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Artykuły w czasopismach na temat „Ubiquitous User Query Model”

Autor: Grafiati
Data publikacji: 6 września 2023
Data aktualizacji: 31 lipca 2025

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Myllynen, Teemu, Eunice Kamau, Sikirat Damilola Mustapha, Gideon Opeyemi Babatunde, and Abidemi Adeleye Alabi. "Developing a Conceptual Model for Cross-Domain Microservices Using Event-Driven and Domain-Driven Design." International Journal of Multidisciplinary Research and Growth Evaluation 5, no. 1 (2024): 1158–71. https://doi.org/10.54660/.ijmrge.2024.5.1.1158-1171.

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The growing complexity of modern software systems necessitates architectural approaches that enhance scalability, flexibility, and maintainability. Microservices architecture has emerged as a leading solution, particularly for cross-domain applications. This paper presents a conceptual model for cross-domain microservices by integrating Event-Driven Design (EDD) and Domain-Driven Design (DDD) principles. The proposed model focuses on structuring microservices around distinct bounded contexts, enabling modular development and fostering a deep alignment between technical architecture and busines
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Myllynen, Teemu, Eunice Kamau, Sikirat Damilola Mustapha, Gideon Opeyemi Babatunde, and Abidemi Adeleye Alabi. "Developing a Conceptual Model for Cross-Domain Microservices Using Event-Driven and Domain-Driven Design." International Journal of Multidisciplinary Research and Growth Evaluation 4, no. 1 (2023): 635–38. https://doi.org/10.54660/.ijmrge.2023.4.1.635-638.

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The growing complexity of modern software systems necessitates architectural approaches that enhance scalability, flexibility, and maintainability. Microservices architecture has emerged as a leading solution, particularly for cross-domain applications. This paper presents a conceptual model for cross-domain microservices by integrating Event-Driven Design (EDD) and Domain-Driven Design (DDD) principles. The proposed model focuses on structuring microservices around distinct bounded contexts, enabling modular development and fostering a deep alignment between technical architecture and busines
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Kritsi, Eftichia, Minos-Timotheos Matsoukas, Constantinos Potamitis, et al. "Novel Hit Compounds as Putative Antifungals: The Case of Aspergillus fumigatus." Molecules 24, no. 21 (2019): 3853. http://dx.doi.org/10.3390/molecules24213853.

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The prevalence of invasive fungal infections has been dramatically increased as the size of the immunocompromised population worldwide has grown. Aspergillus fumigatus is characterized as one of the most widespread and ubiquitous fungal pathogens. Among antifungal drugs, azoles have been the most widely used category for the treatment of fungal infections. However, increasingly, azole-resistant strains constitute a major problem to be faced. Towards this direction, our study focused on the identification of compounds bearing novel structural motifs which may evolve as a new class of antifungal
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Et. al., Niraja Jain,. "Data Membership Identification using Bloom Filter in Cloud Storage for Effective Resource Allocation." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (2021): 102–8. http://dx.doi.org/10.17762/itii.v9i2.308.

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Cloud computing has recently been the buzz word that had changed the entire software industry with its PaaS, IaaS and SaaS architecture model. The legacy systems operational in the organizations across different industries have been using the database that had an overhead in terms of cost of data storage, runtime operation and frequent data maintenance. Cloud database concept had challenged the existing storage and operational norms of data. In the distributed environment, resources used in the cloud databases need to identify whether the requested data belongs to the data nodes of a cluster.
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Zhang, Bo, Xiaoxuan Qi, and Xiaowei Han. "An Advanced User Intent Model Based On User Learning Process." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 09 (2019): 2050024. http://dx.doi.org/10.1142/s021800142050024x.

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User intent analysis is a continuous research hotspot in the field of query expansion. However, the big amount of irrelevant feedbacks in search log has negatively impacted the precision of user intent model. By observing the log, it can be found that tentative click is a major source of irrelevant feedback. It is also observed that a kind of new feedback information can be extracted from the log to recognize the characteristics of tentative clicks. With this new feedback information, this paper proposes an advanced user intent model and applies it into query expansion. Experiment results show
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Yu, Yang Xin. "Personalization Information Retrieval Based on Unigram Language Model." Applied Mechanics and Materials 321-324 (June 2013): 2269–73. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.2269.

