Il sito web del Palio di San Donato: proposta di localizzazione e Search Engine Optimization

La presente tesi illustra e discute due attività legate all'ambito dei siti web, ovvero la localizzazione e l'ottimizzazione per i motori di ricerca (o SEO, dall'inglese "Search Engine Optimization"). Quest'ultima è un'attività mirata a permettere che i siti stessi ottengano un posizionamento migliore nella pagina dei risultati dei motori di ricerca e siano dunque più visibili agli utenti. Poiché la SEO prevede vari interventi sui siti web, alcuni dei quali implicano la manipolazione di codice HTML, essa viene spesso considerata come un'attività strettamente informatica. L'obiettivo della presente tesi, dunque, è quello di illustrare come i traduttori possano sfruttare le proprie competenze linguistiche per dedicarsi non soltanto alla localizzazione di siti web, ma anche alla loro ottimizzazione per i motori di ricerca. Per dimostrare l'applicabilità di tali tecniche è stato utilizzato come esempio pratico il sito web de "Il Palio di San Donato", un sito gestito dal Comune di Cividale del Friuli e finalizzato alla descrizione dell'omonima rievocazione storica cittadina. La tesi si compone di quattro capitoli. Nel primo capitolo vengono introdotti i principi teorici alla base della localizzazione di siti web, della SEO, della scrittura per il web e della traduzione per il settore turistico. Nel secondo capitolo, invece, viene descritto il sito del Palio di San Donato, esaminandone in particolare la struttura e i contenuti. Il terzo capitolo è dedicato alla descrizione del progetto di localizzazione a cui è stato sottoposto il sito in esame. Infine, il quarto capitolo contiene un breve commento relativo alle problematiche linguistiche, culturali e tecnologiche riscontrate durante il processo traduttivo e un elenco di strategie di SEO applicate a cinque pagine del sito web in esame, selezionate sulla base della possibilità di illustrare il maggior numero possibile di interventi di SEO attuabili dai traduttori.

Integrare Search Engine Optimization e Website usability al processo di localizzazione di un sito Web. Proposta di localizzazione dal francese all’italiano del sito del Parc de loisirs du Lac de Maine.

Ce mémoire a comme objectif de montrer le processus de localisation en langue italienne d’un site Internet français, celui du Parc de loisir du Lac de Maine. En particulier, le but du mémoire est de démontrer que, lorsqu’on parle de localisation pour le Web, on doit tenir compte de deux facteurs essentiels, qui contribuent de manière exceptionnelle au succès du site sur le Réseau Internet. D’un côté, l’utilisabilité du site Web, dite également ergonomie du Web, qui a pour objectif de rendre les sites Web plus aisés d'utilisation pour l'utilisateur final, de manière que son rapprochement au site soit intuitif et simple. De l’autre côté, l’optimisation pour les moteurs de recherche, couramment appelée « SEO », acronyme de son appellation anglais, qui cherche à découvrir les meilleures techniques visant à optimiser la visibilité d'un site web dans les pages de résultats de recherche. En améliorant le positionnement d'une page web dans les pages de résultats de recherche des moteurs, le site a beaucoup plus de possibilités d’augmenter son trafic et, donc, son succès. Le premier chapitre de ce mémoire introduit la localisation, avec une approche théorique qui en illustre les caractéristiques principales ; il contient aussi des références à la naissance et l’origine de la localisation. On introduit aussi le domaine du site qu’on va localiser, c’est-à-dire le domaine du tourisme, en soulignant l’importance de la langue spéciale du tourisme. Le deuxième chapitre est dédié à l’optimisation pour les moteurs de recherche et à l’ergonomie Web. Enfin, le dernier chapitre est consacré au travail de localisation sur le site du Parc : on analyse le site, ses problèmes d’optimisation et d’ergonomie, et on montre toutes les phases du processus de localisation, y compris l’intégration de plusieurs techniques visant à améliorer la facilité d’emploi par les utilisateurs finaux, ainsi que le positionnement du site dans les pages de résultats des moteurs de recherche.

Search Engine Optimization: principi e tecniche di ottimizzazione di siti web per i motori di ricerca

La tesi tratta i concetti fondamentali legati alla "Search Engine Optimization", ovvero all’ottimizzazione dei siti web per i motori di ricerca. La SEO è un’attività multidisciplinare che coinvolge aspetti tecnici dello sviluppo web e princìpi di web marketing, allo scopo di migliorare la visibilità di un sito nelle pagine di risposta di un motore di ricerca. All’interno dell’elaborato viene analizzato dapprima il funzionamento dei motori di ricerca, con particolare riferimento al mondo Google; in seguito vengono esaminate le diverse tecniche di ottimizzazione “on-page” di un sito (codice, architettura, contenuti) e le strategie “off-page” volte a migliorare reputazione, popolarità e autorevolezza del sito stesso.

