Service composition integrates existing services to fulfill specific tasks using a set of standards and tools. Existing service composition techniques and tools are mainly designed for SOA professionals. The business processes used in the service composition systems are primarily designed by experienced business analysts who have extensive process knowledge. Process knowledge is the information about a process, including the tasks in a process, the control flow and data flow among tasks. It is challenging for end-users without sufficient service composition skills and process knowledge to find desired services then compose services to perform their daily activities, such as planning a trip. Context-aware techniques provide a promising way to help end-users find services using the context of end-users. However, existing context-aware techniques have limited support for dynamic adapting to new context types (e.g., location, time and activity) and context values (e.g., “New York City”). To shelter end-users from the complexity of service composition, we present our techniques that assist non-IT professional end-users in service composition by dynamically composing and recommending services to meet their requirements. To acquire the desired process knowledge for service composition, we propose an approach to automatically extract process knowledge from existing commercial applications on the Web. By analyzing the context of end-users, our techniques can dynamically adapt to new context types or values and provide personalized service recommendation for end-users. Instead of requiring end-users to specify detailed steps for service composition, the end-users only need to describe their goals using a few keywords. Our approach expands the meaning of an end-user's goal using process knowledge then derives a group of tasks to help the end-user fulfill the goal. The effectiveness of our proposed techniques is demonstrated through a set of case studies. / Thesis (Ph.D, Computing) -- Queen's University, 2011-09-30 11:43:39.151

隨著人們習慣的改變，從網路上獲取新知漸漸取代傳統媒體，這也延伸產生許多新的行為。社群標籤是近幾年流行的一種透過使用者標記來分類與詮釋資訊的方式，相較於傳統分類學要求物件被分類到預先定義好的類別，社群標籤則沒有這樣的要求，因此容易因應內容的變動做出調整。 問答型網站是近年來興起的一種個開放性的知識分享平台，例如quora、Stack Overflow、yahoo 奇摩知識+，使用者可以在平台上與網友做問答的互動，在問與答的討論中，結合大眾的經驗與專長，幫助使用者找到滿意的答案，使用單純的問答系統的好處是可以不必在不同且以分類為主的論壇花費時間尋找答案，和在關鍵字搜索中的結果花費時間尋找答案。 本研究希望能針對問答型網站的文件做自動標籤分類，運用標籤推薦技術來幫助使用者能夠更有效率的找到需要的問題，也讓問答平台可以把這些由使用者所產生的大量問題分群歸類。 在研究過程蒐集Stack Exchange問答網站共20638個問題，使用naïve Bayes演算法與文件相似度計算的方式，進行標籤推薦，推薦適合的標籤給新進文件。在研究結果中，推薦標籤的準確率有64.2% 本研究希望透過自動分類標籤，有效地分類問題。幫助使用者有效率的找到需要的問題，也能把這些由使用者所產生的大量問題分群歸類。 / With User's behavior change. User access to new knowledge from the internet instead of from the traditional media. This Change leads to a lot new behavior. Social tagging is popular in recent years through a user tag to classify and annotate information. Unlike traditional taxonomy requiring items are classified into predefined categories, Social tagging is more elastic to adjust through the content change. Q & A Website is the rise in recent years. Like Quora , Stack Overflow , yahoo Knowledge plus. User can interact with other people form this platform , in Q & A discussion, with People's experience and expertise to help the user find a satisfactory answer. This study hopes to build a tag recommendation system for Q & A Website. The recommendation system can help people find the right problem efficiently , and let Q & A platform can put these numerous problems into the right place. We collect 20,638 questions from Stack Exchange. Use naïve Bayes algorithm and document similarity calculation to recommend tag for the new document. The result of the evaluation show we can effectively recommend relevant tags for the new question.

