No description available.

<p>In a world that is moving faster and faster, a company’s ability to align to market changes is becoming a major competitive factor. Forecasting enables companies to predict what lies ahead, e.g. trend shifts or market turns, and makes it possible to plan for it. But looking into the future is never an easy task.</p><p>“Prediction is very difficult, especially if it’s about the future.” (Niels Bohr, 1885-1962)</p><p>However, progress in the field of forecasting has shown that it is possible for companies to improve on forecasting practices. This master thesis looks at the sales forecasting practices in MNCs primarily operating in emerging and developing countries. We examine the whole process of sales forecasting, also known as forecasting management, in order to develop a comprehensive model for forecasting in this type of companies. The research is based on a single case study, which is then later generalized into broader conclusions.</p><p>The conclusion of this master thesis is that forecasting is a four-step exercise. The four stages we have identified are: Knowledge creation, knowledge transformation, knowledge use and feedback. In the course of these four stages a company’s sales forecast is developed, changed and used. By understanding how each stage works and what to focus on, companies will be able to improve their forecasting practices.</p>

In a world that is moving faster and faster, a company’s ability to align to market changes is becoming a major competitive factor. Forecasting enables companies to predict what lies ahead, e.g. trend shifts or market turns, and makes it possible to plan for it. But looking into the future is never an easy task. “Prediction is very difficult, especially if it’s about the future.” (Niels Bohr, 1885-1962) However, progress in the field of forecasting has shown that it is possible for companies to improve on forecasting practices. This master thesis looks at the sales forecasting practices in MNCs primarily operating in emerging and developing countries. We examine the whole process of sales forecasting, also known as forecasting management, in order to develop a comprehensive model for forecasting in this type of companies. The research is based on a single case study, which is then later generalized into broader conclusions. The conclusion of this master thesis is that forecasting is a four-step exercise. The four stages we have identified are: Knowledge creation, knowledge transformation, knowledge use and feedback. In the course of these four stages a company’s sales forecast is developed, changed and used. By understanding how each stage works and what to focus on, companies will be able to improve their forecasting practices.

The use of hypermedia for educational purposes gained a great deal of importance for educators. There are many opportunities provided to learners by these environments such as independence from time and place, availability and accessability of the course material, non-linear interaction that provides the learner to regulate his/ her own learning and so on. Although many advantages of hypermedia environment are suggested in the literature, there are also many studies concerning with learning in hypermedia environment concluding that many learners face with problems on these settings. This qualitative study aimed to investigate the affects of three important factors in terms of learning with hypermedia revealed by the literature / cognitive styles, computer competency levels, and domain knowledge levels of the students. To the purpose of the study, participants from a web-enhanced course were selected considering these factors, and interviews and observations were conducted to reveal their learning strategies. Results indicated some differences among the different cognitive style groups of students in terms of their preferred learning strategies. Computer competency levels of the students were also found to be quite important in terms of their patterns to use the hypermedia program. Students&rsquo / prior knowledge levels were also important in this study, since different needs and expectations were revealed related to the domain knowledge levels of the participants. Furthermore, a deep understanding about the behaviors, experiences, feelings, and expectations of the students in an instructional hypermedia environment related to suggested different characteristics were gained at the end of the study.

