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Journal articles on the topic 'Business knowledge extraction'
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Höpken, Wolfram, Matthias Fuchs, Dimitri Keil, and Maria Lexhagen. "Business intelligence for cross-process knowledge extraction at tourism destinations." Information Technology & Tourism 15, no. 2 (May 6, 2015): 101–30. http://dx.doi.org/10.1007/s40558-015-0023-2.
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Spruit, Marco, Marcin Kais, and Vincent Menger. "Automated Business Goal Extraction from E-mail Repositories to Bootstrap Business Understanding." Future Internet 13, no. 10 (September 23, 2021): 243. http://dx.doi.org/10.3390/fi13100243.
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The Cross-Industry Standard Process for Data Mining (CRISP-DM), despite being the most popular data mining process for more than two decades, is known to leave those organizations lacking operational data mining experience puzzled and unable to start their data mining projects. This is especially apparent in the first phase of Business Understanding, at the conclusion of which, the data mining goals of the project at hand should be specified, which arguably requires at least a conceptual understanding of the knowledge discovery process. We propose to bridge this knowledge gap from a Data Science perspective by applying Natural Language Processing techniques (NLP) to the organizations’ e-mail exchange repositories to extract explicitly stated business goals from the conversations, thus bootstrapping the Business Understanding phase of CRISP-DM. Our NLP-Automated Method for Business Understanding (NAMBU) generates a list of business goals which can subsequently be used for further specification of data mining goals. The validation of the results on the basis of comparison to the results of manual business goal extraction from the Enron corpus demonstrates the usefulness of our NAMBU method when applied to large datasets.
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Mohamed, Mona, Sharma Pillutla, and Stella Tomasi. "Extraction of knowledge from open government data." VINE Journal of Information and Knowledge Management Systems 50, no. 3 (January 24, 2020): 495–511. http://dx.doi.org/10.1108/vjikms-05-2019-0065.
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Purpose The purpose of this paper is to establish a new conceptual iterative framework for extracting knowledge from open government data (OGD). OGD is becoming a major source for knowledge and innovation to generate economic value, if properly used. However, currently there are no standards or frameworks for applying knowledge continuum tactics, techniques and procedures (TTPs) to improve elicit knowledge extraction from OGD in a consistent manner. Design/methodology/approach This paper is based on a comprehensive review of literature on both OGD and knowledge management (KM) frameworks. It provides insights into the extraction of knowledge from OGD by using a vast array of phased KM TTPs into the OGD lifecycle phases. Findings The paper proposes a knowledge iterative value network (KIVN) as a new conceptual model that applies the principles of KM on OGD. KIVN operates through applying KM TTPs to transfer and transform discrete data into valuable knowledge. Research limitations/implications This model covers the most important knowledge elicitation steps; however, users who are interested in using KIVN phases may need to slightly customize it based on their environment and OGD policy and procedure. Practical implications After its validation, the model allows facilitating systemic manipulation of OGD for both data-consuming industries and data-producing governments to establish new business models and governance schemes to better make use of OGD. Originality/value This paper offers new perspectives on eliciting knowledge from OGD and discussing crucial, but overlooked area of the OGD arena, namely, knowledge extraction through KM principles.
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De Toni, Alberto Felice, Andrea Fornasier, and Fabio Nonino. "The nature and value of knowledge." Kybernetes 46, no. 06 (June 5, 2017): 966–79. http://dx.doi.org/10.1108/k-01-2017-0016.
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Purpose This paper aims to explain and discuss the complex nature and value of knowledge as an exploitable resource for business. Design/methodology/approach The authors propose a conceptual explanation of knowledge based on three pillars: the plurality of its nature, understood to be conservative, multipliable and generative, its contextual value and the duality of carrier incorporating business knowledge, objects or processes. After conceptualizing the nature of knowledge, the authors offer a metaphor based on the classic transformation from “potential” to “kinetic” energy in an inclined plane assuming that the conservative nature of knowledge makes it act as energy. Findings The metaphor uses the concept of potential and kinetic energy: if energy is only potential, it has a potential value not yet effective, whereas if the potential energy (knowledge) becomes kinetic energy (products and/or services), it generates business value. In addition, business value is a function of the speed acquired and caused by the angle of inclined plan, namely, the company’s business model. Knowledge is the source of the value and can be maintained and regenerated only through continuous investments. Several years later the value extraction reaches a null value of the company (potential energy) which will cease to act (kinetic energy) for triggering both the value generated and the value extracted. Originality/value The paper proposes an initial attempt to explain the meaning of the transformation of knowledge using a metaphor derived from physics. The metaphor of the energy of knowledge clearly depicts the managerial dilemma of balancing a company’s resources for both the generating and extracting value. Similarly, future study should try to associate other knowledge peculiarities to physical phenomena.
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Saura, Jose Ramon, Ana Reyes-Menendez, and Ferrão Filipe. "Comparing Data-Driven Methods for Extracting Knowledge from User Generated Content." Journal of Open Innovation: Technology, Market, and Complexity 5, no. 4 (September 24, 2019): 74. http://dx.doi.org/10.3390/joitmc5040074.
