Enterprise Search Research Articles

Does digitalization always benefit cultural, sports, and tourism enterprises quality? Unveiling the inverted U-shaped relationship from a resource and capability perspective

The impact of digitalization on the high-quality development of “technology-sparse” but “labor-intensive” cultural, sports, and tourism enterprises may not follow a linear path. Therefore, there is an urgent need to explore the mechanism by which digitalization contributes to the integrated high-quality development of enterprises from the perspective of resources and capabilities. The majority of studies focus on the theoretical realm, while challenges in acquiring crucial data indicators constrain the advancement of empirical inquiries. This study creates a lexicon of digitized features encompassing strategic, technical, and application layers within cultural, sports, and tourism enterprises. The MD&A texts from annual reports were processed using the TF-IDF algorithm, which is based on machine learning, to assess the level of digitization. Additionally, integration development was incorporated into the evaluation system for high-quality development of cultural, sports, and tourism enterprises via social network analysis. In terms of theoretical contributions, enterprise search and reorganization capabilities were included within the scope of core enterprise capabilities. Furthermore, the study elucidated the roles of human capital and enterprise innovation in the interplay between digitalization and high-quality development, drawing upon the resource-based view and core capabilities theory. Finally, we empirically examined panel data from 116 listed cultural, sports, and tourism companies in Chinese A-share markets from 2010 to 2020. The findings reveal the following: (1) There is an inverted U-shaped association between digitalization and high-quality development, initially promoting then hindering progress, with a persistent pattern over time and robustness against instrumental variable regression and endogeneity tests. Additionally, the majority of cultural, sports, and tourism enterprises fall within a moderate range, indicating a positive impact on high-quality development. (2) Human capital and enterprise innovation are mediators of the inverted U-shaped relationship between digitalization and high-quality development. (3) The influence of digitalization on high-quality development significantly varies depending on the enterprise’s geographical location, life cycle, and risk tolerance. The study reveals the digitalization journey within cultural, sports, and tourism enterprises, offering theoretical insights for fostering collaborative high-quality development within the industry amid digitalization.

Information landscapes, technological ecosystems, enterprise discoverability, humanities open data conundrums and web search analysis for the public good: Reflections on a pandemic

Reports from UKeiG member forums, lectures and events during the pandemic, including: Open data practices in the humanities Analysing web searches for public good Defining and developing the enterprise search experience Managing the information landscape The library technology ecosystem AI - utopia or dystopia?

Artificial Intelligence, Technological Innovation and the Upgrading of China’s Equipment Manufacturing Industry

This article identifies and screens out companies applying Artificial Intelligence (AI) through Enterprise Search, and measures the level of AI empowerment in the industry in terms of the number of companies applying AI as a proportion of the number of companies in the industry as a whole, and based on the panel data of China’s equipment manufacturing industry from 2001-2017, we empirically test the impact effect and mechanism of action of AI empowerment on the upgrading of the equipment manufacturing industry. The research results show that: AI empowerment has a significant positive impact on the upgrading of the equipment manufacturing industry, but there is industry heterogeneity, and AI empowerment has a greater positive impact effect on the upgrading of the high-end equipment manufacturing industry. Technological innovation plays a mediating role in the process of AI empowerment for the upgrading of equipment manufacturing.

Privacy‐preserving clustering of unstructured big data for cloud‐based enterprise search solutions