Spanish Supreme Court Rules in Favour of Google Search Engine... and a Flexible Reading of Copyright Statutes?

On 3 April 2012, the Spanish Supreme Court issued a major ruling in favour of the Google search engine, including its ‘cache copy’ service: Sentencia n.172/2012, of 3 April 2012, Supreme Court, Civil Chamber.* The importance of this ruling lies not so much in the circumstances of the case (the Supreme Court was clearly disgusted by the claimant’s ‘maximalist’ petitum to shut down the whole operation of the search engine), but rather on the court going beyond the text of the Copyright Act into the general principles of the law and case law, and especially on the reading of the three-step test (in Art. 40bis TRLPI) in a positive sense so as to include all these principles. After accepting that none of the limitations listed in the Spanish Copyright statute (TRLPI) exempted the unauthorized use of fragments of the contents of a personal website through the Google search engine and cache copy service, the Supreme Court concluded against infringement, based on the grounds that the three-step test (in Art. 40bis TRLPI) is to be read not only in a negative manner but also in a positive sense so as to take into account that intellectual property – as any other kind of property – is limited in nature and must endure any ius usus inocui (harmless uses by third parties) and must abide to the general principles of the law, such as good faith and prohibition of an abusive exercise of rights (Art. 7 Spanish Civil Code).The ruling is a major success in favour of a flexible interpretation and application of the copyright statutes, especially in the scenarios raised by new technologies and market agents, and in favour of using the three-step test as a key tool to allow for it.

Finding relevant search engine results:a minimax linear programming approach

For a submitted query to multiple search engines finding relevant results is an important task. This paper formulates the problem of aggregation and ranking of multiple search engines results in the form of a minimax linear programming model. Besides the novel application, this study detects the most relevant information among a return set of ranked lists of documents retrieved by distinct search engines. Furthermore, two numerical examples aree used to illustrate the usefulness of the proposed approach.

SemSearch:a search engine for the semantic web

Existing semantic search tools have been primarily designed to enhance the performance of traditional search technologies but with little support for ordinary end users who are not necessarily familiar with domain specific semantic data, ontologies, or SQL-like query languages. This paper presents SemSearch, a search engine, which pays special attention to this issue by providing several means to hide the complexity of semantic search from end users and thus make it easy to use and effective.

A Digital Library of Folklore Songs and Keyword-Based Search Engine

ACM Computing Classification System (1998): H3.3, H.5.5, J5.

Regrouping metric-space search index for search engine size adaptation

This work contributes to the development of search engines that self-adapt their size in response to fluctuations in workload. Deploying a search engine in an Infrastructure as a Service (IaaS) cloud facilitates allocating or deallocating computational resources to or from the engine. In this paper, we focus on the problem of regrouping the metric-space search index when the number of virtual machines used to run the search engine is modified to reflect changes in workload. We propose an algorithm for incrementally adjusting the index to fit the varying number of virtual machines. We tested its performance using a custom-build prototype search engine deployed in the Amazon EC2 cloud, while calibrating the results to compensate for the performance fluctuations of the platform. Our experiments show that, when compared with computing the index from scratch, the incremental algorithm speeds up the index computation 2–10 times while maintaining a similar search performance.

Self-adapting parallel metric-space search engine for variable query loads

This research focuses on automatically adapting a search engine size in response to fluctuations in query workload. Deploying a search engine in an Infrastructure as a Service (IaaS) cloud facilitates allocating or deallocating computer resources to or from the engine. Our solution is to contribute an adaptive search engine that will repeatedly re-evaluate its load and, when appropriate, switch over to a dierent number of active processors. We focus on three aspects and break them out into three sub-problems as follows: Continually determining the Number of Processors (CNP), New Grouping Problem (NGP) and Regrouping Order Problem (ROP). CNP means that (in the light of the changes in the query workload in the search engine) there is a problem of determining the ideal number of processors p active at any given time to use in the search engine and we call this problem CNP. NGP happens when changes in the number of processors are determined and it must also be determined which groups of search data will be distributed across the processors. ROP is how to redistribute this data onto processors while keeping the engine responsive and while also minimising the switchover time and the incurred network load. We propose solutions for these sub-problems. For NGP we propose an algorithm for incrementally adjusting the index to t the varying number of virtual machines. For ROP we present an ecient method for redistributing data among processors while keeping the search engine responsive. Regarding the solution for CNP, we propose an algorithm determining the new size of the search engine by re-evaluating its load. We tested the solution performance using a custom-build prototype search engine deployed in the Amazon EC2 cloud. Our experiments show that when we compare our NGP solution with computing the index from scratch, the incremental algorithm speeds up the index computation 2{10 times while maintaining a similar search performance. The chosen redistribution method is 25% to 50% faster than other methods and reduces the network load around by 30%. For CNP we present a deterministic algorithm that shows a good ability to determine a new size of search engine. When combined, these algorithms give an adapting algorithm that is able to adjust the search engine size with a variable workload.