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This study aimed to compare two techniques of business knowledge extraction for the identification of insights related to the improvement of digital marketing strategies on a sample of 15,731 tweets. The sample was extracted from user generated content (UGC) from Twitter using two methods based on knowledge extraction techniques for business. In Method 1, an algorithm to detect communities in complex networks was applied; this algorithm, in which we applied data visualization techniques for complex networks analysis, used the modularity of nodes to discover topics. In Method 2, a three-phase process was developed for knowledge extraction that included the application of a latent Dirichlet allocation (LDA) model, a sentiment analysis (SA) that works with machine learning, and a data text mining (DTM) analysis technique. Finally, we compared the results of each of the two techniques to see whether or not the results yielded by these two methods regarding the analysis of companies’ digital marketing strategies were mutually complementary.
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Jennex, Murray E., and Summer E. Bartczak. "A Revised Knowledge Pyramid." International Journal of Knowledge Management 9, no. 3 (July 2013): 19–30. http://dx.doi.org/10.4018/ijkm.2013070102.
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The knowledge pyramid has been used for several years to illustrate the hierarchical relationships between data, information, knowledge, and wisdom. This paper posits that the knowledge pyramid is too basic and fails to represent reality and presents a revised knowledge-KM pyramid. One key difference is that the revised knowledge-KM pyramid includes knowledge management as an extraction of reality with a focus on organizational learning. The model also posits that newer initiatives such as business and/or customer intelligence are the result of confusion in understanding the traditional knowledge pyramid that is resolved in the revised knowledge-KM pyramid.
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Deshmukh, Shilpa, P. P. Karde, and V. R. Thakare. "An Improved Approach for Deep Web Data Extraction." ITM Web of Conferences 40 (2021): 03045. http://dx.doi.org/10.1051/itmconf/20214003045.
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The World Wide Web is a valuable wellspring of data which contains information in a wide range of organizations. The different organizations of pages go about as a boundary for performing robotized handling. Numerous business associations require information from the World Wide Web for doing insightful undertakings like business knowledge, item insight, serious knowledge, dynamic, assessment mining, notion investigation, and so on Numerous scientists face trouble in tracking down the most fitting diary for their exploration article distribution. Manual extraction is arduous which has directed the requirement for the computerized extraction measure. In this paper, approach called ADWDE is proposed. This drew closer is essentially founded on heuristic methods. The reason for this exploration is to plan an Automated Web Data Extraction System (AWDES) which can recognize the objective of information extraction with less measure of human intercession utilizing semantic marking and furthermore to perform extraction at a satisfactory degree of precision. In AWDES, there consistently exists a compromise between the degree of human intercession and precision. The objective of this examination is to diminish the degree of human intercession and simultaneously give exact extraction results independent of the business space to which the site page has a place.
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Manolova, Agata, Krasimir Tonchev, Vladimir Poulkov, Sudhir Dixir, and Peter Lindgren. "Context-Aware Holographic Communication Based on Semantic Knowledge Extraction." Wireless Personal Communications 120, no. 3 (June 3, 2021): 2307–19. http://dx.doi.org/10.1007/s11277-021-08560-7.
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AbstractAugmented, mixed and virtual reality are changing the way people interact and communicate. Five dimensional communications and services, integrating information from all human senses are expected to emerge, together with holographic communications (HC), providing a truly immersive experience. HC presents a lot of challenges in terms of data gathering and transmission, demanding Artificial Intelligence empowered communication technologies such as 5G. The goal of the paper is to present a model of a context-aware holographic architecture for real time communication based on semantic knowledge extraction. This architecture will require analyzing, combining and developing methods and algorithms for: 3D human body model acquisition; semantic knowledge extraction with deep neural networks to predict human behaviour; analysis of biometric modalities; context-aware optimization of network resource allocation for the purpose of creating a multi-party, from-capturing-to-rendering HC framework. We illustrate its practical deployment in a scenario that can open new opportunities in user experience and business model innovation.
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Schafer, Brad A., Sarah Bee, and Margaret Garnsey. "The Lemonade Stand: An Elementary Case for Introducing Data Analytics." AIS Educator Journal 13, no. 1 (January 1, 2018): 29–43. http://dx.doi.org/10.3194/1935-8156-13.1.29.
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Accounting education has been encouraged to increase the business knowledge, analytical skills, and data analytic skills of accounting students. This case blends these areas in a single, multi-part project for Accounting Information Systems (AIS) courses. The case includes the technical function of extracting data from databases, integrating multiple data stores and using multiple software tools (MS Access and Tableau). Additionally, students learn to assess the business needs driving the use of integrated data stores to produce quality information for decision making. Using a basic business scenario (lemonade stand), this case provides a stand-alone project focusing on incorporating data analytics into an AIS course. Students assume the role of a professional consultant to a lemonade stand and will become familiar with the business processes and the data of the company, develop queries to answer various business questions, and integrate internal and external data to graphically analyze the combined data for a business analysis. The case allows integration of the course content of data extraction and reporting elements with data analytics. Students indicated that they perceived that they increased their knowledge about business analysis and data analytics tools. Student also indicated they enjoyed the case and had many positive comments about their experience. Results from a pre-/post-test quiz reflect that students did significantly increase their knowledge of business analysis and data analytics.
