Management and Future Technologies | Volume 2, Issue 1 | № 005

Management and Future Technologies journal.umft.uz March 2025 Volume 2, Issue 1 Although paraphrase generation, i.e. creating an artificial paraphrase for a given sentence, is beyond the scope of this article, this issue is being further explored in our work as part of the ParaPhraser project. Our paraphrase segmentation method is based on the approach proposed by Fernando and Stevenson [9]. They proposed a matrix similarity metric that measures the distance between two sentences based on the similarity of words in WordNet. Since a complete Uzbek WordNet is not available, we use a thesaurus instead of WordNet. Figure 1. Sentence paraphrasing tasks Androutsopoulos and Malakasiotis list several methods for paraphrase detection, including logic-based methods, vector-based methods, surface string (text) similarity-based methods, syntactic similarity-based methods, symbolic representation-based methods, machine learning methods, and decoding-based methods. Pham et al. used a distributional semantics approach to identify paraphrases. Furthermore, some approaches are difficult to categorize [10]. In recent years, the development of deep learning methods has also influenced research on paraphrase detection. Attention-based LSTM (attention-based Long Short Time Memory) architectures have been used to automatically match pairs of sentences and measure their similarity [11]. Convolutional neural networks have achieved competitive results in the paraphrase detection 141

Volume 2, Issue 1 142 March 2025 Management and Future Technologies journal.umft.uz task [12]. The analysis by Androutsopoulos and Malakasiotis lists several natural language processing tasks where paraphrasing techniques can be applied, including question-answering systems, text reduction, information extraction, machine translation, and natural language generation. In recent years, more and more areas of application of paraphrasing have begun to emerge.. Barrón-Cedeño et al. have shown the importance of paraphrase in detecting plagiarism and have identified a corpus of plagiarism types [13]. Petrović used paraphrase in the task of first subject identification, i.e. finding the original source describing a particular news event. They have argued that in this task, as in many others, lexical variation is a major obstacle and can be overcome using paraphrase detection techniques.. Pavlick and Nenkova demonstrated the importance of stylistic changes in paraphrases in determining genre [15]. In a paper by Wieting et al., the authors used a corpus of paraphrases to train semantic groups of words (word embeddings) and improved results in a lexical similarity task [16]. Hintz argued that paraphrases can be used to provide stylistic coherence in multi-document text reduction systems [17]. Although most studies have been conducted on English data, there is also interest in research on paraphrase in other languages. For example, Eshkol-Taravella and Grabar studied paraphrased reconstructions in French spoken corpora[18]. Nevěřílová proposed a paraphrase generation system for Czech [19]. There are several publications on Russian paraphrase detection and text reuse [20,21, 22]. However, compared to other languages and natural language processing tasks for Russian, the amount of research in this area is relatively small.. Over the past five years, a number of general tasks on semantic text similarity have been organized within the framework of SemEval conferences[23,24]. Paraphrase detection is very similar to this task, the only difference being that in our task the classification is discrete (paraphrase is not paraphrase), while in textual similarity the semantic distance is required to be calculated on a continuous scale. The paraphrasing of texts can vary depending on the task of the corpus. Paraphrase Corpus Among the existing paraphrase corpora, the most famous is the Microsoft Research Paraphrase Corpus (MSRP) (Dolan et al., 2004). This corpus consists of 5801 sentence pairs collected from news clusters, of which 3900 are paraphrases. Although it is known for its vague definition of paraphrase, bidirectional labeling, and high lexical similarity between sentences (e.g., Rus et al., 2014, Triantafillou et al., 2016, Liang et al., 2016), it is widely used for paraphrase detection. It has also inspired the development of other paraphrase resources (including our own ParaPhraser corpus). MSRP is used as a dataset to track advanced results in paraphrase detection. Other paraphrase corpora can be divided into several groups based on the level of paraphrase they cover. Some corpora are structured only at the sentence level, while others additionally include

Management and Future Technologies journal.umft.uz March 2025 Volume 2, Issue 1 word or phrase level annotations. There are also resources that only contain word and phrase level paraphrases. Based on the origin of the paraphrases, corpora are classified as either automatic or manual. Automatic corpora include parallel corpora in multiple languages and comparative corpora in one language. For example, different translations of the same texts, news articles, answers to questions from social networks or students, social media, Wikipedia, or different descriptions of the same videos. Many corpora have been developed in the world, which serve to increase the importance of natural language. Corpora can be divided into different categories: language, time, modern, future corpora The types of corpora based on how they represent natural language data across languages are presented in the figure below. Figure 2. Types of corpora according to language representation Monolingual corpora are also divided into different groups. They can also be divided into groups according to the type of information they contain: Textual, media, etc. Textual corpora can be divided into the following types: Sentence corpora, Phrase corpora, Word corpora. Sentence corpora are corpora that consist of sentences grouped by category in the database. Table 1 provides information about sentence corpora. Table 1. Examples of sentence corpora. № Naming sentence corpuses Dataset view in the corpus Year 143

