Artykuły w czasopismach: „Ubiquitous User Query Model”

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Akram, Atia, Asma Ahmad Farhan, and Amna Basharat. "Less is more: Efficient behavioral context recognition using Dissimilarity-Based Query Strategy." PLOS ONE 18, no. 6 (2023): e0286919. http://dx.doi.org/10.1371/journal.pone.0286919.

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With the advancement of ubiquitous computing, smartphone sensors are generating a vast amount of unlabeled data streams ubiquitously. This sensor data can potentially help to recognize various behavioral contexts in the natural environment. Accurate behavioral context recognition has a wide variety of applications in many domains like disease prevention and independent living. However, despite the availability of enormous amounts of sensor data, label acquisition, due to its dependence on users, is still a challenging task. In this work, we propose a novel context recognition approach i.e., Dissimilarity-Based Query Strategy (DBQS). Our approach DBQS leverages Active Learning based selective sampling to find the informative and diverse samples in the sensor data to train the model. Our approach overcomes the stagnation problem by considering only new and distinct samples from the pool that were not previously explored. Further, our model exploits temporal information in the data in order to further maintain diversity in the dataset. The key intuition behind the proposed approach is that the variations during the learning phase will train the model in diverse settings and it will outperform when assigned a context recognition task in the natural setting. Experimentation on a publicly available natural environment dataset demonstrates that our proposed approach improved overall average Balanced Accuracy(BA) by 6% with an overall 13% less training data requirement.

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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 antifungals. To fulfil this scope, a combination of in silico techniques and in vitro assays were implemented. Specifically, a ligand-based pharmacophore model was created and served as a 3D search query to screen the ZINC chemical database. Additionally, molecular docking and molecular dynamics simulations were used to improve the reliability and accuracy of virtual screening results. In total, eight compounds, bearing completely different chemical scaffolds from the commercially available azoles, were proposed and their antifungal activity was evaluated using in vitro assays. Results indicated that all tested compounds exhibit antifungal activity, especially compounds 1, 2, and 4, which presented the most promising minimum inhibitory concentration (MIC) and minimum fungicidal concentration (MFC) values and, therefore, could be subjected to further hit to lead optimization.

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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. With databases began to be ubiquitous, the data storage needed to satisfy heterogeneous data structures rather the unstructured data storage support is looked for. The Use of Bloom's filter for data membership identification is the novel approach and can effectively improve the resource organization strategy on cloud. Dynamic resource organization can further improve the query efficiency as well. The concern raised during this is the data privacy which can also be ascertain by maintaining the data access authority levels. Bloom filter uses less memory space against the large dataset to store it's information. A Bloom filter is proposed to be used to determine whether an element is part of a reference set. It is a very compact hash-based data structure with efficient look-up times and a manageable risk of giving false positives.

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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 that the model can effectively decrease the negative impact of irrelevant feedbacks that belong to tentative clicks and increase the precision of query expansion, especially for those informational queries.

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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 results show that the proposed method in this paper is effective.

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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 dynamically created till the user satisfies with the query forms. The important F-measure for measuring the goodness of a query form. A model is developed for estimating the goodness of a query form in DQF. Experimental evaluation and user study demonstrate the accuracy and performance of the system. The ranking of form components is based on the captured user preference. A user can also fill the query form and submit queries to view the query output at each step. This type a query form could be dynamically refined till the user satisfies with the query results.

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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 semantical similarity and co-occurrence which indicates the behavior information from other users in web search. Regarding the current user’s preference to a query, we combine the user’s short-term and long-term search behavior in a linear fashion and deal with the data sparse problem with Bayesian probabilistic matrix factorization (BPMF). In particular, we also investigate the impact of different personalization strategies (the combination of the user’s short-term and long-term search behavior) on the performance of query suggestion reranking. We quantify the improvement of our proposed UB model against a state-of-the-art baseline using the public AOL query logs and show that it beats the baseline in terms of metrics used in query suggestion reranking. The experimental results show that: (i) for personalized ranking, users’ behavioral information helps to improve query suggestion effectiveness; and (ii) given a query, merging information inferred from the short-term and long-term search behavior of a particular user can result in a better performance than both plain approaches.

