A Multi-Agent Personalized Query Refinement Approach for Academic Paper Retrieval in Big Data Environment

As one of the pillar industries of China's economic development, the coal industry has been concerned by all circles of society with the continuous development of economy. At the same time, with the development of information data, various fields have changed under the influence of "big data" environment. Based on the definition of big coal enterprise data environment social responsibility scope, on the basis of combing existing problems in the coal enterprise social responsibility information disclosure, and puts forward the measures to help the coal enterprise under the environment of big data maximum fulfill social responsibility.

Boosted by the invention and wide popularity of SIFT image features and bag-of-visual-words (BoVW) image representation, object retrieval has progressed significantly in the past years and has already found deployment in real-life applications and products, but the traditional BoVW methods have several problems, such as:low time efficiency and large memory consumption, the synonymy and polysemy of visual words. In this article, a method based on E2LSH (Exact Euclidean Locality Sensitive Hashing) and query expansion is proposed. Firstly, E2LSH is used to hash local features of training dataset, and a group of scalable random visual vocabularies are constructed. Then, the visual vocabulary histograms and index files are created according to these random vocabularies. Finally, a query expansion strategy is used to accomplish object retrieval. Experimental results show that the accuracy of the novel method is substantially improved compared to the traditional methods, and it adapts large scale datasets well.

This thesis explores statistical approaches to machine learning in a big data environment. Firstly, the connections and differences between machine learning and statistics in a big data environment are introduced, as well as the statistical foundations in machine learning models. Secondly, the application of statistical methods in big data analysis is discussed, including the combination of traditional data analysis and machine learning. Then, the challenges and limitations of statistical methods in the big data environment, such as high dimensionality and huge amount of data, are discussed. Then, common statistical methods in the big data environment, including linear regression, decision trees, and support vector machines, are described in detail. Finally, the research findings are summarised and future directions and research trends are outlined. Through the research in this paper, a deeper understanding of statistical methods for machine learning in big data environment is provided, which provides an important reference for big data analysis and application.

Much attention has been paid to the relative effectiveness of Interactive Query Expansion (IQE) versus Automatic Query Expansion (AQE). This research has been shown that automatic query expansion (collection dependent) strategy gives better performance than no query expansion. The percentage of queries that are improved by AQE strategy is 57% with average precision equal to 43.2. Compared against AQE (collection dependent) strategy, IQE gives better average precision than AQE strategy. The percentage of queries that are improved by best IQE decision is 86% with average precision equal to 44.1. Evaluation process reveals that the value of n in AQE strategy that gave the optimal value of average precision for the whole query set is equal to one.

In a Big Data environment, in order to study the decision-making problem of Big Data information investment and the effects of using Big Data information to improve industry cost on supply chain coordination, firstly the importance of Data Company in supply chain was analyzed, and the original supply chain model was built. Meanwhile, some changes of consumer behavior were analyzed in a Big Data environment. Based on these, the market demand function and the benefit model of stakeholder were built and analyzed. Findings:(1)The first finding is whether an enterprise was suitable for gaining Big Data to improve its costs, which was determined by the cost improvement coefficient; namely, it was related to the ability of excavating and using the value of Big Data.(2)Whether the supply chain was the decentralized decision-making and the centralized decision-making, the thresholds of acquisition costs on Big Data information were equal. Moreover, the maximum value that they could undertake was same.(3)Meanwhile the fact that the quantity discount contract could achieve a win-win outcome for supply chain members was proved. The discount coefficient was related to consumers’ behavior preference in a Big Data environment.

The existing query expansion (QE) methods cannot find the most users-requested source code version at times due to the over-expansion resulting from noises. To solve this problem, we propose a QE method based on evolving contexts (EC) that are added/deleted terms and their dependent terms during code evolution. On expanding a query, we appended the added terms as relevant terms, and excluded the deleted terms as noisy terms. We also developed a QE-integrating framework based on the Support Vector Machine (SVM) Ranking, called QESR, to simultaneously integrate multiple QE methods. Our experiment shows that QESR outperforms the state-of-the-art QE methods CodeHow and Query Expansion based on Crowd Knowledge (QECK) by 13%–16% in terms of precision when the first query result is inspected.

