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Описание работы

МИНИСТЕРСТВО НАУКИ И ВЫСШЕГО ОБРАЗОВАНИЯ РОССИЙСКОЙ ФЕДЕРАЦИИ
Федеральное государственное автономное образовательное учреждение высшего образования
«ТЮМЕНСКИЙ ГОСУДАРСТВЕННЫЙ УНИВЕРСИТЕТ» ШКОЛА ПЕРСПЕКТИВНЫХ ИССЛЕДОВАНИЙ (SAS)
РЕКОМЕНДОВАНО К ЗАЩИТЕ В ГЭК

ВЫПУСКНАЯ КВАЛИФИКАЦИОННАЯ РАБОТА
бакалаврская работа
DEVELOPING CLOTHING-ACCORDING-TO-WEATHER АРР: ADJUSTING SUGGESTIONS BASED ON ТНЕ USER'S EXPERIENCE /РАЗРАБОТКА ПРИЛОЖЕНИЯ ДЛЯ ОДЕЖДЫ ПО ПОГОДЕ: КОРРЕКТИРОВКА ПРЕДЛОЖЕНИЙ НА ОСНОВЕ ОПЫТА ПОЛЬЗОВАТЕЛЯ

09.03.03 Прикладная информатика
Профиль «Информационные технологии и системный анализ»

Тюмень 2023

TABLE OF CONTENTS
LIST OF TERMS 4
INTRODUCTION. PROBLEM STATEMENT AND PROJECT VALUE 6
CHAPTER 1. AUDIENCE 7
1.1. QUALITATIVE RESEARCH 7
1.1.1. HYPOTHESES 7
1.1.2. INTERVIEW QUESTIONS 9
1.1.3. INTERVIEW FINDINGS 11
1.2. DEFINING TARGET AUDIENCE OF THE SERVICE 12
CHAPTER 2. MARKET ANALYSIS 15
2.1. MARKET SIZE 15
2.2. COMPETITOR ANALYSIS 15
2.2.1. APPLICATIONS FOCUSED ON WEATHER 15
2.2.2. APPLICATIONS FOCUSED ON CLOTHES CAPSULES 18
2.3 DEMAND 20
CHAPTER 3. PROJECT DESIGN 21
3.1. REQUIREMENTS 21
3.1.1. FUNCTIONAL REQUIREMENTS 21
3.1.2. NON-FUNCTIONAL REQUIREMENTS 23
3.2. IMPLEMENTATION 25
CHAPTER 4. METHODS AND TOOLS 27
4.1. RESEARCH 27
4.2. DESIGN 28
4.3. DEVELOPMENT 29
CHAPTER 5. END PRODUCT 31
CHAPTER 6. REFLECTION. CHANGE OF THE SERVICE’S CORE IDEA 32
CONCLUSION 34
BIBLIOGRAPHY 35

LIST OF TERMS
? Purposive sampling: A sampling method where the guiding logic when deciding who to recruit is to achieve the most relevant participants for the research topic, in terms of being rich in information or insights. (Knott et al)
? Snowball sampling: Researchers ask participants to introduce the researcher to others who meet the study’s inclusion criteria. (Knott et al)
INTRODUCTION. PROBLEM STATEMENT AND PROJECT VALUE.
When people are going to have a trip, be it business one or traveling, they face a challenge of deciding what clothes they should take with themselves. This challenge is born from several points. First, people usually are limited in the weight of their luggage, hence they need to take a number of clothes that will be, on the one hand, sufficient for the duration of their trip so that people would not have to buy extra clothes at their destination point (taking into account different weather conditions — for example, some warm clothes might be needed if there is cooling during the trip), and on the other hand, will fit into the mentioned limits. This entails that the perfect formula for a clothing set for a trip is “the less items, the more versatility — the better”. Second, the climate and weather in their usual location and destination point might be different, and just orienting on their habitual clothing choices for a certain weather might be misleading: for example, the same temperature would feel different in Tyumen and Saint-Petersburg because of different humidity and wind strength. Not to mention that it is hard for human beings to convert abstract concepts such as the number of degrees Celcius to practical knowledge. In the case of the weather, this problem is expressed in the fact that frequently people do not know what they should wear in certain conditions. The fact that different people perceive the same weather differently and might hence need distinct, personalized clothes recommendations, adds to the issue. What makes this problem even more pressing is that dring trips, especially if their aim is traveling, people spend a lot of time outdoors, which means that they cannot escape the weather and hence need to adjust to it.
My solution to this problem is a service that suggests a clothing capsule for a trip to a certain location for a certain period of time. This service allows a user to create a set of clothing items that form several outfits, suitable for the weather throughout the whole trip, even if the weather is changing. The service is also able to note user’s feedback on these suggestions (for example, the proposed capsule was too hot for someone) and adjust further ones to the user’s individual perception of the weather.

BIBLIOGRAPHY
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3. Eriksson, Hans-Erik, and Magnus Penker. Business modeling with UML. New York: Wiley, 2000. 480 p.
4. Groff, James R., Paul N. Weinberg, and Andrew J. Oppel. SQL: the complete reference. New York: McGraw Hill, 2002. 980 p.
5. Knott, Eleanor, Aliya Hamid Rao, Kate Summers, and Chana Teeger. Interviews in the social sciences // Nature Reviews Methods Primers v. 2, no. 1, 2022. 73 p.
6. Sawyer, Pete, and Gerald Kotonya. Software requirements. New York: SWEBOK, 2001. 228 p.
7. Sillin, Jack. What Does Humidity Percentage Mean and What Is High Humidity? | Weather Station Advisor. // Weather Station Advisor, February 11, 2023.
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// StatCounter Global Stats.
10. Temperature // Think Metric.
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13. Эксперты выяснили, как часто путешествуют россияне и какие выбирают отели // РИА Новости. December 5, 2022.