In this thesis we propose semantic-social recommendation algorithms, that recommend an input item to users connected by a collaboration social network. These algorithms use two types of information: semantic information and social information.The semantic information is based on the semantic relevancy between users and the input item; while the social information is based on the users position and their type and quality of connections in the collaboration social network. Finally, we use depth-first search and breath-first search strategies to explore the graph.Using the semantic information and the social information, in the recommender system, helps us to partially explore the social network, which leads us to reduce the size of the explored data and to minimize the graph searching time.We apply our algorithms on real datasets: MovieLens and Amazon, and we compare the accuracy an the performance of our algorithms with the classical recommendation algorithms, mainly item-based collaborative filtering and hybrid recommendation.Our results show a satisfying accuracy values, and a very significant performance in execution time and in the size of explored data, compared to the classical recommendation algorithms.In fact, the importance of our algorithms relies on the fact that these algorithms explore a very small part of the graph, instead of exploring all the graph as the classical searching methods, and still give a good accuracy compared to the other classical recommendation algorithms. So, minimizing the size of searched data does not badly influence the accuracy of the results.

Every single day, lots of users actively participate in social media sites (e.g., Facebook, YouTube, Last.fm, Flicker, etc.) upload photos, videos, share bookmarks, write blogs and annotate/comment on content provided by others. With the recent proliferation of social media sites, users are overwhelmed by the huge amount of available content. Therefore, organizing and retrieving appropriate multimedia content is becoming an increasingly important and challenging task. This challenging task led a number of research communities to concentrate on social tagging systems (also known as folksonomy) that allow users to freely annotate their media items (e.g., music, images, or video) with any sort of arbitrary words, referred to as tags. Tags assist users to organize their own content, as well as to find relevant content shared by other users. In this thesis, we first analyze how useful a folksonomy is for improving personalized services such as tag recommendation, tag-based search and item annotation. We then propose two new algorithms for social media retrieval and tag recommendation respectively. The first algorithm computes the latent preferences of tags for users from other similar tags, as well as latent annotations of tags for items from other similar items. We then seamlessly map the tags onto items, depending on an individual user’s query, to find the most desirable content relevant to the user’s needs. The second algorithm improves tag-recommendation and item annotation by adapting the Katz measure, a path-ensemble based proximity measure, for the use in social tagging systems. In this algorithm we model folksonomy as a weighted, undirected tripartite graph. We then apply the Katz measure to this graph, and exploit it to provide personalized tag recommendation for individual users. We evaluate our algorithms on two real-world folksonomies collected from Last.fm and CiteULike. The experimental results demonstrate that the proposed algorithms improve the search and the recommendation performance, and obtain significant gains in cold start situations where relatively little information is known about a user or an item

This thesis proposes a novel recommendation approach to take advantage of the information available in user-created lists. Our approach assumes associations among any two items appearing in a list together. We consider two different ways to calculate the strength of item-item associations: frequency of co-occurrence, and sum of Bayesian ratings (SBR) of all lists containing the item pair. The latter takes into consideration not only the number of lists the items have co-appeared in, but also the quality of the lists. We collected a data set of user ratings for books along with Listmania lists on Amazon.com using Amazon Web Services (AWS). Our method shows superior performance to existing user-based and item-based collaborative filtering approaches according to the resulted Mean Absolute Error (MAE), coverage, precision and recall.

