Knowledge Patterns for the Web: extraction, tranformation and reuse

This thesis aims at investigating methods and software architectures for discovering what are the typical and frequently occurring structures used for organizing knowledge in the Web. We identify these structures as Knowledge Patterns (KPs). KP discovery needs to address two main research problems: the heterogeneity of sources, formats and semantics in the Web (i.e., the knowledge soup problem) and the difficulty to draw relevant boundary around data that allows to capture the meaningful knowledge with respect to a certain context (i.e., the knowledge boundary problem). Hence, we introduce two methods that provide different solutions to these two problems by tackling KP discovery from two different perspectives: (i) the transformation of KP-like artifacts to KPs formalized as OWL2 ontologies; (ii) the bottom-up extraction of KPs by analyzing how data are organized in Linked Data. The two methods address the knowledge soup and boundary problems in different ways. The first method provides a solution to the two aforementioned problems that is based on a purely syntactic transformation step of the original source to RDF followed by a refactoring step whose aim is to add semantics to RDF by select meaningful RDF triples. The second method allows to draw boundaries around RDF in Linked Data by analyzing type paths. A type path is a possible route through an RDF that takes into account the types associated to the nodes of a path. Then we present K~ore, a software architecture conceived to be the basis for developing KP discovery systems and designed according to two software architectural styles, i.e, the Component-based and REST. Finally we provide an example of reuse of KP based on Aemoo, an exploratory search tool which exploits KPs for performing entity summarization.

Definition, realization and evaluation of a software reference architecture for use in space applications

A recent initiative of the European Space Agency (ESA) aims at the definition and adoption of a software reference architecture for use in on-board software of future space missions. Our PhD project placed in the context of that effort. At the outset of our work we gathered all the industrial needs relevant to ESA and all the main European space stakeholders and we were able to consolidate a set of technical high-level requirements for the fulfillment of them. The conclusion we reached from that phase confirmed that the adoption of a software reference architecture was indeed the best solution for the fulfillment of the high-level requirements. The software reference architecture we set on building rests on four constituents: (i) a component model, to design the software as a composition of individually verifiable and reusable software units; (ii) a computational model, to ensure that the architectural description of the software is statically analyzable; (iii) a programming model, to ensure that the implementation of the design entities conforms with the semantics, the assumptions and the constraints of the computational model; (iv) a conforming execution platform, to actively preserve at run time the properties asserted by static analysis. The nature, feasibility and fitness of constituents (ii), (iii) and (iv), were already proved by the author in an international project that preceded the commencement of the PhD work. The core of the PhD project was therefore centered on the design and prototype implementation of constituent (i), a component model. Our proposed component model is centered on: (i) rigorous separation of concerns, achieved with the support for design views and by careful allocation of concerns to the dedicated software entities; (ii) the support for specification and model-based analysis of extra-functional properties; (iii) the inclusion space-specific concerns.

Sviluppo di modelli motore e veicolo per l'analisi di strategie di controllo in applicazioni Software e Hardware In the Loop