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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 record is designed. UTC-tree avoids traversing all user check-in records in the query, accelerating user dynamic preference evaluation. Finally, the DPG query algorithm is used to implement a well-interacted DPG query system. Through a large number of comparative experiments, the validity of UTC-tree and the scalability of DPG query are verified.

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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. We train a state-of-the-art statistical machine translation model on query-snippet pairs from user query logs, and extract expansion terms from the query rewrites produced by the monolingual translation system. We show in an extrinsic evaluation in a real-world Web search task that the combination of a query-to-snippet translation model with a query language model achieves improved contextual query expansion compared to a state-of-the-art query expansion model that is trained on the same query log data.

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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 and topic model, for search in folksonomy. In detail, topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods.

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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 performance of the successful information query is evaluated based on the computing model and network simulation. The results of evaluation and the real experiences of this service validate the feasibility of the system.

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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 alternatives of query terms obtained from the dictionary. The importance factor measures the probability of a translation candidate of being selected as the final translation of a query term. This removes the problem of taking binary decision for translation candidates. At second level disambiguation, the model targets the user query as a single concept and deduces the translation of all query terms simultaneously, taking into account the weights of translation alternatives also. This is contrary to previous researches which select translation for each word in source language query independently. The experimental result with English-Hindi cross language information retrieval shows that the proposed two level disambiguation model achieved 79.53% and 83.50% of monolingual translation and 21.11% and 17.36% improvement compared to greedy disambiguation strategies in terms of MAP for short and long queries respectively.

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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 ambiguous words in natural language such as “Apple.” People with different knowledge backgrounds and interests have personalized understandings of these words. Therefore, we propose a personalized search model with personal word embeddings for each individual user that mainly contain the word meanings that the user already knows and can reflect the user interests. To learn great personal word embeddings, we design a pre-training model that captures both the textual information of the query log and the information about user interests contained in the click-through data represented as a graph structure. With personal word embeddings, we obtain the personalized word and context-aware representations of the query and documents. Furthermore, we also employ the current session as the short-term search context to dynamically disambiguate the current query. Finally, we use a matching model to calculate the matching score between the personalized query and document representations for ranking. Experimental results on two large-scale query logs show that our designed model significantly outperforms state-of-the-art personalization models.

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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. This paper proposes novel approach to vectorize each document and query with normalized TF-IDF method and applying Cosine Similarity function to extract top 3 documents based on user query.

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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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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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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 userâ€Ÿs informationneed. The userâ€Ÿs 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 search thehighly relevant web pages. This paper prepares an Entity-Centric model for the context and proposes a framework forcontext-aware focused web search system that considers thevarious context features and returns highly relevant searchresults to the user.

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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. Coordinately, the authors design a model called PLSA-based Personalized Information Retrieval with Network Regularization. Finally, the model is applied into academic information retrieval and the authors compare it with Baidu Scholar and the personalized information retrieval model based on the probabilistic latent semantic analysis topic model. The experiment results prove that this model can effectively extract topics and retrieves back results more satisfied for users' requirements. Also, this model improves the effect of retrieval results apparently. In addition, the retrieval model can be utilized not only in the academic information retrieval, but also in the personalized information retrieval on microblog search, associate recommendation, etc.

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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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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 presenting more relevant documents on the top retrieved results to mobile users. The main contribution of the improvements comes from the consideration of geographic information, ontological topic information and user interests together to find more relevant documents for satisfying their personal information need.

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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 generalizes existing frameworks for data-parallel computation, while providing the flexibility of query plan generation that is currently absent from these frameworks. In particular, we show how existing, off-the-shelf, AI planning tools can be used for solving DPPS tasks.