Descriptive search is a type of exploratory search that allows users to search for content by providing descriptors. Instead of having a specific target in mind, the user looks for a recommendation of items that matches the given descriptors. However in the music domain, descriptive words do not necessarily have the same semantic meaning as they have in a generic text corpus. In this study, we investigate if we can train a shallow neural model on playlist data for descriptive music search, and if the model can capture music-specific word semantics. We carry out three experiments to evaluate our model. The first and the second experiments evaluate if the model can predict tracks that are relevant to given search queries, and the third experiment evaluates whether the model successfully captures domain-specific word semantics. From our experiments, we conclude that our model trained on playlist data indeed can capture music-specific word semantics and generate reasonable track predictions. For future work, we suggest to explore possibilities to re-rank the top results retrieved by the model and diversify and/or personalize the ordering of the results. / Deskriptiv sökning är en typ av utforskande informationshämtning där användare söker efter material med hjälp av beskrivande sökord. Istället för att ange namnet på ett objekt i söksträngen så kan användaren med ord beskriva objekt som efterfrågas. I ett musiksammanhang har dock många beskrivande ord inte samma betydelse som de har i ett generellt sammanhang. Vi undersöker därför i vår studie om vi kan träna ett grunt neuralt nätverk med spellistsdata för deskriptiv musiksökning, och om modellen kan lära sig musik-specifika betydelser av ord. Vi utför totalt tre olika experiment för att utvärdera modellen. De första två experimenten undersöker om modellen kan föreslå relevanta låtar givet beskrivande söksträngar och det sista experimentet undersöker om modellen fångar domän-specifika betydelser av sökorden. Resultaten från våra experiment tyder på att modellen lyckas fånga musik-specifika språkmönster och kan föreslå rimliga låtar för deskriptiva söksträngar. För att göra modellen mer användningsbar föreslår vi att undersöka möjligheterna att omranka toppresultaten från modellen, och diversifiera samt personalisera ordningen av resultaten efter individuella användare.

No description available.

Då mjukvaruutveckling består av testning av mjukvara innebär det attmjukvarutestning ökar i takt med ny mjukvara. En typ av mjukvarutestning ärregressionstestning som innebär testning av befintlig mjukvara i syfte att säkerställaatt mjukvaran fungerar som planerat efter att ny funktionalitet tillkommer. Agilautvecklingsprocesser blir alltmer populära, dock är det brist på generell forskning sombetraktar regressionstestning ur en agil synvinkel. Denna studie ämnar undersöka hur agila mjukvaruutvecklingsteam arbetar medregressionstestning. Syftet med studien var att bidra med teoretisk kunskap kringregressionstestning ur agil kontext. För att genomföra undersökningen tillämpades enkvalitativ ansats med semi-strukturerade intervjuer. Tre testare och tre utvecklare frånett mjukvaruutvecklingsföretag intervjuades. Tematisk analys applicerades sedan tillstudiens resultat. Resultatet indikerar på att testare utgår ifrån sin domänkunskap samt kommunikationför att välja ut och avgöra omfattningen av testfall. Kommunikationen består avfunktionaliteten som tillkommer, förändringar som utvecklare har utvecklat men sominte var en del av den ursprungliga uppgiften samt potentiella riskområden.Mjukvaruutvecklare förlitar sig på befintliga tester för att säkerställa att nyfunktionalitet inte påverkar det befintliga systemet. Det innebär att för varje nyfunktionalitet som tillkommer behöver utvecklare säkerställa att tillräcklig kod täcksav relevanta enhets- och integrationstest. / Since software development consists of software testing the amount of softwaretesting increases in step with new software. One type of software testing is regressiontesting, which involves testing existing software in order to ensure that the softwareworks as planned after new functionality is added. Agile development processes arebecoming increasingly popular, however, there is a lack of general research thatconsiders regression testing from an agile point of view. This study aims to investigate how agile software development teams work withregression testing. The aim of the study was to contribute with theoretical knowledgeabout regression testing from an agile context. To carry out the survey, a qualitativeapproach was applied with semi-structured interviews. Three testers and threedevelopers from a software development company were interviewed. Thematicanalysis was applied to the results. The result indicates that testers rely on their domain knowledge and communicationto select and determine the scope of test cases. The communication consists of thefunctionality that is added, changes that developers have developed but were not partof the original task and potential risk areas. Software developers rely on existing teststo ensure that new functionality does not affect the existing system. This means thatfor every new functionality that is added, developers need to ensure that sufficientcode is covered by relevant unit and integration tests.

