Optimized trusted information sharing

As the digital world expands the building of trust and the retention of privacy in information sharing becomes paramount. A major impediment to information sharing is a lack of trust between the parties, based on security and privacy concerns, as well as information asymmetry. Several technological solutions have been proposed to solve this problem, including our\u27s: a trusted enclave with a Continuous Compliance Assurance (CCA) mechanism. Of the work surrounding these proposed solutions, no attention has been directed toward studying the issues of performance surrounding processing of this nature. Studies have shown that ignoring the performance of a system can lead to ineffectiveness (i.e. disabling certain features), and can be severely detrimental to system adoption.;To ensure that our trusted enclave and CCA mechanism are viable solutions to the trusted information sharing problem, we have built a prototype CCA mechanism and a test bed. The test bed has allowed us to identify problem areas within our prototype. One such area is compliance verification, which utilizes the XPath language in order to test XML encoded information for compliance to regulatory and corporate policies. The compliance verification problem can be described as the answering of multiple queries over a single XML document. We proposed and tested multiple state-of-the-art algorithmic as well as system-based improvements to XPath evaluation, in order to better the overall performance of this aspect of our system. We integrated each of the improvements into our prototype mechanism and have observed the results. Our experiments have taught us much about the problem of compliance verification, and has led us in new directions as we continue to search for a solution

: Méthodes d'Inférence Symbolique pour les Bases de Données

This dissertation is a summary of a line of research, that I wasactively involved in, on learning in databases from examples. Thisresearch focused on traditional as well as novel database models andlanguages for querying, transforming, and describing the schema of adatabase. In case of schemas our contributions involve proposing anoriginal languages for the emerging data models of Unordered XML andRDF. We have studied learning from examples of schemas for UnorderedXML, schemas for RDF, twig queries for XML, join queries forrelational databases, and XML transformations defined with a novelmodel of tree-to-word transducers.Investigating learnability of the proposed languages required us toexamine closely a number of their fundamental properties, often ofindependent interest, including normal forms, minimization,containment and equivalence, consistency of a set of examples, andfinite characterizability. Good understanding of these propertiesallowed us to devise learning algorithms that explore a possibly largesearch space with the help of a diligently designed set ofgeneralization operations in search of an appropriate solution.Learning (or inference) is a problem that has two parameters: theprecise class of languages we wish to infer and the type of input thatthe user can provide. We focused on the setting where the user inputconsists of positive examples i.e., elements that belong to the goallanguage, and negative examples i.e., elements that do not belong tothe goal language. In general using both negative and positiveexamples allows to learn richer classes of goal languages than usingpositive examples alone. However, using negative examples is oftendifficult because together with positive examples they may cause thesearch space to take a very complex shape and its exploration may turnout to be computationally challenging.Ce mémoire est une courte présentation d’une direction de recherche, à laquelle j’ai activementparticipé, sur l’apprentissage pour les bases de données à partir d’exemples. Cette recherches’est concentrée sur les modèles et les langages, aussi bien traditionnels qu’émergents, pourl’interrogation, la transformation et la description du schéma d’une base de données. Concernantles schémas, nos contributions consistent en plusieurs langages de schémas pour les nouveaumodèles de bases de données que sont XML non-ordonné et RDF. Nous avons ainsi étudiél’apprentissage à partir d’exemples des schémas pour XML non-ordonné, des schémas pour RDF,des requêtes twig pour XML, les requêtes de jointure pour bases de données relationnelles et lestransformations XML définies par un nouveau modèle de transducteurs arbre-à-mot.Pour explorer si les langages proposés peuvent être appris, nous avons été obligés d’examinerde près un certain nombre de leurs propriétés fondamentales, souvent souvent intéressantespar elles-mêmes, y compris les formes normales, la minimisation, l’inclusion et l’équivalence, lacohérence d’un ensemble d’exemples et la caractérisation finie. Une bonne compréhension de cespropriétés nous a permis de concevoir des algorithmes d’apprentissage qui explorent un espace derecherche potentiellement très vaste grâce à un ensemble d’opérations de généralisation adapté àla recherche d’une solution appropriée.L’apprentissage (ou l’inférence) est un problème à deux paramètres : la classe précise delangage que nous souhaitons inférer et le type d’informations que l’utilisateur peut fournir. Nousnous sommes placés dans le cas où l’utilisateur fournit des exemples positifs, c’est-à-dire deséléments qui appartiennent au langage cible, ainsi que des exemples négatifs, c’est-à-dire qui n’enfont pas partie. En général l’utilisation à la fois d’exemples positifs et négatifs permet d’apprendredes classes de langages plus riches que l’utilisation uniquement d’exemples positifs. Toutefois,l’utilisation des exemples négatifs est souvent difficile parce que les exemples positifs et négatifspeuvent rendre la forme de l’espace de recherche très complexe, et par conséquent, son explorationinfaisable

