This thesis presents the architecture and implementation of a query system resulted from a domain-specific conceptual data modeling and querying methodology. The query system is built for a high level conceptual query language that supports dynamically user-defined domain-specific functions and application-specific functions. It is DBMS-independent and can be translated to SQL and OQL through a normal form. Currently, it has been implemented in neuroscience domain and can be applied to any other domain.

As the number of the published web services increase, discovery of the web services with the desired functionality and quality is becoming a challenging process. Selecting the appropriate web services among the ones that oer the same functionality is also a challenging task. The web service repositories like UDDI (Universal Description Discovery and Integration) support only the syntactic searchs. Quality of service parameters for the published web services can not be queried over these repositories. We have proposed a query language that aims to overcome these problems. It enables its users to query the web services both syntactically and semantically. We also allow the users to specify the quality of service criteria which the desired web services should satisfy. We have developed a graphical query interface to assist the users in query sentence formulation process. The proposed work is developed as a submodule of the Domain Specific Web Service Discovery with Semantics (DSWSD-S) System. Aforementioned query language and the query interface are explained in detail in this thesis.

Diploma thesis is focused on elaboration of database solution for saving measured data. In theory part analyses database query language and database management system Microsoft SQL Server 2017. Further in theory part is focused on programming environment for application development using C# .NET. Thesis includes database solution for saving measured data, service console application for saving data into the database and user application for creating new measuring, representation of data and user administration.

The purpose of this thesis is to design a Java AST query language and implement tool that uses the query language. This work overviews graph databases and their libraries with focus on Neo4J and Titan. This thesis overviews tools Java bytecode analysis as well. Libraries Procyon and BCEL are described in detail. The work includes a proposal the query language and detailed description of the tool implementation, together with the detailed description of the way how Java entities are stored into the graph databases. In the end, the work deals with experiments and the evaluation of the time complexity of the library.

Traditional modeling approaches and information systems assume static entities that represent all information and attributes at once. However, due to the evolution of information systems to increasingly context-aware and self-adaptive systems, this assumption no longer holds. To cope with the required flexibility, the role concept was introduced. Although researchers have proposed several role modeling approaches, they usually neglect the contextual characteristics of roles and their representation in database management systems. Unfortunately, these systems do not rely on a conceptual model of an information system, rather they model this information by their own means leading to transformation and maintenance overhead. So far, the challenges posed by dynamic complex entities, their first class implementation, and their contextual characteristics lack detailed investigations in the area of database management systems. Hence, this paper, presents an approach that ties a conceptual role-based data model and its database implementation together, to directly represent the information modeled conceptually inside a database management system. In particular, we propose a formal database model to describe roles and their contextual information in compartments. Moreover, to provide a context-dependent role-based database interface, we extend RSQL by compartments. Finally, we introduce RSQL Result Net to preserve the contextual role semantics as well as enable users and applications to both iterate and navigate over results produced by RSQL. In sum, these means allow for a coherent design of more dynamic, complex software systems.