本研究根據對公共關係實務工作者訪談，瞭解實務工作者認為大學公共關係本科畢業生應具備那些公關核心知識與技能，以及實務工作者對本科教育是否能達成實務專業任用標準的看法。並透過對同時在相關科系兼任授課的實務工作者深度訪談，整理這些具備公關產學雙重身份的受訪者對目前公共關係相關科系在課程目標及課程規劃安排上的看法與建議。 本研究發現，組織及商業管理知識是被較常提及的必備知識，媒體生態及議題建構的知識是所有的受訪者都會提到而且獨立強調的核心知識。研究方法、寫作技巧及語文能力則是最常被受訪者提及的重要必備技能。受訪者者也認為應該要讓學生知道公共關係有那些特定領域（「領域知識」），以及會遭遇那些溝通情境。應以各類公共關係個案情境（情境知識）來讓學生瞭解工具策略的多元性而不是生硬的學習SOP操作程序。 另外，公關本科生並未有就業優勢，但在校任教的實務工作者會更支持本科教育及人才任用。受訪者認為公關課程目標、課程基礎配套資訊以及課程銜接等相關問題都不清楚，業界老師對公共關係教育的付出並未被善用。而學生的學習地圖也並未能被做一個更好的規劃安排，學生無法掌握學習目標及學習策略。 本研究提出的建議為，1.對公關教育及課程任務與規劃應有明確的管理指標，以利教師授課、學生學習及實務業界溝通。2.應善用業界師資資源強化公關領域知識及情境知識教育。3.應鼓勵學生參與實習及實作課程並透過參與校外相關競賽瞭解教學實力。4.應務實面對科系現況補足重要核心知識課程。 關鍵詞：公共關係、公共關係教育、核心知識技能、課程目標、領域知識、情境知識 / This study aims to learn public relations professionals’ 1) perspectives on core knowledge and skills that students majoring in public relations should be equipped with; and 2) viewpoints on whether current university public relations education has been sufficient to assist public relations graduates for related job qualification. Through in-depth interviews with public professionals who currently teach public relations-related courses in universities, I probe their philosophies of teaching, perspectives on course objectives, strategies and plans of teaching. The results of the study indicate that organization theory and business administration were the frequently mentioned as essential knowledge. Meanwhile, getting to know of ever-changing media environment and ability of agenda setting among various communication contexts were essential capability that had been particularly emphasized by all interviewees. On the other hand, research methods, writing skills, and language proficiency were skills frequently addressed by interviewees. Interviewees point out the importance of advancing public relations students’ ‘domain knowledge’ as well as communication scenarios in public relations education. In other words, for the purpose of understanding the strategies and tools of public relations, ‘knowledge of contexts’ in public relations case studies, instead of knowledge of standard operating procedures of public relations practices, should be provided to students during teaching cases. This study also shows that public relations graduates have not owned advantages in competing for public relations job applications. However, professionals who also teach in universities tend to value public relations education more and favor recruiting public relations majors than those who haven’t had the experiences of teaching. Interviewees also expressed their concern of lacking of the public relations course objectives, basic course supplement information, and curriculum linkage, it is why they tend to think public relations education still have much room to improve. They stated that students will not be able to grasp the meaning of learning if they have not developed a knowledge map of public relations. In addition, public relations educators’ contributions to the learning of public relations should be better leveraged. Based on the study, the author offers the following suggestions. 1. Instructors should make teaching objectives and class management strategies clear, as well as course assignments and project arrangement logical. This will benefit lecturers in teaching, students in learning, and public relations professionals, who may serve as guest speakers in class. 2. Department should make the best use of the resources of lecturers from public relations practicum to strengthen the education of both domain and contextual knowledge in the public relations industry. 3. Department should encourage students to take the opportunities of internship programs and to compete for off-campus public relations contests so that students will have chances to reflect what they have learned in classroom. 4. Departments should provide core public relations courses based on what public relations has to offer. Key words: public relations,education,core knowledge and skills, course objectives, domain knowledge, knowledge of contexts

