TY - GEN A1 - Welearegai, Gebrehiwet B. A1 - Schlueter, Max A1 - Hammer, Christian T1 - Static security evaluation of an industrial web application T2 - Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing N2 - JavaScript is the most popular programming language for web applications. Static analysis of JavaScript applications is highly challenging due to its dynamic language constructs and event-driven asynchronous executions, which also give rise to many security-related bugs. Several static analysis tools to detect such bugs exist, however, research has not yet reported much on the precision and scalability trade-off of these analyzers. As a further obstacle, JavaScript programs structured in Node. js modules need to be collected for analysis, but existing bundlers are either specific to their respective analysis tools or not particularly suitable for static analysis. KW - JavaScript KW - WALA KW - SAFE KW - comparison Y1 - 2019 SN - 978-1-4503-5933-7 U6 - https://doi.org/10.1145/3297280.3297471 SP - 1952 EP - 1961 PB - Association for Computing Machinery CY - New York ER - TY - GEN A1 - Ullrich, Andre A1 - Enke, Judith A1 - Teichmann, Malte A1 - Kress, Antonio A1 - Gronau, Norbert T1 - Audit - and then what? BT - a roadmap for digitization of learning factories T2 - Procedia Manufacturing N2 - Current trends such as digital transformation, Internet of Things, or Industry 4.0 are challenging the majority of learning factories. Regardless of whether a conventional learning factory, a model factory, or a digital learning factory, traditional approaches such as the monotonous execution of specific instructions don‘t suffice the learner’s needs, market requirements as well as especially current technological developments. Contemporary teaching environments need a clear strategy, a road to follow for being able to successfully cope with the changes and develop towards digitized learning factories. This demand driven necessity of transformation leads to another obstacle: Assessing the status quo and developing and implementing adequate action plans. Within this paper, details of a maturity-based audit of the hybrid learning factory in the Research and Application Centre Industry 4.0 and a thereof derived roadmap for the digitization of a learning factory are presented. KW - Audit KW - Digitization KW - Learning Factory KW - Roadmap Y1 - 2019 U6 - https://doi.org/10.1016/j.promfg.2019.03.025 SN - 2351-9789 VL - 31 SP - 162 EP - 168 PB - Elsevier CY - Amsterdam ER - TY - GEN A1 - Torkura, Kennedy A. A1 - Sukmana, Muhammad Ihsan Haikal A1 - Strauss, Tim A1 - Graupner, Hendrik A1 - Cheng, Feng A1 - Meinel, Christoph T1 - CSBAuditor BT - proactive security risk analysis for cloud storage broker systems T2 - 17th International Symposium on Network Computing and Applications (NCA) N2 - Cloud Storage Brokers (CSB) provide seamless and concurrent access to multiple Cloud Storage Services (CSS) while abstracting cloud complexities from end-users. However, this multi-cloud strategy faces several security challenges including enlarged attack surfaces, malicious insider threats, security complexities due to integration of disparate components and API interoperability issues. Novel security approaches are imperative to tackle these security issues. Therefore, this paper proposes CSBAuditor, a novel cloud security system that continuously audits CSB resources, to detect malicious activities and unauthorized changes e.g. bucket policy misconfigurations, and remediates these anomalies. The cloud state is maintained via a continuous snapshotting mechanism thereby ensuring fault tolerance. We adopt the principles of chaos engineering by integrating Broker Monkey, a component that continuously injects failure into our reference CSB system, Cloud RAID. Hence, CSBAuditor is continuously tested for efficiency i.e. its ability to detect the changes injected by Broker Monkey. CSBAuditor employs security metrics for risk analysis by computing severity scores for detected vulnerabilities using the Common Configuration Scoring System, thereby overcoming the limitation of insufficient security metrics in existing cloud auditing schemes. CSBAuditor has been tested using various strategies including chaos engineering failure injection strategies. Our experimental evaluation validates the efficiency of our approach against the