA.
In: Journal of Machine Learning Research, 16(3): 3757-3811,
2016
Min, B. K.; Dähne, S.; Ahn, M. H.; Noh, Y. K.; Müller, K. R.
Decoding of top-down cognitive processing for SSVEP-controlled BMI.
In: Sci. Rep. 6, 36267,
2016
Lapuschkin, S.; Binder, A.; Montavon, G.; Müller, K. R.; Samek, w.
Analyzing classifiers: fisher vectors and deep neural networks.
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , page 2912-2920.
2016
Lapuschkin, Sebastian; Binder, Alexander; Montavon, Grégoire; Müller, Klaus-Robert; Samek, Wojciech
The LRP Toolbox for Artificial Neural Networks
In: Journal of Machine Learning Research, 17(114):1 -5
2016
Krause, S.; Xu, F.; Uszkoreit, H.; Weißenborn, D.
Event Linking with Sentential Features from Convolutional Neural Networks.
In: Proceedings of CoNLL, Association for Computational Linguistics
2016
Krause, Sebastian; Hennig, Leonhard; Moro, Andrea; Weissenborn, Dirk; Xu, Feiyu; Uszkoreit, Hans; Navigli, Roberto
Sar-graphs: A language resource connecting linguistic knowledge with semantic relations from knowledge graphs.
In: K, Journal of Web Semantics: Science, Services and Agents on the World Wide Web,
2016
Koltai, P.; Ciccotti, G.; Schütte, Ch.
On Markov state models for non-equilibrium molecular dynamics.
In: The Journal of Chemical Physics 145, 174103,
2016

Note: (Editors' Choice of The Journal of Chemical Physics)

Kindermans, Pieter-Jan; Schütt, Kristof T.; Müller, Klaus R.; Dähne, Sven
Investigating the influence of noise and distractors on the interpretation of neural networks
Presented at NIPS 2016 Workshop on Interpretable Machine Learning in Complex Systems,
November 2016
Jugel, U.; Jerzak, Z.; Markl, V.
Big data on a few pixels.
In: IEEE International Conference on Big Data (Big Data) , page 895-900.
2016
Jugel, U.; Jerzak, Z.; Hackenbroich, G.; Markl, V.
VDDA: automatic visualization-driven data aggregation in relational databases
In: VLDB J. 25(1): 53-77,
2016

Note: (VLDB Best Paper Award)