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Personalization information retrieval is very useful in information retrieval system, the user profile can be used to represent the favorites or interests of user. Many methods to personalization have been studied in extending query with user profile. A proposed navel method which use the context of long-term user profile with multiple domain to extend query model under the unigram language model framework, uses the new query model to retrieve and get more interesting results for users. Combined with psudo relevance feedback model, the proposed method get better performance. Experimental resul
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Bhalerao, R. S., Dhananjay Kumbhakarna, Ashvini Avhad, Kirti Shinde, and Dipalee Tidke. "Design Enrichment of Query Forms for Database Query." Asian Journal of Computer Science and Technology 5, no. 1 (2016): 30–35. http://dx.doi.org/10.51983/ajcst-2016.5.1.1761.

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The scientific databases & web databases maintain huge and large amount of data. The real-world databases contain over thousands of relations & attributes. predefined database query forms are not able to satisfy various queries from users on those databases. The review of DQF is to capture a user’s preference and rating query form components, assisting to take decisions. The creation of a query form is an faster process and is given by the user. A user can also create the query form and submit queries to view the query output at each iteration. This way, a query form could be dynamical
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Zhou, Yinglian, and Jifeng Chen. "Time Series Geographic Social Network Dynamic Preference Group Query." International Journal of Information Systems in the Service Sector 13, no. 4 (2021): 18–39. http://dx.doi.org/10.4018/ijisss.2021100102.

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Driven by experience and social impact of the new life, user preferences continue to change over time. In order to make up for the shortcomings of existing geographic social network models that often cannot obtain user dynamic preferences, a time-series geographic social network model was constructed to detect user dynamic preferences, a dynamic preference value model was built for user dynamic preference evaluation, and a dynamic preferences group query (DPG) was proposed in this paper . In order to optimize the efficiency of the DPG query algorithm, the UTC-tree index user timing check-in re
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Chen, Wanyu, Zepeng Hao, Taihua Shao, and Honghui Chen. "Personalized query suggestion based on user behavior." International Journal of Modern Physics C 29, no. 04 (2018): 1850036. http://dx.doi.org/10.1142/s0129183118500365.

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Query suggestions help users refine their queries after they input an initial query. Previous work mainly concentrated on similarity-based and context-based query suggestion approaches. However, models that focus on adapting to a specific user (personalization) can help to improve the probability of the user being satisfied. In this paper, we propose a personalized query suggestion model based on users’ search behavior (UB model), where we inject relevance between queries and users’ search behavior into a basic probabilistic model. For the relevance between queries, we consider their semantica
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Riezler, Stefan, and Yi Liu. "Query Rewriting Using Monolingual Statistical Machine Translation." Computational Linguistics 36, no. 3 (2010): 569–82. http://dx.doi.org/10.1162/coli_a_00010.

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Long queries often suffer from low recall in Web search due to conjunctive term matching. The chances of matching words in relevant documents can be increased by rewriting query terms into new terms with similar statistical properties. We present a comparison of approaches that deploy user query logs to learn rewrites of query terms into terms from the document space. We show that the best results are achieved by adopting the perspective of bridging the “lexical chasm” between queries and documents by translating from a source language of user queries into a target language of Web documents. W
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Gou, Zhinan, and Yan Li. "A method of query expansion based on topic models and user profile for search in folksonomy." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 1701–11. http://dx.doi.org/10.3233/jifs-210508.

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With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile an
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Zhang, Tianle, Chunlu Wang, ZongWei Luo, Shuihua Han, and Mengyuan Dong. "RFID Enabled Vehicular Network for Ubiquitous Travel Query." International Journal of Systems and Service-Oriented Engineering 2, no. 3 (2011): 47–62. http://dx.doi.org/10.4018/jssoe.2011070104.