A domain-specific search engine for the construction sector

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.

A science-oriented search engine could solve web problems

Lawrence and Giles [1] eloquently define the current problems with the World-Wide Web, but could "Nature" provide the solution ?

Searching for return: How Google search engine query data can beat the market

Throughout the last years technologic improvements have enabled internet users to analyze and retrieve data regarding Internet searches. In several fields of study this data has been used. Some authors have been using search engine query data to forecast economic variables, to detect influenza areas or to demonstrate that it is possible to capture some patterns in stock markets indexes. In this paper one investment strategy is presented using Google Trends’ weekly query data from major global stock market indexes’ constituents. The results suggest that it is indeed possible to achieve higher Info Sharpe ratios, especially for the major European stock market indexes in comparison to those provided by a buy-and-hold strategy for the period considered.

Using interaction data for improving the offline and online evaluation of search engines

This thesis investigates how web search evaluation can be improved using historical interaction data. Modern search engines combine offline and online evaluation approaches in a sequence of steps that a tested change needs to pass through to be accepted as an improvement and subsequently deployed. We refer to such a sequence of steps as an evaluation pipeline. In this thesis, we consider the evaluation pipeline to contain three sequential steps: an offline evaluation step, an online evaluation scheduling step, and an online evaluation step. In this thesis we show that historical user interaction data can aid in improving the accuracy or efficiency of each of the steps of the web search evaluation pipeline. As a result of these improvements, the overall efficiency of the entire evaluation pipeline is increased. Firstly, we investigate how user interaction data can be used to build accurate offline evaluation methods for query auto-completion mechanisms. We propose a family of offline evaluation metrics for query auto-completion that represents the effort the user has to spend in order to submit their query. The parameters of our proposed metrics are trained against a set of user interactions recorded in the search engine’s query logs. From our experimental study, we observe that our proposed metrics are significantly more correlated with an online user satisfaction indicator than the metrics proposed in the existing literature. Hence, fewer changes will pass the offline evaluation step to be rejected after the online evaluation step. As a result, this would allow us to achieve a higher efficiency of the entire evaluation pipeline. Secondly, we state the problem of the optimised scheduling of online experiments. We tackle this problem by considering a greedy scheduler that prioritises the evaluation queue according to the predicted likelihood of success of a particular experiment. This predictor is trained on a set of online experiments, and uses a diverse set of features to represent an online experiment. Our study demonstrates that a higher number of successful experiments per unit of time can be achieved by deploying such a scheduler on the second step of the evaluation pipeline. Consequently, we argue that the efficiency of the evaluation pipeline can be increased. Next, to improve the efficiency of the online evaluation step, we propose the Generalised Team Draft interleaving framework. Generalised Team Draft considers both the interleaving policy (how often a particular combination of results is shown) and click scoring (how important each click is) as parameters in a data-driven optimisation of the interleaving sensitivity. Further, Generalised Team Draft is applicable beyond domains with a list-based representation of results, i.e. in domains with a grid-based representation, such as image search. Our study using datasets of interleaving experiments performed both in document and image search domains demonstrates that Generalised Team Draft achieves the highest sensitivity. A higher sensitivity indicates that the interleaving experiments can be deployed for a shorter period of time or use a smaller sample of users. Importantly, Generalised Team Draft optimises the interleaving parameters w.r.t. historical interaction data recorded in the interleaving experiments. Finally, we propose to apply the sequential testing methods to reduce the mean deployment time for the interleaving experiments. We adapt two sequential tests for the interleaving experimentation. We demonstrate that one can achieve a significant decrease in experiment duration by using such sequential testing methods. The highest efficiency is achieved by the sequential tests that adjust their stopping thresholds using historical interaction data recorded in diagnostic experiments. Our further experimental study demonstrates that cumulative gains in the online experimentation efficiency can be achieved by combining the interleaving sensitivity optimisation approaches, including Generalised Team Draft, and the sequential testing approaches. Overall, the central contributions of this thesis are the proposed approaches to improve the accuracy or efficiency of the steps of the evaluation pipeline: the offline evaluation frameworks for the query auto-completion, an approach for the optimised scheduling of online experiments, a general framework for the efficient online interleaving evaluation, and a sequential testing approach for the online search evaluation. The experiments in this thesis are based on massive real-life datasets obtained from Yandex, a leading commercial search engine. These experiments demonstrate the potential of the proposed approaches to improve the efficiency of the evaluation pipeline.