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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 the local interactions, we explicitly incorporate all contexts of a term through the graph-of-word text format. Matching patterns can be revealed accordingly to provide a more accurate relevance score. Our approach significantly outperforms strong baselines on two ad-hoc benchmarks. We also experimentally compare our model with BERT and show our advantages on long documents.

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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 used to construct different types of structured queries. First, a generic query structure known as semantic query structure is designed to store query contents given by user. Then, generation of a target language is carried out by mapping the contents in semantic query structure to query syntax templates stored in knowledge base. Findings Evaluations were carried out based on how well information needs are captured and transformed into a target query language. In summary, the proposed model is able to express information needs specified using query like NEXI. Xquery records a lower percentage because of its language complexity. The authors also achieve satisfactory query construction rate with an example-based method, i.e. 86 per cent (for NEXI IMDB topics) and 87 per cent (NEXI Wiki topics), respectively, compare to benchmark of 78 per cent by Sumita and Iida in language translation. Originality/value The proposed semantic-syntax query model allows flexibility of accommodating new query language by separating the semantic of query from its syntax.

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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. Set up a personalized Chinese search engineer model.

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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 restrictions on the desired documents simultaneously, incorporating more flexibility in the user-IR system interaction.

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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 intent by building two profiles: A positive user profile for the probabilities of the user is interested in the topics and a corresponding negative user profile for the probabilities of being not interested in the the topics. The profiles are estimated based on the user’s search logs. A clicked webpage is assumed to include interesting topics. A skipped (viewed but not clicked) webpage is assumed to cover some non-interesting topics to the user. Such estimations are performed in the latent topic space generated by LDA. Moreover, a new approach is proposed to estimate the correlation between a given query and the user’s search history so as to determine how much personalization should be considered for the query. We compare our proposed models with several strong baselines including state-of-the-art personalization approaches. Experiments conducted on a large-scale real user search log collection illustrate the effectiveness of the proposed models.

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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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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 user segmentation. Different from traditional user segmentation methods, which take little semantics of user behaviors into consideration, the authors propose a novel user segmentation strategy by incorporating the query enhancement mechanism with a topic model to mine the relationships between users and their behaviors in order to segment users in a semantic manner. Comparing with a classical clustering algorithm, K-means, the experimental results indicate that the proposed user segmentation strategy helps improve behavioral targeting effectiveness significantly. This paper also proposes an alternative to define user's search intent for the evaluation purpose, in the case that the dataset is sanitized. This approach automatically labels users in a click graph, which are then used in training an intent-based user classifier.

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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 is a top-k tuple for the scoring function adopted by the user. Further, we generalize UrkHit and propose the (r,k)-hit (rkHit) problem with an additional penalty function to model the user satisfaction with the tuple ranked i-th. rkHit aims to maximize the expected user satisfaction with the representative set. In 2D space, we design an exact algorithm 2DH for rkHit, indicating rkHit is in P for d=2. We show that rkHit is NP-hard when d\ge3. In 3D space, assuming a uniform preference distribution, we propose a (1-1/e)-approximation algorithm 3DH based on space partitioning. In addition, we propose an approximate algorithm MDH suitable for any dimension and distribution, which creatively combines the ideas of sampling and clustering. It relaxes the approximation guarantee slightly. Comprehensive experiments demonstrate the efficiency and effectiveness of our algorithms.

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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 provide the infrastructure and architectural support for realizing ubiquitous GIS services; and 2) Designing and developing ubiquitous GIS applications by utilizing the UbiSound framework to let users experience and benefit from the context aware services. We use scenario to illustrate how mobile/wireless and sensor technologies can enable ubiquitous GIS services in UbiSound. Some of the examples included in UbiSound are: Noise mapping, soundscape mapping and wellbeing data acquisition and analysis.