L’intérêt croissant pour les environnements connectés (bâtiments, villes, usines intelligents) etl’évolution des réseaux de capteurs, technologies de gestion/communication de données ont ouvertla voie à des applications intéressantes et utiles qui aident les utilisateurs dans leurs tâchesquotidiennes (augmenter la productivité dans une usine, réduire la consommation d’énergie).Cependant, diverses améliorations sont encore nécessaires. Par exemple, comment améliorer lareprésentation de ces environnements complexes, dynamiques et hétérogènes. En outre, commentfaciliter l’interaction entre les utilisateurs et leurs environnements connectés et comment fournir desoutils de surveillance et de gestion de tels environnements.Dans cette thèse, nous nous concentrons sur quatre défis principaux: (i) représenter un ensemblediversifié de composants et d’éléments liés à l’environnement et à son réseau de capteurs; (ii) fournirun langage de requête qui gère les interactions utilisateur/environnement connecté (pour la définitionde l’environnement, la gestion de données, la définition d’événements); (iii) faire face à la dynamiquede l’environnement et à son évolution dans le temps; et (iv) proposer un mécanisme générique dedétection d’événements pour mieux surveiller l’environnement.Pour ce faire, nous présentons d’abord un modèle de données basé sur une ontologie qui représentedes environnements et réseaux de capteurs hybrides. Couvrant ainsi divers capteurs (statique, mobile),environnements (infrastructures, équipements) et données (scalaires, multimédia). Ensuite, nousintroduisons un langage de requête que l’on pourrait utiliser pour diverses tâches (définirl’environnement connecté, la recherche d’informations, la définition d’événements, la gestion dedonnées). De plus, afin de suivre les changements d’environnement, nous fournissons un optimiseurde requêtes qui permet aux requêtes soumises de gérer la dynamique de l’environnement avant leurexécution. Enfin, nous proposons un noyau de détection d’événement qui prend en entrée lesdéfinitions d’événement et détecte les événements ciblés.Nous regroupons les modules susmentionnés dans un framework global pour la détectiond’événements dans des environnements connectés. Notre proposition est générique, extensible, etpourrait être utilisée avec différents environnements connectés tels que des bâtiments, des villes. . . / The rising interest in smart connected environments (e.g., smart buildings, cities, factories) and theevolution of sensors, data management/communication technologies have paved the way forinteresting and useful applications that help users in their every day tasks (e.g. increasing comfort,reducing energy consumption). However, various improvements are still required. For instance, howto enhance the representation of such complex, dynamic, and heterogeneous environments.Moreover, how to facilitate the interaction between users and their connected environments, and howto provide tools for environment monitoring and management.In this thesis, we focus on four main challenges: (i) representing a diverse set of components andelements related to the environment and its sensor network; (ii) providing a query language thathandles user/connected environment interactions (e.g., environment definition, data management,event definition); (iii) coping with the dynamicity of the environment and its evolution over time; and(iv) proposing a generic event detection mechanism for improved environment monitoring.To do so, we first present an ontology-based data model that represents hybrid environments/sensornetworks. Thus covering diverse sensors (e.g., static, mobile), environments (e.g., infrastructures,devices), and data (e.g., scalar, multimedia). Then, we introduce a query language that one might usefor various tasks (e.g., defining the connected environment, information retrieval, event definition,data management). Furthermore, to keep up with the environment changes we provide a queryoptimizer that allows the submitted queries to cope with the dynamicity of the environment prior totheir execution. Finally, we propose an event detection core that takes event definitions as input anddetects the targeted events.We group the aforementioned modules in one global framework for event detection in connectedenvironments. Our proposal is generic, extensible, and could be used with different connectedenvironments such as buildings, cities. . .

Masters of Science / Coders of databases repeatedly face the problem of checking their Structured Query Language (SQL) code. Instructors face the difficulty of checking student projects and lab assignments in database courses. We collect and categorize common MySQL programming errors into three groups: data definition errors, data manipulation errors, and transaction control errors. We build these into a comprehensive list of MySQL errors, which novices are inclined make during database programming. We collected our list of common MySQL errors both from the technical literature and directly by noting errors made in assignments handed in by students. In the results section of this research, we check and summarize occurrences of these errors based on three characteristics as semantics, syntax, and logic. These data form the basis of a future static MySQL checker that will eventually assist database coders to correct their code automatically. These errors also form a useful checklist to guide students away from the mistakes that they are prone to make.

With heightened security concerns across the globe and the increasing need to monitor, preserve and protect infrastructure and public spaces to ensure proper operation, quality assurance and safety, numerous video cameras have been deployed. Accordingly, they also need to be monitored effectively and efficiently. However, relying on human operators to constantly monitor all the video streams is not scalable or cost effective. Humans can become subjective, fatigued, even exhibit bias and it is difficult to maintain high levels of vigilance when capturing, searching and recognizing events that occur infrequently or in isolation. These limitations are addressed in the Live Video Database Management System (LVDBMS), a framework for managing and processing live motion imagery data. It enables rapid development of video surveillance software much like traditional database applications are developed today. Such developed video stream processing applications and ad hoc queries are able to "reuse" advanced image processing techniques that have been developed. This results in lower software development and maintenance costs. Furthermore, the LVDBMS can be intensively tested to ensure consistent quality across all associated video database applications. Its intrinsic privacy framework facilitates a formalized approach to the specification and enforcement of verifiable privacy policies. This is an important step towards enabling a general privacy certification for video surveillance systems by leveraging a standardized privacy specification language. With the potential to impact many important fields ranging from security and assembly line monitoring to wildlife studies and the environment, the broader impact of this work is clear. The privacy framework protects the general public from abusive use of surveillance technology; iii success in addressing the "trust" issue will enable many new surveillance-related applications. Although this research focuses on video surveillance, the proposed framework has the potential to support many video-based analytical applications.