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Ezeife, C. I., and Titas Mutsuddy. "Towards Comparative Mining of Web Document Objects with NFA." International Journal of Data Warehousing and Mining 8, no. 4 (October 2012): 1–21. http://dx.doi.org/10.4018/jdwm.2012100101.
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The process of extracting comparative heterogeneous web content data which are derived and historical from related web pages is still at its infancy and not developed. Discovering potentially useful and previously unknown information or knowledge from web contents such as “list all articles on ’Sequential Pattern Mining’ written between 2007 and 2011 including title, authors, volume, abstract, paper, citation, year of publication,” would require finding the schema of web documents from different web pages, performing web content data integration, building their virtual or physical data warehouse before web content extraction and mining from the database. This paper proposes a technique for automatic web content data extraction, the WebOMiner system, which models web sites of a specific domain like Business to Customer (B2C) web sites, as object oriented database schemas. Then, non-deterministic finite state automata (NFA) based wrappers for recognizing content types from this domain are built and used for extraction of related contents from data blocks into an integrated database for future second level mining for deep knowledge discovery.
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Baiao, Fernanda, Kate Revoredo, Brunno Silveira, and Felipe Klussmann. "Effort Estimation of Business Process Modeling through Clustering Techniques." iSys - Brazilian Journal of Information Systems 7, no. 1 (October 22, 2014): 34–47. http://dx.doi.org/10.5753/isys.2014.239.
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A critical activity in project planning, especially in business process modeling (BPM) projects, is effort estimation. It involves several dimensions such as business domain complexity, team and technology characteristics, turning estimation into a difficult and inaccurate task. In order to reduce this difficulty, background knowledge about past projects is typically applied; however, it is too costly to be carried out manually. On the other hand, Data Mining enables the automatic extraction of new nontrivial and useful knowledge from existing data. This paper presents a new approach for BPM project effort estimation using data mining through clustering technique. This approach was successfully applied to real data
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Garcia-Garcia, Julian A., C. Arevalo Maldonado, Ayman Meidan, Esteban Morillo-Baro, and Maria Jose Escalona. "gPROFIT: A Tool to Assist the Automatic Extraction of Business Knowledge From Legacy Information Systems." IEEE Access 9 (2021): 94934–52. http://dx.doi.org/10.1109/access.2021.3093356.
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Botha, Daniel F. "Rethinking the knowledge bearing capacity of e-Business systems." South African Journal of Business Management 38, no. 1 (March 31, 2007): 37–44. http://dx.doi.org/10.4102/sajbm.v38i1.576.
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Research was conducted in the area of sustainable knowledge extraction from e-business systems and technologies by exploring differentiating approaches from three notable authors seeking some common denominator to apply to a convergent approach in system conceptualization and design. It will be argued that most of e-business system upgrades and modification cost could be averted if the knowledge bearing capacity of proposed systems is realized and included as primary design parameters during the System Development Life Cycle. It will furthermore be argued that if this inclusive and integrative approach is followed it would lead to building a capacity to act that could be utilized for creating sustainable competitive advantage. Most e-Business system development is approached from an information processing and efficiency dimension, which more often than not exclude the knowledge utilization and effectiveness component as a design parameter. In most cases the primary focus is on information intensive functions, business process reengineering/automation and transaction processing whilst the use of information to discover knowledge assets and to innovate only comes into prominence after system implementation. This line of design thinking leads to the emergence of dominant designs which extend the scope for standardization whilst simultaneously limiting the scope for system variation. It will be proposed that re Boisot (1999), N-learning (neo-classical) thinking is normally dominant to S-learning (Schumpeterian) thinking during the e-business system design phase. The paper primarily draw on Max Boisot’s Evolutionary Production Function and I-Space theoretical approach, Donald Marchand’s Four Fundamental Principles of using Information to Create Business Value and Yogesh Malhotra’s model on Balancing Design and Emergence for EBusiness Model Innovation. A new construct called the Knowledge Prospect Domain (KPD) will be identified and introduced as a common denominator between the models of the three authors on which to ground the approach to new thinking on e-business system design. To facilitate argumentation an attempt will be made to position the extant status of e-business systems in the I-Space, referring to what will be proposed as proprietary technologies and emergent technologies.
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Xu, Hong Sheng, and Jia Song. "Constructing Domain Ontology of E-Business Based on Rough Concept Lattices." Advanced Materials Research 219-220 (March 2011): 202–5. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.202.
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Variable precision rough set (VPRS) model and formal concept analysis are studied in this paper, include algorithm of reduction attribute and extraction rule. The traditional algorithms about attribute reduction based on discernibility matrix and extraction rule in VPRS are discussed, there are problems in these traditional algorithms which are improved. Rough concept lattice model is proposed based on integrating of variable precision rough set model and formal concept analysis, and is used to reduce formal context. The domain ontology model of e-business is built combined with knowledge of domain expert, and original ontology model of the United Nations Standard Products and Services Classification Code by way of core ontology in order to enhance system robustness and efficiency.