Höhne, Johannes; Bartz, Daniel; Hebart, Martin N.; Müller, Klaus-Robert; Blankertz, Benjamin
Analyzing neuroimaging data with subclasses: a shrinkage approach
NeuroImage, 1 2 4::740 - 751
2016
Herb, Tobias; Thamsen, Lauritz; Renner, Thomas; Kao, Odej
Aura: A Flexible Dataflow Engine for Scalable Data Processing.
in Andreas Knüpfer, Tobias Hilbrich, Christoph Niethammer, José Gracia, Wolfgang E. Nagel, Michael M. Resch (eds.), Tools for High Performance Computing 2015. Springer.
2016
Hennig, L.; Thomas, Ph.; Ai, R.; Kirschnick, J.; Wang, H.; Pannier, J.; Zimmermann, N.; Schmeier, S.; Xu, F.; Ostwald, J.; Uszkoreit, H.
Real-Time Discovery and Geospatial Visualization of Mobility and Industry Events from Large-Scale, Heterogeneous Data Streams.
In: Proceedings of ACL
2016
Gül, S.; Meyer, J. T.; Hellge, C.; Schierl, Th.; Samek, w.
Hybrid Video Object Tracking in H.265/HEVC Video Streams.
In: Proceedings of the International Workshop on Multimedia Signal Processing (MMSP), 1-5
2016
Ghiringhelli, L. M.; Carbogno, C.; Levchenko, S.; Mohamed, F.; Huhs, G.; Lueders, M.; Oliveira, M.; Scheffler,, M.
Towards a Common Format for Computational Materials Science Data.
Published as: "?k Scientific Highlight of the Month", 131,
2016
Gabryszak, Aleksandra; Krause, Sebastian; Hennig, Leonhard; Xu, Feiyu; Uszkoreit, Hans
Relation- and phrase-level linking of FrameNet with Sar-graphs.
In: The 10th International Conference on Language Resources and Evaluation, LREC
2016
Fuerst, Carlo; Schmid, Stefan; Suresh, Lalith; Costa, Paolo
Kraken: Online and Elastic Resource Reservations for Multitenant Datacenters.
Proc. 35th IEEE Conference on Computer Communications (INFOCOM),,
2016
Fuerst, Carlo; Schmid, Stefan; Suresh, Lalith; Costa, Paolo
Kraken: Online and Elastic Resource Reservations for Multitenant Datacenters.
Proc. 35th IEEE Conference on Computer Communications (INFOCOM).
2016
Fajerski, J.; Noack, M.; Reinefeld, A.; Schintke, F.; Steinke, Th.
Fast In-Memory Checkpointing with POSIX API for Legacy Exascale-Applications.
H.J. Bungartz, P. Neumann, W.E. Nagel (Eds): Software for Exascale Computing, 2013-2015, Springer Lecture Notes in Computational Science and Engineering, 113: 427-441,
2016
Eichler, Kathrin; Xu, Feiyu; Uszkoreit, Hans; Hennig, Leonhard; Krause, Sebastian
TEG-REP: A corpus of Textual Entailment Graphs based on Relation Extraction Patterns.
In: The 10th International Conference on Language Resources and Evaluation, LREC
2016
Bosse, S.; Maniry, D.; Wiegand, Th.; Samek, w.
A Deep Neural Network for Image Quality Assessment.
In: Proceedings of the IEEE International Conference on Image Processing (ICIP) , page 3773-77.
2016
Bosse, S.; Maniry, D.; Müller, K.-R.; Wiegand, Th.; Samek, w.
Neural Network-Based Full-Reference Image Quality Assessment.
In: Proceedings of the Picture Coding Symposium (PCS), 1-5 , page 3773-77.
2016
Bosse, S.; Chen, Q.; Siekmann, M.; Samek, w.; Wiegand, Th.
Shearlet-based reduced reference image quality assessment.
In: IEEE International Conference on Image Processing (ICIP)
2016
Blythe, Duncan A. J.; Nikulin, Vadim V.; Müller, Klaus-Robert
Robust Statistical Detection of Power-Law Cross-Correlation
Scientific reports, 6:27089
2016
Binder, Alexander; Bach, Sebastian; Montavon, Grégoire; Müller, Klaus-Robert; Samek, Wojciech
Layer-Wise Relevance Propagation for Deep Neural Network Architectures
In: Information Science and Applications (ICISA), 9 13 - 922., :913 - 922
2016
Bauer, Alexander; Nakajima, Shinichi; Müller, K. R.
Efficient Exact Inference With Loss Augmented Objective in Structured Learning.
IEEE transactions on neural networks and learning systems Volume PP, no. 99 , page 1 - 14.
August 2016
Arbabzadah, Farhad; Montavon, Grégoire; Müller, Klaus-Robert; Samek, Wojciech
Identifying individual facial expressions by deconstructing a neural network
In: German Conference on Pattern Recognition , page 344 - 354.
Springer International Publishing
2016
Alexandrov, A.; Salzmann, A.; Krastev, G.; Katsifodimos, A.; Markl, V.
Emma in Action: Declarative Dataflows for Scalable Data Analysis.
In: SIGMOD Volume Record 45(1) , page 51-58.
2016
Alber, Maximilian; Zimmert, Julian; Dogan, Urun; Kloft, Marius
Distributed Optimization of Multi-Class SVMs
Extreme Classification NIPS 2016 Workshop,
November 2016
Abedjan, Z.:; Chu,, X.; Deng, D.; Fernandez, R.; Ilyas, R.; Ouzzani, I.; Papotti, M.; Stonebraker, M.; Tang, N.
Detecting Data Errors: Where are we and What needs to be done?
PVLDB 9(12):993-1004,
2016

