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Uncomputation is an essential part of reversible computing and plays a vital role in quantum computing. Using this technique, memory resources can be safely deallocated without performing a nonreversible deletion process. For the case of quantum computing, several algorithms depend on this as they require disentangled states in the course of their execution. Thus, uncomputation is not only about resource management, but is also required from an algorithmic point of view. However, synthesizing uncomputation circuits is tedious and can be automated. In this paper, we describe the interface for automated generation of uncomputation circuits in our Qrisp framework. Our algorithm for synthesizing uncomputation circuits in Qrisp is based on an improved version of "Unqomp", a solution presented by Paradis et. al. Our paper also presents some improvements to the original algorithm, in order to make it suitable for the needs of a high-level programming framework. Qrisp itself is a fully compilable, high-level programming language/framework for gate-based quantum computers, which abstracts from many of the underlying hardware details. Qrisp's goal is to support a high-level programming paradigm as known from classical software development.

Quantum information processing architectures typically only allow for nearest-neighbour entanglement creation. In many cases, this prevents the direct generation of GHZ states, which are commonly used for many communication and computation tasks. Here, we show how to obtain GHZ states between nodes in a network that are connected in a straight line, naturally allowing them to initially share linear cluster states. We prove a strict upper bound of $\lfloor (n+3)/2 \rfloor$ on the size of the set of nodes sharing a GHZ state that can be obtained from a linear cluster state of $n$ qubits, using local Clifford unitaries, local Pauli measurements, and classical communication. Furthermore, we completely characterize all selections of nodes below this threshold that can share a GHZ state obtained within this setting. Finally, we demonstrate these transformations on the IBMQ Montreal quantum device for linear cluster states of up to $n=19$ qubits., Comment: 6 pages, 4 figures, Jupyter notebook available at https://github.com/hahnfrederik/Extracting-maximal-entanglement-from-linear-cluster-states

We present CQELS 2.0, the second version of Continuous Query Evaluation over Linked Streams. CQELS 2.0 is a platform-agnostic federated execution framework towards semantic stream fusion. In this version, we introduce a novel neural-symbolic stream reasoning component that enables specifying deep neural network (DNN) based data fusion pipelines via logic rules with learnable probabilistic degrees as weights. As a platform-agnostic framework, CQELS 2.0 can be implemented for devices with different hardware architectures (from embedded devices to cloud infrastructures). Moreover, this version also includes an adaptive federator that allows CQELS instances on different nodes in a network to coordinate their resources to distribute processing pipelines by delegating partial workloads to their peers via subscribing continuous queries

In this paper, we present our ideas on how to best support researchers in every phase of the research process when dealing with their research data. We propose a Research Data Portal as the central data infrastructure. With the help of this portal, a researcher can easily manage and update his or her research data, share it with collaborators, and reach out to the public. We further propose a Citizen Science Portal, which includes some new and innovative concepts and methods. In this portal, Citizen Science and Auto Science concepts are applied, and support to bring together the best of both worlds is provided. Citizen Science promises to entail the individual (scientists and hobby scientists) to help with research. Auto Science is meant to help analyze research data, e.g., to help publish the data and to help improve its quality, by applying methods from artificial intelligence., Weizenbaum Conference

P-Grid is a decentralized DHT-based peer-to-peer system with logarithmic search complexity. As it is intended to provide a platform for distributed information management beyond mere file-sharing it supports an update functionality with lazy consistency guarantees, identity management disentangling the DHT from the underlying networking infrastructure, and decentralized load balancing. In this paper we first give a brief overview of the theoretical foundations of P-Grid and the supporting functionalities. Then we highlight some interesting implementation details and present briefly some experimental evaluation results. To our knowledge this is one of the first reports on experimental evaluation results of a structured peer-to-peer system in a real-world networking environment.

This paper provides a quantative comparison of the efficiency of the self-organized construction processes of the P-Grid and FreeNet peer-to-peer systems. Starting from a defined, realistic network topology we simulate the construction of their access structures and measure the incurred message load and memory usage for routing tables. Besides these results our experimental setup may also be used as a starting point for defining a standard test and evaluation suite for P2P systems.

