This Master’s thesis presents an algorithm that finds the semantic differences between two versions of files, an older version and a new modified version of the file. The algorithm is responsible for finding changes in the program’s behavior and displaying them graphically. By this a lot of time can be saved because it is not necessary to go through the whole file to find the differences.   The program, Semantic Diff, developed in this master thesis uses the Javacc parser generator which is used to parse files and generate the abstract syntax tree for them. Using this tree it is possible to see all the methods, classes, constructors and parameters for both older version and modified version.  By comparing all the methods, classes and interfaces of both the versions it is possible to find the differences that change the program behavior.   The algorithm for finding semantic differences has been evaluated by testing it on various test cases. By making changes in the original file and in the modified file. Like adding methods and deleting methods and adding classes to the files. The algorithm highlights those methods with green color which are added newly in the modified file and highlights the methods with red color in the original file which got deleted in modified file. This algorithm also finds the textual difference between two files and highlights those lines which are changed in modified file and which got deleted from original file.

<p>This Master’s thesis presents an algorithm that finds the semantic differences between two versions of files, an older version and a new modified version of the file. The algorithm is responsible for finding changes in the program’s behavior and displaying them graphically. By this a lot of time can be saved because it is not necessary to go through the whole file to find the differences.</p><p> </p><p>The program, Semantic Diff, developed in this master thesis uses the Javacc parser generator which is used to parse files and generate the abstract syntax tree for them. Using this tree it is possible to see all the methods, classes, constructors and parameters for both older version and modified version.  By comparing all the methods, classes and interfaces of both the versions it is possible to find the differences that change the program behavior.</p><p> </p><p>The algorithm for finding semantic differences has been evaluated by testing it on various test cases. By making changes in the original file and in the modified file. Like adding methods and deleting methods and adding classes to the files. The algorithm highlights those methods with green color which are added newly in the modified file and highlights the methods with red color in the original file which got deleted in modified file. This algorithm also finds the textual difference between two files and highlights those lines which are changed in modified file and which got deleted from original file.</p><p> </p>

ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / Telemetry missions spanning multiple years of tests often require access to archived configuration data for replay and analysis purposes. The needs for versioning vary from simple file-naming conventions to advanced global database versioning based on the scale and complexity of the mission. This paper focuses on a flexible approach to allow access to current and past versions of multiple test article configurations. Specifically, this paper discusses the characteristics of a versioning system for user-friendly and feature-rich solutions. It analyzes the tradeoffs of various versioning options to meet the needs of a given mission and provides a simple framework for users to identify their versioning requirements and implementation.

A current popular paradigm in e-learning is that of the "learning object". Broadly de-fined, a learning object is a reusable piece of educational material intended to be strung together with other learning objects to form larger educational units such as activities, lessons, or whole courses. This aggregating of learning objects together is a recursive process small objects can be combined to form medium sized objects, medium sized objects can be combined to form large objects, and so on. Once objects have been com-bined appropriately, they are generally serialized into content packages, and deployed into an online course for delivery to learners.<p>Learning objects are often stored in distributed and decentralized repositories throughout the Internet. This provides unique challenges when managing the history of such an ob-ject, as traditional versioning techniques (e.g. CVS, RCS, etc.) rely on centralized man-agement. These challenges have been largely ignored by the educational technology community, but are becoming more important as sharing of learning objects increases.<p>This thesis explores these issues by providing a formal version model for learning ob-jects, a set of data bindings for this model, and a prototype authoring environment which implements these bindings. In addition, the work explores the potential benefits of ver-sion control by implementing a visualization of a learning object revision tree. This visualization includes the relationship between objects and their aggregates, the struc-tural history of an object, and the semantic changes that an object has undergone.

A current popular paradigm in e-learning is that of the "learning object". Broadly de-fined, a learning object is a reusable piece of educational material intended to be strung together with other learning objects to form larger educational units such as activities, lessons, or whole courses. This aggregating of learning objects together is a recursive process small objects can be combined to form medium sized objects, medium sized objects can be combined to form large objects, and so on. Once objects have been com-bined appropriately, they are generally serialized into content packages, and deployed into an online course for delivery to learners.<p>Learning objects are often stored in distributed and decentralized repositories throughout the Internet. This provides unique challenges when managing the history of such an ob-ject, as traditional versioning techniques (e.g. CVS, RCS, etc.) rely on centralized man-agement. These challenges have been largely ignored by the educational technology community, but are becoming more important as sharing of learning objects increases.<p>This thesis explores these issues by providing a formal version model for learning ob-jects, a set of data bindings for this model, and a prototype authoring environment which implements these bindings. In addition, the work explores the potential benefits of ver-sion control by implementing a visualization of a learning object revision tree. This visualization includes the relationship between objects and their aggregates, the struc-tural history of an object, and the semantic changes that an object has undergone.