Volume 2, Issue 1 144 1. 2. 3. 4. 5. 6. 7. March 2025 Corpus KMC User Language Paraphrase Corpus Question Paraphrase Corpus Microsoft Research Video Description Corp Regneri va Wang korpusi Twitter Paraphrase Corpus Student Response Analysis Corpus Management and Future Technologies assembled from pairs of texts and their abbreviated forms composed of student paraphrases of biology textbook sentences Includes sentence pairs taken from WikiAnswers and marked by social media users journal.umft.uz 2002 2008 Compiled from short video descriptions set on the Amazon Mechanical Turk crowdsourcing platform 2011 compiled from the synopsis of TV show episodes 2014 from tweets related to the same events 2013 collected from students' answers to explanation and definition questions collected from multiple sources, such as news texts, FrameNet-WordNet glossaries, and OntoNotesWordNet glossaries 2013 8. Semantic Textual Similarity Corpus 9. Japanese Paraphrase Corpus for Speech Translation composed of travel conversational phrases and their variants paraphrased by humans 2004 10. Turkish Paraphrase Corpus compiled from translations of popular novels, subtitles, translations from the English-Turkish parallel corpus, and articles from news sites 2012 2013 Phrase corpora are corpuses of phrases. Many phrase corpora can be found in the world of computational linguistics. WiCoPaCo is a corpus of French paraphrases compiled from Wikipedia’s edit history. WRPA is also based on Wikipedia and uses its structure [25]. However, unlike WiCoPaCo, it only covers paraphrases between certain relationships (authorship, birth date, and person, etc.). The SEMILAR Corpus is also based solely on the MSRP (Microsoft Research Paraphrase Corpus) and is enriched with word-level similarity and concordance. The Paraphrase Database (PPDB) is a rich paraphrase resource, containing billions of paraphrase pairs. This database is compiled from bilingual parallel corpora for Russian and over 20 other languages. The authors use a language-independent method to extract paraphrases from bilingual parallel texts: paraphrases are found by “pivoting” from a common translation in one language to a common translation in the other [26]. This approach was proposed by scholars such as Bannard and Callison-Burch and has been successfully used by many researchers [27]. The PPDB includes lexical, phraseological, and syntactic paraphrases, all of which are marked with metrics derived from machine translation. ParaPhraser systems. It is known that there are no publicly available paraphrase resources for many natural languages, including Uzbek. The only exception is the dataset published by Ganitkevitch and Callison-Burch as part of The Paraphrase Database project. Although this database

Management and Future Technologies journal.umft.uz March 2025 Volume 2, Issue 1 contains paraphrases at the word, phrase, and syntactic levels, it lacks information about the context of paraphrases [26]. Therefore, in this study, we aimed to develop a corpus of paraphrases at the sentence level.. The research aims to study the phenomenon of paraphrase in the Uzbek language, and includes the extraction of paraphrases, the creation of paraphrase corpora, and the construction of models for the detection and generation of paraphrases. A two-step process is required to develop a corpus: 1. Automatically collect paraphrase pairs, 2. Manually tag them using crowdsourcing. Below we describe both stages in detail.. Automatic collection of paraphrase pairs In developing the ParaPhraser corpus, sentence pairs are collected in real time. We analyze the websites of several Uzbek news agencies and extract the headlines of the articles. The headlines are compared to each other according to the strategy proposed by Wubben et al. [28]. We add sentence pairs to the corpus whose similarity metric value is above a certain threshold value. In order to provide more negative samples, a small random subset of sentence pairs whose similarity metric value is below the threshold value is also added to the corpus. Crowdsourcing Sentence paraphrases are tagged by annotators. Annotators are native speakers of Uzbek, most of whom are ordinary speakers, but some also include linguistics specialists and students. The annotator tags two sentences according to the following classes: Table 2. Classificationa of tegging sentences. № Classes Teg 1 The sentences mean the same thing. 1 2 means similar meanings 0 3 means different things -1 No special instructions are given during the tagging process, i.e. experts use their knowledge and intuition. We exclude pairs of sentences with opposite judgments (-1 and 1). The paraphrase class of a sentence pair in the corpus is calculated as the median of all the ratings given to this pair by experts. 145