aforementioned security issues with a detection and recovery rate of over 96 %. KW - Cloud-Security KW - Cloud Audit KW - Security Metrics KW - Security Risk Assessment KW - Secure Configuration Y1 - 2018 SN - 978-1-5386-7659-2 U6 - https://doi.org/10.1109/NCA.2018.8548329 PB - IEEE CY - New York ER - TY - GEN A1 - Torkura, Kennedy A. A1 - Sukmana, Muhammad Ihsan Haikal A1 - Meinig, Michael A1 - Kayem, Anne V. D. M. A1 - Cheng, Feng A1 - Meinel, Christoph A1 - Graupner, Hendrik T1 - Securing cloud storage brokerage systems through threat models T2 - Proceedings IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA) N2 - Cloud storage brokerage is an abstraction aimed at providing value-added services. However, Cloud Service Brokers are challenged by several security issues including enlarged attack surfaces due to integration of disparate components and API interoperability issues. Therefore, appropriate security risk assessment methods are required to identify and evaluate these security issues, and examine the efficiency of countermeasures. A possible approach for satisfying these requirements is employment of threat modeling concepts, which have been successfully applied in traditional paradigms. In this work, we employ threat models including attack trees, attack graphs and Data Flow Diagrams against a Cloud Service Broker (CloudRAID) and analyze these security threats and risks. Furthermore, we propose an innovative technique for combining Common Vulnerability Scoring System (CVSS) and Common Configuration Scoring System (CCSS) base scores in probabilistic attack graphs to cater for configuration-based vulnerabilities which are typically leveraged for attacking cloud storage systems. This approach is necessary since existing schemes do not provide sufficient security metrics, which are imperatives for comprehensive risk assessments. We demonstrate the efficiency of our proposal by devising CCSS base scores for two common attacks against cloud storage: Cloud Storage Enumeration Attack and Cloud Storage Exploitation Attack. These metrics are then used in Attack Graph Metric-based risk assessment. Our experimental evaluation shows that our approach caters for the aforementioned gaps and provides efficient security hardening options. Therefore, our proposals can be employed to improve cloud security. KW - Cloud-Security KW - Threat Models KW - Security Metrics KW - Security Risk Assessment KW - Secure Configuration Y1 - 2018 SN - 978-1-5386-2195-0 U6 - https://doi.org/10.1109/AINA.2018.00114 SN - 1550-445X SP - 759 EP - 768 PB - IEEE CY - New York ER - TY - GEN A1 - Torkura, Kennedy A. A1 - Sukmana, Muhammad Ihsan Haikal A1 - Kayem, Anne V. D. M. A1 - Cheng, Feng A1 - Meinel, Christoph T1 - A cyber risk based moving target defense mechanism for microservice architectures T2 - IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom) N2 - Microservice Architectures (MSA) structure applications as a collection of loosely coupled services that implement business capabilities. The key advantages of MSA include inherent support for continuous deployment of large complex applications, agility and enhanced productivity. However, studies indicate that most MSA are homogeneous, and introduce shared vulnerabilites, thus vulnerable to multi-step attacks, which are economics-of-scale incentives to attackers. In this paper, we address the issue of shared vulnerabilities in microservices with a novel solution based on the concept of Moving Target Defenses (MTD). Our mechanism works by performing risk analysis against microservices to detect and prioritize vulnerabilities. Thereafter, security risk-oriented software diversification is employed, guided by a defined diversification index. The diversification is performed at runtime, leveraging both model and template based automatic code generation techniques to automatically transform programming languages and container images of the microservices. Consequently, the microservices attack surfaces are altered thereby introducing uncertainty for attackers while reducing the attackability of the microservices. Our experiments demonstrate the efficiency of our solution, with an average success rate of over 70% attack surface randomization. KW - Security Risk Assessment KW - Security Metrics KW - Moving Target Defense KW - Microservices Security KW - Application