Cloud-based enterprise search services (e.g., Amazon Kendra) are enchanting to big data owners by providing them with convenient search solutions over their enterprise big datasets. However, individuals and businesses dealing with confidential big data (e.g., criminal reports) are reluctant to fully embrace such cloud services due to valid data privacy concerns. Solutions based on client-side encryption have been developed to mitigate these concerns. Nonetheless, such solutions hinder data processing, especially, data clustering, which is pivotal in applications such as real-time search on large corpora (e.g., big datasets). To cluster encrypted big data, we propose privacy-preserving clustering schemes, called ClusPr, for three forms of unstructured datasets, namely static, semi-dynamic, and dynamic. ClusPr functions based on statistical characteristics of the datasets to: (A) determine the suitable number of clusters; (B) populate the clusters with topically relevant tokens; and (C) adapt the cluster set based on the dynamism of the underlying dataset. Experimental results, obtained from evaluating ClusPr against other schemes in the literature, on three different test datasets demonstrate between 30 % $$ 30\% $$ and 60 % $$ 60\% $$ improvement on the cluster coherency. Moreover, we notice that employing ClusPr within a privacy-preserving enterprise search system can reduce the search time by up to 78 % $$ 78\% $$ , while improving the search accuracy by up to 35 % $$ 35\% $$ .

The role of historical and contextual knowledge in enterprise search

PurposeThe purpose of the study is to examine enterprise searching practices across different work areas and work tasks in an enterprise search system in an international biotechnology company.Design/methodology/approachA mixed-method approach studying employees' authentic search activities during a 4-month period by log data, questionnaire survey and interviews. The log data analysed the entire active searcher group, whereas the questionnaire and interviews focused on frequent searchers.FindingsThe three studies provided insight into the searching activities and an understanding of the way searchers used the enterprise search system to search for information as part of their work tasks. The data identified three searcher groups, each with specific search characteristics. Four work task types were identified, and for all four types the searchers applied a tracing searching technique with use of contextual and historical relationships as paths.Practical implicationsThe findings point to the importance of knowledge on historical and contextual relations in enterprise search.Originality/valueThe work sheds new light on enterprise searchers' information search practices. A significant contribution is the identification of a tracing search method used in relation to four essential work task types. Another contribution is the importance of historical and contextual knowledge to support the tracing search and decide what paths to follow.

Impact of COVID-19 on search in an organisation.

COVID-19 has created unprecedented organisational challenges, yet no study has examined the impact on information search. A case study in a knowledge-intensive organisation was undertaken on 2.5 million search queries during the pandemic. A surge of unique users and COVID-19 search queries in March 2020 may equate to 'peak uncertainty and activity', demonstrating the importance of corporate search engines in times of crisis. Search volumes dropped 24% after lockdowns; an 'L-shaped' recovery may be a surrogate for business activity. COVID-19 search queries transitioned from awareness, to impact, strategy, response and ways of working that may influence future search design. Low click through rates imply some information needs were not met and searches on mental health increased. In extreme situations (i.e. a pandemic), companies may need to move faster, monitoring and exploiting their enterprise search logs in real time as these reflect uncertainty and anxiety that may exist in the enterprise.

A Detailed Study of Distributed Indexed Search Techniques using SOLR

For any web application running on RDBMS databases as the backend, it might be a huge performance impact if a search needs to be performed on a table with millions of rows or if a query needs to be executed which joins multiple tables. In general, such kind of backend services make the website extremely slow. Document based reverse indexing can be a useful solution in these cases. SOLR is a standalone enterprise search server with a REST-like API. It has major features which include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, NoSQL features and rich document (e.g., Word, PDF and more) parsing, geospatial search, Security built in. Databases and SOLR have complementary strengths and weaknesses. SQL supports very simple wildcard-based text search with some simple normalization like matching upper case to lowercase. The problem is that these are full table scans. In SOLR all searchable words are stored in an "inverse index based", which searches orders of magnitude faster. However, designing this framework is quite challenging. This paper discusses the techniques that are highly reliable, scalable and fault tolerant which can help in setting up the distributed indexing, replication and load-balanced querying with a centralized configuration.

Media Information, Investor Attention, Stock Price Changes and Liquidity: An Empirical Analysis Based on the Technology Enterprise Search Index

With the development of Internet media, the information asymmetry between listed companies and individual investors has been greatly improved. However, the impact of information on the stock market is relatively complex, and that is different on disparate industries. This paper takes the stock data of science and technology enterprise BOE as a sample. Firstly, use text mining to measure media information and investor attention, and then construct multivariate regression model by adjusting the non-linear and heteroscedasticity to analyze correlation between media information, investor attention and stock price changes as well as stock liquidity. Also, breakpoint regression model is made. Finally, the influence mode is obtained, and combined with the actual situation, the relevant suggestions are put forward.