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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 model is best method in information retrieval for document ranking with efficient document representation which combines simplicity and clarity. VSM adopts similarity function to measure the matching between documents and user intent, and assign scores from the biggest to smallest. The documents and query are assigned with weights using term frequency and inverse document frequency method. To retrieve most relevant document to the user query term, document ranking function cosine similarity score is applied for every document and user query. The documents having more similarity scores will be considered as relevant documents to the query term and they are ranked based on these scores. This paper emphasizes on different techniques of information retrieval and Vector Space Model offers a realistic compromise in IR processing. It allows best weighing scheme which ranks the set of documents in order of relevance based on user query.

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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 strategy and comprehensively considering users’ interests according to the intelligent devices they use by implementing a user-device-based query expansion strategy and a weighted query expansion strategy in order to obtain the optimized query expansion set. We compare our method with the conceptual retrieval method as well as other two lexical methods for query expansion, and we prove that our method has better average recall and average precision ratios.

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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, presenting entity type-based query suggestions; second, clustering aggregated attributes; and third, ranking attributes based on their importance to a given query. To evaluate the model, 36 subjects were recruited to experience entity search with and without AIM. Findings – The experimental results show that the model achieves significant improvements over the default method of semantic aggregated search provided by Sig.ma, a leading entity search and navigation tool. Originality/value – This proposal develops more informative views for aggregated entity search and exploration to enhance users’ understanding of semantic data. The user study is the first to evaluate user interaction with Sig.ma's search capabilities in a systematic way.

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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 enhance data manipulation using SQL and to efficiently get the required query, the paper proposes a method for the generation of SQL query from natural language input, spoken(audio input) by the user. The model is constructed on NLP (Natural Language Processing) and Neural Networks (Deep Learning) technologies. Long Short Term Memory(LSTM) Model is used for predicting queries and is trained on the dataset with natural language as input and returns outline skeletal structure of the query as output. Then the output will be processed and the final query will be displayed to the user. The project also aims to benefit the people who are suffering from Repetitive Stress Injury (RSI), causing pain in the finger joints, which has been attributed to work requiring a long period of typing and also to those who are not familiar with SQL queries. As this system will readily provide the required query.

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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 enhance data manipulation using SQL and to efficiently get the required query, the paper proposes a method for the generation of SQL query from natural language input, spoken(audio input) by the user. The model is constructed on NLP (Natural Language Processing) and Neural Networks (Deep Learning) technologies. Long Short Term Memory(LSTM) Model is used for predicting queries and is trained on the dataset with natural language as input and returns outline skeletal structure of the query as output. Then the output will be processed and the final query will be displayed to the user. The project also aims to benefit the people who are suffering from Repetitive Stress Injury (RSI), causing pain in the finger joints, which has been attributed to work requiring a long period of typing and also to those who are not familiar with SQL queries. As this system will readily provide the required query.

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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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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 database. Our model is able to achieve comparable results to its discriminative counterpart, while being able to dynamically generate visual explanations. In addition to our searching and ranking method, we present an explanation interface that enables the user to successfully explore the model’s explanations and its confidence by revealing query-based, model-generated motion capture clips that contributed to the model’s decision. Finally, we conducted a user study with 44 participants to show that by using our model and interface, participants benefit from a deeper understanding of the model’s conceptualization of the search query. We discovered that the XAI system yielded a comparable level of efficiency, accuracy, and user-machine synchronization as its black-box counterpart, if the user exhibited a high level of trust for AI explanation.

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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 classifier (NBC) model for the CLMDS. An extensive set of experimentation analysis was performed and the results are investigated under distinct aspects. The resultant experimental values ensured the supremacy of the presented CLMDS model.

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Yu, Jongtae, Chengqi Guo, and Mincheol Kim. "Developing a User Centered Model for Ubiquitous Healthcare System Implementation." International Journal of Healthcare Information Systems and Informatics 3, no. 3 (2008): 58–76. http://dx.doi.org/10.4018/jhisi.2008070104.