The P-Grid overlay network: From a simple idea to a complex system.

The availability of cheap and smart wireless sensing devices provides unprecedented possibilities to monitor the physical world. On the technical side these devices introduce several original research problems, many of them related to the integration of the rampant technology proposals. Global Sensor Network (GSN) is a platform which provides a scalable infrastructure for integrating heterogeneous sensor network technologies using a small set of powerful abstractions. GSN supports the integration and discovery of sensor networks and sensor data, provides distributed querying, filtering, and combination of sensor data, and supports the dynamic adaption of the system configuration during operation through a declarative XML-based language

We present a framework for the autonomous discovery and selection of Semantic Web services based on their QoS properties. The novelty of our approach is the wide use of semantic technologies for a customizable discovery, which enables both the service users and providers to flexibly specify their matching models for QoS and the corresponding environmental conditions. In the presented approach, the discovery and ranking of services can be personalized via the use of domain ontologies detailing the user's preferences and the provider's specification. The discovery component is modeled as an adaptive query processing system in which the basic steps of filtering, matchmaking, reputation-based QoS assessment, and ranking of services correspond to logical algebraic operators, which facilitates the introduction of different discovery algorithms and the automatic generation of appropriate parallelized matchmaking evaluations, enabling the scalability of our solution up to unpredictable arrival rate of user queries against high numbers of published service descriptions in the system.

Gnutella is still one of the most popular P2P systems with millions of users. The advantages of Gnutella are its low maintenance overhead, its excellent robustness properties, and its query processing flexibility. Recent improvements, such as the introduction of ultrapeers and augmented node degrees also significantly reduced its excessive network bandwidth usage which was one of Gnutella's major drawbacks. Despite these improvements, Gnutella is still inefficient for rare queries in terms of low success rates and massive message propagation overhead. In this paper we augment the unstructured Gnutella network with a structured overlay network of ultrapeers based on the Kademlia DHT to address the problem of rare queries in Gnutella. We present the required query, maintenance, and ultrapeer election algorithms which use both overlays at their optimal efficiency, describe the protocols and architecture of our hybrid system, and present our implementation on the basis of the LimeWire Gnutella client and the Azureus Kademlia implementation. To demonstrate the advantages and efficiency of our hybrid approach we provide experimental results from large-scale experiments with hybrid ultrapeers running on PlanetLab which were connected to the live LimeWire Gnutella and Azureus Kademlia networks, with approximately 4 million (LimeWire) and 800 thousand (Azureus) connected users during the experiments.

The lack of standardization and the continuous inflow of novel sensor network technologies have made their deployment the main factor of manpower consumption, considerably complicate the interconnection of heterogeneous sensor networks, and make portable application development a challenging and time-consuming task. To address these problems we propose our Global Sensor Networks middleware which supports the rapid and simple deployment of a wide range of sensor network technologies, facilitates the flexible integration and discovery of sensor networks and sensor data, enables fast deployment and addition of new platforms, provides distributed querying, filtering, and combination of sensor data, and supports the dynamic adaption of the system configuration during operation. GSN offers virtual sensors as a simple and powerful abstraction which enables the user to declaratively specify XML-based deployment descriptors in combination with the possibility to integrate sensor network data through plain SQL queries over local and remote sensor data sources. The paper describes GSN's conceptual model and system architecture, and demonstrates the efficiency of the implementation through experiments with typical high-load application profiles. The GSN implementation is available from http://globalsn.sourceforge.net/.

This paper describes our Gnutella compatible P2P system Gridella which is based on the P-Grid approach. P-Grid has several advantages over the standard Gnutella infrastructure, for example, that probabilistic estimates can be given for successful search requests and that it scales gracefully in the total number of nodes and data items. Gridella is designed for interoperability with Gnutella and the long-term goal is to replace the existing Gnutella infrastructure with Gridella to make it more self-organizing and get around known problems of Gnutella. On the basis of an analysis of Gnutella we describe the P-Grid approach and the mapping algorithm that form the algorithmic foundations of Gridella. We present Gridella’s architecture and communication patterns and compare it with Gnutella.

