Refine
Year of publication
- 2018 (89) (remove)
Document Type
- Other (52)
- Article (19)
- Doctoral Thesis (14)
- Monograph/Edited Volume (4)
Language
- English (89)
Is part of the Bibliography
- yes (89)
Keywords
- E-Learning (3)
- Security Metrics (3)
- Security Risk Assessment (3)
- real-time rendering (3)
- 3D printing (2)
- Angriffserkennung (2)
- Answer set programming (2)
- Big Data (2)
- Cloud-Security (2)
- Energy (2)
Institute
- Hasso-Plattner-Institut für Digital Engineering GmbH (89) (remove)
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.
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.
Live migration is an important feature in modern software-defined datacenters and cloud computing environments. Dynamic resource management, load balance, power saving and fault tolerance are all dependent on the live migration feature. Despite the importance of live migration, the cost of live migration cannot be ignored and may result in service availability degradation. Live migration cost includes the migration time, downtime, CPU overhead, network and power consumption. There are many research articles that discuss the problem of live migration cost with different scopes like analyzing the cost and relate it to the parameters that control it, proposing new migration algorithms that minimize the cost and also predicting the migration cost. For the best of our knowledge, most of the papers that discuss the migration cost problem focus on open source hypervisors. For the research articles focus on VMware environments, none of the published articles proposed migration time, network overhead and power consumption modeling for single and multiple VMs live migration. In this paper, we propose empirical models for the live migration time, network overhead and power consumption for single and multiple VMs migration. The proposed models are obtained using a VMware based testbed.
For the last ten years, almost every theoretical result concerning the expected run time of a randomized search heuristic used drift theory, making it the arguably most important tool in this domain. Its success is due to its ease of use and its powerful result: drift theory allows the user to derive bounds on the expected first-hitting time of a random process by bounding expected local changes of the process - the drift. This is usually far easier than bounding the expected first-hitting time directly. Due to the widespread use of drift theory, it is of utmost importance to have the best drift theorems possible. We improve the fundamental additive, multiplicative, and variable drift theorems by stating them in a form as general as possible and providing examples of why the restrictions we keep are still necessary. Our additive drift theorem for upper bounds only requires the process to be nonnegative, that is, we remove unnecessary restrictions like a finite, discrete, or bounded search space. As corollaries, the same is true for our upper bounds in the case of variable and multiplicative drift.
One of the most important aspects of a randomized algorithm is bounding its expected run time on various problems. Formally speaking, this means bounding the expected first-hitting time of a random process. The two arguably most popular tools to do so are the fitness level method and drift theory. The fitness level method considers arbitrary transition probabilities but only allows the process to move toward the goal. On the other hand, drift theory allows the process to move into any direction as long as it move closer to the goal in expectation; however, this tendency has to be monotone and, thus, the transition probabilities cannot be arbitrary. We provide a result that combines the benefit of these two approaches: our result gives a lower and an upper bound for the expected first-hitting time of a random process over {0,..., n} that is allowed to move forward and backward by 1 and can use arbitrary transition probabilities. In case that the transition probabilities are known, our bounds coincide and yield the exact value of the expected first-hitting time. Further, we also state the stationary distribution as well as the mixing time of a special case of our scenario.
For theoretical analyses there are two specifics distinguishing GP from many other areas of evolutionary computation. First, the variable size representations, in particular yielding a possible bloat (i.e. the growth of individuals with redundant parts). Second, the role and realization of crossover, which is particularly central in GP due to the tree-based representation. Whereas some theoretical work on GP has studied the effects of bloat, crossover had a surprisingly little share in this work. We analyze a simple crossover operator in combination with local search, where a preference for small solutions minimizes bloat (lexicographic parsimony pressure); the resulting algorithm is denoted Concatenation Crossover GP. For this purpose three variants of the wellstudied Majority test function with large plateaus are considered. We show that the Concatenation Crossover GP can efficiently optimize these test functions, while local search cannot be efficient for all three variants independent of employing bloat control.