Container Security Y1 - 2018 SN - 978-1-7281-1141-4 U6 - https://doi.org/10.1109/BDCloud.2018.00137 SN - 2158-9178 SP - 932 EP - 939 PB - Institute of Electrical and Electronics Engineers CY - Los Alamitos ER - TY - GEN A1 - Teusner, Ralf A1 - Matthies, Christoph A1 - Staubitz, Thomas T1 - What Stays in Mind? BT - Retention Rates in Programming MOOCs T2 - IEEE Frontiers in Education Conference (FIE) Y1 - 2018 SN - 978-1-5386-1174-6 U6 - https://doi.org/10.1109/FIE.2018.8658890 SN - 0190-5848 PB - IEEE CY - New York ER - TY - GEN A1 - Teichmann, Malte A1 - Ullrich, Andre A1 - Gronau, Norbert T1 - Subject-oriented learning BT - a new perspective for vocational training in learning factories T2 - Procedia Manufacturing N2 - The transformation to a digitized company changes not only the work but also social context for the employees and requires inter alia new knowledge and skills from them. Additionally, individual action problems arise. This contribution proposes the subject-oriented learning theory, in which the employees´ action problems are the starting point of training activities in learning factories. In this contribution, the subject-oriented learning theory is exemplified and respective advantages for vocational training in learning factories are pointed out both theoretically and practically. Thereby, especially the individual action problems of learners and the infrastructure are emphasized as starting point for learning processes and competence development. KW - Subject-oriented learning KW - action problems KW - vocational training KW - learning factories Y1 - 2019 U6 - https://doi.org/10.1016/j.promfg.2019.03.012 SN - 2351-9789 VL - 31 SP - 72 EP - 78 PB - Elsevier CY - Amsterdam ER - TY - GEN A1 - Tala, Mahdi A1 - Schrape, Oliver A1 - Krstić, Miloš A1 - Bertozzi, Davide T1 - Exploring the Performance-Energy Optimization Space of a Bridge Between 3D-Stacked Electronic and Optical Networks-on-Chip T2 - XXXIII Conference on Design of Circuits and Integrated Systems (DCIS) N2 - The relentless improvement of silicon photonics is making optical interconnects and networks appealing for use in miniaturized systems, where electrical interconnects cannot keep up with the growing levels of core integration due to bandwidth density and power efficiency limitations. At the same time, solutions such as 3D stacking or 2.5D integration open the door to a fully dedicated process optimization for the photonic die. However, an architecture-level integration challenge arises between the electronic network and the optical one in such tightly-integrated parallel systems. It consists of adapting signaling rates, matching the different levels of communication parallelism, handling cross-domain flow control, addressing re-synchronization concerns, and avoiding protocol-dependent deadlock. The associated energy and performance overhead may offset the inherent benefits of the emerging technology itself. This paper explores a hybrid CMOS-ECL bridge architecture between 3D-stacked technology-heterogeneous networks-on-chip (NoCs). The different ways of overcoming the serialization challenge (i.e., through an improvement of the signaling rate and/or through space-/wavelength division multiplexing options) give rise to a configuration space that the paper explores, in search for the most energy-efficient configuration for high-performance. Y1 - 2018 SN - 978-1-7281-0171-2 U6 - https://doi.org/10.1109/DCIS.2018.8681461 SN - 2471-6170 SN - 2640-5563 PB - IEEE CY - New York ER - TY - GEN A1 - Sukmana, Muhammad Ihsan Haikal A1 - Torkura, Kennedy A. A1 - Cheng, Feng A1 - Meinel, Christoph A1 - Graupner, Hendrik T1 - Unified logging system for monitoring multiple cloud storage providers in cloud storage broker T2 - 32ND International Conference on Information Networking (ICOIN) N2 - With the increasing demand for personal and enterprise data storage service, Cloud Storage Broker (CSB) provides cloud storage service using multiple Cloud Service Providers (CSPs) with guaranteed Quality of Service (QoS), such as data availability and security. However monitoring cloud storage usage in multiple CSPs has become a challenge for CSB due to lack of standardized logging format for cloud services that causes each CSP to implement its own format. In this paper we propose a unified logging system that can be used by CSB to monitor