The proliferation of digital camera devices (stand-alone or combined with cell phones), new protocols such as MMS and the desire of people to com-municate and share their experience call for new systems to support these needs in new Internet-scale infrastructures. In this paper we outline our plans for an Internet-scale P2P system that enables users to share (globally or only within a group) and efficiently locate photographs based on semi-automatically provided meta-information (by the devices and the user).

A key problem in current sensor network technology is the heterogeneity of the available software and hardware platforms which makes deployment and application development a tedious and time consuming task. To minimize the unnecessary and repetitive implementation of identical functionalities for different platforms, we present our Global Sensor Networks (GSN) middleware which supports the flexible integration and discovery of sensor networks and sensor data, enables fast deployment and addition of new platforms, provides distributed querying, filtering, and combination of sensor data, and supports the dynamic adaption of the system configuration during operation. In this demonstration, we specifically focus on the deployment aspects and allow users to dynamically reconfigure the running system, to add new sensor networks on the fly, and to monitor the effects of the changes via a graphical interface.

Many new applications, for example Wikis, social networks, and distributed recommender systems, require the efficient integration of decentralized and heterogenous data sources at a large scale. In this paper, we present our vision of a universal storage for RDF-like triple data based on a structured P2P system and the universal relation model as its key enabling technologies to achieve flexibility, robustness, and efficiency for large-scale distributed data storage and query processing. We outline the steps we have already accomplished successfully and discuss a roadmap to achieve the final goal of a practical, light-weight implementation of our storage system.

Structured peer-to-peer (P2P) systems are considered as the next generation application backbone on the Internet. An important problem of these systems is load balancing in the presence of non-uniform data distributions. In this paper we propose a completely decentralized mechanism that in parallel addresses a local and a global load balancing problem: (1) balancing the storage load uniformly among peers participating in the network and (2) uniformly replicating different data items in the network while optimally exploiting existing storage capacity. Our approach is based on the P-Grid P2P system which is our variant of a structured P2P network. Problem (1) is solved by directly adapting the search structure to the data distribution. This may result in an unbalanced search structure, but we will show that the expected search cost in P-Grid in number of messages remains logarithmic under all circumstances. Problem (2) is solved by a dynamic, reactive balancing method based on sampling the P-Grid structure. Through simulations we show that our solution provides a scalable approach to these load balancing problems. Finally we discuss issues that had to be addressed beyond the theoretical aspects when implementing our approach as part of a practical P2P system.

Web services are inherently heterogeneous at both data and behavioral levels because of the nature of the Web, which is the main obstacle to the usability of Web services. The heterogeneity at a behavioral level is generally addressed by process mediation, in which the message flow is adjusted to suit the behavior of Web services involved in a given interaction. In this paper, we present a novel approach for process mediation, and propose an architectural for process mediation based on Triple Space Computing to solve resolvable message sequence mismatches. These resolvable mismatches can be classified into five classes for unveiling their essence. This work provides a basis for the generalization of mismatches themselves, as well as a potentially uniform solution to address these mismatches. peer-reviewed