Volume 2, Issue 1 146 March 2025 Management and Future Technologies journal.umft.uz The implementation of this task consists of two important tasks: Three-class classification: identifying given sentence pairs, predicting whether they are exact paraphrases, close paraphrases, or non-paraphrases. Two-class classification: Identifying a given pair of sentences, predicting whether they are paraphrases (exact or close paraphrases) or non-paraphrases. For each task, we allowed standard and non-standard tasks. For standard tasks, participants were allowed to use only the ParaPhraser corpus or any derivatives (e.g., embeddings) from these corpora. However, for standard tasks, we allowed the use of any language processing tools or manually constructed dictionaries. For non-standard tasks, any resources were allowed. Participants’ submitted tasks were evaluated for accuracy and F1-score (F1micro and F1-macro for the three-class classification task). The general processes in developing a sentence paraphrasing information system are shown in the figure below. Figure 3. Processes of paraphrasing sentences Each of these processes is performed using special algorithms and language models. The following algorithms are used to paraphrase Uzbek sentences:. • Rule-based semantic parser

Management and Future Technologies journal.umft.uz March 2025 Volume 2, Issue 1 • SVM or Random Forest classifiers, on thesaurus-based similarity features • SVM classifier on unigrams, bigrams, and trigrams of words and symbols • Gradient Boosting classifier, on features derived from existing tools, including English machine translation and similarity detection tools • Convolutional neural networks The task organizers submitted work for both tasks as Team3448. Our approach to the paraphrase detection problem is based on using three types of sentence similarity measures as features in the paraphrase classification task [29]: 1) superficial or surface similarity measures, which are based on comparing n-grams, words, and symbols in sentences; 2) semantic similarity measures, which include the relationships of synonymy and derivational morphology; 3) distributional measures, which use vector representations of words and phrases. Conclusion The study presents the ParaPhraser corpus, which is available for independent use, and the first steps in paraphrasing Uzbek. This paper shows for which tasks the Paraphrasing corpus can be used, which means that practical applications that require a paraphrasing step, such as inter-document text reduction or information retrieval, are useful for increasing the volume of Uzbek resources. The overall task results show that paraphrasing methods developed for other languages can be applied to Uzbek, and the results may be only slightly inferior to the state-of-the-art approach in English. According to the overall task results, various methods, from rule-based systems to deep learning, can be used for paraphrasing, and most of them are quite successful in the given task. Since our training dataset is not large, it is reasonable to use the traditional (classifier + language features) approach to achieve higher results. To increase the level of accuracy, it is required to increase the size of the training data. 147

Volume 2, Issue 1 148 March 2025 Management and Future Technologies journal.umft.uz References 1. Bhagat, R., Hovy, E. What is a paraphrase? Computational Linguistics, 39 (3): 463–472 (2013). 2. Agirre, E., Banea, C., Cardie, C., Cer, D., Diab, M., Gonzalez-Agirre, A., Guo, W., LopezGazpio, I., Maritxalar, M., Mihalcea, R., Rigau, G.; Uria, L., Wiebe, J. SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability. Proceedings of SemEval 2015 (2015) 3. Vila, M., Martí, M. A., Rodríguez, H. Is this a paraphrase? What kind? Paraphrase boundaries and typology. Open Journal of Modern Linguistics, 4 (01), 205 (2014) 4. Pronoza E., Yagunova E., Kochetkova N. Sentence Paraphrase Graphs: Classification Based on Predictive Models or Annotators’ Decisions?. In: Sidorov G., Herrera-Alcántara O. (eds) Advances in Computational Intelligence. MICAI 2016. Lecture Notes in Computer Science, vol 10061. Springer, Cham (2017). 5. Pronoza, E., Yagunova, E. Comparison of sentence similarity measures for Russian paraphrase identification. In Artificial Intelligence and Natural Language and Information Extraction, Social Media and Web Search FRUCT Conference (AINL-ISMW FRUCT), pp. 74–82, IEEE (2015a). 6. Pronoza, E., Yagunova, E. Low-Level Features for Paraphrase Identification. In: Sidorov, G., Galicia-Haro, Sofía N. (eds.) MICAI 2015. LNCS, vol. 9413, pp. 59–71. Springer, Cham (2015b). 7. Pronoza, E., Yagunova, E., Pronoza, A. Construction of a Russian Paraphrase Corpus: Unsupervised Paraphrase Extraction. Proceedings of the 9th Russian Summer School in Information Retrieval, August 24–28, 2015, Saint-Petersburg, Russia, (RuSSIR 2015, Young Scientist Conference), Springer CCIS (2015). 8. Androutsopoulos, I., Prodromos Malakasiotis, P. A survey of paraphrasing and textual entailment methods. Journal of Artificial Intelligence Research, v. 38: 135–187 (2010). 9. Fernando, S., Stevenson, M. A semantic similarity approach to paraphrase detection. In Proceedings of the 11th Annual Research Colloquium of the UK Special Interest Group for Computational Linguistics, pp. 45–52, (2008). 10. Pham, N., Bernardi, R., Zhang, Y. Z., Baroni, M. Sentence paraphrase detection: When determiners and word order make the difference. In Proceedings of the Towards a Formal Distributional Semantics Workshop at IWCS 2013, pp. 21–29 (2013). 11. Rocktäschel, T., Grefenstette, E., Hermann, K. M., Kočiský, T., Blunsom, P. Reasoning about entailment with neural attention. arXiv preprint arXiv:1509.06664. (2015) 12. He, H., Gimpel, K., Lin, J. Multi-perspective sentence similarity modeling with convolutional neural networks. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1576–1586 (2015). 13. Barrón-Cedeño, A., Vila, M., Martí, M. A., Rosso, P. Plagiarism meets paraphrasing: Insights for the next generation in automatic plagiarism detection. Computational Linguistics, 39(4), 917–947 (2013). 14. Petrović, S., Osborne, M., Lavrenko, V. Using paraphrases for improving first story detection in news and Twitter. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 338–346. Association for Computational Linguistics (2012) 15. Pavlick, E., Nenkova, A. Inducing lexical style properties for paraphrase and genre differentiation. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 218– 224 (2015)