2015

Weißenborn, Dirk; Hennig, Leonhard; Xu, Feiyu; Uszkoreit, Hans
Multi-objective Optimization for the Joint Disambiguation of Nouns and Named Entities
In: 53nd Annual Meeting of the Association for Computational Linguistics, ACL , page 596-605.
2015
Weißenborn, Dirk; Xu, Feiyu; Uszkoreit, Hans
DFKI: Multi-objective Optimization for the Joint Disambiguation of Entities and Nouns & Deep Verb Sense Disambiguation
In: 9th International Workshop on Semantic Evaluations (SemVal2015)
2015
Rosenfeld, Viktor; Heimel, Max; Viebig, Christoph; Markl, Volker
The Operator Variant Selection Problem on Heterogeneous Hardware,
In: ADMS@VLDB, 2015. , page 1-12.
2015
Renner, Thomas; Thamsen, Lauritz; Kao, Odej
Network-Aware Resource Management for Scalable Data Analytics Frameworks.
In Proceedings of the First Workshop on Data-Centric Infrastructure for Big Data Science (DIBS) 2015, co-located with the 2015 IEEE International Conference on BigData (BigData). IEEE.
2015
Reinefeld, A.; Schütt, Ch.; Döbbelin, R.
Fast Memory Access for Data Intensive Applications.
In: Forschung im HLRN-Verbund 2015, Konrad-Zuse-Zentrum für Informationstechnik Berlin (Hrsg.): 304-305,
2015
Pujol, Enric; Hohlfeld, Oliver; Feldmann, Anja
Annoyed Users: Ads and Ad-Block Usage in the Wild.
In Proceedings of the 2015 Internet Measurement Conference (IMC '15), ACM, New York, NY, USA, :93-106
2015
Nakajima, S.; Tomioka, R.; Sugiyama, M.; Babacan, S.D.
Condition for Perfect Dimensionality Recovery by Variational Bayesian PCA,
Journal of Machine Learning Research, vol.16, pp.3757-3811
2015
Lohrmann, Björn; Janacik, Peter; Kao, Odej
Elastic Stream Processing with Latency Guarantees
In: IEEE 35th International Conference on Distributed Computing Systems (ICDCS) , page 399-410.
July 2015
Li, Hong; Krause, Sebastian; Xu, Feiyu; Moro, Andrea; Uszkoreit, Hans; Navigli, Roberto
Improvement of n-ary Relation Extraction by Adding Lexical Semantics to Distant-Supervision Rule Learning.
In: International Conference on Agents and Artificial Intelligence (ICAART)
2015
Krause, Sebastian; Hennig, Leonhard; Gabryszak, Aleksandra; Xu, Feiyu; Uszkoreit, Hans
Sar-graphs: A Linked Linguistic Knowledge Resource Connecting Facts with Language.
Workshop on Linked Data in Linguistics: Resources and Applications, co-located with the Annual Meeting of the Association for Computational Linguistics (LDL @ ACL),
2015
Krause, Sebastian; Alfonseca, Enrique; Filippova, Katja; Pighin, Daniele
Learning a Distributed Representation for Event Patterns.
In: IIdest. Conference of the North American Chapter of the ACL – Human Language Technologies (NAACL HLT)
2015
Herb, Tobias; Renner, Thomas; Kao, Odej
Aura: A Flexible Dataflow Engine for Scalable Data Processing.
In: Tools for High Performance Computing: 117-126,
2015
Hennig, Leonhard; Li, Hong; Krause, Sebastian; Xu, Feiyu; Uszkoreit, Hans
A Web-based Collaborative Evaluation Tool for Automatically Learned Relation Extraction Patterns.
Annual Meeting of the Association for Computational Linguistics (ACL), System Demonstrations,
2015
Hansen, S.T.; Winkler, I.; Hansen, L.K.; Müller, K.-R.; Dähne, S.