External knowledge search paths in open innovation processes of small and medium enterprises

PurposeThe purpose of this paper is to advance knowledge by exploring how small and medium enterprises (SMEs) search for external knowledge in their open innovation processes, and how the search can be advanced.Design/methodology/approachThis exploratory research employs a qualitative multiple case study design. A literature review of open innovation in SMEs and external knowledge search is used to build the premises of this study. Semi-structured interviews with eight SMEs are employed to collect subsequent exploratory empirical data.FindingsThis exploratory study revealed that SMEs adopted a combination of cognitive and experiential search heuristics where cognitive search was practiced during the innovation research process when searching for external knowledge, whilst experiential search was practiced during the innovation development process. Concerning the search space, this study found that SMEs mainly explored local knowledge, and occasionally pursued distant knowledge when confronted with complex problems. The reason for the above behavior was explained to be related to the reduction of costs and risks associated with innovation activities.Originality/valueExternal knowledge plays a pivotal role in open innovation. Although extant studies have shed some light on how large firms search for external knowledge, however, it is not clear how SMEs search for external knowledge. Moreover, this study focuses on learning about both the search space and the search heuristics at both the research and the development stages of the innovation process.

Performance Analysis of Elastic Search Technique in Identification and Removalof Duplicate Data

Elastic search is a way to organize the data and make it easily accessible. It is a server based search on Lucene. It is a highly scalable, distributed and full-text search engine. Elastic search is developed in Java. It is published as open source under the terms of the Apache License. Elastic search is the most popular enterprise search engine. Elastic search includes all advances in speed, security, scalability, and hardware efficiency. Elastic search is a tool for querying written words. It can perform some other smart tasks, but its principal is returning text similar to a given query and statistical analyses of a quantity of text. Elasticsearch is a standalone database server, which is written in Java and using HTTP/JSON protocol,it’s takes data and optimized the data according to language based searches and stores it in a sophisticated format. Elastic search is very convenient, supporting clustering and leader selection out of the box. Whether it’s searching a database of trade products by description, finding similar text in a body of crawled web pages. In this manuscript elastic search capability of copied data identification and its removing techniques performance are analyzed.

Enterprise search: A state of the art

Enterprise search is changing. The explosion of information within organizations, technological advances and availability of free OpenSource machine learning libraries offer many possibilities. Eighteen informants from practice, academia, search technology vendors and large organizations (Oil and Gas, Governments, Pharmaceuticals, Aerospace and Retail) were interviewed to assess challenges and future directions. The findings confirmed the existence of the ‘Google Habitus’, technology propaganda and a need to transcend disciplines for a Systems thinking approach toward enterprise search. This encompasses information management, user search literacy, governance, learning feedback loops as well as technology. A novel four-level model for enterprise search use cases is presented, covering search as a utility, search as an answer machine, search task apps and a discovery engine. This could be used to reframe enterprise search perceptions, expanding possibilities and improving business outcomes.

GeoTxt: A scalable geoparsing system for unstructured text geolocation

AbstractIn this article we present GeoTxt, a scalable geoparsing system for the recognition and geolocation of place names in unstructured text. GeoTxt offers six named entity recognition (NER) algorithms for place name recognition, and utilizes an enterprise search engine for the indexing, ranking, and retrieval of toponyms, enabling scalable geoparsing for streaming text. GeoTxt offers a flexible application programming interface (API), allowing for customized attribute and/or spatial ranking of retrieved toponyms. We evaluate the system on a corpus of manually geo‐annotated tweets. First, we benchmark the performance of the six NERs that GeoTxt provides access to. Second, we assess GeoTxt toponym resolution accuracy incrementally, demonstrating improvements in toponym resolution achieved (or not achieved) by adding specific heuristics and disambiguation methods. Compared to using the GeoNames web service, GeoTxt's toponym resolution demonstrates a 20% accuracy gain. Our results show that places mentioned in the same tweet do not tend to be geographically proximate.