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Pajić Simović, Ana, Slađan Babarogić, Ognjen Pantelić, and Stefan Krstović. "Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems." Applied Sciences 11, no. 12 (June 12, 2021): 5476. http://dx.doi.org/10.3390/app11125476.
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Enterprise resource planning (ERP) systems are often seen as viable sources of data for process mining analysis. To perform most of the existing process mining techniques, it is necessary to obtain a valid event log that is fully compliant with the eXtensible Event Stream (XES) standard. In ERP systems, such event logs are not available as the concept of business activity is missing. Extracting event data from an ERP database is not a trivial task and requires in-depth knowledge of the business processes and underlying data structure. Therefore, domain experts require proper techniques and tools for extracting event data from ERP databases. In this paper, we present the full specification of a domain-specific modeling language for facilitating the extraction of appropriate event data from transactional databases by domain experts. The modeling language has been developed to support complex ambiguous cases when using ERP systems. We demonstrate its applicability using a case study with real data and show that the language includes constructs that enable a domain expert to easily model data of interest in the log extraction step. The language provides sufficient information to extract and transform data from transactional ERP databases to the XES format.
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Bratianu, Constantin, Shahrazad Hadad, and Ruxandra Bejinaru. "Paradigm Shift in Business Education: A Competence-Based Approach." Sustainability 12, no. 4 (February 12, 2020): 1348. http://dx.doi.org/10.3390/su12041348.
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The fast and unpredictable changes in the business environment lead to significant changes in the future job market. For current business students, the future will offer many new opportunities for their employment but, at the same time, it will also create many threats disguised in the disappearing jobs. Business education centered mainly on knowledge transmission is challenged to switch towards a competence-based approach which includes knowledge, skills, and attitudes. The present research focuses on the need to change the paradigm of business education by creating a new learning environment centered on business competencies, and on a new knowledge ecosystem dynamics. The approach uses both qualitative and quantitative methods. In the first phase the research is focused on a critical literature review, and extraction of ideas for the next phase based on quantitative methods. In order to evaluate the students’ perception on the need of competence-based business education, a questionnaire has been designed and applied to undergraduate and graduate students enrolled in business and management programs. Data is processed by using SPSS and deriving six logistic regressions based on the conceptual model designed similar to a hierarchy Findings coming from students show a significant awareness for the need of paradigm shift in business education, from knowledge transfer to business competence development.
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Gailly, Frederik, and Guido L. Geerts. "Ontology-Driven Business Rule Specification." Journal of Information Systems 27, no. 1 (February 1, 2013): 79–104. http://dx.doi.org/10.2308/isys-50428.
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ABSTRACT Discovering business rules is a complex task for which many approaches have been proposed including analysis, extraction from code, and data mining. In this paper, a novel approach is presented in which business rules for an enterprise model are generated based on the semantics of a domain ontology. Starting from an enterprise model for which the business rules need to be defined, the approach consists of four steps: (1) classification of the enterprise model in terms of the domain ontology (semantic annotation), (2) matching of the enterprise model constructs with ontology-based Enterprise Model Configurations (EMCs), (3) determination of Business Rule Patterns (BRPs) associated with the EMCs, and (4) use of the semantic annotations to instantiate the business rule patterns; that is, to specify the actual business rules. The success of this approach depends on two factors: (1) the existence of a semantically rich domain ontology, and (2) the strength of the knowledge base consisting of EMC-BRP associations. The focus of this paper is on defining and illustrating the new business rule discovery approach: Ontology-Driven Business Rule Specification (ODBRS). The domain of interest is enterprise systems, and an extended version of the Resource-Event-Agent Enterprise Ontology (REA-EO) is used as the domain ontology. A small set of EMC-BRP associations—i.e., an example knowledge base—is developed for illustration purposes. The new approach is demonstrated with an example.
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Triantafyllou, Ioannis, Ioannis C. Drivas, and Georgios Giannakopoulos. "How to Utilize My App Reviews? A Novel Topics Extraction Machine Learning Schema for Strategic Business Purposes." Entropy 22, no. 11 (November 17, 2020): 1310. http://dx.doi.org/10.3390/e22111310.
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Acquiring knowledge about users’ opinion and what they say regarding specific features within an app, constitutes a solid steppingstone for understanding their needs and concerns. App review utilization helps project management teams to identify threads and opportunities for app software maintenance, optimization and strategic marketing purposes. Nevertheless, app user review classification for identifying valuable gems of information for app software improvement, is a complex and multidimensional issue. It requires foresight and multiple combinations of sophisticated text pre-processing, feature extraction and machine learning methods to efficiently classify app reviews into specific topics. Against this backdrop, we propose a novel feature engineering classification schema that is capable to identify more efficiently and earlier terms-words within reviews that could be classified into specific topics. For this reason, we present a novel feature extraction method, the DEVMAX.DF combined with different machine learning algorithms to propose a solution in app review classification problems. One step further, a simulation of a real case scenario takes place to validate the effectiveness of the proposed classification schema into different apps. After multiple experiments, results indicate that the proposed schema outperforms other term extraction methods such as TF.IDF and χ2 to classify app reviews into topics. To this end, the paper contributes to the knowledge expansion of research and practitioners with the purpose to reinforce their decision-making process within the realm of app reviews utilization.