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Martinez-Villaseñor, Maria, Miguel Gonzalez-Mendoza, and Neil Hernandez-Gress. "Towards a Ubiquitous User Model for Profile Sharing and Reuse." Sensors 12, no. 10 (2012): 13249–83. http://dx.doi.org/10.3390/s121013249.

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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 deploys the sparse vector space model to represent each query, which focuses on reducing the storage and representation overheads. And the proposed scheme adds a random number to each query vector. Hence, the cloud server cannot distinguish between queries with the same keywords, which ensures the privacy of the query. Experimental results show that the proposed scheme outperforms other relevant state-of-the-art schemes.

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Li, Jingfei, Peng Zhang, Dawei Song, and Yue Wu. "Understanding an enriched multidimensional user relevance model by analyzing query logs." Journal of the Association for Information Science and Technology 68, no. 12 (2017): 2743–54. http://dx.doi.org/10.1002/asi.23868.

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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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Singh, Jagendra, and Aditi Sharan. "Relevance Feedback Based Query Expansion Model Using Borda Count and Semantic Similarity Approach." Computational Intelligence and Neuroscience 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/568197.

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Pseudo-Relevance Feedback (PRF) is a well-known method of query expansion for improving the performance of information retrieval systems. All the terms of PRF documents are not important for expanding the user query. Therefore selection of proper expansion term is very important for improving system performance. Individual query expansion terms selection methods have been widely investigated for improving its performance. Every individual expansion term selection method has its own weaknesses and strengths. To overcome the weaknesses and to utilize the strengths of the individual method, we used multiple terms selection methods together. In this paper, first the possibility of improving the overall performance using individual query expansion terms selection methods has been explored. Second, Borda count rank aggregation approach is used for combining multiple query expansion terms selection methods. Third, the semantic similarity approach is used to select semantically similar terms with the query after applying Borda count ranks combining approach. Our experimental results demonstrated that our proposed approaches achieved a significant improvement over individual terms selection method and related state-of-the-art methods.

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Ezzat, Mostafa, Tarek Ahmed ElGhazaly, and Mervat Gheith. "A Word & Character N-Gram based Arabic OCR Error Simulation model." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 8 (2014): 3758–67. http://dx.doi.org/10.24297/ijct.v12i8.2999.

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This paper provides a new model aimed to enhanceArabic OCR degraded text retrieval effectiveness. The proposed model based onsimulating the Arabic OCR recognition mistakesbased on both, word based and Character N-Gram approaches. Then we expand the user search query using the expected OCR errors. The resulting search query expanded gives high precision and recall values in searching Arabic OCR-Degraded text rather than the original query. The proposed model showed a significant increase in the degraded text retrieval effectiveness over the previous models. The retrieval effectiveness of the newmodel is %93, while the best effectiveness published for word based approach was %84 and the best effectiveness for character based approach was %56.

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Jin, Cang-hong, Ze-min Liu, Ming-hui Wu, and Jing Ying. "FastFlow: Efficient Scalable Model-Driven Framework for Processing Massive Mobile Stream Data." Mobile Information Systems 2015 (2015): 1–18. http://dx.doi.org/10.1155/2015/818307.

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Massive stream data mining and computing require dealing with an infinite sequence of data items with low latency. As far as we know, current Stream Processing Engines (SPEs) cannot handle massive stream data efficiently due to their inability of horizontal computation modeling and lack of interactive query. In this paper, we detail the challenges of stream data processing and introduce FastFlow, a model-driven infrastructure. FastFlow differs from other existing SPEs in terms of its user-friendly interface, support of complex operators, heterogeneous outputs, extensible computing model, and real-time deployment. Further, FastFlow includes optimizers to reorganize the execution topology for batch query to reduce resource cost rather than executing each query independently.

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Li, Su Duo, Jing Lian Huang, Ying Xing Li, Jing Wei Deng, and Kai Ying Deng. "A Model of LBS Privacy Protection Based on Collaboration." Applied Mechanics and Materials 411-414 (September 2013): 172–76. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.172.