A User-Aware and Semantic Approach for Enterprise Search

This article describes how in addition to general purposes search engines, specialized search engines have appeared and have gained their part of the market. An enterprise search engine enables the search inside the enterprise information, mainly web pages but also other kinds of documents; the search is performed by people inside the enterprise or by customers. This article proposes an enterprise search engine called AMBIT1-SE that relies on two enhancements: first, it is user-aware in the sense that it takes into consideration the profile of the users that perform the query; second, it exploits semantic techniques to consider not only exact matches but also synonyms and related terms. It performs two main activities: (1) information processing to analyse the documents and build the user profile and (2) search and retrieval to search for information that matches user's query and profile. An experimental evaluation of the proposed approach is performed on different real websites, showing its benefits over other well-established approaches.

Academic research and its relevance to enterprise search success

Martin White emphasises the relevance of academic research on the success of enterprise search projects. “Academic research may not provide definitive answers to very difficult issues but it can provide a vendor-independent framework for discussion and inspiration.” He emphasises valuable research that has significant implications for enterprise search success, introducing us to concepts like “information scent” along the way. If you have responsibility for search management in your organisation, Martin’s article is a must read.
 Keywords: Research, Enterprise Search, Intranets, Information Retrieval

Improvement of Production Processes with the Use of Lean Manufacturing Tools

Abstract Growing competition, clients’ requirements as well as advances in technology make modern industrial enterprises search for a way of achieving and maintaining strong position on the market. One of the principal and at the same time necessary conditions to achieve this aim is continuous development of all of the processes in order to fulfil various clients’ expectations. Enterprises make use of different concepts and management methods which in significant way upgrade ongoing processes by raising their efficiency. One of such is the concept of ‘lean’ production - Lean Manufacturing. Management according to Lean Management rules allows to produce more with the use of less resources - at the same time approaching the aim which is to deliver clients the exact product they want. The process of resources use limitation is not easy to perform however, the concept of Lean Manufacturing by the application of chosen tools allows successfully and effectively to introduce ‘lean’ model of management in an enterprise and undertaking following improvement activities in every of its field.

Enterprise search and discovery capability: The factors and generative mechanisms for user satisfaction

Many organisations are re-creating the ‘Google-like’ experience behind their firewall to exploit their information. However, surveys show dissatisfaction with enterprise search is commonplace. No prior study has investigated unsolicited user feedback from an enterprise search user interface to understand the underlying reasons for dissatisfaction. A mixed-methods longitudinal study was undertaken analysing feedback from over 1000 users and interviewing search service staff in a multinational corporation. Results show that 62% of dissatisfaction events were due to human (information and search literacy) rather than technology factors. Cognitive biases and the ‘Google Habitus’ influence expectations and information behaviour and are postulated as deep underlying generative mechanisms. The current literature focuses on ‘structure’ (technology and information quality) as the reason for enterprise search satisfaction, agency (search literacy) appears downplayed. Organisations which emphasise ‘systems thinking’ and bimodal approaches towards search strategy and information behaviour may improve capabilities.

Search Engine, Inc.: Growth Versus Exit Strategy for a Mature Tech Start-Up

This case is about an information technology start-up that has grown into a global tech company in 10 years with revenue growth of 35.4% from 2008 to 2011. However, during that same period, four major competitors were acquired by integrated search providers like Microsoft and Oracle. These firms integrate enterprise search into broader information services like data mining, analytic, and predictive services. This case enables students to role-play as a consulting team hired by Search Engine, Inc.’s Board to assess the company’s strengths and weaknesses relative to its changing competitive environment and to evaluate whether industry consolidation should be viewed as a threat to its long-term viability or as an opportunity for further growth. If the team recommends selling the company, it needs to identify a potential buyer who would benefit the most from synergistic effects of acquiring their technology, talents, and clients. If the team recommends a growth strategy, it has to specify whether the company should grow within the specialized search market or compete in the integrated search market and whether to finance such growth with private equity or with an IPO. Whether a growth or exit strategy is recommended, the team needs to come up with a fair market valuation.