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Akbar, Zeeshan, Jun Liu, and Zahida Latif. "Discovering Knowledge by Comparing Silhouettes Using K-Means Clustering for Customer Segmentation." International Journal of Knowledge Management 16, no. 3 (July 2020): 70–88. http://dx.doi.org/10.4018/ijkm.2020070105.
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A large amount of data is generated every day from different sources. Knowledge extraction is the discovery of some useful and potential information from data that can help to make better decisions. Today's business process requires a technique that is intelligent and has the capability to discover useful patterns in data called data mining. This research is about using silhouettes created from K-means clustering to extract knowledge. This paper implements K-means clustering technique in order to group customers into K clusters according to deals purchased in two different scenarios using evolutionary algorithm for optimization and compare silhouettes for different K values to analyze the improvement in extracted knowledge.
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Istiqamah, A. Nurul, and Kemas Rahmat Saleh Wiharja. "a Schema Extraction of Document-Oriented Database for Data Warehouse." International Journal on Information and Communication Technology (IJoICT) 7, no. 2 (December 31, 2021): 36–47. http://dx.doi.org/10.21108/ijoict.v7i2.584.
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The data warehouse is a very famous solution for analyzing business data from heterogeneous sources. Unfortunately, a data warehouse only can analyze structured data. Whereas, nowadays, thanks to the popularity of social media and the ease of creating data on the web, we are experiencing a flood of unstructured data. Therefore, we need an approach that can "structure" the unstructured data into structured data that can be processed by the data warehouse. To do this, we propose a schema extraction approach using Google Cloud Platform that will create a schema from unstructured data. Based on our experiment, our approach successfully produces a schema from unstructured data. To the best of our knowledge, we are the first in using Google Cloud Platform for extracting a schema. We also prove that our approach helps the database developer to understand the unstructured data better.
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Pajić, Ana, and Dragana Bečejski-Vujaklija. "Metamodel of the Artifact-Centric Approach to Event Log Extraction from ERP Systems." International Journal of Decision Support System Technology 8, no. 2 (April 2016): 18–28. http://dx.doi.org/10.4018/ijdsst.2016040102.
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Enterprise Resource Planning (ERP) systems handle a huge amount of data related to the actual execution of business processes and the goal is to discover from transaction log a model of how the business processes are actually carried out. The authors' work captures the knowledge of existing approaches and tools in converting the data from transaction logs to event logs for process mining techniques. They conduct a detailed analysis of the artifact-centric approach concepts and describe its constructs by the ontological metamodel. The underlying logical and semantically rich structure of the approach is presented through the model definition. The paper specifies how concepts of the data source are mapped onto the concept of the event log. Dynamics NAV ERP system is used as an example to illustrate the data-oriented structure of ERP system.
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Lin, Po Hung, Wen Hung Kuo, and Sheue Ling Hwang. "The development of a two-phase knowledge extraction framework on liquid crystal display image quality with knowledge-creation process." International Journal of Business and Systems Research 4, no. 1 (2010): 22. http://dx.doi.org/10.1504/ijbsr.2010.029946.
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Gonzalez-Lopez, Fernanda, and Guillermo Bustos. "Business process architecture design methodologies – a literature review." Business Process Management Journal 25, no. 6 (September 17, 2019): 1317–34. http://dx.doi.org/10.1108/bpmj-09-2017-0258.
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PurposeThe purpose of this paper is to describe the current state of the research field of business process architecture (BPA) and its design methodologies.Design/methodology/approachA systematic literature review (SLR) was conducted using meta- and content-based perspectives.FindingsFrom over 6,000 candidate studies, 89 were selected. A fifth of these primary works corresponded to BPA design methodologies. Though the BPA research field remains in an early stage of development, it bears promising growth potential. Regarding BPA design methodologies, the following aspects susceptible for further research were detected: identification and modeling of business process relationships; specification of inputs; standardization of models, notations and tool support; consideration of managerial concerns; integration of knowledge from other areas; and validation of methodological and product quality aspects.Research limitations/implicationsThe main limitation of the work lies in not being fully reproducible due to the fixed number of data sources and their digital nature, together with subjective decisions in work selection, data extraction and data analysis.Originality/valueTo the best of the authors’ knowledge no study has yet analyzed the BPA research field by means of an SLR. This study will benefit practitioners and research groups working on this topic by allowing them to get a rigorous overview of the BPA research field with an emphasis on available BPA design methodologies, and become aware of research gaps within the BPA field to position further research.
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Liu, Yang, Qingguo Zeng, Joaquín Ordieres Meré, and Huanrui Yang. "Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach." Complexity 2019 (August 7, 2019): 1–15. http://dx.doi.org/10.1155/2019/9202457.