cloud storage usage across multiple CSPs. We gather cloud storage log files from three different CSPs and normalise these into our proposed log format that can be used for further analysis process. We show that our work enables a coherent view suitable for data navigation, monitoring, and analytics. KW - Unified logging system KW - Cloud Service Provider KW - cloud monitoring KW - data integration KW - security analytics Y1 - 2018 SN - 978-1-5386-2290-2 U6 - https://doi.org/10.1109/ICOIN.2018.8343081 SP - 44 EP - 49 PB - IEEE CY - New York ER - TY - GEN A1 - Staubitz, Thomas A1 - Teusner, Ralf A1 - Meinel, Christoph T1 - MOOCs in Secondary Education BT - Experiments and Observations from German Classrooms T2 - 2019 IEEE Global Engineering Education Conference (EDUCON) N2 - Computer science education in German schools is often less than optimal. It is only mandatory in a few of the federal states and there is a lack of qualified teachers. As a MOOC (Massive Open Online Course) provider with a German background, we developed the idea to implement a MOOC addressing pupils in secondary schools to fill this gap. The course targeted high school pupils and enabled them to learn the Python programming language. In 2014, we successfully conducted the first iteration of this MOOC with more than 7000 participants. However, the share of pupils in the course was not quite satisfactory. So we conducted several workshops with teachers to find out why they had not used the course to the extent that we had imagined. The paper at hand explores and discusses the steps we have taken in the following years as a result of these workshops. KW - MOOC KW - Secondary Education KW - School KW - Teamwork KW - K-12 KW - Programming course KW - Java KW - Python Y1 - 2019 SN - 978-1-5386-9506-7 U6 - https://doi.org/10.1109/EDUCON.2019.8725138 SN - 2165-9567 SP - 173 EP - 182 PB - IEEE CY - New York ER - TY - GEN A1 - Staubitz, Thomas A1 - Meinel, Christoph T1 - Graded Team Assignments in MOOCs BT - Effects of Team Composition and Further Factors on Team Dropout Rates and Performance T2 - SCALE N2 - The ability to work in teams is an important skill in today's work environments. In MOOCs, however, team work, team tasks, and graded team-based assignments play only a marginal role. To close this gap, we have been exploring ways to integrate graded team-based assignments in MOOCs. Some goals of our work are to determine simple criteria to match teams in a volatile environment and to enable a frictionless online collaboration for the participants within our MOOC platform. The high dropout rates in MOOCs pose particular challenges for team work in this context. By now, we have conducted 15 MOOCs containing graded team-based assignments in a variety of topics. The paper at hand presents a study that aims to establish a solid understanding of the participants in the team tasks. Furthermore, we attempt to determine which team compositions are particularly successful. Finally, we examine how several modifications to our platform's collaborative toolset have affected the dropout rates and performance of the teams. KW - Teamwork KW - MOOCs KW - Team-based Learning KW - Team Assessment KW - Peer Assessment KW - Project-based learning Y1 - 2019 SN - 978-1-4503-6804-9 U6 - https://doi.org/10.1145/3330430.3333619 PB - Association for Computing Machinery CY - New York ER - TY - GEN A1 - Staubitz, Thomas A1 - Meinel, Christoph T1 - Collaborative Learning in MOOCs - Approaches and Experiments T2 - 2018 IEEE Frontiers in Education (FIE) Conference N2 - This Research-to-Practice paper examines the practical application of various forms of collaborative learning in MOOCs. Since 2012, about 60 MOOCs in the wider context of Information Technology and Computer Science have been conducted on our self-developed MOOC platform. The platform is also used by several customers, who either run their own platform instances or use our white label platform. We, as well as some of our partners, have experimented with different approaches in collaborative learning in these courses. Based on the results of early experiments, surveys amongst our participants, and requests by our business partners we have integrated several options to offer forms of collaborative learning to the system. The results of our experiments are directly fed back to the platform