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Fixed infrastructure based wireless network is very expensive to provide total coverage and offer ubiquitous communication capacity. RFID enabled Vehicular Network emerges as an alternative which can leverage mobile nodes to bridge the gap between information isolated islands. The mobility and low duty cycle activity of nodes may destroy the network connectivity. This paper proposes RFID Enabled Vehicular Network for Ubiquitous Travel Query over Mobile Relay Network (MRN) to facilitate the needed information access for drivers on the road. The ubiquitous service is introduced and the performan
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Yao, Jing, Zhicheng Dou, and Ji-Rong Wen. "Clarifying Ambiguous Keywords with Personal Word Embeddings for Personalized Search." ACM Transactions on Information Systems 40, no. 3 (2022): 1–29. http://dx.doi.org/10.1145/3470564.

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Personalized search tailors document ranking lists for each individual user based on her interests and query intent to better satisfy the user’s information need. Many personalized search models have been proposed. They first build a user interest profile from the user’s search history, and then re-rank the documents based on the personalized matching scores between the created profile and candidate documents. In this article, we attempt to solve the personalized search problem from an alternative perspective of clarifying the user’s intention of the current query. We know that there are many
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Bajpai, Pratibha, Parul Verma, and Syed Q. Abbas. "Two Level Disambiguation Model for Query Translation." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 3923. http://dx.doi.org/10.11591/ijece.v8i5.pp3923-3932.

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Selection of the most suitable translation among all translation candidates returned by bilingual dictionary has always been quiet challenging task for any cross language query translation. Researchers have frequently tried to use word co-occurrence statistics to determine the most probable translation for user query. Algorithms using such statistics have certain shortcomings, which are focused in this paper. We propose a novel method for ambiguity resolution, named ‘two level disambiguation model’. At first level disambiguation, the model properly weighs the importance of translation alternat
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Pratibha, Bajpai, Verma Parul, and Q. Abbas Syed. "Two Level Disambiguation Model for Query Translation." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 3923–32. https://doi.org/10.11591/ijece.v8i5.pp3923-3932.

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Selection of the most suitable translation among all translation candidates returned by bilingual dictionary has always been quiet challenging task for any cross language query translation. Researchers have frequently tried to use word co-occurrence statistics to determine the most probable translation for user query. Algorithms using such statistics have certain shortcomings, which are focused in this paper. We propose a novel method for ambiguity resolution, named „two level disambiguation model". At first level disambiguation, the model properly weighs the importance of translation al
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Zhu, Qiuyu, Dongmei Li, Cong Dai, Qichen Han, and Yi Lin. "PLSA-Based Personalized Information Retrieval with Network Regularization." Journal of Information Technology Research 12, no. 1 (2019): 105–16. http://dx.doi.org/10.4018/jitr.2019010108.

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With the rapid development of the Internet, the information retrieval model based on the keywords matching algorithm has not met the requirements of users, because people with various query history always have different retrieval intentions. User query history often implies their interests. Therefore, it is of great importance to enhance the recall ratio and the precision ratio by applying query history into the judgment of retrieval intentions. For this sake, this article does research on user query history and proposes a method to construct user interest model utilizing query history. Coordi
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Yogish, Deepa, T. N. Manjunath, H. K. Yogish, and Ravindra S. Hegadi. "Ranking Top Similar Documents for User Query Based on Normalized Vector Cosine Similarity Model." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4531–34. http://dx.doi.org/10.1166/jctn.2020.9330.

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As the technology is developing information in each fields like literature, technology, science, medicine etc., also increasing in high pace. To extract related document in huge collection of documents based on user query in digital world is an interesting problem. Documents similarity Technique used in many applications like text categorization, plagiarism discernment, document clustering, information retrieval, machine translation and question answering system. Many algorithms have been developed for this purpose that take a document or input query and match it with the document databases. T
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Zhang, Yong Hua. "Anonymity Query Method of Outsourced Database." Advanced Materials Research 798-799 (September 2013): 837–41. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.837.

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This paper analyzes the traditional outsourcing model (TOM). Aiming at that TOM has disadvantages such as low security on User Privacy, this paper proposes a new access model in outsourced database. Using the trusted third party (TTP ) makes all operations in the database become anonymous in order to achieve the purpose of user privacy protection.
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Kumar, Sushil, and Naresh Chauhan. "A Context Model For Focused Web Search." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 3 (2012): 155–62. http://dx.doi.org/10.24297/ijct.v2i3c.2715.