High-throughput RNA sequencing (RNAseq) produces large data sets containing expression levels of thousands of genes. The analysis of RNAseq data leads to a better understanding of gene functions and interactions, which eventually helps to study diseases like cancer and develop effective treatments. Large-scale RNAseq expression studies on cancer comprise samples from multiple cancer types and aim to identify their distinct molecular characteristics. Analyzing samples from different cancer types implies analyzing samples from different tissue origin. Such multi-tissue RNAseq data sets require a meaningful analysis that accounts for the inherent tissue-related bias: The identified characteristics must not originate from the differences in tissue types, but from the actual differences in cancer types. However, current analysis procedures do not incorporate that aspect. As a result, we propose to integrate a tissue-awareness into the analysis of multi-tissue RNAseq data. We introduce an extension for gene selection that provides a tissue-wise context for every gene and can be flexibly combined with any existing gene selection approach. We suggest to expand conventional evaluation by additional metrics that are sensitive to the tissue-related bias. Evaluations show that especially low complexity gene selection approaches profit from introducing tissue-awareness.
In rural/remote areas, resource constrained smart micro-grid (RCSMG) architectures can provide a cost-effective power supply alternative in cases when connectivity to the national power grid is impeded by factors such as load shedding. RCSMG architectures can be designed to handle communications over a distributed lossy network in order to minimise operation costs. However, due to the unreliable nature of lossy networks communication data can be distorted by noise additions that alter the veracity of the data. In this chapter, we consider cases in which an adversary who is internal to the RCSMG, deliberately distorts communicated data to gain an unfair advantage over the RCSMG’s users. The adversary’s goal is to mask malicious data manipulations as distortions due to additive noise due to communication channel unreliability. Distinguishing malicious data distortions from benign distortions is important in ensuring trustworthiness of the RCSMG. Perturbation data anonymisation algorithms can be used to alter transmitted data to ensure that adversarial manipulation of the data reveals no information that the adversary can take advantage of. However, because existing data perturbation anonymisation algorithms operate by using additive noise to anonymise data, using these algorithms in the RCSMG context is challenging. This is due to the fact that distinguishing benign noise additions from malicious noise additions is a difficult problem. In this chapter, we present a brief survey of cases of privacy violations due to inferences drawn from observed power consumption patterns in RCSMGs centred on inference, and propose a method of mitigating these risks. The lesson here is that while RCSMGs give users more control over power management and distribution, good anonymisation is essential to protecting personal information on RCSMGs.
In this chapter, we provide a framework to specify how cheating attacks can be conducted successfully on power marketing schemes in resource constrained smart micro-grids. This is an important problem because such cheating attacks can destabilise and in the worst case result in a breakdown of the micro-grid. We consider three aspects, in relation to modelling cheating attacks on power auctioning schemes. First, we aim to specify exactly how in spite of the resource constrained character of the micro-grid, cheating can be conducted successfully. Second, we consider how mitigations can be modelled to prevent cheating, and third, we discuss methods of maintaining grid stability and reliability even in the presence of cheating attacks. We use an Automated-Cheating-Attack (ACA) conception to build a taxonomy of cheating attacks based on the idea of adversarial acquisition of surplus energy. Adversarial acquisitions of surplus energy allow malicious users to pay less for access to more power than the quota allowed for the price paid. The impact on honest users, is the lack of an adequate supply of energy to meet power demand requests. We conclude with a discussion of the performance overhead of provoking, detecting, and mitigating such attacks efficiently.
Resource constrained smart micro-grid architectures describe a class of smart micro-grid architectures that handle communications operations over a lossy network and depend on a distributed collection of power generation and storage units. Disadvantaged communities with no or intermittent access to national power networks can benefit from such a micro-grid model by using low cost communication devices to coordinate the power generation, consumption, and storage. Furthermore, this solution is both cost-effective and environmentally-friendly. One model for such micro-grids, is for users to agree to coordinate a power sharing scheme in which individual generator owners sell excess unused power to users wanting access to power. Since the micro-grid relies on distributed renewable energy generation sources which are variable and only partly predictable, coordinating micro-grid operations with distributed algorithms is necessity for grid stability. Grid stability is crucial in retaining user trust in the dependability of the micro-grid, and user participation in the power sharing scheme, because user withdrawals can cause the grid to breakdown which is undesirable. In this chapter, we present a distributed architecture for fair power distribution and billing on microgrids. The architecture is designed to operate efficiently over a lossy communication network, which is an advantage for disadvantaged communities. We build on the architecture to discuss grid coordination notably how tasks such as metering, power resource allocation, forecasting, and scheduling can be handled. All four tasks are managed by a feedback control loop that monitors the performance and behaviour of the micro-grid, and based on historical data makes decisions to ensure the smooth operation of the grid. Finally, since lossy networks are undependable, differentiating system failures from adversarial manipulations is an important consideration for grid stability. We therefore provide a characterisation of potential adversarial models and discuss possible mitigation measures.