Fusing simultaneous EEG and fMRI using functional and anatomical information,
In International Workshop on Pattern Recognition in Neuroimaging, 2015. IEEE.
2015
Ghiringhelli, L.M.; Vybiral, J.; Levchenko, S.V.; Draxl, C.; Scheffler, M.
Big Data of Materials Science: Critical Role of the Descriptor,
Phys. Rev. Lett. 114 (10),
March 2015
Dudoladov, Sergey; Katsifodimos, Asterios; Xu, Chen; Ewen, Stephan; Markl, Volker; Schelter, Sebastian; Tzoumas, Kostas
Optimistic Recovery for Iterative Dataflows in Action.
In Proceedings of the 2015 ACM SIGMOD International conference on Management of Data (SIGMOD '15).
2015
Djurdjevac, C.; Banisch, R.; Schütte, Ch.
Modularity of Directed Networks: Cycle Decomposition Approach.
In: Journal of Computational Dynamics 2(1): 1-24,
2015
Dähne, S.; Goltz, D.; Gundlach, C.; Mehnert, J.; Villringer, A.; Haufe, S.; Müller, K.-R.
Multivariate Fusion of EEG oscillations and fMRI using multimodal Source Power Co-modulation (mSPoC),
In Annual Meeting of the Organization for Human Brain Mapping (OHBM).
2015
Carbone, P.; Katsifodimos, A.; Ewen, St.; Markl, V.; Haridi, S.; Tzoumas, K.
Apache Flink™: Stream and Batch Processing in a Single Engine.
In: IEEE Data Eng. Bull. 38(4),
2015
Bhattacharya, S.; Sonin, B.; Jumonville, C.J.; Ghiringhelli, L.M.; Marom, N.
Computational Design of Nanoclusters by Property-Based Genetic Algorithms: Tuning the Electronic Properties of (TiO2)n Clusters.
In: Phys. Rev. B 91, 241115.,
June 2015
Bergen, E.; Edlich, St.
Towards a Taxonomy for Error Management in Big Data Analytics Systems.
n: Proceedings of the Research Day 2015, Beuth University of Applied Sciences Berlin, :80-83
2015
Bauer, Alexander; Braun, Mikio; Müller, Klaus-Robert
Accurate Max-Margin Training for Parsing with Context-Free Grammars,
IEEE Transactions on Neural Networks and Learning Systems. Volume 28(1) , page 44-45.
2015
Bach, S.; Binder, A.; Montavon, G.; Klauschen, F.; Müller, K.-R.; Samek, w.
On Pixel-wise Explanations for Non-Linear Classifier Decisions by Layer-wise Relevance Propagation,
In: PLOS ONE 10(7),,
2015
Alexandrov, Alexander; Kunft, Andreas; Katsifodimos, Asterios; Schüler, Felix; Thamsen, Lauritz; Kao, Odej; Herb, Tobias; Markl, Volker
Implicit Parallelism through Deep Language Embedding
In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (SIGMOD '15).
2015
Ai, Renlong; Krause, Sebastian; Kasper, Walter; Xu, Feiyu; Uszkoreit, Hans
Semi-automatic Generation of Multiple-Choice Tests from Mentions of Semantic Relations.
Workshop on Natural Language Processing Techniques for Educational Applications at the Annual Meeting of the Association for Computational Linguistics (NLP-TEA @ ACL),
2015

2014

Nakajima, S.; Sato, I.; Sugiyama, M.; Watanabe, K.; Kobayashi, H.
Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity than MAP,
Twenty-Eighth Annual Conference on Neural Information Processing Systems (NIPS2014).
2014
Bauer, A.; Gornitz, N.; Biegler, F.; Müller, K.-R.; Kloft, M.
Efficient algorithms for exact inference in sequence labeling SVMs.
In: IEEE transactions on neural networks and learning systems 25(5): 870-881,
2014