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In the existing web search systems, the information retrieval isperformed using a single query and mapping it to a set ofdocuments. From a single query, however, the search systemscan only have very limited clue about the user‟s informationneed. The user‟s context and his environment are ignored whilesearching the information resulting in irrelevant search results.These irrelevant search results increase the cognitive overheadof the user in filtering them out and finding useful information.Therefore, the search systems must incorporate contextinformation regarding user and his environment
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Pu, Qiang, Ahmed Lbath, and Da Qing He. "Mobile Geographic Web Search Personalization with Language Model." Applied Mechanics and Materials 303-306 (February 2013): 1420–25. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1420.

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Mobile personalized web search has been introduced for the purpose of distinguishing mobile user's personal different search interest. We first take the user's location information into account to do a geographic query expansion, then present an approach to personalizing web search for mobile users within language modeling framework. We estimate a user mixed model estimated according to both activated ontological topic model-based feedback and user interest model to re-rank the results from geographic query expansion. Experiments show that language model based re-ranking method is effective in
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Moran, Stuart, and Keiichi Nakata. "Ubiquitous monitoring and user behaviour: A preliminary model." Journal of Ambient Intelligence and Smart Environments 2, no. 1 (2010): 67–80. http://dx.doi.org/10.3233/ais-2010-0049.

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Karpas, Erez, Tomer Sagi, Carmel Domshlak, Avigdor Gal, Avi Mendelson, and Moshe Tennenholtz. "Data-Parallel Computing Meets STRIPS." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 474–80. http://dx.doi.org/10.1609/aaai.v27i1.8590.

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The increased demand for distributed computations on “big data” has led to solutions such as SCOPE, DryadLINQ, Pig, and Hive, which allow the user to specify queries in an SQL-like language, enriched with sets of user-defined operators. The lack of exact semantics for user-defined operators interferes with the query optimization process, thus putting the burden of suggesting, at least partial, query plans on the user. In an attempt to ease this burden, we propose a formal model that allows for data-parallel program synthesis (DPPS) in a semantically well-defined manner. We show that this model
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Zhang, Yufeng, Jinghao Zhang, Zeyu Cui, Shu Wu, and Liang Wang. "A Graph-based Relevance Matching Model for Ad-hoc Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 4688–96. http://dx.doi.org/10.1609/aaai.v35i5.16599.

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To retrieve more relevant, appropriate and useful documents given a query, finding clues about that query through the text is crucial. Recent deep learning models regard the task as a term-level matching problem, which seeks exact or similar query patterns in the document. However, we argue that they are inherently based on local interactions and do not generalise to ubiquitous, non-consecutive contextual relationships. In this work, we propose a novel relevance matching model based on graph neural networks to leverage the document-level word relationships for ad-hoc retrieval. In addition to
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Wildannissa Pinasti and Lya Hulliyyatus Suadaa. "Named Entity Recognition in Statistical Dataset Search Queries." Jurnal Nasional Teknik Elektro dan Teknologi Informasi 13, no. 3 (2024): 171–77. http://dx.doi.org/10.22146/jnteti.v13i3.11580.

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Search engines must understand user queries to provide relevant search results. Search engines can enhance their understanding of user intent by employing named entity recognition (NER) to identify the entity in the query. Knowing the types of entities in the query can be the initial step in helping search engines better understand search intent. In this research, a dataset was constructed using search query history from the Statistics Indonesia (Badan Pusat Statistik, BPS) website, and NER in query modeling was employed to extract entities from search queries related to statistical datasets.
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Zhu, Shengrong, Xiangguang Meng, Feixiang Chen, and Xuan Tian. "Personalized Semantic Query Expansion Based on Dynamic User Query Profile and Spreading Activation Model." International Journal of Hybrid Information Technology 10, no. 6 (2017): 33–46. http://dx.doi.org/10.14257/ijhit.2017.10.6.04.

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HERRERA-VIEDMA, E. "AN INFORMATION RETRIEVAL MODEL WITH ORDINAL LINGUISTIC WEIGHTED QUERIES BASED ON TWO WEIGHTING ELEMENTS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 09, supp01 (2001): 77–87. http://dx.doi.org/10.1142/s0218488501001009.

