Reason Maintenance - State of the Art

This paper describes state of the art in reason maintenance with a focus on its future usage in the KiWi project. To give a bigger picture of the field, it also mentions closely related issues such as non-monotonic logic and paraconsistency. The paper is organized as follows: first, two motivating scenarios referring to semantic wikis are presented which are then used to introduce the different reason maintenance techniques

Navigating Diverse Datasets in the Face of Uncertainty

When exploring big volumes of data, one of the challenging aspects is their diversity of origin. Multiple files that have not yet been ingested into a database system may contain information of interest to a researcher, who must curate, understand and sieve their content before being able to extract knowledge. Performance is one of the greatest difficulties in exploring these datasets. On the one hand, examining non-indexed, unprocessed files can be inefficient. On the other hand, any processing before its understanding introduces latency and potentially un- necessary work if the chosen schema matches poorly the data. We have surveyed the state-of-the-art and, fortunately, there exist multiple proposal of solutions to handle data in-situ performantly. Another major difficulty is matching files from multiple origins since their schema and layout may not be compatible or properly documented. Most surveyed solutions overlook this problem, especially for numeric, uncertain data, as is typical in fields like astronomy. The main objective of our research is to assist data scientists during the exploration of unprocessed, numerical, raw data distributed across multiple files based solely on its intrinsic distribution. In this thesis, we first introduce the concept of Equally-Distributed Dependencies, which provides the foundations to match this kind of dataset. We propose PresQ, a novel algorithm that finds quasi-cliques on hypergraphs based on their expected statistical properties. The probabilistic approach of PresQ can be successfully exploited to mine EDD between diverse datasets when the underlying populations can be assumed to be the same. Finally, we propose a two-sample statistical test based on Self-Organizing Maps (SOM). This method can outperform, in terms of power, other classifier-based two- sample tests, being in some cases comparable to kernel-based methods, with the advantage of being interpretable. Both PresQ and the SOM-based statistical test can provide insights that drive serendipitous discoveries

System Optimisation for Multi-access Edge Computing Based on Deep Reinforcement Learning

Multi-access edge computing (MEC) is an emerging and important distributed computing paradigm that aims to extend cloud service to the network edge to reduce network traffic and service latency. Proper system optimisation and maintenance are crucial to maintaining high Quality-of-service (QoS) for end-users. However, with the increasing complexity of the architecture of MEC and mobile applications, effectively optimising MEC systems is non-trivial. Traditional optimisation methods are generally based on simplified mathematical models and fixed heuristics, which rely heavily on expert knowledge. As a consequence, when facing dynamic MEC scenarios, considerable human efforts and expertise are required to redesign the model and tune the heuristics, which is time-consuming. This thesis aims to develop deep reinforcement learning (DRL) methods to handle system optimisation problems in MEC. Instead of developing fixed heuristic algorithms for the problems, this thesis aims to design DRL-based methods that enable systems to learn optimal solutions on their own. This research demonstrates the effectiveness of DRL-based methods on two crucial system optimisation problems: task offloading and service migration. Specifically, this thesis first investigate the dependent task offloading problem that considers the inner dependencies of tasks. This research builds a DRL-based method combining sequence-to-sequence (seq2seq) neural network to address the problem. Experiment results demonstrate that our method outperforms the existing heuristic algorithms and achieves near-optimal performance. To further enhance the learning efficiency of the DRL-based task offloading method for unseen learning tasks, this thesis then integrates meta reinforcement learning to handle the task offloading problem. Our method can adapt fast to new environments with a small number of gradient updates and samples. Finally, this thesis exploits the DRL-based solution for the service migration problem in MEC considering user mobility. This research models the service migration problem as a Partially Observable Markov Decision Process (POMDP) and propose a tailored actor-critic algorithm combining Long-short Term Memory (LSTM) to solve the POMDP. Results from extensive experiments based on real-world mobility traces demonstrate that our method consistently outperforms both the heuristic and state-of-the-art learning-driven algorithms on various MEC scenarios

Edge Computing for Internet of Things

The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond

A Policy-Based Resource Brokering Environment for Computational Grids

With the advances in networking infrastructure in general, and the Internet in particular, we can build grid environments that allow users to utilize a diverse set of distributed and heterogeneous resources. Since the focus of such environments is the efficient usage of the underlying resources, a critical component is the resource brokering environment that mediates the discovery, access and usage of these resources. With the consumer\u27s constraints, provider\u27s rules, distributed heterogeneous resources and the large number of scheduling choices, the resource brokering environment needs to decide where to place the user\u27s jobs and when to start their execution in a way that yields the best performance for the user and the best utilization for the resource provider. As brokering and scheduling are very complicated tasks, most current resource brokering environments are either specific to a particular grid environment or have limited features. This makes them unsuitable for large applications with heterogeneous requirements. In addition, most of these resource brokering environments lack flexibility. Policies at the resource-, application-, and system-levels cannot be specified and enforced to provide commitment to the guaranteed level of allocation that can help in attracting grid users and contribute to establishing credibility for existing grid environments. In this thesis, we propose and prototype a flexible and extensible Policy-based Resource Brokering Environment (PROBE) that can be utilized by various grid systems. In designing PROBE, we follow a policy-based approach that provides PROBE with the intelligence to not only match the user\u27s request with the right set of resources, but also to assure the guaranteed level of the allocation. PROBE looks at the task allocation as a Service Level Agreement (SLA) that needs to be enforced between the resource provider and the resource consumer. The policy-based framework is useful in a typical grid environment where resources, most of the time, are not dedicated. In implementing PROBE, we have utilized a layered architecture and façade design patterns. These along with the well-defined API, make the framework independent of any architecture and allow for the incorporation of different types of scheduling algorithms, applications and platform adaptors as the underlying environment requires. We have utilized XML as a base for all the specification needs. This provides a flexible mechanism to specify the heterogeneous resources and user\u27s requests along with their allocation constraints. We have developed XML-based specifications by which high-level internal structures of resources, jobs and policies can be specified. This provides interoperability in which a grid system can utilize PROBE to discover and use resources controlled by other grid systems. We have implemented a prototype of PROBE to demonstrate its feasibility. We also describe a test bed environment and the evaluation experiments that we have conducted to demonstrate the usefulness and effectiveness of our approach

Database architectures for modern hardware: report from Dagstuhl Seminar 18251

The requirements of emerging applications on the one hand and the trends in computing hardware and systems on the other hand demand a fundamental rethinking of current data management architectures. Based on the broad consensus that this rethinking requires expertise from different research disciplines, the goal of this seminar was to bring together researchers and practitioners from these areas representing both the software and hardware sides and to foster cross-cutting architectural discussions. The outcome of this seminar was not only an identification of promising hardware technologies and their exploitation in data management systems but also a set of use cases, studies, and experiments for new architectural concepts