A técnica de mineração de regras de associação surgiu com o objetivo de encontrar conhecimento novo, útil e previamente desconhecido em bancos de dados transacionais, e uma grande quantidade de algoritmos de mineração de regras de associação tem sido proposta na última década. O maior e mais bem conhecido problema destes algoritmos é a geração de grandes quantidades de conjuntos freqüentes e regras de associação. Em bancos de dados geográficos o problema de mineração de regras de associação espacial aumenta significativamente. Além da grande quantidade de regras e padrões gerados a maioria são associações do domínio geográfico, e são bem conhecidas, normalmente explicitamente representadas no esquema do banco de dados. A maioria dos algoritmos de mineração de regras de associação não garantem a eliminação de dependências geográficas conhecidas a priori. O resultado é que as mesmas associações representadas nos esquemas do banco de dados são extraídas pelos algoritmos de mineração de regras de associação e apresentadas ao usuário. O problema de mineração de regras de associação espacial pode ser dividido em três etapas principais: extração dos relacionamentos espaciais, geração dos conjuntos freqüentes e geração das regras de associação. A primeira etapa é a mais custosa tanto em tempo de processamento quanto pelo esforço requerido do usuário. A segunda e terceira etapas têm sido consideradas o maior problema na mineração de regras de associação em bancos de dados transacionais e tem sido abordadas como dois problemas diferentes: “frequent pattern mining” e “association rule mining”. Dependências geográficas bem conhecidas aparecem nas três etapas do processo. Tendo como objetivo a eliminação dessas dependências na mineração de regras de associação espacial essa tese apresenta um framework com três novos métodos para mineração de regras de associação utilizando restrições semânticas como conhecimento a priori. O primeiro método reduz os dados de entrada do algoritmo, e dependências geográficas são eliminadas parcialmente sem que haja perda de informação. O segundo método elimina combinações de pares de objetos geográficos com dependências durante a geração dos conjuntos freqüentes. O terceiro método é uma nova abordagem para gerar conjuntos freqüentes não redundantes e sem dependências, gerando conjuntos freqüentes máximos. Esse método reduz consideravelmente o número final de conjuntos freqüentes, e como conseqüência, reduz o número de regras de associação espacial. / The association rule mining technique emerged with the objective to find novel, useful, and previously unknown associations from transactional databases, and a large amount of association rule mining algorithms have been proposed in the last decade. Their main drawback, which is a well known problem, is the generation of large amounts of frequent patterns and association rules. In geographic databases the problem of mining spatial association rules increases significantly. Besides the large amount of generated patterns and rules, many patterns are well known geographic domain associations, normally explicitly represented in geographic database schemas. The majority of existing algorithms do not warrant the elimination of all well known geographic dependences. The result is that the same associations represented in geographic database schemas are extracted by spatial association rule mining algorithms and presented to the user. The problem of mining spatial association rules from geographic databases requires at least three main steps: compute spatial relationships, generate frequent patterns, and extract association rules. The first step is the most effort demanding and time consuming task in the rule mining process, but has received little attention in the literature. The second and third steps have been considered the main problem in transactional association rule mining and have been addressed as two different problems: frequent pattern mining and association rule mining. Well known geographic dependences which generate well known patterns may appear in the three main steps of the spatial association rule mining process. Aiming to eliminate well known dependences and generate more interesting patterns, this thesis presents a framework with three main methods for mining frequent geographic patterns using knowledge constraints. Semantic knowledge is used to avoid the generation of patterns that are previously known as non-interesting. The first method reduces the input problem, and all well known dependences that can be eliminated without loosing information are removed in data preprocessing. The second method eliminates combinations of pairs of geographic objects with dependences, during the frequent set generation. A third method presents a new approach to generate non-redundant frequent sets, the maximal generalized frequent sets without dependences. This method reduces the number of frequent patterns very significantly, and by consequence, the number of association rules.