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An Information Retrieval (IR) model defined using an ordinal fuzzy linguistic approach is proposed. It accepts ordinal linguistic weighted queries based on two weighting elements: the query terms and the query sub-expressions. In such a way, users may easily express simultaneously several semantic restrictions in a query. A symmetrical threshold semantic is associated to the weights of the query terms and an importance semantic is associated to the weights of the query sub-expressions. The advantage of this IR model with respect to others is the facility for expressing different semantic restr
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Gan, Keng Hoon, and Keat Keong Phang. "A semantic-syntax model for XML query construction." International Journal of Web Information Systems 13, no. 2 (2017): 155–72. http://dx.doi.org/10.1108/ijwis-06-2016-0034.

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Purpose When accessing structured contents in XML form, information requests are formulated in the form of special query languages such as NEXI, Xquery, etc. However, it is not easy for end users to compose such information requests using these special queries because of their complexities. Hence, the purpose of this paper is to automate the construction of such queries from common query like keywords or form-based queries. Design/methodology/approach In this paper, the authors address the problem of constructing queries for XML retrieval by proposing a semantic-syntax query model that can be
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Xiong, Wei, Michael Recce, and Brook Wu. "Intent-Based User Segmentation with Query Enhancement." International Journal of Information Retrieval Research 3, no. 4 (2013): 1–17. http://dx.doi.org/10.4018/ijirr.2013100101.

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With the rapid advancement of the internet, accurate prediction of user's online intent underlying their search queries has received increasing attention from online advertising community. This paper aims to address the major challenges with user queries in the context of behavioral targeting advertising by proposing a query enhancement mechanism that augments user's queries by leveraging a user query log. The empirical evaluation demonstrates that the authors' methodology for query enhancement achieves greater improvement than the baseline models in both intent-based user classification and u
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Yogish, Deepa, T. N. Manjunath, and Ravindra S. Hegadi. "Analysis of Vector Space Method in Information Retrieval for Smart Answering System." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4468–72. http://dx.doi.org/10.1166/jctn.2020.9099.

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In the world of internet, searching play a vital role to retrieve the relevant answers for the user specific queries. The most promising application of natural language processing and information retrieval system is Question answering system which provides directly the accurate answer instead of set of documents. The main objective of information retrieval is to retrieve relevant document from a huge volume of data sets underlying in the internet using appropriatemodel. There are many models proposed for retrieval process such as Boolean, Vector space and Probabilistic method. Vector space mod
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Zhao, Jiashu, Jimmy Xiangji Huang, Hongbo Deng, Yi Chang, and Long Xia. "Are Topics Interesting or Not? An LDA-based Topic-graph Probabilistic Model for Web Search Personalization." ACM Transactions on Information Systems 40, no. 3 (2022): 1–24. http://dx.doi.org/10.1145/3476106.

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In this article, we propose a Latent Dirichlet Allocation– (LDA) based topic-graph probabilistic personalization model for Web search. This model represents a user graph in a latent topic graph and simultaneously estimates the probabilities that the user is interested in the topics, as well as the probabilities that the user is not interested in the topics. For a given query issued by the user, the webpages that have higher relevancy to the interested topics are promoted, and the webpages more relevant to the non-interesting topics are penalized. In particular, we simulate a user’s search inte
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Gao, Qian, and Young Im Cho. "A Multi-Agent Personalized Query Refinement Approach for Academic Paper Retrieval in Big Data Environment." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 7 (2012): 874–80. http://dx.doi.org/10.20965/jaciii.2012.p0874.

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This paper proposes a multi-agent query refinement approach to realize personalized query expansion effective for academic paper retrieval in a Big Data environment. First, we use Hadoop as a platform to develop a formalized model to represent different types of large caches of data in order to analyze and process Big Data efficiently. Second, we use a client agent to verify user identities and monitor whether a device is ready to run a query-expanded task. We then use a query expansion agent to determine the domain that the initial query belongs to by applying a knowledgebased query expansion
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Han, Meng, and Xiao Hu Qiu. "Personalized Search Engineer Model." Advanced Materials Research 268-270 (July 2011): 1216–21. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1216.