development, allowing to fine tune existing and to add new tools where necessary. In the paper at hand, we discuss the benefits and disadvantages of decisions in the design of a MOOC with regard to the various forms of collaborative learning. While the focus of the paper at hand is on forms of large group collaboration, two types of small group collaboration on our platforms are briefly introduced. KW - MOOC KW - Collaborative learning KW - Peer assessment KW - Team based assignment KW - Teamwork Y1 - 2018 SN - 978-1-5386-1174-6 SN - 0190-5848 PB - IEEE CY - New York ER - TY - GEN A1 - Sianipar, Johannes Harungguan A1 - Sukmana, Muhammad Ihsan Haikal A1 - Meinel, Christoph T1 - Moving sensitive data against live memory dumping, spectre and meltdown attacks T2 - 26th International Conference on Systems Engineering (ICSEng) N2 - The emergence of cloud computing allows users to easily host their Virtual Machines with no up-front investment and the guarantee of always available anytime anywhere. But with the Virtual Machine (VM) is hosted outside of user's premise, the user loses the physical control of the VM as it could be running on untrusted host machines in the cloud. Malicious host administrator could launch live memory dumping, Spectre, or Meltdown attacks in order to extract sensitive information from the VM's memory, e.g. passwords or cryptographic keys of applications running in the VM. In this paper, inspired by the moving target defense (MTD) scheme, we propose a novel approach to increase the security of application's sensitive data in the VM by continuously moving the sensitive data among several memory allocations (blocks) in Random Access Memory (RAM). A movement function is added into the application source code in order for the function to be running concurrently with the application's main function. Our approach could reduce the possibility of VM's sensitive data in the memory to be leaked into memory dump file by 2 5% and secure the sensitive data from Spectre and Meltdown attacks. Our approach's overhead depends on the number and the size of the sensitive data. KW - Virtual Machine KW - Memory Dumping KW - Security KW - Cloud Computing KW - Spectre KW - Meltdown Y1 - 2019 SN - 978-1-5386-7834-3 PB - IEEE CY - New York ER - TY - GEN A1 - Shaabani, Nuhad A1 - Meinel, Christoph T1 - Improving the efficiency of inclusion dependency detection T2 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management N2 - The detection of all inclusion dependencies (INDs) in an unknown dataset is at the core of any data profiling effort. Apart from the discovery of foreign key relationships, INDs can help perform data integration, integrity checking, schema (re-)design, and query optimization. With the advent of Big Data, the demand increases for efficient INDs discovery algorithms that can scale with the input data size. To this end, we propose S-INDD++ as a scalable system for detecting unary INDs in large datasets. S-INDD++ applies a new stepwise partitioning technique that helps discard a large number of attributes in early phases of the detection by processing the first partitions of smaller sizes. S-INDD++ also extends the concept of the attribute clustering to decide which attributes to be discarded based on the clustering result of each partition. Moreover, in contrast to the state-of-the-art, S-INDD++ does not require the partition to fit into the main memory-which is a highly appreciable property in the face of the ever growing datasets. We conducted an exhaustive evaluation of S-INDD++ by applying it to large datasets with thousands attributes and more than 266 million tuples. The results show the high superiority of S-INDD++ over the state-of-the-art. S-INDD++ reduced up to 50 % of the runtime in comparison with BINDER, and up to 98 % in comparison with S-INDD. KW - Algorithms KW - Data partitioning KW - Data profiling KW - Data mining Y1 - 2018 SN - 978-1-4503-6014-2 U6 - https://doi.org/10.1145/3269206.3271724 SP - 207 EP - 216 PB - Association for Computing Machinery CY - New York ER - TY - GEN A1 - Schlosser, Rainer A1 - Kossmann, Jan A1 - Boissier, Martin T1 - Efficient Scalable Multi-Attribute Index Selection Using Recursive Strategies T2 - 2019 IEEE 35th International Conference on Data Engineering (ICDE) N2 - An efficient selection of indexes is indispensable for database