To evaluate the effectiveness of generative query expansion for biomedical literature retrieval. We thoroughly examined eight generative query expansion methods using three large language models across five datasets for biomedical literature retrieval. We further performed a quantitative analysis, including performance comparisons, rank transition analysis, and article-type effect analysis. We also conducted a qualitative examination of representative cases, from which we derived an error taxonomy. On BioASQ-Y/N, GPT-4o-based query expansion shifts Recall@10 to 0.417-0.512 and nDCG@10 to 0.358-0.479, relative to a baseline of 0.491 and 0.456. For PubMedQA, Precision@1 ranges from 0.764 to 0.876 and nDCG@10 from 0.847 to 0.931, compared with baseline values of 0.893 and 0.935. For 2019-Trec-PM, query expansion yields Recall@100 of 0.217-0.256 and nDCG@100 of 0.272-0.312, versus a baseline of 0.227 and 0.274. Similarly, for 2018-TREC-PM, Recall@100 spans 0.169-0.227 and nDCG@100 spans 0.195-0.250, relative to baseline scores of 0.164 and 0.191. For 2017-TREC-PM, Recall@100 and nDCG@100 fall within 0.111-0.139 and 0.154-0.191 under query expansion, compared with baseline metrics of 0.102 and 0.147. Both general-purpose and domain-specific Llama-based models demonstrate similar performance to GPT-4o. The impact of query expansion varies significantly by the expansion methods and type of evidence, but is relatively agnostic to backbone model choice. Notably, query expansion primarily affects article ranking but has a limited impact on the screening stage. Our findings underscore the unique challenges of biomedical literature retrieval and highlight the need to develop domain-specific information retrieval techniques.

This paper study about a review of the literature on Query Expansion (QE) methods. The main aspects of the review study are the methods, limitations of the QE methods, and datasets used in QE. As the results of this study, we can conclude several methods used on QE i.e., Ontology, Association Rules, Wordnet, Methathesaurus, Synonym mapping, Concept-based, Local Co-occurrence, and Latent Semantic Indexing (LSI). Each of the methods still has limitations. For datasets used in QE, many researchers use public datasets.

Queries to search engines on the Web are usually short. They do not provide sufficient information for an effective selection of relevant documents. Previous research has proposed the utilization of query expansion to deal with this problem. However, expansion terms are usually determined on term co-occurrences within documents. In this study, we propose a new method for query expansion based on user interactions recorded in user logs. The central idea is to extract correlations between query terms and document terms by analyzing user logs. These correlations are then used to select high-quality expansion terms for new queries. Compared to previous query expansion methods, ours takes advantage of the user judgments implied in user logs. The experimental results show that the log-based query expansion method can produce much better results than both the classical search method and the other query expansion methods.

In recent years, with the continuous development of the country’s Internet platforms, China has gradually entered the e-commerce era of national online shopping, and more and more e-commerce platforms and stores have adopted intelligent recommendation systems to increase transaction rates. However, it is not easy for consumers to filter out the products they want from a large amount of information. The emergence of intelligent recommendation systems provides great convenience for people to screen out personalized products that meet their own characteristics. However, the algorithms used in traditional recommendation technology focus on the single-computer environment and do not consider the performance of the recommendation method when distributed parallel processing is required in the big data environment, so it cannot meet the personalized needs of users in the big data environment. Aiming at the new requirements for the development of e-commerce intelligent recommendation technology in the big data environment, this paper uses the big data processing technology based on cloud computing and focuses on the realization technology of the e-commerce intelligent recommendation algorithm and the comprehensive evaluation method of the recommendation system in the big data environment. A prototype system of personalized intelligent recommendation based on cloud computing has been developed, which is of great importance to meet the needs of e-commerce personalized intelligent recommendation in the big data environment, improve the effectiveness, scale, and real-time performance of the personalized intelligent recommendation system, and improve the level of personalized precision marketing., which is of theoretical significance and economic value.