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To improve the accuracy of query result of search engineer and satisfy personalized requirements of users, we proposed the method of building and updating user personalized model. This method based on certain information which mine from users’ behaviors and customs in using search engineer. Through mining information from users’ query customs, visit frequency and browse Web in using Chinese search engineer, we pick up characters of use and interest of users, and then build personalized interest model of users. This paper studies technique details of building and updating personalized model. Se
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Ahmad, Luky Ramdani, Djatna Taufik, and Sukoco Heru. "Selecting User Influence on Twitter Data Using Skyline Query under MapReduce Framework." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 3 (2018): 1416–25. https://doi.org/10.12928/TELKOMNIKA.v16i3.4624.

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The aim of this research was to select and identify user influence on Twitter data. In identification stage, the method proposed in this study was matrix Twitter approach, sentiment analysis, and characterization of the opinion leader. The importan characteristics included external communication, accessibility, and innovation. Based on these characteristics and information from Twitter data through matrix Twitter and sentiment analysis, a algorithm of skyline query was constructed for the selection stage. Algorithm of skyline query selected user influence by comparing with other users accordin
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Soleimani, S., E. Keshtehgar, and M. R. Malek. "UBISOUND: DESIGN A USER GENERATED MODEL IN UBIQUITOUS GEOSPATIAL INFORMATION ENVIRONMENT FOR SOUND MAPPING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2/W3 (October 22, 2014): 243–47. http://dx.doi.org/10.5194/isprsarchives-xl-2-w3-243-2014.

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In this paper, we study how mobile computing and wireless technologies can be explored to provide effective ubiquitous GIS services. Instead of reinventing the wheels, we make use of smartphones, off-the-shelf components, and existing technologies in ubiquitous computing (i.e. wireless and mobile positioning technologies, and data acquisition techniques and processing via sensors) to develop a middleware, and tools for the development of systems and applications to provide effective ubiquitous GIS services. Two main tasks to be studied are: 1) Developing a framework, called UbiSound, to provid
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Kim, Chris, Xiao Lin, Christopher Collins, Graham W. Taylor, and Mohamed R. Amer. "Learn, Generate, Rank, Explain: A Case Study of Visual Explanation by Generative Machine Learning." ACM Transactions on Interactive Intelligent Systems 11, no. 3-4 (2021): 1–34. http://dx.doi.org/10.1145/3465407.

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While the computer vision problem of searching for activities in videos is usually addressed by using discriminative models, their decisions tend to be opaque and difficult for people to understand. We propose a case study of a novel machine learning approach for generative searching and ranking of motion capture activities with visual explanation. Instead of directly ranking videos in the database given a text query, our approach uses a variant of Generative Adversarial Networks (GANs) to generate exemplars based on the query and uses them to search for the activity of interest in a large dat
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Sivani, JC, S. Sathyalakshmi, B. Sthuthi, and Kamleshwar Kumar Yadav Prof. "A Comprehensive Survey on Human-to-Database Communication using NLP." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 597–601. https://doi.org/10.5281/zenodo.7950915.

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In recent years, there is an exponential growth in the amount of data that is being generated every day. Nowadays there is the widespread use of technology in all fields. As data is growing accessing data that is required out of the huge amount of data is an important task. Structured query language (SQL) is commonly used to access data from a database. Even though these help in fetching the required data, it is not as user-friendly as using natural language. In this paper, the query writing task will be done by the model which will reduce the burden of a user who does not have any prior knowl
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Xiao, Xingxing, and Jianzhong Li. "rkHit: Representative Query with Uncertain Preference." Proceedings of the ACM on Management of Data 1, no. 2 (2023): 1–26. http://dx.doi.org/10.1145/3589271.

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A top-k query retrieves the k tuples with highest scores according to a user preference, defined as a scoring function. It is difficult for a user to precisely specify the scoring function. Instead, obtaining the distribution on scoring functions, i.e., the preference distribution, has been extensively explored in many fields. Motivated by this, we introduce the uniform (r,k)-hit (UrkHit) problem. Given a preference distribution, UrkHit aims to select a representative set of r tuples to maximize the probability of containing a tuple attractive to the user. We say a tuple attracts a user, if it
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Sawant, Aditya, Rohit Raina, Anuja Patil, and Anand Pardeshi. "AI Model to Generate SQL Queries from Natural Language Instructions through Voice." Journal of Physics: Conference Series 2273, no. 1 (2022): 012014. http://dx.doi.org/10.1088/1742-6596/2273/1/012014.