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An increasing number of the renowned company’s investors are turning attention to stock prediction in the search for new efficient ways of hypothesizing about markets through the application of behavioral finance. Accordingly, research on stock prediction is becoming a popular direction in academia and industry. In this study, the goal is to establish a model for predicting stock price movement through knowledge graph from the financial news of the renowned companies. In contrast to traditional methods of stock prediction, our approach considers the effects of event tuple characteristics on stocks on the basis of knowledge graph and deep learning. The proposed model and other feature selection models were used to perform feature extraction on the websites of Thomson Reuters and Cable News Network. Numerous experiments were conducted to derive evidence of the effectiveness of knowledge graph embedding for classification tasks in stock prediction. A comparison of the average accuracy with which the same feature combinations were extracted over six stocks indicated that the proposed method achieves better performance than that exhibited by an approach that uses only stock data, a bag-of-words method, and convolutional neural network. Our work highlights the usefulness of knowledge graph in implementing business activities and helping practitioners and managers make business decisions.
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Bafna, Prafulla, Dhanya Pramod, Shailaja Shrwaikar, and Atiya Hassan. "Semantic key phrase-based model for document management." Benchmarking: An International Journal 26, no. 6 (August 5, 2019): 1709–27. http://dx.doi.org/10.1108/bij-04-2018-0102.
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Purpose Document management is growing in importance proportionate to the growth of unstructured data, and its applications are increasing from process benchmarking to customer relationship management and so on. The purpose of this paper is to improve important components of document management that is keyword extraction and document clustering. It is achieved through knowledge extraction by updating the phrase document matrix. The objective is to manage documents by extending the phrase document matrix and achieve refined clusters. The study achieves consistency in cluster quality in spite of the increasing size of data set. Domain independence of the proposed method is tested and compared with other methods. Design/methodology/approach In this paper, a synset-based phrase document matrix construction method is proposed where semantically similar phrases are grouped to reduce the dimension curse. When a large collection of documents is to be processed, it includes some documents that are very much related to the topic of interest known as model documents and also the documents that deviate from the topic of interest. These non-relevant documents may affect the cluster quality. The first step in knowledge extraction from the unstructured textual data is converting it into structured form either as term frequency-inverse document frequency matrix or as phrase document matrix. Once in structured form, a range of mining algorithms from classification to clustering can be applied. Findings In the enhanced approach, the model documents are used to extract key phrases with synset groups, whereas the other documents participate in the construction of the feature matrix. It gives a better feature vector representation and improved cluster quality. Research limitations/implications Various applications that require managing of unstructured documents can use this approach by specifically incorporating the domain knowledge with a thesaurus. Practical implications Experiment pertaining to the academic domain is presented that categorizes research papers according to the context and topic, and this will help academicians to organize and build knowledge in a better way. The grouping and feature extraction for resume data can facilitate the candidate selection process. Social implications Applications like knowledge management, clustering of search engine results, different recommender systems like hotel recommender, task recommender, and so on, will benefit from this study. Hence, the study contributes to improving document management in business domains or areas of interest of its users from various strata’s of society. Originality/value The study proposed an improvement to document management approach that can be applied in various domains. The efficacy of the proposed approach and its enhancement is validated on three different data sets of well-articulated documents from data sets such as biography, resume and research papers. These results can be used for benchmarking further work carried out in these areas.
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Yusuf, Ah, Rizki Fitryasari, Esti Yunitasari, and Rr Dian Tristiana. "TRAINING SANITATION HYGIENE SKILLS AND PRODUCTION MANAGEMENT OF "JAHE SEHAT" BEVERAGE IN AL HIDAYAH SENIOR HIGH SCHOOL STUDENTS, MOJOKERTO." Jurnal Pengabdian Masyarakat Dalam Kesehatan 3, no. 1 (February 22, 2021): 6. http://dx.doi.org/10.20473/jpmk.v3i1.23139.
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Most home and micro-scale industries have poor business management. Good management is able to produce the desired result or good in the business being run. The partner in this community service activity is students of Al Hidayah Senior High School in Mojokerto, who has a business in making ginger drinks “Jahe Sehat”. This training aimed to increase partner’s knowledge and skill about sanitation hygiene and production management. The manufacturing process starts from the preparation of raw materials, namely ginger, extraction, cooking, sieving, packaging and labeling to marketing. It was found that healthy ginger producers still do not understand the importance of hygiene and sanitation in the production process, unattractive packaging designs and no good financial cash planning system. The community service team conducts training and outreach to solve the partner's problems. By carrying out this service activity, partners increase partner’ skills and knowledge and the healthy ginger product "jahe sehat al-hidayah" has an attractive packaging.
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Zhou, Yilu, and Yuan Xue. "ACRank: a multi-evidence text-mining model for alliance discovery from news articles." Information Technology & People 33, no. 5 (June 22, 2020): 1357–80. http://dx.doi.org/10.1108/itp-06-2018-0272.