Management and Future Technologies journal.umft.uz March 2025 Volume 2, Issue 1 16. Wieting, J., Bansal, M., Gimpel, K., Livescu, K. Transactions of the Association for Computational Linguistics, vol. 3, pp. 345–358 (2015) 17. Hintz, G. Data-driven Paraphrasing and Stylistic Harmonization. In Proceedings of NAACL-HLT, pp. 37–44 (2016). 18. Eshkol-Taravella, I., Grabar, N. Paraphrastic Reformulations in Spoken Corpora. In International Conference on Natural Language Processing, pp. 425–437, Springer International Publishing (2014). 19. Nevěřilová, Z. Paraphrase and textual entailment generation in Czech. Computación y Sistemas, 18 (3): 555–568 (2014) 20. Bakhteev, O., Kuznetsova, R., Romanov, A., Khritankov, A. A monolingual approach to detection of text reuse in Russian-English collection. In Artificial Intelligence and Natural Language and Information Extraction, Social Media and Web Search FRUCT Conference (AINL-ISMW FRUCT), pp. 3–10, IEEE (2015). 21. Khritankov, A., Botov, P., Surovenko, N., Tsarkov, S., Viuchnov, D., Chekhovich, Y. Discovering Text Reuse in Large Collections of Documents: a Study of Theses in History Sciences. In Artificial Intelligence and Natural Language and Information Extraction, Social Media and Web Search FRUCT Conference (AINL-ISMW FRUCT), pp. 26–32, IEEE (2015). 22. Malykh, V. Robust Word Vectors for Russian Language. In Proceedings of Artificial Intelligence and Natural Language AINL FRUCT 2016 Conference, Saint-Petersburg, Russia, 10–12 November 2016, pp.95–98 (2016). 23. Agirre, E., Cer, D., Diab, M., Gonzalez-Agirre, A SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity. Proceedings of SemEval 2012 (2012). 24. Agirre, E., Cer, D., Diab, M., Gonzalez-Agirre, A., Guo. W. *SEM 2013 shared task: Semantic Textual Similarity, Proceedings of *SEM 2013 (2013). 25. Vila, M., Rodriguez, H., Marti, M. A. WRPA: A System for Relational Paraphrase Acquisition from Wikipedia. Procesamiento del Lenguaje Natural, Revista No 45, septiembre 2010, pp. 11–19 (2010). 26. Ganitkevitch, J., Callison-Burch, Ch.: The Multilingual Paraphrase Database. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14). European Language Resources Association (ELRA). Reykjavik, Iceland (2014). 27. Bannard, C., Callison-Burch, Ch.: Paraphrasing with Bilingual Parallel Corpora. In Proceedings of the 43rd Annual Meeting of the ACL, pp. 597–604 (2005). 28. Wubben, S., van den Bosch, A., Krahmer, E., Marsi, E.: Clustering and Matching Headlines for Automatic Paraphrase Acquisition. In: Proceedings of the 12th European Workshop on Natural, language Generation, pp. 122–125, Athens, Greece (2009) 29. Pronoza, E., Yagunova, E., Pronoza, A. Construction of a Russian Paraphrase Corpus: Unsupervised Paraphrase Extraction. Proceedings of the 9th Russian Summer School in Information Retrieval, August 24–28, 2015, Saint-Petersburg, Russia, (RuSSIR 2015, Young Scientist Conference), Springer CCIS (2015). 149