Studier kring rekommendationsmotorer är ett område med större signifikans i en växande digital verklighet. Mängden med information ökar och med mer information blir det svårare att hitta det som för individen är av intresse. Vissa specifika områden med tillämpning av rekommendationsmotorer är mer välstuderade än andra, domäner som sysslar med försäljning hamnar i den mer studerade kategorin. Andra domäner som är i behov av rekommendationsmotorer, som inte är lika välstuderade är verksamheter som tillhandahåller möjlighet för lärande via internet. En av dessa verksamheter heter Nomp och erbjuder ett läroverktyg för barn och ungdomar inom matematik. Målet med denna studie är därför att implementera en rekommendationsmotor inom denna mindre utforskade domän. Målet är även att undersöka nyttan med rekommendationsmotorn för applikationens användare. Studien har baserats på ett ramverk inom designforskning, vilket inkluderar olika typer av experiment samt en undersökning. Resultaten från dessa aktiviteter utgjorde empirin för den analys som sedan genomfördes. Resultatet ger visst stöd för att det är möjligt att implementera en rekommendationsmotor för denna domän. De visade däremot inget entydigt svar i vilken omfattning dess nytta har för slutanvändaren. Studiens målsättning uppfylldes till viss del, däremot kunde nyttan för slutanvändaren utforskats i större omfattning. Förhoppningen är att denna studie ska ha effekter i form av praktiska konsekvenser, där användare kan spendera mindre tid på att leta efter information som kan vara till nytta. Det som skiljer sig i denna studie från tidigare liknande studier är att rekommendationsmotorn är implementerad för att passa en verklig verksamhet. I jämförelse med andra studier är denna studie även baserad på data direkt från verksamhetens användare. Vissa liknande artefakter har blivit implementerade, men då är de ofta mer generella eller har använt sig av data som inte är relevant för domänen. Det är också vanligare att liknande rekommendationsmotorer använder sig av direkt användarfeedback för att göra rekommendationer, vilket inte används i denna studie. / Studies regarding recommendation engines have gained greater importance in our reality of the digital community. With regards to the continuously growing amount of digital information it has become harder to find information that’s of importance to the individual. Some specific domains with enforcement of recommendation engines are more studied than others, domains that distribute services or items usually end up in this category. Other domains that are in need of recommendation engines, that’s not as well explored is business which enables learning through the internet. One of these business is called Nomp and provides a learning tool for kids and young teenagers in mathematics. The goal with this study is therefore to implement a recommendation engine for a business that is within this lesser explored domain. The goal is also to explore the advantages a recommendation engine would provide for its users. The study is based on a framework within design science research, which included various kinds of experiments and a survey. The results from these activities represented the empirics for the analysis that was conducted. The results show some signs that it’s possible to implement an artifact for this domain. However, it does not clearly show to what extent it’s valuable for the end user. For some part, the objectives for this study was met. Although, the advantages for the users could have been explored in greater depth. The overall prospects by conducting this study is that it will have some practical consequences, that the user can or will spend lesser time to search for important information. Differences between this study and other similar studies is that the recommendation engine is implemented to fit the needs of a real business. Also, compared to others, this study is based on data collected directly from the end users. Some similar systems have been implemented but the artefact is often more general or might have used data that’s not relevant the domain. It’s also more common that similar recommendation engines are using direct user feedback to make recommendations, which is not used in this study.

Les donn ́ees utilisateurs sont devenue de plus en plus disponibles dans plusieurs do- maines tels que les traces d'usage des smartphones et le Web social. Les donn ́ees util- isateurs, sont un type particulier de donn ́ees qui sont d ́ecrites par des informations socio-d ́emographiques (ex., ˆage, sexe, m ́etier, etc.) et leurs activit ́es (ex., donner un avis sur un restaurant, voter, critiquer un film, etc.). L'analyse des donn ́ees utilisa- teurs int ́eresse beaucoup les scientifiques qui travaillent sur les ́etudes de la population, le marketing en-ligne, les recommandations et l'analyse des donn ́ees `a grande ́echelle. Cependant, les outils d'analyse des donn ́ees utilisateurs sont encore tr`es limit ́es.Dans cette th`ese, nous exploitons cette opportunit ́e et proposons d'analyser les donn ́ees utilisateurs en formant des groupes d'utilisateurs. Cela diff`ere de l'analyse des util- isateurs individuels et aussi des analyses statistiques sur une population enti`ere. Un groupe utilisateur est d ́efini par un ensemble des utilisateurs dont les membres parta- gent des donn ́ees socio-d ́emographiques et ont des activit ́es en commun. L'analyse au niveau d'un groupe a pour objectif de mieux g ́erer les donn ́ees creuses et le bruit dans les donn ́ees. Dans cette th`ese, nous proposons un cadre de gestion de groupes d'utilisateurs qui contient les composantes suivantes: d ́ecouverte de groupes, analyse de groupes, et recommandation aux groupes.La premi`ere composante concerne la d ́ecouverte des groupes d'utilisateurs, c.