This diploma thesis is about versioning of databases. Firsts chapters are shortly describing the biggest problems what will need to solve. Thr second part refers to choosen database systems and introcution to SQL language. The last unit describes the development of program solving sketched problems.

zlomekFS is a distributed file system that supports disconnected operation using local cache. During synchronization of local changes it offers easyto-use conflict resolution mechanism. Further improved it became a file system with no specific kernel code. It has therefore a good potential in future public use. As the content of this file system can be updated by many users, keeping history of the changes can be a useful feature. This thesis implements file versioning in zlomekFS, answering the questions such as how to store and access the history. On top of the versioning, the possibility of consistent backup is introduced into the file system. New functionality is derived from the analysis of other file systems with similar features and selection of the most suitable approach for zlomekFS.

zlomekFS is a distributed file system that supports disconnected operation using local cache. During synchronization of local changes it offers easytouse conflict resolution mechanism. Further improved it became a file system with no specific kernel code. It has therefore a good potential in future public use. As the content of this file system can be updated by many users, keeping history of the changes can be a useful feature. This thesis implements file versioning in zlomekFS, answering the questions such as how to store and access the history. On top of the versioning, the possibility of consistent backup is introduced into the file system. New functionality is derived from the analysis of other file systems with similar features and selection of the most suitable approach for zlomekFS.

O objetivo geral desta tese é a detecção, o gerenciamento e a consulta às réplicas e às versões de documentos XML. Denota-se por réplica uma cópia idêntica de um objeto do mundo real, enquanto versão é uma representação diferente, mas muito similar, deste objeto. Trabalhos prévios focam em gerenciamento e consulta a versões conhecidas, e não no problema da detecção de que dois ou mais objetos, aparentemente distintos, são variações (versões) do mesmo objeto. No entanto, o problema da detecção é crítico e pode ser observado em diversos cenários, tais como detecção de plágio, ranking de páginas Web, identificação de clones de software e busca em sistemas peer-to-peer (P2P). Nesta tese assume-se que podem existir diversas réplicas de um documento XML. Documentos XML também podem ser modificados ao longo do tempo, ocasionando o surgimento de versões. A detecção de réplicas é relativamente simples e pode ser feita através do uso de funções hash. Já a detecção de versões engloba conceitos de similaridade, a qual pode ser medida por várias métricas, tais como similaridade de conteúdo, de estrutura, de assunto, etc. Além da análise da similaridade entre os arquivos também se faz necessária a definição de um mecanismo de detecção de versões. O mecanismo deve possibilitar o gerenciamento e a posterior consulta às réplicas e às versões detectadas. Para que o objetivo da tese fosse alcançado foram definidos um conjunto de funções de similaridade para arquivos XML e o mecanismo de detecção de réplicas e de versões. Também foi especificado um framework onde tal mecanismo pode ser inserido e os seus respectivos componentes, que possibilitam o gerenciamento e a consulta às réplicas e às versões detectadas. Foi realizado um conjunto de experimentos que validam o mecanismo proposto juntamente com a implementação de protótipos que demonstram a eficácia dos componentes do framework. Como diferencial desta tese, o problema de detecção de versões é tratado como um problema de classificação, para o qual o uso de limiares não é necessário. Esta abordagem é alcançada pelo uso da técnica baseada em classificadores Naïve Bayesianos. Resultados demonstram a boa qualidade obtida com o mecanismo proposto na tese. / The overall goals of this thesis are the detection, management and querying of replicas and versions of XML documents. We denote by replica an identical copy of a real-world object, and by version a different but very similar representation of this object. Previous works focus on version management and querying rather than version detection. However, the version detection problem is critical in many scenarios, such as plagiarism detection, Web page ranking, software clone identification, and peer-to-peer (P2P) searching. In this thesis, we assume the existence of several replicas of a XML document. XML documents can be modified over time, causing the creation of versions. Replica detection is relatively simple and can be achieved by using hash functions. The version detection uses similarity concepts, which can be assessed by some metrics such as content similariy, structure similarity, subject similarity, and so on. Besides the similarity analysis among files, it is also necessary to define the version detection mechanism. The mechanism should allow the management and the querying of the detected replicas and versions. In order to achieve the goals of the thesis, we defined a set of similarity functions for XML files, the replica and version detection mechanism, the framework where such mechanism can be included and its components that allow managing and querying the detected replicas and versions. We performed a set of experiments for evaluating the proposed mechanism and we implemented tool prototypes that demonstrate the accuracy of some framework components. As the main distinguishing point, this thesis considers the version detection problem as a classification problem, for which the use of thresholds is not necessary. This approach is achieved by using Naïve Bayesian classifiers.

JavaScript Object Notation (JSON) is a format for representing data. In this thesis we show how to capture the history of changes to a JSON document. Capturing the history is important in many applications, where not only the current version of a document is required, but all the previous versions. Conceptually the history can be thought of as a sequence of non-temporal JSON documents, one for each instant of time. Each document in the sequence is called a snapshot. Since changes to a document are few and infrequent, the sequence of snapshots largely duplicates a document across many time instants, so the snapshot model is (wildly) inefficient in terms of space needed to represent the history and time taken to navigate within it. A more efficient representation can be achieved by “gluing" the snapshots together to form a temporal model. Data that remains unchanged across snapshots is represented only once in a temporal model. But we show that the temporal model is not a JSON document, and it is important to represent a history as JSON to ensure compatibility with web services and scripting languages that use JSON. So we describe a representational model that captures the information in a temporal model. We implement the representational model in Python and extensively experiment with the model. Our experiments show that the model is efficient.