Volume 2, Issue 1 March 2025 150 Mualliflar Asror Ulashev Kompyuter ilmlari va dasturlash kafedrasi, O‘zbekiston Milliy universiteti Jizzax filiali, Jizzax, O‘zbekiston; ulashevasror8@gmail. com Aynakulov Toxir Kompyuter ilmlari va dasturlash kafedrasi, O‘zbekiston Milliy universiteti Jizzax filiali, Jizzax, O‘zbekiston; aynaqulovtohir@gmail. com Mas’ul: ulashevasror8@gmail.com Management and Future Technologies journal.umft.uz KITOB SAVDOSI JARAYONINI OPTIMALLASH TIRISHDA AVTOMAT LASHTIRILGAN AXBOROT TIZIMINING O‘RNI A nnotatsiya: Ushbu ilmiy tadqiqot maqolasida kitob savdosi jarayonlarini avtomatlashtirishning nazariy va amaliy jihatlari tahlil qilinadi. Kitob do‘konlari faoliyatini optimallashtirish uchun ishlab chiqilgan avtomatlashtirilgan axborot tizimlari sotuv, izlash va hisobkitob jarayonlarini samarali tashkil etish imkoniyatini yaratadi. Tadqiqot axborot texnologiyalarining kitob savdosi sohasiga ta’siri, biznes jarayonlarini optimallashtirishga xizmat qiluvchi ma’lumotlar bazasi va dasturiy ta’minot imkoniyatlari, shuningdek, avtomatlashtirishning iqtisodiy samaradorligini o‘rganishga qaratilgan. Ish davomida C# dasturlash tili yordamida kitob do‘konlari uchun mo‘ljallangan avtomatlashtirilgan axborot tizimi ishlab chiqildi va tahlil qilindi. Ushbu tizim kitoblarni izlash jarayonini osonlashtirish, sotuv va hisob-kitoblarda inson omilidan kelib chiqadigan xatolarni kamaytirish, mijozlar va sotuvchilar uchun qulay ma’lumot taqdim etish kabi muhim funksiyalarni o‘z ichiga oladi. Tadqiqot natijalari shuni ko‘rsatadiki, avtomatlashtirilgan axborot tizimlaridan foydalanish kitob savdosi jarayonining samaradorligini oshirish, vaqtni tejash va resurslardan oqilona foydalanish imkonini beradi. Kalit so‘zlar: C#, BPMN, UML, login, parol, ISBN, microservices, admin, Message.box, Menu, interfeys. Copyright: © 2024 by the authors. Ushbu maqola Creative Commons Attribution (CC BY) litsenziyasi shartlari asosida tarqatiladigan ochiq foydalanish maqolasi hisoblanadi (https://creativecommons.org/ licenses/by/4.0/).

Management and Future Technologies journal.umft.uz March 2025 Volume 2, Issue 1 Kirish Axborot texnologiyalarining tez rivojlanishi savdo va xizmat ko‘rsatish tarmoqlarida avtomatlashtirish jarayonlarini muhim bosqichga olib chiqdi. Zamonaviy biznesda sotuv jarayonlarini optimallashtirish, ma’lumotlar almashinuvi tezligini oshirish va hisob-kitob tizimini soddalashtirish asosiy maqsadlarga aylandi. Kitob do‘konlari bu jarayonlardan mustasno emas, chunki an’anaviy savdo tizimlarida kitoblarni qo‘lda izlash, ombor hisobini yuritish, xaridorlarga tezkor ma’lumot taqdim etish va hisob-kitoblarni aniq yuritish katta vaqt va mehnat talab qiladi[3]. Kitob do‘konlarining avtomatlashtirilgan axborot tizimiga o‘tishi quyidagi muammolarni hal qilishga xizmat qiladi: • Kitoblarni izlash jarayonini tezlashtirish va soddalashtirish; • Sotuv jarayonlarida inson omilidan kelib chiqadigan xatolarni kamaytirish; • Hisob-kitob va moliyaviy operatsiyalarni avtomatlashtirish; • Ombor hisobini aniq yuritish va mavjud mahsulotlar to‘g‘risidagi ma’lumotni real vaqt rejimida yangilash; • Mijozlarga ko‘rsatiladigan xizmat sifatini oshirish va biznes jarayonlarini optimallashtirish. Ushbu ilmiy tadqiqot kitob savdosi jarayonlarini avtomatlashtirish uchun dasturiy ta’minot va ma’lumotlar bazasi texnologiyalarini tadbiq etish masalalarini o‘rganishga qaratilgan. Shuningdek, C# dasturlash tili asosida ishlab chiqilgan avtomatlashtirilgan axborot tizimining tuzilishi, funksional imkoniyatlari va samaradorlik jihatlari tahlil qilinadi[7]. Tadqiqotning maqsadi va vazifalari Tadqiqotning asosiy maqsadi – kitob savdosi jarayonlarini avtomatlashtirish orqali savdo samaradorligini oshirish va ish jarayonlarini optimallashtirish masalasini ilmiy asosda o‘rganishdir[6]. Ushbu maqsadga erishish uchun quyidagi ilmiy va amaliy vazifalar belgilangan: • Do‘konlarda sotuv jarayonlarini avtomatlashtirish orqali navbatlarni kamaytirish; • Kitoblarni izlash jarayonini tezlashtirish va chalkashliklarning oldini olish; • Hisob-kitob ishlarida inson omilining ta’sirini kamaytirish; • Dasturiy ta’minot orqali ombor boshqaruv tizimini soddalashtirish; • Qo‘lda bajariladigan hisob-kitoblarni avtomatlashtirish va bu orqali hisoblash jarayonlari uchun sarflanadigan vaqtni qisqartirish; • Dasturiy ta’minot yaratish jarayonida algoritmlash va ma’lumotlar bazasi bilan ishlash bo‘yicha nazariy va amaliy bilimlarni mustahkamlash. 151