- `a-d., compte tenu des donn ́ees utilisateurs brutes, obtenir les groupes d'utilisateurs en op- timisantuneouplusieursdimensionsdequalit ́e. Ledeuxi`emecomposant(c.-`a-d., l'analyse) est n ́ecessaire pour aborder le probl`eme de la surcharge de l'information: le r ́esultat d'une ́etape d ́ecouverte des groupes d'utilisateurs peut contenir des millions de groupes. C'est une tache fastidieuse pour un analyste `a ́ecumer tous les groupes trouv ́es. Nous proposons une approche interactive pour faciliter cette analyse. La question finale est comment utiliser les groupes trouv ́es. Dans cette th`ese, nous ́etudions une applica- tion particuli`ere qui est la recommandation aux groupes d'utilisateurs, en consid ́erant les affinit ́es entre les membres du groupe et son ́evolution dans le temps.Toutes nos contributions sont ́evalu ́ees au travers d'un grand nombre d'exp ́erimentations `a la fois pour tester la qualit ́e et la performance (le temps de r ́eponse). / User data is becoming increasingly available in multiple domains ranging from phone usage traces to data on the social Web. User data is a special type of data that is described by user demographics (e.g., age, gender, occupation, etc.) and user activities (e.g., rating, voting, watching a movie, etc.) The analysis of user data is appealing to scientists who work on population studies, online marketing, recommendations, and large-scale data analytics. However, analysis tools for user data is still lacking.In this thesis, we believe there exists a unique opportunity to analyze user data in the form of user groups. This is in contrast with individual user analysis and also statistical analysis on the whole population. A group is defined as set of users whose members have either common demographics or common activities. Group-level analysis reduces the amount of sparsity and noise in data and leads to new insights. In this thesis, we propose a user group management framework consisting of following components: user group discovery, analysis and recommendation.The very first step in our framework is group discovery, i.e., given raw user data, obtain user groups by optimizing one or more quality dimensions. The second component (i.e., analysis) is necessary to tackle the problem of information overload: the output of a user group discovery step often contains millions of user groups. It is a tedious task for an analyst to skim over all produced groups. Thus we need analysis tools to provide valuable insights in this huge space of user groups. The final question in the framework is how to use the found groups. In this thesis, we investigate one of these applications, i.e., user group recommendation, by considering affinities between group members.All our contributions of the proposed framework are evaluated using an extensive set of experiments both for quality and performance.

Le Web d’aujourd’hui est formé, entre autres, de deux types de contenus que sont les données structurées et liées du Web sémantique et les contributions subjectives des utilisateurs du Web social. L’approche ViewpointS a été conçue comme un formalisme creuset apte à intégrer ces deux types de contenus, en préservant la subjectivité des interactions du Web Social. ViewpointS est une approche de représentation subjective des connaissances. Les connaissances sont représentées sous forme de points de vue – des viewpoints – qui sont des éléments de base d’une sémantique individuelle déclarant la proximité de deux ressources. L’approche propose aussi un second degré de subjectivité. En effet, viewpoints peuvent être interprétés différemment selon l’utilisateur grâce au mécanisme de perspective. Il y a une subjectivité dans la connaissance capturée ainsi que dans la manière de l’exploiter. En complément aux approches top-down où la sémantique collective d’un groupe est établie par consensus, la sémantique collective d’une communauté ViewpointS émerge de façon « bottom-up » de l’échange et la confrontation des viewpoints et évolue de manière fluide au fur et à mesure de leur émission. Les ressources du Web sont représentées et liées par les viewpoints dans le Graphe de Connaissances. A l’utilisation, les viewpoints entre deux ressources sont agrégés pour créer une « synapse ». A partir du Graphe de Connaissances contenant les viewpoints et les ressources du Web une Carte de Connaissances composée de synapses et de ressources est créée qui est le fruit de l’interprétation et de l’agrégation des viewpoints. Chaque viewpoint contribue à la création, au renforcement ou à l’affaiblissement d’une synapse qui relie deux ressources. L’échange de viewpoints est le processus de sélection qui permet l’évolution des synapses d’une manière analogue à celles qui évoluent dans le cerveau au fil d’un sélectionnisme neuronal. Nous investiguons dans cette étude l’impact que peut avoir la représentation subjective des connaissances dans divers scénarii de construction collective des connaissances. Les domaines traités sur les