Sensors, mobile devices and social platforms generate an immense amount of stream data in various formats and schemata. For these areas, the idea of Linked Stream Data is to extend RDF data model to cope with the heterogeneity of data sources and to enable the data integration¿not only among themselves, but also with other existing sources. This would enable a vast range of new, near real-time applications. Such applications drive the demand for processing engines that support continuous queries over Linked Stream Data and Linked Data. These engines must not only support the necessary functionalities but also meet the typical low-latency response requirement of stream processing applications. Since unmodified data stream management systems (DSMSs) and triple storages do not provide full functionalities required by Linked Stream Data processing, the rewriting approach could be used to delegate the processing to those systems. However, this suffers from the overhead of data transformation and does not enable full control over the query execution process. The overhead might be prohibitively expensive for the low-latency response requirement and the lack of full control of the execution process restricts optimisations partially and locally in each underlying sub-system. Moreover, the graph-based model of RDF data poses many challenges to designing a physical storage and optimising the processing when mapped to a relation-based data model. Nevertheless, most techniques and algorithms of DSMSs assume stream data being represented in that way. Therefore, algorithms and techniques for DSMSs and triple stores need to be carefully re-engineered to build an efficient and scalable processing engine for Linked Stream Data and Linked Data. In this work, we present an adaptive and native execution framework for Linked Stream Data and Linked Data, called CQELS (Continuous Query Evaluation over Linked Streams). The framework introduces one of the first continuous query languages over Linked Stream Data and Linked Data which is compatible with SPARQL 1.1. The flexibility of our execution framework enables performance gains of several orders of magnitudes over other related systems. For dealing with large RDF datasets and high update throughput RDF streams, we propose an efficient hybrid physical data organisation using novel data structures that support algorithms for efficient incremental evaluation of continuous query operators over Linked Stream Data. The framework also provides several adaptive optimisation algorithms. To demonstrate the advantages of the framework and of the CQELS processing engine in terms of performance, the thesis provides extensive experimental evaluations. The evaluations cover a comprehensive set of parameters that dictate the performance of a continuous queries over Linked Stream Data and Linked Data.

P2P network architectures have gained popularity as applications for sharing files between users. A P2P network provides a scalable, robust, and economical storage architecture. These features have led to the extended use of P2P network applications, ranging from file sharing to data sharing for video and telecommunication domains. The shift in storage system being used from high cost, reliable servers to usercentered storage devices led to reliability and availability problems for the P2P network. Peers are machines of users that can go offline at any time. The data stored on the machines are not available during the offline time. Data replication is a common approach for handling data unavailability, which is where multiple copies of files are placed on different peers in the network. In data replication, peers transfer complete/partial data to other nodes. Therefore, data replication provides higher data availability in case of churn. I present data replication algorithms in this thesis to improve the availability of data in the network. With an increase in availability and overhead, the basic challenges faced during the development of data replication algorithm are: (i) How many replicas for a data object should be created? (ii) On which peer(s) should the replicated data objects be stored? (iii) Which files should be replicated? Initial work in data replication considered the static replication of data based on the overall availability of nodes in the network. These approaches overestimated the number of replicas, which lead to high maintenance costs. Dynamic approaches for estimating replica numbers were developed to handle this issue. From the analysis of the current approaches, I found that the proposed mechanisms for dynamic approaches to replication did not provide a balanced replication of data. Data were only replicated to highly available nodes, which were overloaded with data. The second issue was the inability to adapt to the changing behaviour of peers. In this thesis, I present an approach that selects a node set comprised of both highly available and lowly available nodes, in order to provide load balancing in the network. I provide a feedback-based approach where previous behaviours are incorporated in the next behavioural analysis. Compared to the existing approaches to replica calculation, this approach is able to determine the appropriate number of replicas and placement locations with the changing dynamics of the system. The replication system relies on node behaviour prediction algorithms using Monte Carlo simulation and Time series analysis. Each node performs an analysis on the historical traces of its online and offline times in the network. Each node shares the availability log with the replication initiator node, and the prediction of future behaviour is made based on the logs received. The data-owning peer uses this information to run the replica placement algorithm to select nodes that are present for a particular duration, supporting the presence of each others in the network. Partial data replication is supported by the system by applying Zipf distribution to calculate the most popular files. I performed the evaluation using my replication approach and dynamic replica placement algorithms, based on the following parameters: replica count, reliability of data, average availability of nodes in the replica set, and failure analysis for querying data. The replica count analysis shows that the number of replicas required were almost half compared to the previous dynamic approaches. The reliability analysis shows that overall reliability of the data was better in this approach compared to the other dynamic replica placement algorithms. My replication algorithm produced replica sets with a lower average availability compared to the replica set of the other approaches, but the reliability analysis suggests that my approach distributes data more evenly between nodes, resulting in better overall data availability. The availability of data in the network was higher than other approaches. The failure analysis for request failures for data shows that my replication algorithm has a better node selection mechanism compared to other approaches, with better data availability.