Volume 2, Issue 1 March 2025 Management and Future Technologies journal.umft.uz Ilmiy asoslari 152 Kitob do‘konlarida avtomatlashtirilgan axborot tizimlarining joriy etilishi savdo sohasida qo‘llaniladigan zamonaviy texnologiyalar bilan bog‘liq. Ushbu tadqiqot quyidagi ilmiy asoslarga tayanadi: 1. Axborot tizimlari va biznes jarayonlarini optimallashtirish Zamonaviy biznesni avtomatlashtirishning BPMN (Business Process Model and Notation) va UML (Unified Modeling Language) kabi modellashtirish texnologiyalari kitob do‘konlari uchun axborot tizimini ishlab chiqishda qo‘llanilishi mumkin. 2. Ma’lumotlar bazasi boshqaruvi Ma’lumotlar bazasi dizayni va normalizatsiya jarayoni kitob do‘konlarining ma’lumotlarni samarali saqlashi va ulardan tezkor foydalanishi uchun muhim ahamiyatga ega. 3. Dasturiy ta’minot arxitekturasi Axborot tizimining klient-server modeli, uch pog‘onali arxitektura (three-tier architecture) yoki mikroxizmatlar (microservices) modeli asosida ishlab chiqilishi uning kengayuvchanligini ta’minlaydi. 4. Savdo jarayonlarini avtomatlashtirishning iqtisodiy samaradorligi Ilmiy tadqiqotlarda savdo jarayonlarining avtomatlashtirilishi biznes samaradorligini 20-30% gacha oshirish mumkinligi aniqlangan. Bu kitob do‘konlari uchun ham muhim omildir. Amaliy yechim va tizimning tuzilishi Tadqiqot doirasida C# dasturlash tili asosida kitob do‘konlari uchun avtomatlashtirilgan axborot tizimi ishlab chiqildi[2]. Ushbu tizim quyidagi asosiy modullardan iborat: Ma’lumotlar bazasi – kitoblar, mijozlar, buyurtmalar, to‘lovlar va hisobotlar haqida ma’lumotlarni saqlaydi. Izlash va filtr – kitob nomi, muallif, janr yoki ISBN bo‘yicha tezkor qidirish imkonini beradi. Savdo moduli – buyurtmalarni qayd etish, hisob-kitob qilish va mijozlarga elektron hisobfaktura taqdim etish funksiyalarini o‘z ichiga oladi. Statistik tahlil va hisobotlar – sotuv dinamikasi, ombordagi mahsulot qoldig‘i va boshqa biznes ko‘rsatkichlarini real vaqt rejimida kuzatish imkonini yaratadi. Avtomatlashtirilgan “Kitob do‘koni” dasturiga login va parol orqali kiriladi. Dastur asosiy 2 qismdan iborat bular: admin va sotuvchi.

Management and Future Technologies journal.umft.uz March 2025 Volume 2, Issue 1 153 1-rasm. Kirish qism. Ushbu rasm foydalanuvchi avtorizatsiyasi (kirish tizimi) interfeysini aks ettiradi. Tizim foydalanuvchilarni tanlash, parol kiritish va tizimga kirish yoki ma’lumotlarni tozalash imkoniyatlarini taqdim etadi. Asosiy funksiyalar: Foydalanuvchi tanlash – Ro‘yxatga olingan foydalanuvchilar ichidan biri tanlanadi (avazov, asadbek). Parol kiritish maydoni – Maxfiy parolni kiritish yoki parolni “ko‘rmoq” opsiyasi bilan ochib ko‘rish imkoniyati mavjud. “Kirish” tugmasi – Kiritilgan ma’lumotlar tekshiriladi va foydalanuvchi tizimga kiradi. “Tozalash” tugmasi – Kiritilgan ma’lumotlarni o‘chirish va yangidan kiritish imkonini beradi. 2-rasm. Foydalanuvchi interfeysi. Ushbu rasm foydalanuvchi avtorizatsiyasi (kirish tizimi) interfeysini aks ettiradi. Tizim foydalanuvchilarga tizimga kirish, rollarni tanlash, parolni kiritish va boshqarish imkoniyatlarini taqdim etadi.