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Abstract Data plays the most important role in the development of industries, small businesses. Even world leaders need the data to make analyses and make better policies for people. In almost every field where the work process is digitized need to store data and then retrieve it. According to statistics most of the data is stored in the relational database and for manipulations of the data, Structured Query Language(SQL) is commonly used. So for handling databases a person need to have specialized knowledge regarding the queries and had to remember the syntax of many complex queries. So to en
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Sawant, Aditya, Rohit Raina, Anuja Patil, and Anand Pardeshi. "AI Model to Generate SQL Queries from Natural Language Instructions through Voice." Journal of Physics: Conference Series 2273, no. 1 (2022): 012014. http://dx.doi.org/10.1088/1742-6596/2273/1/012014.

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Abstract Data plays the most important role in the development of industries, small businesses. Even world leaders need the data to make analyses and make better policies for people. In almost every field where the work process is digitized need to store data and then retrieve it. According to statistics most of the data is stored in the relational database and for manipulations of the data, Structured Query Language(SQL) is commonly used. So for handling databases a person need to have specialized knowledge regarding the queries and had to remember the syntax of many complex queries. So to en
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Alahmari, Fahad, James A. Thom, and Liam Magee. "A model for ranking entity attributes using DBpedia." Aslib Journal of Information Management 66, no. 5 (2014): 473–93. http://dx.doi.org/10.1108/ajim-12-2013-0148.

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Purpose – Previous work highlights two key challenges in searching for information about individual entities (such as persons, places and organisations) over semantic data: query ambiguity and redundant attributes. The purpose of this paper is to consider these challenges and proposes the Attribute Importance Model (AIM) for clustering and ranking aggregated entity search to improve the overall users’ experience of finding and navigating entities over the Web of Data. Design/methodology/approach – The proposed model describes three distinct techniques for augmenting semantic search: first, pre
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Abdallah, Hanya M., Ahmed Taha, and Mazen M. Selim. "Cloud-Based Fuzzy Keyword Search Scheme Over Encrypted Documents." International Journal of Sociotechnology and Knowledge Development 13, no. 4 (2021): 82–100. http://dx.doi.org/10.4018/ijskd.2021100106.

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With the rapid growth and adoption of cloud computing, more sensitive information is centralized onto the cloud every day. For protecting this sensitive information, it must be encrypted before being outsourced. Current search schemes allow the user to query encrypted data using keywords, but these schemes do not guarantee the privacy of queries (i.e., when the user hits query more than once with the same keywords, the server can capture information about the data). This paper focuses on the secure storage and retrieval of ciphered data with preserving query privacy. The proposed scheme deploy
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Tatyana, Ivanova. "Ontology-Based Metasearch Engine in Electronics Area." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 9, no. 12 (2020): 273–77. https://doi.org/10.35940/ijitee.L8014.1091220.

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Paper The goal of search engines is to return accurate and complete results. Satisfying concrete user information needs becomes more and more difficult because of inability in it complete explicit specification and short comes of keyword-based searching and indexing. General search engines have indexed millions of web resources and often return thousands of results to the user query (most of them often inadequate). To increase result’s precession, users sometimes choose search engines, specialized in searching concrete domain, personalized or semantic search. A grand variety of specializ
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Giannakouris, Victor, and Immanuel Trummer. "DBG-PT: A Large Language Model Assisted Query Performance Regression Debugger." Proceedings of the VLDB Endowment 17, no. 12 (2024): 4337–40. http://dx.doi.org/10.14778/3685800.3685869.

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In this paper we explore the ability of Large Language Models (LLMs) in analyzing and comparing query plans, and resolving query performance regressions. We present DBG-PT, a query regression debugging framework powered by LLMs. DBG-PT keeps track of query execution instances, and detects slowdowns according to a user-defined regression factor. Once a regression is detected, DBG-PT leverages the capabilities of the underlying LLM in order to compare the regressed plan with a previously effective one, and comes up with tuning knob configurations in order to alleviate the regression. By exploiti
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Meng, Jian Liang, and Da Wei Li. "Improve and Optimize Query Recommendation System by MST Algorithm and its MapReduce Implementation." Applied Mechanics and Materials 701-702 (December 2014): 50–53. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.50.