Griffin

Interactive information retrieval services, such as enterprise search and document search, must provide relevant results with consistent, low response times in the face of rapidly growing data sets and query loads. These growing demands have led researchers to consider a wide range of optimizations to reduce response latency, including query processing parallelization and acceleration with co-processors such as GPUs. However, previous work runs queries either on GPU or CPU, ignoring the fact that the best processor for a given query depends on the query's characteristics, which may change as the processing proceeds. We present Griffin, an IR systems that dynamically combines GPU- and CPU-based algorithms to process individual queries according to their characteristics. Griffin uses state-of-the-art CPU-based query processing techniques and incorporates a novel approach to GPU-based query evaluation. Our GPU-based approach, as far as we know, achieves the best available GPU search performance by leveraging a new compression scheme and exploiting an advanced merge-based intersection algorithm. We evaluate Griffin with real world queries and datasets, and show that it improves query performance by 10x compared to a highly optimized CPU-only implementation, and 1.5x compared to our GPU-approach running alone. We also find that Griffin helps reduce the 95th-, 99th-, and 99.9th-percentile query response time by 10.4x, 16.1x, and 26.8x, respectively.

Vocabulary Management at the Department for Education

UKeiG is committed to promoting the theory and practice of organising digital knowledge and information. ISKO UK, the UK Chapter of the International Society for Knowledge Organisation, is one of our partner organisations, overseeing the presentation of the prestigious Tony Kent Strix Award featured in this issue. ISKO brings together researchers and practitioners who develop and apply conceptual tools like semantic analysis and faceted classification. In previous issues we have emphasised that taxonomy and effective and systematically applied metadata continue to be key to breaking down information silos in organisations by enriching and structuring content, providing semantic context and integrating and linking disparate external and internal information resources. They underpin effective data management, information management and enterprise search. Helen Challinor contributes a fascinating case study of a project - Vocabulary management at the Department for Education - that takes us back to the essential basics of information retrieval, of nomenclature and thesauri. “This case study considers the use made of controlled vocabularies at the Department for Education. It outlines the principles of vocabulary management, before explaining the uses made of controlled vocabularies within the department. It includes explanations about why decisions were taken, how users were engaged and a forward look.”
 Helen writes: “Most developers have no idea that there is a science underpinning the management of subject vocabularies, and why would they? Neither do end users, and why would they? Devising innovative ways of explaining the nuances of vocabulary construction to developers and end users has been challenging, enjoyable and a significant deliverable from the project.”
 Keywords: Taxonomies, Vocabularies, Thesauri, Knowledge Management, Information Management

Improving retrieval of structured and unstructured information

Every day, information users struggle to find relevant documents and data needed to perform their jobs effectively and efficiently. This paper presents a strategic, practical approach to information architecture, focusing on developing and adding metadata to improve information retrieval. The review starts with a discussion of common information management problems and potential business benefits, and addresses the need for overarching principles and policies to be aligned with a comprehensive enterprise architecture. Next, the paper outlines a solution based on combining elements of information architecture – the hierarchy of a taxonomy, the synonyms from a thesaurus and the relationships in an ontology. It discusses how this hybrid structure can improve content classification, user navigation and enterprise search. The final sections will explain how taxonomy-generated rules can improve content classification and search, and present the results of a recent Proof of Concept (PoC) for automated content classification. The article also includes two sidebars – one offering taxonomy best practice guidelines and the other exploring an advanced classification weighting to improve search precision.