Volume 2, Issue 1 154 March 2025 Management and Future Technologies journal.umft.uz Asosiy funksiyalar: Foydalanuvchi tanlash – Ro‘yxatdan mavjud foydalanuvchilar (avazov, asadbek) tanlanadi. Rol tanlash – Foydalanuvchi tizimga ADMIN yoki SOTUVCHI sifatida kirish imkoniyatiga ega. Parol kiritish maydoni – Foydalanuvchi o‘z parolini kiritishi va “ko‘rmoq” opsiyasi orqali parolni ko‘rsatishi yoki yashirish imkoniyatiga ega. “Kirish” tugmasi – Kiritilgan ma’lumotlar tekshiriladi va foydalanuvchi tizimga kiradi. “Tozalash” tugmasi – Kiritilgan ma’lumotlarni o‘chirish va yangidan kiritish imkonini beradi. 3-rasm. Messageboxda xato xabarini berish. Ushbu rasm foydalanuvchi avtorizatsiyasi (kirish tizimi) interfeysini aks ettiradi. Tizim foydalanuvchilarga tizimga kirish uchun login va parolni kiritish, rolni tanlash va autentifikatsiya qilish imkonini beradi. Asosiy jihatlar: Foydalanuvchi tanlash – Ro‘yxatda avazov va asadbek ismli foydalanuvchilar mavjud. Rol tanlash – ADMIN yoki boshqa rollar mavjud bo‘lishi mumkin. Parol kiritish – Foydalanuvchi tizimga kirish uchun parolni kiritishi talab qilinadi. Xatolik xabari – Rasmda “Login yoki Parol xato!!” degan bildirishnoma aks etgan, bu foydalanuvchining noto‘g‘ri ma’lumot kiritganini bildiradi. Boshqaruv tugmalari: o “Kirish” – Kiritilgan ma’lumotlar asosida tizimga kirishga harakat qiladi. o “Tozalash” – Kiritilgan login yoki parolni o‘chirish uchun ishlatiladi.

Management and Future Technologies journal.umft.uz March 2025 Volume 2, Issue 1 155 4-rasm. Messageboxda to‘g‘ri xabarini berish. Rasmda foydalanuvchi avtorizatsiyasi bilan bog‘liq interfeys aks etgan. Unda tizimga kirish huquqini tekshirish va kirish yoki tozalash amallarini bajarish funksiyalari mavjud. Interfeys tafsilotlari: Foydalanuvchi belgisi (ko‘k rangda joylashgan odam silueti). Foydalanuvchi nomlari (ehtimol, tizimda mavjud foydalanuvchilar): o evazov o esad o ADMIN Kirish huquqi haqida bildirishnoma: o Oyna sarlavhasi: “Kirish” o Xabar: “Kirish huquqingiz bor!!” o Tasdiqlash tugmasi: “Ha” Quyidagi tugmalar mavjud: o “Kirish” (qizil tugma) – Tizimga kirish uchun. o “Tozalash” (yashil tugma) – Ehtimol, foydalanuvchi nomini yoki parol maydonini tozalash uchun.

Volume 2, Issue 1 March 2025 Management and Future Technologies journal.umft.uz 156 5-rasm. Menu admin bo‘limi. Ushbu rasm “Menu Admin” nomli dastur interfeysini aks ettiradi. Tizim kitoblarni boshqarish, sotish jarayonlarini kuzatish, buyurtmalarni yuritish va tarixiy ma’lumotlarni saqlash uchun mo‘ljallangan. Asosiy bo‘limlar: Kitob kiritish – Yangi kitoblarni tizimga qo‘shish va ma’lumotlarini saqlash. Sotuvchi – Kitob savdosini boshqarish va sotuv jarayonlarini nazorat qilish. Zakaslar – Buyurtmalarni qabul qilish va boshqarish. Tarix – Oldingi savdo va buyurtmalar tarixini ko‘rish va tahlil qilish. 6-rasm. Menu Sotuvchi bo‘limi. Ushbu rasm “Menu Sotuvchi” nomli dasturiy interfeysni aks ettiradi. Bu tizim kitoblarni sotish

