http://dl.gi.de/handle/20.500.12116/36620 2026-07-23T16:55:56Z http://dl.gi.de/handle/20.500.12116/36627 OriginStamp: A blockchain-backed system for decentralized trusted timestamping Hepp, Thomas; Schoenhals, Alexander; Gondek, Christopher; Gipp, Bela Currently, timestamps are certified by central timestamping authorities, which have disadvantages of centralization. The concept of the decentralized trusted timestamping (DTT) was developed by Gipp et al. to address these drawbacks. The paper provides insights into the architecture and implementation of a decentralized timestamp service taking the integration of multiple blockchain types into account. Furthermore, the components are introduced and the versatile application scenarios are presented. A future direction of research is the evaluation of blockchain technology and their suitability for timestamping. 2018-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/36629 Brokerless inter-domain virtual network embedding: A blockchain-based approach Rizk, Amr; Bisbal, Jordi; Bergsträßer, Sonja; Steinmetz, Ralf The blockchain technology enables entities to query and alter information without trusting a middle party while providing a secure data storage in a decentralized manner. In this paper, we focus on an IT data supply chain scenario, where multiple actors negotiate a tenancy agreement for virtualized network resources. This process consists of service providers (SPs) requesting the embedding of specified virtual networks across multiple infrastructure providers (InPs). Since InPs are typically not willing to disclose detailed internal network information, this is a major deal breaker that hampers the efficiency of the service negotiation process. After reviewing the related work on centralized and decentralized virtual network embedding (VNE) approaches, we briefly discuss the reasons for a blockchain approach for this problem. Our approach comprises of a brokerless blockchain based system, that uses smart contracts and a VN partitioning algorithm based on the Vickrey auction model. Finally, we investigate the feasibility of our approach, by first analyzing the behavior of the introduced auction model in adverse conditions and by secondly evaluating the blockchain performance given different consensus protocols. 2018-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/36628 On-chain vs. off-chain storage for supply- and blockchain integration Hepp, Thomas; Sharinghousen, Matthew; Ehret, Philip; Schoenhals, Alexander; Gipp, Bela Supply chains are the basis of most everyday life products. Both data integrity and authenticity of related information have severe implications for quality and safety of end-products. Hence, tamper-proof storage is necessary that prevents unauthorized modifications. We examine peer-reviewed blockchain technologies according to four criteria relevant to supply chains: On-chain storage, off-chain storage, verification cost and secure data sharing. Our evaluation yields an overview of concepts for modeling supply chain processes and points out that on-chain storage is currently not practical. 2018-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/36631 The Berlin Big Data Center (BBDC) Boden, Christoph; Rabl, Tilmann; Markl, Volker The last decade has been characterized by the collection and availability of unprecedented amounts of data due to rapidly decreasing storage costs and the omnipresence of sensors and data-producing global online-services. In order to process and analyze this data deluge, novel distributed data processing systems resting on the paradigm of data flow such as Apache Hadoop, Apache Spark, or Apache Flink were built and have been scaled to tens of thousands of machines. However, writing efficient implementations of data analysis programs on these systems requires a deep understanding of systems programming, prohibiting large groups of data scientists and analysts from efficiently using this technology. In this article, we present some of the main achievements of the research carried out by the Berlin Big Data Cente (BBDC). We introduce the two domain-specific languages Emma and LARA, which are deeply embedded in Scala and enable declarative specification and the automatic parallelization of data analysis programs, the PEEL Framework for transparent and reproducible benchmark experiments of distributed data processing systems, approaches to foster the interpretability of machine learning models and finally provide an overview of the challenges to be addressed in the second phase of the BBDC. 2018-01-01T00:00:00Z