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The traditional distinction between known item search and subject search is analyzed drawing on Robert Pages' distinction between particulars and specimens, the distinction between objects and their properties, Robert Fugmann's distinction between individual concepts and general concepts, and the difference between referring and describing. The resulting analysis coincides with E. J. Lowe's four-category ontology.

In the last two decades, word-based text compression has shown to be the key to efficiently handling large collections of text not only due to yielding important storage savings but, more importantly, because it allowed boosting the performance of some traditional text retrieval systems. The reason is that when the appropriate compression techniques are chosen, compressed text search becomes much faster than searches on plain text, and retrieval/decompression could start at any part of the compressed data, hence allowing to keep the text collection compressed all the time. Word-based text compressors have been typically compared in terms of their compression effectiveness, encoding/decoding speed, and performance when searching for words. In this paper, we show that compression also has benefits in terms of energy efficiency when performing word-based searches. Particularly, our experiments considering searches performed over uncompressed text and text compressed with the most well-suited compressors for text databases showed energy savings of around 30–70%. These savings are obtained thanks to improvements in search time but also, rather unexpectedly, because compressed text searches typically require less power from the processor. We have also analyzed how small modifications to the Horspool search algorithm can lead to time savings and can reduce even further the power needs of the processor (up to 5–10%), and, consequently, the overall energy consumption.

This paper presents a hybrid algorithm for pattern matching in text, which combines word length preprocessing with the Knuth-Morris-Pratt (KMP) algorithm. Its performance was evaluated against KMP and Boyer-Moore (BM) in two scenarios: synthetic texts and real-world texts. In the former, classical algorithms proved more efficient due to the uniform structure of the data. However, in real-world texts, the hybrid algorithm significantly reduced search times, thanks to its ability to filter matches by length patterns before performing character-by-character comparisons. The algorithm also demonstrated flexibility in recognizing patterns with different delimiters. Among its limitations is the difficulty in detecting substrings within longer words. As future work, the incorporation of partial matching techniques and the adaptation of the approach to multilingual environments and machine learning systems are proposed. The dataset used is provided to encourage reproducibility.

Google Books Ngram Viewer (GNV) is an analytical tool that uses quantitative methods to analyze digitized text. This paper looks at the possibilities and limitations of using GNV in management fashion research, an area of management research that examines the lifecycle and evolution of management concepts and ideas. GNV provides a historical big picture of the lifecycle and popularity of specific terms and phrases in books. It is argued that this tool could have a natural application in the study of management fashions, since books are a medium through which popular management concepts and ideas have traditionally been diffused. The paper provides several illustrative examples of how GNV can be applied to study management fashions and identifies the tool’s main possibilities and limitations. Although GNV has obvious advantages such as accessibility and user-friendliness, researchers should exercise caution, as it only provides a partial picture of the impact of management fashions.

In order to overcome the inefficiency and resource consumption of full-text search in relational databases, a light full-text search model with auxiliary cache is developed. Specially, we utilize the MySQL as the data storage layer and the Redis as the index cache layer. We first design a full-index cache mechanism by the Redis-based inverted indexes construction methods to augment the efficient memory processing capability of relational databases. In addition, an increment-index synchronization mechanism is implemented to fit the dynamic update of relation database. For hot data, an index update optimization mechanism is provided to guarantee the fast response and accuracy of full-text search. The proposed Redis-based auxiliary cache method has also been put into practical industrial applications and achieved promising results. Finally, we evaluate our method from index space occupation, time consumption and the accuracy of retrieval results. The experimental results show that the proposed model outperforms MySQL Full-Text method 2–3 times and surpasses ElasticSearch 12 times in space resource consumption.

Person search by natural language description is a challenging problem because of demands for modelling and learning visual-text semantic embedding. While several works have been dedicated to person search by English description, very few attempts have been made for other languages. This paper presents the first work towards person search by Vietnamese description. The contribution of the paper is threefold. First, the first and large-scale dataset for person search by Vietnamese natural language named 3000VnPersonSearch is built. Second, inspired by dual-path architecture (Zheng et al. ACM Trans Multimed Comput Commun Appl (TOMM) 16(2):1–23, 2020 ), in which single loss for intra-modal and triple loss for cross-modal learning of text and image data distribution were considered, in this paper, we employ this architecture for Vietnamese description-based person search. However, as Vietnamese language is under-resource, the existing word embedding model is still modest compared to that of English. Therefore, instead of using word2vec model as in Zheng et al. ( ACM Trans Multimed Comput Commun Appl (TOMM) 16(2):1–23, 2020 ), we modify the initialization process of the first convolution layer of the text-CNN path. In addition, we investigate in detail two online triplet mining strategies that are batch all and batch hard triplet. Extensive experiments have been conducted on benchmark datasets as well as on 3000VnPersonSearch. Experimental results show that the proposed method obtains 2.42% of improvement over the baseline method on CUHK-PEDES dataset and achieved state of the art results on VnPersonSearch dataset with a significant margin in comparison with the method in Pham et al. ( 2020 ). Finally, in order to illustrate the practical usage of person search by Vietnamese description language, a web-based application of person search is implemented and deployed.

Learning to rank (LTR) is an important artificial intelligence (AI) approach supporting the operation of many search engines. In large-scale search systems, the ranking results are continually improved with the introduction of more factors to be considered by LTR. However, the more factors being considered, the more computation resources required, which in turn, results in increased system response latency. Therefore, removing redundant factors can significantly improve search engine efficiency. In this paper, we report on our experience incorporating our Contextual Factor Selection (CFS) deep reinforcement learning approach into the Taobao e-commerce platform to optimize the selection of factors based on the context of each search query to simultaneously maintaining search result quality while significantly reducing latency. Online deployment on Taobao.com demonstrated that CFS is able to reduce average search latency under everyday use scenarios by more than 40% compared to the previous approach with comparable search result quality. Under peak usage during the Singles Day Shopping Festival (November 11th) in 2017, CFS reduced the average search latency by 20% compared to the previous approach.

This paper discusses the importance of search engine optimization (SEO) for digital repositories. We first describe the importance of SEO in the academic environment. Online systems, such as institutional digital repositories, are established and used to disseminate scientific information. Next, we present a case study of our own institution’s DSpace repository, performing several SEO tests and identifying the potential SEO issues through a group of three independent audit tools. In this case study, we attempt to resolve most of the SEO problems that appeared within our research and propose solutions to them. After making the necessary adjustments, we were able to improve the quality of SEO variables by more than 59% compared to the non-optimized state (a fresh installation of DSpace). Finally, we apply the same software audit tools to a sample of global institutional repositories also based on DSpace. In the discussion, we compare the SEO sample results with the average score of the semi-optimized DSpace repository (from the case study) and make conclusions.

The paper considers the problem of finding a given substring in a text. It is known that the complexity of a classical search query in an unordered database is linear in the length of the text and a given substring. At the same time, Grover’s quantum search provides a quadratic speed-up in the complexity of the query and gives the correct result with a high probability. We propose a hybrid classical–quantum algorithm (hybrid random–quantum algorithm, to be more precise) that implements Grover’s search to find a given substring in a text. As expected, the algorithm works (a) with a high probability of obtaining the correct result and (b) with a quadratic query acceleration compared to the classical one. What is new is that our algorithm uses the uniform hash family functions technique. As a result, our algorithm is much more memory efficient (in terms of the number of qubits used) compared to previously known quantum algorithms.