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Graphs, as simple yet powerful data structures, play a pivotal role in modeling and analyzing relationships among real-world entities. In the data representation and analysis landscape, graph data structures have established themselves as a fundamental paradigm for modeling and understanding complex relationships in various domains. The intrinsic domain independence, expressiveness, and the wide variety of analysis options based on graph theory have gained significant attention in both research and industry. In recent years, companies have increasingly leveraged graph technology to represent, store, query, and analyze graph-shaped data. This has been notably impactful in uncovering hidden patterns and predicting relationships within diverse domains such as social networks, Internet of Things (IoT), biological systems, and medical networks. However, the dynamic nature of most real-world graphs is often neglected in existing approaches, which might lead to inaccurate analytical results or an incomplete understanding of evolving patterns within the graph over time. Temporal graphs, in contrast, are a particular type of graphs that maintain changing structures and properties over time. They have gained significant attention in various domains, from financial networks over micromobility networks to supply chains and biological networks. A majority of these real-world networks are not static but rather exhibit high dynamics, which are rarely considered in data models, query languages, and analyses, although analytical questions often require an evaluation of the network's evolution. This doctoral thesis addresses this critical gap by presenting a comprehensive study on analyzing and exploring temporal property graphs. It focuses on scalability and proposes novel methodologies to enhance accuracy and comprehensiveness in analyzing evolving graph patterns over time. It also offers insights into real-time querying, addressing various challenges that emerge when the time dimension is treated as an integral part of the graph. This thesis introduces the Temporal Property Graph Model (TPGM), a sophisticated data model designed for bitemporal modeling of vertices and edges, as well as logical abstractions of subgraphs and graph collections. The reference implementation of this model, namely Gradoop, is a graph dataflow system explicitly designed for scalable and distributed analysis of static and temporal property graphs. Gradoop empowers analysts to construct comprehensive and flexible temporal graph processing workflows through a declarative analytical language. The system supports various analytical temporal graph operators, such as snapshot retrieval, temporal graph pattern matching, time-dependent grouping, and temporal metrics such as degree evolution. The thesis provides an in-depth analysis of the data model, system architecture, and implementation details of Gradoop and its operators. Comprehensive performance evaluations have been conducted on large real-world and synthetic temporal graphs, providing valuable insights into the system's capabilities and efficiency. Furthermore, this thesis demonstrates the flexibility of the temporal graph model and its operators through a practical use case based on a call center network. In this scenario, a TPGM operator pipeline is developed to answer a complex and time-dependent analytical question. We also showcase the Temporal Graph Explorer (TGE), a web-based user interface designed to explore temporal graphs, leveraging Gradoop as a backend. The TGE empowers users to delve into temporal graph dynamics by enabling the retrieval of snapshots from the graph's past states, computing differences between these snapshots, and providing temporal summaries of graphs. This functionality allows for a comprehensive understanding of graph evolution through diverse visualizations. Real-world temporal graph data from bicycle rentals highlight the system's flexibility and configurability of the selected temporal operators. The impact of graph changes on its characteristics can also be explored by examining centrality measures over time. Centrality measures, encompassing both node and graph metrics, quantify the characteristics of individual nodes or the entire graph. In the dynamic context of temporal graphs, where the graph changes over time, node and graph metrics also undergo implicit changes. This thesis tackles the challenge of adapting static node and graph metrics to temporal graphs. It proposes temporal extensions for selected degree-dependent metrics and aggregations, emphasizing the importance of including the time dimension in the metrics. This thesis demonstrates that a metric conventionally representing a scalar value for static graphs results in a time series when applied to temporal graphs. It introduces a baseline algorithm for calculating the degree evolution of vertices within a temporal graph, and its practical implementation in Gradoop is presented. The scalability of this algorithm is evaluated using both real-world and synthetic datasets, providing valuable insights into its performance across diverse scenarios. Such time series data can also be captured from the application scenario as properties of nodes and edges, such as sensor readings in the IoT domain. In light of this, we showcase significant advancements, including an extended version of the TPGM that supports time series data in temporal graphs. Additionally, we introduce a temporal graph query language based on Oracle's language PGQL to facilitate seamless querying of time-oriented graph structures. Furthermore, we present a novel continuous graph-based event detection approach to support scenarios involving more time-sensitive use cases. The increasing frequency of graph changes and the need to react quickly to emerging patterns leads to the need for a unified declarative graph query language that can evaluate queries on graph streams. To address the increasing importance of real-time data analysis and management, the thesis introduces the syntax and semantics of Seraph, a Cypher-based language that supports native streaming features within property graph query languages. The semantics of Seraph combine stream processing with property graphs and time-varying relations, treating time as a first-class citizen. Real-world industrial use cases demonstrate the usage of Seraph for graph-based continuous queries. After evaluating lessons learned from the development and research on Gradoop, a dissertation summary and an outlook on future work are given in a final section. This doctoral thesis comprehensively examines scalable analysis and exploration techniques for temporal property graphs, focusing on Gradoop and its system architecture, data model, operators, and performance evaluations. It also explores the evolution of node and graph metrics and the theoretical foundation of a real-time query language, contributing to the advancement of temporal graph analysis in various domains.:1 Introduction 2 Background and Related Work 3 The TPGM and Gradoop 4 Gradoop Application Examples 5 Evolution of Degree Metrics 6 The Fusion of Graph and Time-Series Data 7 Seraph: Continuous Queries on Property Graph Streams 8 Lessons Learned from Gradoop 9 Conclusion and Outlook Bibliography