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PurposeStrategic alliances among organizations are some of the central drivers of innovation and economic growth. However, the discovery of alliances has relied on pure manual search and has limited scope. This paper proposes a text-mining framework, ACRank, that automatically extracts alliances from news articles. ACRank aims to provide human analysts with a higher coverage of strategic alliances compared to existing databases, yet maintain a reasonable extraction precision. It has the potential to discover alliances involving less well-known companies, a situation often neglected by commercial databases.Design/methodology/approachThe proposed framework is a systematic process of alliance extraction and validation using natural language processing techniques and alliance domain knowledge. The process integrates news article search, entity extraction, and syntactic and semantic linguistic parsing techniques. In particular, Alliance Discovery Template (ADT) identifies a number of linguistic templates expanded from expert domain knowledge and extract potential alliances at sentence-level. Alliance Confidence Ranking (ACRank)further validates each unique alliance based on multiple features at document-level. The framework is designed to deal with extremely skewed, noisy data from news articles.FindingsIn evaluating the performance of ACRank on a gold standard data set of IBM alliances (2006–2008) showed that: Sentence-level ADT-based extraction achieved 78.1% recall and 44.7% precision and eliminated over 99% of the noise in news articles. ACRank further improved precision to 97% with the top20% of extracted alliance instances. Further comparison with Thomson Reuters SDC database showed that SDC covered less than 20% of total alliances, while ACRank covered 67%. When applying ACRank to Dow 30 company news articles, ACRank is estimated to achieve a recall between 0.48 and 0.95, and only 15% of the alliances appeared in SDC.Originality/valueThe research framework proposed in this paper indicates a promising direction of building a comprehensive alliance database using automatic approaches. It adds value to academic studies and business analyses that require in-depth knowledge of strategic alliances. It also encourages other innovative studies that use text mining and data analytics to study business relations.
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Alfhaid, Mashaal A., and Manal Abdullah. "Classification of Imbalanced Data Stream: Techniques and Challenges." Transactions on Machine Learning and Artificial Intelligence 9, no. 2 (April 23, 2021): 36–52. http://dx.doi.org/10.14738/tmlai.92.9964.
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As the number of generated data increases every day, this has brought the importance of data mining and knowledge extraction. In traditional data mining, offline status can be used for knowledge extraction. Nevertheless, dealing with stream data mining is different due to continuously arriving data that can be processed at a single scan besides the appearance of concept drift. As the pre-processing stage is critical in knowledge extraction, imbalanced stream data gain significant popularity in the last few years among researchers. Many real-world applications suffer from class imbalance including medical, business, fraud detection and etc. Learning from the supervised model includes classes whether it is binary- or multi-classes. These classes are often imbalance where it is divided into the majority (negative) class and minority (positive) class, which can cause a bias toward the majority class that leads to skew in predictive performance models. Handles imbalance streaming data is mandatory for more accurate and reliable learning models. In this paper, we will present an overview of data stream mining and its tools. Besides, summarize the problem of class imbalance and its different approaches. In addition, researchers will present the popular evaluation metrics and challenges prone from imbalanced streaming data.
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Dunne, Jack. "Design Knowledge Extraction: A Community-Based Approach to Public Open Space Design in Mexico City." Architectural Engineering and Design Management 3, no. 3 (January 2007): 160–68. http://dx.doi.org/10.1080/17452007.2007.9684639.
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Li, Rui, Cheng Yang, Tingwei Li, and Sen Su. "MiDTD: A Simple and Effective Distillation Framework for Distantly Supervised Relation Extraction." ACM Transactions on Information Systems 40, no. 4 (October 31, 2022): 1–32. http://dx.doi.org/10.1145/3503917.
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Relation extraction (RE), an important information extraction task, faced the great challenge brought by limited annotation data. To this end, distant supervision was proposed to automatically label RE data, and thus largely increased the number of annotated instances. Unfortunately, lots of noise relation annotations brought by automatic labeling become a new obstacle. Some recent studies have shown that the teacher-student framework of knowledge distillation can alleviate the interference of noise relation annotations via label softening. Nevertheless, we find that they still suffer from two problems: propagation of inaccurate dark knowledge and constraint of a unified distillation temperature . In this article, we propose a simple and effective Multi-instance Dynamic Temperature Distillation (MiDTD) framework, which is model-agnostic and mainly involves two modules: multi-instance target fusion (MiTF) and dynamic temperature regulation (DTR). MiTF combines the teacher’s predictions for multiple sentences with the same entity pair to amend the inaccurate dark knowledge in each student’s target. DTR allocates alterable distillation temperatures to different training instances to enable the softness of most student’s targets to be regulated to a moderate range. In experiments, we construct three concrete MiDTD instantiations with BERT, PCNN, and BiLSTM-based RE models, and the distilled students significantly outperform their teachers and the state-of-the-art (SOTA) methods.
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Farida, Yeni, Heru Sasongko, Sugiyarto, and Agung Budihardjo. "Granulasi dengan Matrix dari Residu Ekstraksi Kunyit sebagai Upaya Produksi Pakan Ayam Pedaging." Agrokreatif Jurnal Ilmiah Pengabdian kepada Masyarakat 2, no. 2 (February 22, 2017): 87. http://dx.doi.org/10.29244/agrokreatif.2.2.87-91.