A defining feature of the response to the COVID-19 pandemic was a pervasive and palpable collapse of sense-making in individuals and organisations. A key factor underlying this chaos was the inability of humans to make sense of the big and noisy data environment. Such big data environments are especially amenable to interpretation by Artificial intelligence. In this study, a theoretical framework has been developed for the use of AI for sense-making in survival supply chains. The proposed framework, ‘The Cognitive Model of Survival Supply Chains’, is articulated as consisting of scan, store, interpret, execute, and learn as its purposive components. This framework is presented as a scaffolding to organise extant knowledge, identify gaps, synthesise new knowledge, and guide future research and practice. Research questions have been identified to develop a systematic research agenda. Implications for research, policy and practice for managing pandemics have been drawn out for researchers, policy makers, and practitioners, respectively. The paper also presents the design and architecture of an AI based solution for sense-making in survival supply chains. Such a solution is urgently needed for organising the efforts of diverse stakeholders in presenting a concerted and effective response to disasters that may unfold in the future.

Accepted: 24.11.2015 In this paper, a hybrid approach is developed for geospatial information retrieval which combines query expansion (QE) and latent semantic indexing (LSI) methods. The hybrid method uses the advantages of Query Expansion and Semantic indexing methods for improving search results. LSI establishes relations depending on the similarities between queries and the documents where QE helps by adding extra similar terms to the queries. The dataset is populated using data extracted from Wikipedia and USGS (The United States Geological Survey). Automation is programmed to get all the information from the web and to extract the meaningful words. The significant terms are the name of the places, directions, and nearby locations. The dataset includes mountains and places with their latitudes, longitudes, locations and directions. The extracted data is also taken into Protege for semantically querying. The approach is applied to geographical data (popular search items on search engines), and by querying the data with the combination of LSI and QE more related results are handled than the results of methods when they are applied alone. Many queries are run, and the results are listed and compared. At the end of the study it is understood that the hybrid method considerably improves the search results.

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.

We propose a new method for query expansion called "term similarity tree model" (TSTM). Term similarity tree is built to represent and estimate similarities between terms. Based on TSTM, we use similarity restriction and overlay restriction to implement query expansion. This method can cluster terms automatically, make the process of query expansion more flexible and controllable, and control noise effectively. In addition, the parameters of TSTM can be adjusted easily to meet the requirements of different types of queries. TREC data is used to test the method. The experiments show that TSTM method outperforms the existing methods in query expansion, such as WordNet-based method, local cooccurrence method and latent semantic indexing (LSI-based) method.

PurposeThe role of big data and data analytics in the audit engagement process is evident. Notwithstanding, understanding how big data influences cognitive processes and, consequently, on the auditors’ judgment decision-making process is limited. The purpose of this paper is to present a conceptual framework on the cognitive process that may influence auditors’ judgment decision-making in the big data environment. The proposed framework predicts the relationships among data visualization integration, data processing modes, task complexity and auditors’ judgment decision-making.Design/methodology/approachThe methodology to accomplish the conceptual framework is based on a thorough literature review that consists of theoretical discussions and comparative studies of other authors’ works and thinking. It also involves summarizing and interpreting previous contributions subjectively and narratively and extending the work in some fashion. Based on this approach, this paper formulates four propositions about data visualization integration, data processing modes, task complexity and auditors’ judgment decision-making. The proposed framework was built from cognitive theory addressing how auditors process data into useful information to make judgment decision-making.FindingsThe proposed framework expects that the cognitive process of data visualization integration and intuitive data processing mode will improve auditors’ judgment decision-making. This paper also contends that task complexity may influence the cognitive process of data visualization integration and processing modes because of the voluminous nature of data and the complexity of business processes. Hence, it is also expected that the relationships between data visualization integration and audit judgment decision-making and between processing mode and audit judgment decision-making will be moderated by task complexity.Research limitations/implicationsThere is a dearth of studies examining how big data and big data analytics affect auditors’ cognitive processes in making decisions. This paper will help researchers and auditors understand the behavioral consequences of data visualization integration and data processing mode in making judgment decision-making, given a certain level of task complexity.Originality/valueWith the advent of big data and the evolution of innovative audit procedures, the constructed framework can be used as a theoretical foundation for future empirical studies concerning auditors’ judgment decision-making. It highlights the potential of big data to transform the nature and practice of accounting and auditing.