A técnica de mineração de regras de associação surgiu com o objetivo de encontrar conhecimento novo, útil e previamente desconhecido em bancos de dados transacionais, e uma grande quantidade de algoritmos de mineração de regras de associação tem sido proposta na última década. O maior e mais bem conhecido problema destes algoritmos é a geração de grandes quantidades de conjuntos freqüentes e regras de associação. Em bancos de dados geográficos o problema de mineração de regras de associação espacial aumenta significativamente. Além da grande quantidade de regras e padrões gerados a maioria são associações do domínio geográfico, e são bem conhecidas, normalmente explicitamente representadas no esquema do banco de dados. A maioria dos algoritmos de mineração de regras de associação não garantem a eliminação de dependências geográficas conhecidas a priori. O resultado é que as mesmas associações representadas nos esquemas do banco de dados são extraídas pelos algoritmos de mineração de regras de associação e apresentadas ao usuário. O problema de mineração de regras de associação espacial pode ser dividido em três etapas principais: extração dos relacionamentos espaciais, geração dos conjuntos freqüentes e geração das regras de associação. A primeira etapa é a mais custosa tanto em tempo de processamento quanto pelo esforço requerido do usuário. A segunda e terceira etapas têm sido consideradas o maior problema na mineração de regras de associação em bancos de dados transacionais e tem sido abordadas como dois problemas diferentes: “frequent pattern mining” e “association rule mining”. Dependências geográficas bem conhecidas aparecem nas três etapas do processo. Tendo como objetivo a eliminação dessas dependências na mineração de regras de associação espacial essa tese apresenta um framework com três novos métodos para mineração de regras de associação utilizando restrições semânticas como conhecimento a priori. O primeiro método reduz os dados de entrada do algoritmo, e dependências geográficas são eliminadas parcialmente sem que haja perda de informação. O segundo método elimina combinações de pares de objetos geográficos com dependências durante a geração dos conjuntos freqüentes. O terceiro método é uma nova abordagem para gerar conjuntos freqüentes não redundantes e sem dependências, gerando conjuntos freqüentes máximos. Esse método reduz consideravelmente o número final de conjuntos freqüentes, e como conseqüência, reduz o número de regras de associação espacial. / The association rule mining technique emerged with the objective to find novel, useful, and previously unknown associations from transactional databases, and a large amount of association rule mining algorithms have been proposed in the last decade. Their main drawback, which is a well known problem, is the generation of large amounts of frequent patterns and association rules. In geographic databases the problem of mining spatial association rules increases significantly. Besides the large amount of generated patterns and rules, many patterns are well known geographic domain associations, normally explicitly represented in geographic database schemas. The majority of existing algorithms do not warrant the elimination of all well known geographic dependences. The result is that the same associations represented in geographic database schemas are extracted by spatial association rule mining algorithms and presented to the user. The problem of mining spatial association rules from geographic databases requires at least three main steps: compute spatial relationships, generate frequent patterns, and extract association rules. The first step is the most effort demanding and time consuming task in the rule mining process, but has received little attention in the literature. The second and third steps have been considered the main problem in transactional association rule mining and have been addressed as two different problems: frequent pattern mining and association rule mining. Well known geographic dependences which generate well known patterns may appear in the three main steps of the spatial association rule mining process. Aiming to eliminate well known dependences and generate more interesting patterns, this thesis presents a framework with three main methods for mining frequent geographic patterns using knowledge constraints. Semantic knowledge is used to avoid the generation of patterns that are previously known as non-interesting. The first method reduces the input problem, and all well known dependences that can be eliminated without loosing information are removed in data preprocessing. The second method eliminates combinations of pairs of geographic objects with dependences, during the frequent set generation. A third method presents a new approach to generate non-redundant frequent sets, the maximal generalized frequent sets without dependences. This method reduces the number of frequent patterns very significantly, and by consequence, the number of association rules.