Complexity of Lexical Descriptions and its Relevance to Partial Parsing

In this dissertation, we have proposed novel methods for robust parsing that integrate the flexibility of linguistically motivated lexical descriptions with the robustness of statistical techniques. Our thesis is that the computation of linguistic structure can be localized if lexical items are associated with rich descriptions (supertags) that impose complex constraints in a local context. However, increasing the complexity of descriptions makes the number of different descriptions for each lexical item much larger and hence increases the local ambiguity for a parser. This local ambiguity can be resolved by using supertag co-occurrence statistics collected from parsed corpora. We have explored these ideas in the context of Lexicalized Tree-Adjoining Grammar (LTAG) framework wherein supertag disambiguation provides a representation that is an almost parse. We have used the disambiguated supertag sequence in conjunction with a lightweight dependency analyzer to compute noun groups, verb groups, dependency linkages and even partial parses. We have shown that a trigram-based supertagger achieves an accuracy of 92.1‰ on Wall Street Journal (WSJ) texts. Furthermore, we have shown that the lightweight dependency analysis on the output of the supertagger identifies 83‰ of the dependency links accurately. We have exploited the representation of supertags with Explanation-Based Learning to improve parsing effciency. In this approach, parsing in limited domains can be modeled as a Finite-State Transduction. We have implemented such a system for the ATIS domain which improves parsing eciency by a factor of 15. We have used the supertagger in a variety of applications to provide lexical descriptions at an appropriate granularity. In an information retrieval application, we show that the supertag based system performs at higher levels of precision compared to a system based on part-of-speech tags. In an information extraction task, supertags are used in specifying extraction patterns. For language modeling applications, we view supertags as syntactically motivated class labels in a class-based language model. The distinction between recursive and non-recursive supertags is exploited in a sentence simplification application