performance. For large problem instances with hundreds of tables, existing approaches are not suitable: They either exhibit prohibitive runtimes or yield far from optimal index configurations by strongly limiting the set of index candidates or not handling index interaction explicitly. We introduce a novel recursive strategy that does not exclude index candidates in advance and effectively accounts for index interaction. Using large real-world workloads, we demonstrate the applicability of our approach. Further, we evaluate our solution end to end with a commercial database system using a reproducible setup. We show that our solutions are near-optimal for small index selection problems. For larger problems, our strategy outperforms state-of-the-art approaches in both scalability and solution quality. Y1 - 2019 SN - 978-1-5386-7474-1 U6 - https://doi.org/10.1109/ICDE.2019.00113 SN - 1084-4627 SP - 1238 EP - 1249 PB - IEEE CY - New York ER - TY - GEN A1 - Sahlmann, Kristina A1 - Scheffler, Thomas A1 - Schnor, Bettina T1 - Ontology-driven Device Descriptions for IoT Network Management T2 - 2018 Global Internet of Things Summit (GIoTS) N2 - One particular challenge in the Internet of Things is the management of many heterogeneous things. The things are typically constrained devices with limited memory, power, network and processing capacity. Configuring every device manually is a tedious task. We propose an interoperable way to configure an IoT network automatically using existing standards. The proposed NETCONF-MQTT bridge intermediates between the constrained devices (speaking MQTT) and the network management standard NETCONF. The NETCONF-MQTT bridge generates dynamically YANG data models from the semantic description of the device capabilities based on the oneM2M ontology. We evaluate the approach for two use cases, i.e. describing an actuator and a sensor scenario. KW - Internet of Things KW - Interoperability KW - oneM2M KW - Ontology KW - Semantic Web KW - NETCONF KW - YANG KW - MQTT Y1 - 2018 SN - 978-1-5386-6451-3 U6 - https://doi.org/10.1109/GIOTS.2018.8534569 SP - 295 EP - 300 PB - IEEE CY - New York ER - TY - GEN A1 - Risch, Julian A1 - Krestel, Ralf T1 - My Approach = Your Apparatus? BT - Entropy-Based Topic Modeling on Multiple Domain-Specific Text Collections T2 - Libraries N2 - Comparative text mining extends from genre analysis and political bias detection to the revelation of cultural and geographic differences, through to the search for prior art across patents and scientific papers. These applications use cross-collection topic modeling for the exploration, clustering, and comparison of large sets of documents, such as digital libraries. However, topic modeling on documents from different collections is challenging because of domain-specific vocabulary. We present a cross-collection topic model combined with automatic domain term extraction and phrase segmentation. This model distinguishes collection-specific and collection-independent words based on information entropy and reveals commonalities and differences of multiple text collections. We evaluate our model on patents, scientific papers, newspaper articles, forum posts, and Wikipedia articles. In comparison to state-of-the-art cross-collection topic modeling, our model achieves up to 13% higher topic coherence, up to 4% lower perplexity, and up to 31% higher document classification accuracy. More importantly, our approach is the first topic model that ensures disjunct general and specific word distributions, resulting in clear-cut topic representations. KW - Topic modeling KW - Automatic domain term extraction KW - Entropy Y1 - 2018 SN - 978-1-4503-5178-2 U6 - https://doi.org/10.1145/3197026.3197038 SN - 2575-7865 SN - 2575-8152 SP - 283 EP - 292 PB - Association for Computing Machinery CY - New York ER - TY - GEN A1 - Richly, Keven T1 - Leveraging spatio-temporal soccer data to define a graphical query language for game recordings T2 - IEEE International Conference on Big Data (Big Data) N2 - For professional soccer clubs, performance and video analysis are an integral part of the preparation and post-processing of games. Coaches, scouts, and video analysts extract information about strengths and weaknesses of their team as well as opponents by manually analyzing video recordings of past games. Since video recordings are an