Management and Future Technologies journal.umft.uz March 2025 Volume 2, Issue 1 va buyurtmalarni boshqarish jarayonlarini yuritish uchun mo‘ljallangan bo‘lib, foydalanuvchilarga sotuv va buyurtmalar bo‘limlari orqali ishlash imkonini beradi. Asosiy bo‘limlar: 1. Sotish – Kitob savdosini amalga oshirish, mavjud mahsulotlarni sotish va tranzaksiyalarni boshqarish. 2. Zakas (Buyurtma) – Kitoblarni oldindan buyurtma qilish va ularni kuzatish jarayonlarini boshqarish. Dasturdan foydalanishning katta qismlari admin bo‘limida joylashgan, sotuvchida esa faqat sotish va zakas qabul qilishdan iborat. 7-rasm. Sotuvchi interfeysidagi kitob sotish oynasi Sotuvchi bo‘limidagi sotish qismini ko‘rib chiqsak, uning vazifasi kitoblarni sotishdan iborat. Unda kitoblar haqida ma’lumotlar mavjud ular: Kitob nomi; Kitob muallifi; Kitob chop etilgan nashriyoti; Turi; Kelgan vaqti; Kelgan narxi; Minimal sotish narxi; Sotish narxi; 157

Volume 2, Issue 1 158 March 2025 Management and Future Technologies journal.umft.uz Sotiladigan kitoblar soni kiritiladi va kitob sotilayotgan narxni kiritamiz va sotish tugmasi bosilganda umumiy summa ko‘rinadi. 8-rasm. Kitob sotish. Ushbu rasm “Kitob sotish o‘yini sotuvchi” nomli dasturiy interfeysni aks ettiradi. Tizim kitob savdosini boshqarish uchun mo‘ljallangan bo‘lib, asosiy funksiyalari quyidagilardan iborat: 1. Kitob ma’lumotlarini kiritish o Kitob nomi, muallifi, nashr yili, nashriyot, kitob turi, narx va sotish sanasi kabi ma’lumotlar qo‘shiladi. 2. Qidirish tizimi o Yuqori o‘ng qismda kitoblarni turli mezonlar bo‘yicha qidirish imkoniyati mavjud (masalan, oddiy yoki buyurtma asosida sotish). 3. Jadval o Sotilgan kitoblar ro‘yxati, to‘lov turi, to‘lov miqdori va sotuv turi (Oddiy/Zakas) haqidagi ma’lumotlar keltiriladi. 4. Summani ko‘rsatish o Umumiy savdo summasi hisoblab, foydalanuvchiga ko‘rsatib turiladi. 5. Yangi savdo o Oldingi ma’lumotlarni tozalab, yangi kitob sotish jarayonini boshlash funksiyasi.

Management and Future Technologies journal.umft.uz March 2025 Volume 2, Issue 1 159 9-rasm. Kitob turiga qarab izlash. Ushbu dasturiy interfeys kitob savdosini avtomatlashtirish va boshqarish uchun mo‘ljallangan. U foydalanuvchilarga kitob ma’lumotlarini kiritish, narxlarni belgilash, sotuv jarayonlarini kuzatish va turli to‘lov usullarini qo‘llash imkoniyatini beradi. Asosiy xususiyatlar: Kitob haqida to‘liq ma’lumotlar bazasi – Muallif, nashr yili, nashriyot, janr va narxlar kiritiladi. Qidiruv tizimi – Kitoblarni topish va sotuv turini tanlash imkoniyati mavjud. Sotuvlar jadvali – Sotilgan kitoblar, narxlar va to‘lov usullari avtomatik ro‘yxatga olinadi. Umumiy summa hisoboti – Jami savdo hajmi hisoblanadi va ko‘rsatiladi. Yangi savdo boshlash imkoniyati – Yangi mijozlar uchun qayta savdo jarayoni qulay tarzda yo‘lga qo‘yiladi. Bu tizim kitob do‘konlari, elektron savdo platformalari va kutubxonalar uchun foydali vosita bo‘lib, savdo jarayonlarini soddalashtiradi, samaradorlikni oshiradi va hisob-kitoblarni avtomatlashtiradi.

Volume 2, Issue 1 March 2025 Management and Future Technologies journal.umft.uz 160 10-rasm. Kitob ma’lumotlariga qarab izlash. Ushbu dasturiy interfeys kitob savdosini avtomatlashtirish va tartibga solish uchun ishlab chiqilgan. Foydalanuvchilar kitob ma’lumotlarini kiritish, qidirish, saralash va sotuv jarayonlarini kuzatish imkoniyatiga ega. Asosiy funksiyalar: Kitob haqida to‘liq ma’lumotlar – Muallif, nashr yili, nashriyot, janr va narxlar kiritiladi. Qidiruv tizimi – Foydalanuvchi kitoblarni nomi, muallifi, yili, nashriyoti, turi va soni bo‘yicha qidirishi mumkin. Sotuvlar jadvali – Tizim orqali sotilgan kitoblar, ularning narxlari va to‘lov usullari avtomatik ravishda ro‘yxatga olinadi. Umumiy summa hisobi – Jami savdo miqdori hisoblab ko‘rsatiladi. Yangi savdo boshlash imkoniyati – Yangi mijozlar uchun savdo jarayonini yangilash va ma’lumotlarni yangidan kiritish imkoniyati mavjud.