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Query recommendation as an important tool to enhance the user search efficiency has gradually become a hotspot. In the context of big data, using the MapReduce programming model, combined with distributed minimum spanning tree algorithm, a parallel query recommended method based on MapReduce was proposed in this paper. The final results show that the efficiency of query recommendation was greatly improved through parallel computing.
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45

Et.al, Ms P. Mahalakshmi. "Cross - Language based Multi-Document Summarization Model using Machine Learning Technique." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 331–35. http://dx.doi.org/10.17762/turcomat.v12i6.1393.

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Cross-Language Multi-document summarization (CLMDS) process produces a summary generated from multiple documents in which the summary language is different from the source document language. The CLMDS model allows the user to provide query in a particular language (e.g., Tamil) and generates a summary in the same language from different language source documents. The proposed model enables the user to provide a query in Tamil language, generate a summary from multiple English documents, and finally translate the summary into Tamil language. The proposed model makes use of naïve Bayes classifie
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Feng, Lizhou. "Novel Query Intent Identification Method Based on User Interest Model." Journal of Information and Computational Science 12, no. 10 (2015): 3881–88. http://dx.doi.org/10.12733/jics20106165.

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Hwang, Myunggwon, Do-Heon Jeong, Jinhyung Kim, Sa-Kwang Song, and Hanmin Jung. "Activity inference for constructing user intention model." Computer Science and Information Systems 10, no. 2 (2013): 767–78. http://dx.doi.org/10.2298/csis121101033h.

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User intention modeling is a key component for providing appropriate services within ubiquitous and pervasive computing environments. Intention modeling should be concentrated on inferring user activities based on the objects a user approaches or touches. In order to support this kind of modeling, we propose the creation of object-activity pairs based on relatedness in a general domain. In this paper, we show our method for achieving this and evaluate its effectiveness.
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Zhao, Qiwen, Zhongwen Zhou, and Yibang Liu. "PALM: Personalized Attention-based Language Model for Long-tail Query Understanding in Enterprise Search Systems." Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930) 2, no. 1 (2024): 44–59. https://doi.org/10.60087/vol2iisue1.p009.

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Enterprise search systems face significant challenges in handling long-tail queries, which constitute a substantial portion of search traffic but often receive inadequate attention in traditional systems. This paper introduces PALM (Personalized Attention-based Language Model), a novel framework designed to enhance long-tail query understanding in enterprise search environments. PALM integrates personalization capabilities with an advanced attention mechanism to improve search accuracy for infrequent queries while maintaining high performance on common queries. The framework employs a unique h
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Zhang, Kepu, Zhongxiang Sun, Xiao Zhang, et al. "Trigger3:Refining Query Correction via Adaptive Model Selector." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 13260–68. https://doi.org/10.1609/aaai.v39i12.33447.

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In search scenarios, user experience can be hindered by erroneous queries due to typos, voice errors, or knowledge gaps. Therefore, query correction is crucial for search engines. Current correction models, usually small models trained on specific data, often struggle with queries beyond their training scope or those requiring contextual understanding. While the advent of Large Language Models (LLMs) offers a potential solution, they are still limited by their pre-training data and inference cost, particularly for complex queries, making them not always effective for query correction. To tackl
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Deshpande, Padmaja M., Raghvendra Sharma, and Swati Sinha. "Spread Identity to Achieve Location Privacy in Query Search." International Journal of Microsystems and IoT 2, no. 2 (2024): 607–13. https://doi.org/10.5281/zenodo.10809121.

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In present scenario of 4G, 5G communications and IoT development, Location based services are playing important roles for providing the comfort to the consumers. These services provide the nearest locations or location information, based on the user present location and query being asked for. Multiple times queries being asked from the same user can give access regarding the user’s day to day activities/habits and leakage of any important information related to his job(s)/work. This will make the user vulnerable to cyber/physical attacks by any social commotion. Multiple methods have bee
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