Efficient Storage and Domain-Specific Information Discovery on Semistructured Documents

The increasing amount of available semistructured data demands efficient mechanisms to store, process, and search an enormous corpus of data to encourage its global adoption. Current techniques to store semistructured documents either map them to relational databases, or use a combination of flat files and indexes. These two approaches result in a mismatch between the tree-structure of semistructured data and the access characteristics of the underlying storage devices. Furthermore, the inefficiency of XML parsing methods has slowed down the large-scale adoption of XML into actual system implementations. The recent development of lazy parsing techniques is a major step towards improving this situation, but lazy parsers still have significant drawbacks that undermine the massive adoption of XML. Once the processing (storage and parsing) issues for semistructured data have been addressed, another key challenge to leverage semistructured data is to perform effective information discovery on such data. Previous works have addressed this problem in a generic (i.e. domain independent) way, but this process can be improved if knowledge about the specific domain is taken into consideration. This dissertation had two general goals: The first goal was to devise novel techniques to efficiently store and process semistructured documents. This goal had two specific aims: We proposed a method for storing semistructured documents that maps the physical characteristics of the documents to the geometrical layout of hard drives. We developed a Double-Lazy Parser for semistructured documents which introduces lazy behavior in both the pre-parsing and progressive parsing phases of the standard Document Object Model's parsing mechanism. The second goal was to construct a user-friendly and efficient engine for performing Information Discovery over domain-specific semistructured documents. This goal also had two aims: We presented a framework that exploits the domain-specific knowledge to improve the quality of the information discovery process by incorporating domain ontologies. We also proposed meaningful evaluation metrics to compare the results of search systems over semistructured documents.

A domain-specific search engine for the construction sector

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Domain-specific question answering system : an application to the construction sector

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Using domain-specific knowledge to improve information retrieval performance

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

A domain-specific search engine for the construction sector

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Domain-specific question answering system : an application to the construction sector

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Using domain-specific knowledge to improve information retrieval performance

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Software languages engineering: experimental evaluation

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Informática

Framework-specific DSL para sensoriamento participado

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Expressiveness improvements of OutSystems DSL query primitives

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Modeling information in software languages

Ohjelmiston kehitystyökalut käyttävät infromaatiota kehittäjän tuottamasta lähdekoodista. Informaatiota hyödynnetään ohjelmistoprojektin eri vaiheissa ja eri tarkoituksissa. Moderneissa ohjelmistoprojekteissa käytetyn informaation määrä voi kasvaa erittäin suureksi. Ohjelmistotyökaluilla on omat informaatiomallinsa ja käyttömekanisminsa. Informaation määrä sekä erilliset työkaluinformaatiomallit tekevät erittäin hankalaksi rakentaa joustavaa työkaluympäristöä, erityisesti ongelma-aluekohtaiseen ohjelmiston kehitysprosessiin. Tässä työssä on analysoitu perusinformaatiometamalleja Unified Modeling language kielestä, Python ohjelmointikielestä ja C++ ohjelmointikielestä. Metainformaation taso on rajoitettu rakenteelliselle tasolle. Ajettavat rakenteet on jätetty pois. ModelBase metamalli on yhdistetty olemassa olevista analysoiduista metamalleista. Tätä metamallia voidaan käyttää tulevaisuudessa ohjelmistotyökalujen kehitykseen.

CALV3 - Uma linguagem específica de domínio para segurança em sistemas corporativos: um estudo de caso sistemático na indústria

The academic community and software industry have shown, in recent years, substantial interest in approaches and technologies related to the area of model-driven development (MDD). At the same time, continues the relentless pursuit of industry for technologies to raise productivity and quality in the development of software products. This work aims to explore those two statements, through an experiment carried by using MDD technology and evaluation of its use on solving an actual problem under the security context of enterprise systems. By building and using a tool, a visual DSL denominated CALV3, inspired by the software factory approach: a synergy between software product line, domainspecific languages and MDD, we evaluate the gains in abstraction and productivity through a systematic case study conducted in a development team. The results and lessons learned from the evaluation of this tool within industry are the main contributions of this work

Embedding Languages Without Breaking Tools

Domain-specific languages (DSLs) are increasingly used as embedded languages within general-purpose host languages. DSLs provide a compact, dedicated syntax for specifying parts of an application related to specialized domains. Unfortunately, such language extensions typically do not integrate well with the development tools of the host language. Editors, compilers and debuggers are either unaware of the extensions, or must be adapted at a non-trivial cost. We present a novel approach to embed DSLs into an existing host language by leveraging the underlying representation of the host language used by these tools. Helvetia is an extensible system that intercepts the compilation pipeline of the Smalltalk host language to seamlessly integrate language extensions. We validate our approach by case studies that demonstrate three fundamentally different ways to extend or adapt the host language syntax and semantics.

Mining the Ecosystem to Improve Type Inference For Dynamically Typed Languages

Dynamically typed languages lack information about the types of variables in the source code. Developers care about this information as it supports program comprehension. Ba- sic type inference techniques are helpful, but may yield many false positives or negatives. We propose to mine information from the software ecosys- tem on how frequently given types are inferred unambigu- ously to improve the quality of type inference for a single system. This paper presents an approach to augment existing type inference techniques by supplementing the informa- tion available in the source code of a project with data from other projects written in the same language. For all available projects, we track how often messages are sent to instance variables throughout the source code. Predictions for the type of a variable are made based on the messages sent to it. The evaluation of a proof-of-concept prototype shows that this approach works well for types that are sufficiently popular, like those from the standard librarie, and tends to create false positives for unpopular or domain specific types. The false positives are, in most cases, fairly easily identifiable. Also, the evaluation data shows a substantial increase in the number of correctly inferred types when compared to the non-augmented type inference.