This work describes the development of a simulation tool which allows the simulation of the Internal Combustion Engine (ICE), the transmission and the vehicle dynamics. It is a control oriented simulation tool, designed in order to perform both off-line (Software In the Loop) and on-line (Hardware In the Loop) simulation. In the first case the simulation tool can be used in order to optimize Engine Control Unit strategies (as far as regard, for example, the fuel consumption or the performance of the engine), while in the second case it can be used in order to test the control system. In recent years the use of HIL simulations has proved to be very useful in developing and testing of control systems. Hardware In the Loop simulation is a technology where the actual vehicles, engines or other components are replaced by a real time simulation, based on a mathematical model and running in a real time processor. The processor reads ECU (Engine Control Unit) output signals which would normally feed the actuators and, by using mathematical models, provides the signals which would be produced by the actual sensors. The simulation tool, fully designed within Simulink, includes the possibility to simulate the only engine, the transmission and vehicle dynamics and the engine along with the vehicle and transmission dynamics, allowing in this case to evaluate the performance and the operating conditions of the Internal Combustion Engine, once it is installed on a given vehicle. Furthermore the simulation tool includes different level of complexity, since it is possible to use, for example, either a zero-dimensional or a one-dimensional model of the intake system (in this case only for off-line application, because of the higher computational effort). Given these preliminary remarks, an important goal of this work is the development of a simulation environment that can be easily adapted to different engine types (single- or multi-cylinder, four-stroke or two-stroke, diesel or gasoline) and transmission architecture without reprogramming. Also, the same simulation tool can be rapidly configured both for off-line and real-time application. The Matlab-Simulink environment has been adopted to achieve such objectives, since its graphical programming interface allows building flexible and reconfigurable models, and real-time simulation is possible with standard, off-the-shelf software and hardware platforms (such as dSPACE systems).

An architecture for scaling ontology networks

Constructing ontology networks typically occurs at design time at the hands of knowledge engineers who assemble their components statically. There are, however, use cases where ontology networks need to be assembled upon request and processed at runtime, without altering the stored ontologies and without tampering with one another. These are what we call "virtual [ontology] networks", and keeping track of how an ontology changes in each virtual network is called "multiplexing". Issues may arise from the connectivity of ontology networks. In many cases, simple flat import schemes will not work, because many ontology managers can cause property assertions to be erroneously interpreted as annotations and ignored by reasoners. Also, multiple virtual networks should optimize their cumulative memory footprint, and where they cannot, this should occur for very limited periods of time. We claim that these problems should be handled by the software that serves these ontology networks, rather than by ontology engineering methodologies. We propose a method that spreads multiple virtual networks across a 3-tier structure, and can reduce the amount of erroneously interpreted axioms, under certain raw statement distributions across the ontologies. We assumed OWL as the core language handled by semantic applications in the framework at hand, due to the greater availability of reasoners and rule engines. We also verified that, in common OWL ontology management software, OWL axiom interpretation occurs in the worst case scenario of pre-order visit. To measure the effectiveness and space-efficiency of our solution, a Java and RESTful implementation was produced within an Apache project. We verified that a 3-tier structure can accommodate reasonably complex ontology networks better, in terms of the expressivity OWL axiom interpretation, than flat-tree import schemes can. We measured both the memory overhead of the additional components we put on top of traditional ontology networks, and the framework's caching capabilities.

Application Platforms for the Internet of Things: Theory, Architecture, Protocols, Data Formats, and Privacy

The Internet of Things (IoT) is the next industrial revolution: we will interact naturally with real and virtual devices as a key part of our daily life. This technology shift is expected to be greater than the Web and Mobile combined. As extremely different technologies are needed to build connected devices, the Internet of Things field is a junction between electronics, telecommunications and software engineering. Internet of Things application development happens in silos, often using proprietary and closed communication protocols. There is the common belief that only if we can solve the interoperability problem we can have a real Internet of Things. After a deep analysis of the IoT protocols, we identified a set of primitives for IoT applications. We argue that each IoT protocol can be expressed in term of those primitives, thus solving the interoperability problem at the application protocol level. Moreover, the primitives are network and transport independent and make no assumption in that regard. This dissertation presents our implementation of an IoT platform: the Ponte project. Privacy issues follows the rise of the Internet of Things: it is clear that the IoT must ensure resilience to attacks, data authentication, access control and client privacy. We argue that it is not possible to solve the privacy issue without solving the interoperability problem: enforcing privacy rules implies the need to limit and filter the data delivery process. However, filtering data require knowledge of how the format and the semantics of the data: after an analysis of the possible data formats and representations for the IoT, we identify JSON-LD and the Semantic Web as the best solution for IoT applications. Then, this dissertation present our approach to increase the throughput of filtering semantic data by a factor of ten.