Main aim of this thesis is to study existing systems of faceted search and to design own system based on faceted search in the index of Wikipedia. In this thesis we can meet with existing solutions of faceted search. From mistakes and failures of existing solutions was designed our own system, that is output of this thesis. Designed system is described in way of design and implementation. Product of thesis is application and graphical interface. Application interface can be integrated into existing informational system, where it can be used as multidimensional filter. Graphical interface provides option how can application interface be used in real system. System was created focusing on usefullness and simplicity, for using in existing information systems.

La complexité du logiciel augmente tout le temps: les systèmes deviennent plus grands et plus complexes. La modélisation est un élément central de génie logicielle pour relever les défis de la complexité. Cependant, un défi majeur auquel est confronté le développement de logiciels axés sur les modèles est l'évolutivité des outils de modélisation avec une taille croissante de modèles. Certaines initiatives ont commencé à explorer la modélisation tout en stockant des modèles dans une base de données de graphes. Dans cette thèse, nous présentons NMF, un framework pour créer et éditer des modèles dans une base de données Neo4j élevée à l'abstraction du langage de modélisation. / Software complexity increases all the time: systems become larger and more complex. Modeling is a central part of software engineering to tackle challenges of complexity. However, a prominent challenge model-driven software development is facing is scalability of modeling tools with a growing size of models. Some initiatives started exploring modeling while storing models in a graph database. In this thesis, we present NMF, a framework to create and edit MDE models in a Neo4j database lifted to the abstraction of the modeling language.

In the realm of software engineering, understanding the architecture and measuringthe quality of a source code is essential for maintaining and enhancing softwaresystems. However, many existing tools fall short in evaluating architectural aspects,such as detecting architectural erosion or addressing architecture-related metrics andconstraints tailored to the unique context of their systems or organisations. Thisdeficiency restricts proactive architecture governance and hinders the mitigation ofarchitecture-related risks, creating a critical gap in the analysis of software sourcecode. This thesis presents a novel approach to tackle these challenges. It proposes a graphdatabase as a data structure for analysing the source code and architecture quality andcalculating various architectural metrics of interest. The tool developed in this thesisrepresents the source code structural elements and their relationships in the graphdatabase, enabling an intuitive analysis of the source code architecture. The tool also integrates different code quality metrics parsed from Visual Studiocode metrics results, mapped to their correspondent nodes to assess the source codeoverall quality and identify potential areas of improvement. This empowers softwareengineers and developers to make informed decisions regarding refactoring, codeoptimisation, and architectural enhancements. Furthermore, the tool allows users to define the intended architecture in terms ofmodules to reveal any Architecture erosion (AEr). It also provides the flexibility toestablish custom constraints and metrics through tailored queries, accommodating theunique requirements of their system and company. A case study conducted on a real-world software project validates the effectivenessand usefulness of the proposed approach. The case study demonstrates how the toolanalysis reveals valuable insights into the source code health and identifies patternsthat can impact maintainability and scalability. The results of this research showcasethe potential of our tool as a powerful instrument for analysing the code qualityand architecture of source code, fostering more resilient and sustainable softwaresystems.

<div>Graph-structured data is pervasive. Modeling large-scale network-structured datasets require graph processing and management systems such as graph databases. Further, the analysis of graph-structured data often necessitates bulk downloads/uploads from/to the cloud or edge nodes. Unfortunately, experience has shown that malicious actors can compromise the confidentiality of highly-sensitive data stored in the cloud or shared nodes, even in an encrypted form. For particular use cases —multi-modal knowledge graphs, electronic health records, finance— network-structured datasets can be highly sensitive and require auditability, authentication, integrity protection, and privacy-preserving computation in a controlled and trusted environment, i.e., the traditional cloud computation is not suitable for these use cases. Similarly, many modern applications utilize a "shared, replicated database" approach to provide accountability and traceability. Those applications often suffer from significant privacy issues because every node in the network can access a copy of relevant contract code and data to guarantee the integrity of transactions and reach consensus, even in the presence of malicious actors.</div><div><br></div><div>This dissertation proposes breaking from the traditional cloud computation model, and instead ship certified pre-approved trusted code closer to the data to protect graph-structured data confidentiality. Further, our technique runs in a controlled environment in a trusted data owner node and provides proof of correct code execution. This computation can be audited in the future and provides the building block to automate a variety of real use cases that require preserving data ownership. This project utilizes trusted execution environments (TEEs) but does not rely solely on TEE's architecture to provide privacy for data and code. We thoughtfully examine the drawbacks of using trusted execution environments in cloud environments. Similarly, we analyze the privacy challenges exposed by the use of blockchain technologies to provide accountability and traceability.</div><div><br></div><div>First, we propose AGAPECert, an Auditable, Generalized, Automated, Privacy-Enabling, Certification framework capable of performing auditable computation on private graph-structured data and reporting real-time aggregate certification status without disclosing underlying private graph-structured data. AGAPECert utilizes a novel mix of trusted execution environments, blockchain technologies, and a real-time graph-based API standard to provide automated, oblivious, and auditable certification. This dissertation includes the invention of two core concepts that provide accountability, data provenance, and automation for the certification process: Oblivious Smart Contracts and Private Automated Certifications. Second, we contribute an auditable and integrity-preserving graph processing model called AuditGraph.io. AuditGraph.io utilizes a unique block-based layout and a multi-modal knowledge graph, potentially improving access locality, encryption, and integrity of highly-sensitive graph-structured data. Third, we contribute a unique data store and compute engine that facilitates the analysis and presentation of graph-structured data, i.e., TruenoDB. TruenoDB offers better throughput than the state-of-the-art. Finally, this dissertation proposes integrity-preserving streaming frameworks at the edge of the network with a personalized graph-based object lookup.</div>