An illumination of the template enigma : software code generation with templates

Creating software is a process of refining a concept to an implementation. This process consists of several stages represented by documents, models and plans at several levels of abstraction. Mostly, the refinement process requires creativity of the programmers, but sometimes the task is boring and repetitive. This repetitive work is an indication that the program is not written at the most suitable level of abstraction. The level of abstraction offered by the used programming language might be too low to remove the recurring code. Code generators can be used to raise the level of abstraction of program specifications and to automate the repetitive work. This thesis focuses on code generators based on templates. Templates are one of the techniques to implement a code generator. Templates allow extension of the syntax of a programming language, enabling generative programming without modifying the underlying compiler. Four artifacts are involved in a template based generator: templates, input data, a template evaluator and output code. The templates we consider are a concrete (incomplete) representation of the output document, i.e. object code, that contains holes, i.e. the meta code. These holes are filled by the template evaluator using information from the input data to obtain the output code. Templates are widely used to generate HTML code in web applications. They can be used for generating all kinds of text, like e-mails or (source) code. In this thesis we limit the scope to the generation of source code. The central research question is how the quality of template based code generators can be improved. Quality, in general, is a broad notion and our scope is limited to the technical quality of templates and generated code. We focused on improving the maintainability of template based code generators and the correctness of the generated code. This is facilitated by the three main contributions provided by this thesis. First, the maintainability of template based code generators is increased by specifying the following requirement for our metalanguage. Our metalanguage should not be rich enough to allow programming in templates, without being too restrictive to express some code generators. We used the theory of formal languages to specify our metalanguage. Second, we ensure correctness of the templates and generated code. Third, the presented theory and techniques are validated by case studies. These case studies show application of templates in real world applications, increased maintainability and syntactical correctness of generated code. Our metalanguage should not be rich enough to allow programming in templates, without being too restrictive to express some code generators. The theory of formal languages is used to specify the requirements for our metalanguage. As we only consider to generate programming languages, it is sufficient to support the generation of languages defined by context-free grammars. This assumption is used to derive a metalanguage, that is rich enough to specify code generators that are able to instantiate all possible sentences of a context-free language. A specific case of a code generator, the unparser, is a program that can instantiate all sentences of a context-free language. We proved that an unparser can be implemented using a linear deterministic topdown tree-to-string transducer. We call this property unparser-completeness. Our metalanguage is based on a linear deterministic top-down tree-to-string transducer. Recall that the goal of specifying the requirements of the metalanguage is to increase the maintainability of template based code generators, without being too restrictive. To validate that our metalanguage is not too restrictive and leads to better maintainable templates, we compared it with four off-the-shelf text template systems by implementing an unparser. We have observed that the industrial template evaluators provide a Turing complete metalanguage, but they do not contain a block scoping mechanism for the meta-variables. This results in undesired additional boilerplate meta code in their templates. The second contribution is guaranteeing the correctness of the generated code. Correctness of the generated code can be divided in two concerns: syntactical correctness and semantical correctness. We start with syntactical correctness of the generated code. The use of text templates implies that syntactical correctness of the generated code can only be detected at compilation time. This means that errors detected during the compilation are reported on the level of the generated code. The developer is required to trace back manually the errors to their origin in the template or input data. We believe that programs manipulating source code should not consider the object code as text to detect errors as early as possible. We present an approach where the grammars of the object language and metalanguage can be combined in a modular way. Combining both grammars allows parsing both languages simultaneously. Syntax errors in both languages of the template will be found while parsing it. Moreover, only parsing a template is not sufficient to ensure that the generated code will be free of syntax errors. The template evaluator must be equipped with a mechanism to guarantee its output will be syntactically correct. We discuss our mechanism in short. A parse tree is constructed during the parsing of the template. This tree contains subtrees for the object code and subtrees for the meta code. While evaluating the template, subtrees of the meta code are substituted by object code subtrees. The template evaluator checks whether the root nonterminal of the object code subtree is equal to the root nonterminal of the meta code subtree. When both are equal, it is allowed to substitute the meta code. When the root nonterminals are distinct an accurate error message is generated. The template evaluator terminates when all meta code subtrees are substituted. The result is a parse tree of the object language and thus syntactically correct. We call this process syntax safe code generation. In order to validate that the presented techniques increase maintainability and ensure syntactical correctness, we implemented our ideas in a syntax safe template evaluator called Repleo. Repleo has been applied in four case studies. The first case is a real world situation, where it is required to generate a three tier web application from a data model. This case showed that multiple layers of an applications defined in different programming languages can be generated from a single model. The second case and third case are used to show that our metalanguage results in a better maintainable code generator. Our metalanguage forces to use a two layer code generator with separation of concerns between the two layers, where the original implementations are less modular. The last case study shows that ensuring syntactical correctness results in the prevention of cross-site scripting attacks in dynamic generation of web pages. Recall that one of our goals was ensuring the correctness of the generated code. We also showed that is possible to check static semantic properties of templates. Static semantic checks are defined for the metalanguage, for the object language and checks for the situations where the object language is dependent on the metalanguage. We implemented a prototype of a static semantic checker for PicoJava templates using attribute grammars. The use of attribute grammars leads to re-use of the original PicoJava checker. Summarizing, in this thesis we have formulated the requirements for a metalanguage and discussed how to implement a syntax safe template evaluator. This results in better maintainable template based code generators and more reliable generated code