unstructured data source, it is a complex and time-intensive task to find specific game situations and identify similar patterns. In this paper, we present a novel approach to detect patterns and situations (e.g., playmaking and ball passing of midfielders) based on trajectory data. The application uses the metaphor of a tactic board to offer a graphical query language. With this interactive tactic board, the user can model a game situation or mark a specific situation in the video recording for which all matching occurrences in various games are immediately displayed, and the user can directly jump to the corresponding game scene. Through the additional visualization of key performance indicators (e.g.,the physical load of the players), the user can get a better overall assessment of situations. With the capabilities to find specific game situations and complex patterns in video recordings, the interactive tactic board serves as a useful tool to improve the video analysis process of professional sports teams. KW - Spatio-temporal data analysis KW - soccer analytics KW - graphical query language Y1 - 2019 SN - 978-1-5386-5035-6 U6 - https://doi.org/10.1109/BigData.2018.8622159 SN - 2639-1589 SP - 3456 EP - 3463 PB - IEEE CY - New York ER - TY - GEN A1 - Richly, Keven T1 - A survey on trajectory data management for hybrid transactional and analytical workloads T2 - IEEE International Conference on Big Data (Big Data) N2 - Rapid advances in location-acquisition technologies have led to large amounts of trajectory data. This data is the foundation for a broad spectrum of services driven and improved by trajectory data mining. However, for hybrid transactional and analytical workloads, the storing and processing of rapidly accumulated trajectory data is a non-trivial task. In this paper, we present a detailed survey about state-of-the-art trajectory data management systems. To determine the relevant aspects and requirements for such systems, we developed a trajectory data mining framework, which summarizes the different steps in the trajectory data mining process. Based on the derived requirements, we analyze different concepts to store, compress, index, and process spatio-temporal data. There are various trajectory management systems, which are optimized for scalability, data footprint reduction, elasticity, or query performance. To get a comprehensive overview, we describe and compare different exciting systems. Additionally, the observed similarities in the general structure of different systems are consolidated in a general blueprint of trajectory management systems. KW - Trajectory Data Management KW - Spatio-Temporal Data KW - Survey Y1 - 2019 SN - 978-1-5386-5035-6 U6 - https://doi.org/10.1109/BigData.2018.8622394 SN - 2639-1589 SP - 562 EP - 569 PB - IEEE CY - New York ER - TY - GEN A1 - Repke, Tim A1 - Krestel, Ralf A1 - Edding, Jakob A1 - Hartmann, Moritz A1 - Hering, Jonas A1 - Kipping, Dennis A1 - Schmidt, Hendrik A1 - Scordialo, Nico A1 - Zenner, Alexander T1 - Beacon in the Dark BT - a system for interactive exploration of large email Corpora T2 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management N2 - The large amount of heterogeneous data in these email corpora renders experts' investigations by hand infeasible. Auditors or journalists, e.g., who are looking for irregular or inappropriate content or suspicious patterns, are in desperate need for computer-aided exploration tools to support their investigations. We present our Beacon system for the exploration of such corpora at different levels of detail. A distributed processing pipeline combines text mining methods and social network analysis to augment the already semi-structured nature of emails. The user interface ties into the resulting cleaned and enriched dataset. For the interface design we identify three objectives expert users have: gain an initial overview of the data to identify leads to investigate, understand the context of the information at hand, and have meaningful filters to iteratively focus onto a subset of emails. To this end we make use of interactive visualisations based on rearranged and aggregated extracted information to reveal salient patterns. Y1 - 2018 SN - 978-1-4503-6014-2 U6 - https://doi.org/10.1145/3269206.3269231 SP - 1871 EP - 1874 PB - Association for Computing Machinery CY - New York ER -