We present Strabon, a new RDF store that supports the state of the art semantic geospatial query languages stSPARQL and GeoSPARQL. To illustrate the expressive power offered by these query languages and their implementation in Strabon, we concentrate on the new version of the data model stRDF and the query language stSPARQL that we have developed ourselves. Like GeoSPARQL, these new versions use OGC standards to represent geometries where the original versions used linear constraints. We study the performance of Strabon experimentally and show that it scales to very large data volumes and performs, most of the times, better than all other geospatial RDF stores it has been compared with.

The concept of context originated in the ubiquitous computing domain where information regarding the user's situation was gathered using sensing devices, allowing the application to automatically adapt according to the user's current situation. Context awareness enabled the development of smart spaces in a multitude of different environments such as offices, homes, cars, hospitals and universities. Traditionally, the smart space that produced the context data was the sole consumer of that data and accessing the context data from third-party applications was cumbersome. Consequently, smart spaces became stovepipes; having little integration beyond their isolated application scenarios. The development of context-aware applications was limited to environments with access to rich sources of context data and which had large resources available for the development of context-aware applications. The development of these context-aware applications required expertise in the areas of context modelling, context gathering, context management and data privacy. As a result, the inclusion of context-aware behaviours in applications outside of ubiquitous computing research prototypes has been limited. This thesis defines and presents a distributed web-based model for the controlled shar-ing of context data between heterogeneous third-parties. A web-based approach is adopted in order to maximize the variety of potential applications, such as mobile, desktop, smart space and web-based applications. This `Context as a Service' (CxaaS) approach to context management enables applications to access context-data from domains beyond their usual operational boundaries. The support for ubiquitous computing scenario oriented sharing of context data has been addressed in three key areas: (i) discovery of context data from remote sources concerning new and returning users, (ii) enabling the privacy sensitive sharing of context data between heterogeneous third-parties, and (iii) provision of a formal context model appropriate for use with the discovery and privacy mechanisms. The model defined in this thesis is evaluated with an implementation of a comprehensive business use-case. A RESTful approach is adopted in this particular instantiation of the CxaaS model which facilitates the rapid development of context-aware applications through the use of popular libraries. The core CxaaS context ontology is supported and mechanisms are provided to extend this model if necessary.

This dissertation presents a component model, reference architecture and peer-evaluated prototype for Semantic Service Oriented Architecture (SSOA). Semantic Web service technology, derived from the combination of the Semantic Web and Web services, is the enabling-technology for SSOA. Allowing the semantics of the public data and process model of Web services to be unambiguously communicated to potential service-consumers, provides the basis for Web services to be discovered, composed, and invoked as needed. The alternative is that these actions must be carried out manually in advance. The thesis is motivated by the fact that, although a body of research has developed into many aspects of Semantic Web services, a gap remains between conceptual models and languages for Semantic Web services on the one hand, and prototype implementations using those models and languages, primarily for service discovery, on the other. Filling this gap requires a combination of top-down and bottom-up approaches. From a top-down perspective, the absence of a reference software architecture model, as distinct from a conceptual model, for Semantic Web services gives rise to a number of shortcomings in the body of knowledge. These include the identification of a minimal component model for an SSOA, defining corresponding behavioural and component communication models and, validating this architecture, both experimentally and qualitatively so that it can be regarded as a reference point for further research. Experimental validation is achieved by peer-review, through the application of a prototype implementation of the SSOA to a set of open and independently specified system-integration problems. Qualitative evaluation is carried out by first determining a matrix of requirements on which the SSOA is based, and using this as the basis for relating five contemporary Semantic Web service frameworks to the SSOA. The thesis moves on to describe how research challenges arose in the course of building and validating the prototype SSOA implementation. Two specific challenges were in the areas of (1) Semantic Web service discovery where incomplete data, available in the service description, must be augmented by fetching additional data at the time the service is required, and (2) the enactment of process mediation between the public interface descriptions of a service requester and a matching Web service offer. Devising solutions to these problems, applying them to real-world problems and evaluating their outcomes both experimentally and qualitatively represents problem-driven contributions of this thesis from a bottom-up perspective.