bénéfices de la subjectivité des connaissances représentées sont la recherche d’information, la recommandation, l’alignement multilingue d’ontologies et les méthodes de calcul de distance sémantique. / Nowadays, the Web is formed by two types of content which are linked: structured data of the so-called Semantic Web and users’ contributions of the Social Web. The ViewpointS approach was de-signed as an integrative formalism capable of mixing these two types of content while preserving the subjectivity of the interactions of the Social Web. ViewpointS is a subjective knowledge repre-sention approach. Knowledge is represented by means of viewpoints which are micro-expressions of individual semantics tying the relation between two Web resources. The approach also provides a second level of subjectivity. Indeed, the viewpoints can be interpreted differently according to the user through the perspective mechanism. In addition to a top-down approach where collective semantics of a group is established by consensus, collective semantics of a ViewpointS community is emerging from the exchange and confrontation of viewpoints and evolve fluidly. In our frame-work, resources from the Web are tied by viewpoints in a Knowledge Graph. From the Knowledge Graph containing viewpoints and Web resources a Knowledge Map consisting of “synapses” and re-sources is created as a result of the interpretation and aggregation of viewpoints. The evolution of the ViewpointS synapses may be considered analog to the ones in the brain in the very simple sense that each viewpoint contributes to the establishment, strengthening or weakening of a syn-apse that connects two resources. The exchange of viewpoints is the selection process ruling the synapses evolution like the selectionist process within the brain.We investigate in this study the potential impact of our subjective representation of knowledge in various fields: information search, recommendation, multilingual ontology alignment and methods for calculating semantic distances.

Syfte: Forskning visar att byggbranschen inte utvecklas i den takt den borde göra sett till produktiviteten. I försök att öka det värdeskapande arbetet försöker företag att implementera metoder som 5S för att minimera slöseri. Små och medelstora företag (SME-företag) besitter inte samma kompetens och resurser som större företag och därmed försvåras effektiviseringen av produktionsprocessen. Förändringsarbeten av människors vanor samt företagskulturer är en svår process som grundar sig på djupt rotade roller och strukturer. Denna rapports syfte är att introducera Lean genom implementering av 5S på en byggarbetsplats. Målet är sedan att utforma en rekommendationsmanual för hur implementering av Lean-verktyget 5S kan gå tillväga i byggproduktionsskedet. Metod: De valda metoderna för datainsamling är litteraturstudie, fokusgruppsintervjuer och deltagande observationer. Med hjälp av litteraturstudien har kunskap erhållits. Fokusgruppsintervjuerna har bidragit till att genomföra en nulägesanalys, samt utvärdera implementeringen av 5S på arbetsplatsen. Deltagande observationer har använts för att studera utvecklingen på arbetsplatsen. Dessa metoder har legat till grund för att besvara och uppfylla rapportens syfte, mål och frågeställningar. Resultat: Resultatet av den genomförda studien mynnade ut i framtagandet av en rekommendationsmanual för hur 5S kan implementeras i byggproduktionsskedet. Därigenom har kritiska faktorer identifierats och förslag på hur företag kan gå tillväga med implementeringen av 5S. Konsekvenser: Slutsatser som dragits utifrån studien är att förändringsprocesser är svåra att genomföra. Ansvaret för att öka produktiviteten och minimera slöseri bör inte enbart ligga hos de större entreprenörerna. Alla aktörer inom byggbranschen måste samarbeta mot ett gemensamt mål för att förbättra byggprocessen och minska kostnaderna. Lean och 5S kan vara lösningen på problemen. På grund av bristande resurser och kunskap kan därför företag behöva extern hjälp med implementeringen. Redan vid projekteringen av ett byggprojekt är det viktigt att tänka på planeringen för att förhindra slöseri. Innan ett företag genomför en förändring av organisationen är det viktigt att ledningen engagerar sig och förstår Lean för att motivera personalen. Vidare krävs ständiga förbättringar och problemlösning för att engagera personalen. Begränsningar: Studien avgränsades till att fokusera på införandet av 5S och därigenom introducera Lean och dess koncept med förbättringsarbete och problemlösning. Resultatet av implementeringen blev lyckad i detta fall men resultatet kan variera då varje fall är unikt. / Purpose: Studies shows that the construction industry doesn’t develop in the pace that it should regarding productivity. To increase the value creating work companies tries to implement methods as 5S to minimize waste. Small and medium size enterprises (SME) lack the competence and resources that larger companies have and thereby the efficiency of the production process is more difficult. Change management of human’s habits and business culture is a complicated process which is based on deeply rooted roles and structures. The purpose of this report is to introduce Lean by implementing 5S at a construction site. The goal is then to design a recommendations manual for how implementation of the Lean-tool 5S can be done in the construction phase.   