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How to protect users privacy effectively while providing location based service (LBS) is a very important study of the mobile Internet. Where location k-anonymity algorithm is the most commonly used algorithm at present. However the algorithm also has some disadvantages. Therefore, this paper presents a model of LBS privacy protection based on collaboration. It divides a location-based query into two parts and sends to two entities separately. One is the location cloaking server (LCS) which calculates k-anonymous locations, and the other is location service provider (LSP) which implements the query. Except the user none of the servers could have the complete query information. By this way it protects the users privacy very well. The algorithm partly solved the problems existing in the traditional k-anonymity algorithm, and the algorithms performance was significantly increased.

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Li, Xiaohui. "Realization of English Instructional Resources Clusters Reconstruction System Using the Machine Learning Model." Computational Intelligence and Neuroscience 2022 (July 9, 2022): 1–9. http://dx.doi.org/10.1155/2022/2838935.

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Based on ML algorithm, this paper puts forward a method that can search instructional resources through keyword indexing technology, and then cluster and recombine the related results and present them centrally. In this paper, the semantic processing of user query based on the subject index of educational resources is adopted to make up for the deficiency of query semantics, solve the problem of mismatch between query words and document words, and improve the recall and precision of resource retrieval. It is proposed to select the category feature items manually and establish the category feature model. In the environment of small sample set, the weight of category feature items is trained by ML method. The research shows that the user rating of this system is ideal, reaching 93.21% at the highest. In addition, the stability of the system can still reach 89.31% under the condition of relatively large usage, and its performance is excellent. This system can effectively solve the problem of scattered distribution of English instructional resources and make the presentation of knowledge more in line with the needs of users, thereby further improving the utilization rate of English instructional resources and users’ satisfaction.

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Liu, Bin, Zhengyu Yang, Jiaqing Wu, and Jie Gu. "OLAP analysis of user energy consumption based on multitemporal distribution characteristics." Journal of Physics: Conference Series 2290, no. 1 (2022): 012045. http://dx.doi.org/10.1088/1742-6596/2290/1/012045.

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Abstract With the development of databases, online transaction processing (OLTP) can no longer meet the needs of end users for database query and analysis, and the simple query of large databases by SQL can not meet the requirements of end user analysis. Therefore, online analytical processing (OLAP) is proposed. concept. On the one hand, we explained the basic knowledge of OLAP, including OLAP multidimensional data concept, multidimensional data structure, multidimensional data analysis, characteristics, etc. On the other hand, we established an OLAP analysis model of the multi-temporal and spatial distribution characteristics of user energy consumption, which is refined provide support for the analysis of user energy consumption behavior.

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Sheoran, Nikhil, Subrata Mitra, Vibhor Porwal, et al. "Conditional Generative Model Based Predicate-Aware Query Approximation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8259–66. http://dx.doi.org/10.1609/aaai.v36i8.20800.

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The goal of Approximate Query Processing (AQP) is to provide very fast but "accurate enough" results for costly aggregate queries thereby improving user experience in interactive exploration of large datasets. Recently proposed Machine-Learning-based AQP techniques can provide very low latency as query execution only involves model inference as compared to traditional query processing on database clusters. However, with increase in the number of filtering predicates (WHERE clauses), the approximation error significantly increases for these methods. Analysts often use queries with a large number of predicates for insights discovery. Thus, maintaining low approximation error is important to prevent analysts from drawing misleading conclusions. In this paper, we propose ELECTRA, a predicate-aware AQP system that can answer analytics-style queries with a large number of predicates with much smaller approximation errors. ELECTRA uses a conditional generative model that learns the conditional distribution of the data and at run-time generates a small (≈ 1000 rows) but representative sample, on which the query is executed to compute the approximate result. Our evaluations with four different baselines on three real-world datasets show that ELECTRA provides lower AQP error for large number of predicates compared to baselines.

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