Method: The chosen methods for collecting data is literature study, focus group interviews and participatory observations. With help from the literature study knowledge has been gathered. The focus group interviews have contributed with performing zero-state analysis and evaluating the implementation of 5S on the working site. Participatory observations have been used to study the development on the working site. These methods contributed to answering and fulfilling the purpose, goal and the issues of this report.  Results: Results from the study has ended up with the development of a recommendations manual for how 5S can be implemented in the construction phase. Thereby critical factors have been identified and suggestions for how companies can proceed implementation of 5S.  Consequences: Conclusions that have been made from the study is that processes of change are hard to do. The responsibility to increase the productivity and minimize waste should not solely lie with the larger entrepreneurs. Every actor within the construction business must work toward a common goal to improve the construction process and reduce costs.  Lean and 5S can be the solution to these problems. Due to lack of resources and knowledge companies may need external help when it comes to the implementation. Already in the design of a construction project it’s important to think about the planning to prevent waste. Before a company makes a change of the organization it’s important that the management is committed and understands Lean to be able to motivate the staff. Further it’s necessary that continuous improvements and problem-solving is done to engage the staff. Limitations: The study was limited to focusing on the implementation of 5S and thereby introduce Lean and its concept with improvement work and problem-solving. The result of the implementation was a success in this case, but the result can vary from every case since they all are unique.

[pt] O volume de dados RDF publicados na Web aumentou consideravelmente, o que ressaltou a importância de seguir os princípios de dados interligados para promover a interoperabilidade. Um dos princípios afirma que todo novo conjunto de dados deve ser interligado com outros conjuntos de dados publicados na Web. Esta tese contribui para abordar este princípio de duas maneiras. Em primeiro lugar, utiliza algoritmos de detecção de comunidades e técnicas de criação de perfis para a criação e análise automática de um diagrama da nuvem da LOD (Linked Open Data), o qual facilita a localização de conjuntos de dados na nuvem da LOD. Em segundo lugar, descreve três abordagens, apoiadas por ferramentas totalmente implementadas, para recomendar conjuntos de dados a serem interligados com um novo conjunto de dados, um problema conhecido como problema de recomendação de interligação de conjunto de dados. A primeira abordagem utiliza medidas de previsão de links para produzir recomendações de interconexão. A segunda abordagem emprega algoritmos de aprendizagem supervisionado, juntamente com medidas de previsão de links. A terceira abordagem usa algoritmos de agrupamento e técnicas de criação de perfil para produzir recomendações de interconexão. Essas abordagens são implementadas, respectivamente, pelas ferramentas TRT, TRTML e DRX. Por fim, a tese avalia extensivamente essas ferramentas, usando conjuntos de dados do mundo real. Os resultados mostram que estas ferramentas facilitam o processo de criação de links entre diferentes conjuntos de dados. / [en] The volume of RDF data published on the Web increased considerably, which stressed the importance of following the Linked Data principles to foster interoperability. One of the principles requires that a new dataset should be interlinked with other datasets published on the Web. This thesis contributes to addressing this principle in two ways. First, it uses community detection algorithms and profiling techniques for the automatic creation and analysis of a Linked Open Data (LOD) diagram, which facilitates locating datasets in the LOD cloud. Second, it describes three approaches, backed up by fully implemented tools, to recommend datasets to be interlinked with a new dataset, a problem known as the dataset interlinking recommendation problem. The first approach uses link prediction measures to provide a list of datasets recommendations for interlinking. The second approach employs supervised learning algorithms, jointly with link prediction measures. The third approach uses clustering algorithms and profiling techniques to produce dataset interlinking recommendations. These approaches are backed up, respectively, by the TRT, TRTML and DRX tools. Finally, the thesis extensively evaluates these tools, using real-world datasets, reporting results that show that they facilitate the process of creating links between disparate datasets.