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Feed is an important aspect of the farm. Feeding not only consider nutritional factors but also economic factors. The evidence suggests that drugs such as antibiotics are added to feed for fattening purposes. Utilization of natural materials have a minimal risk of side effects. Turmeric (Curcuma domestica) is the most dominant medicinal plant that produces a lot of waste extraction. This activity aimed to reprocessing of residual extraction becomes an added value commodity such as feed. Patner of this programe was UKOT Naturafit Thibbunnabawi. The feed that produced meets the standar of feed for broilers. Counseling and training can improve participants' knowledge about the use of traditional medicine residues to modify animal feed and motivate participants to develop a farm business.
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Bhaumik, Soumya Suvra, and Ramachandran Rajagopalan. "Elicitation techniques to overcome knowledge extraction challenges in 'as-is' process modelling: perspectives and practices." International Journal of Process Management and Benchmarking 3, no. 1 (2009): 47. http://dx.doi.org/10.1504/ijpmb.2009.026408.
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Al-Merri, Hamad Salem. "The Impact of Business Intelligence on Strategic Performance in Commercial Banks Operating in the Sate of Kuwait." International Business Research 13, no. 8 (July 21, 2020): 91. http://dx.doi.org/10.5539/ibr.v13n8p91.
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This study aimed to identify the impact of business intelligence on strategic performance in commercial banks operating in the State of Kuwait the researcher used the descriptive analytical approach to introduce both business intelligence and strategic performance. The study population consisted of employees working in top and middle management in commercial banks operating in State of Kuwait. Stratified random sample amounting 363 subjects was used. 270 questionnaires were collected representing 74.3% of the total sample.
The study concluded that business intelligence system ensures data processing using data storage techniques and data extraction to obtain consistent and qualified information, thus providing the required knowledge to achieve the strategic goals and objectives by end users and executives in the future. The researcher recommends that Kuwaiti banks should keep pace with developments in the field of business intelligence to be employed in a better way in enhancing its strategic performance, in addition to conduct future studies that follow the analytical approach to deepen its utilization in Kuwaiti commercial banking sector.
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Wauer, Matthias, Johannes Meinecke, Daniel Schuster, Andreas Konzag, Markus Aleksy, and Till Riedel. "Semantic Federation of Product Information from Structured and Unstructured Sources." International Journal of Business Data Communications and Networking 7, no. 2 (April 2011): 69–97. http://dx.doi.org/10.4018/jbdcn.2011040105.
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Product-related information can be found in various data sources and formats across the product lifecycle. Effectively exploiting this information requires the federation of these sources, the extraction of implicit information, and the efficient access to this comprehensive knowledge base. Existing solutions for product information management (PIM) are usually restricted to structured information, but most of the business-critical information resides in unstructured documents. We present a generic architecture for federating heterogeneous information from various sources, including the Internet of Things, and argue how this process benefits from using semantic representations. A reference implementation tailor-made to business users is explained and evaluated. We also discuss several issues we experienced that we believe to be valuable for researchers and implementers of semantic information systems, as well as the information retrieval community.
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Rikowski, Ruth. "Value — The Life Blood of Capitalism: Knowledge is the Current Key." Policy Futures in Education 1, no. 1 (March 2003): 160–78. http://dx.doi.org/10.2304/pfie.2003.1.1.5.
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This article considers the knowledge revolution, the knowledge economy, knowledge, knowledge management and value within global capitalism. It argues that the knowledge revolution is the latest phase of capitalism and that the success of the knowledge revolution depends on the creation of value that is extracted from knowledge, and that this includes the knowledge that is in people's heads. It considers philosophical issues surrounding knowledge. It then considers the meaning of ‘value’, by first considering some of the current business and information literature, and second, returning to a Marxist analysis of value. It argues that we can only really begin to fully explain and understand the concept of value, as well as its significance within the knowledge revolution, by returning to a Marxist theoretical analysis. The article concludes by arguing that we need to become more conscious of the fact that ‘value’ is the essential ingredient upon which all forms of capitalism rest, and furthermore, that today value is being extracted from knowledge, particularly in the industrialised world. Once the human race becomes more conscious of this, it can then endeavour to create a better, kinder, fairer social and economic system that does not depend on the extraction of value from and exploitation of human labour.
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Makhija, Veena, and Swapnil Ahuja. "Rule based Text Extraction from a Bibliographic Database." DESIDOC Journal of Library & Information Technology 38, no. 1 (January 2, 2018): 5. http://dx.doi.org/10.14429/djlit.38.1.12307.
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<p>The emergent concept of ‘ Big Data’ has shifted the paradigm from information retrieval to information extraction techniques. The information extraction techniques enables corpus analysis to draw useful interpretations and its possible applications. Selection of appropriate information extraction technique depends upon the type of data being dealt with and its possible applications. In an R&D environment, the published information is considered as an authenticated benchmark to study and analyse the growth pattern in that field of science, medicine, business. A rule based information extraction process, on the selected data extracted from a bibliographic database of published R&D papers is proposed in this paper. Aim of the study is to build up a database on relevant concepts, cleaning of retrieved data and automate the process of information retrieval in the local database. For this purpose, a concept based ‘subject profiles’ in the area of advanced semiconductors as well as the rules for text extraction from metadata retrieved from the bibliographic database was developed. This subset was used as an input to the knowledge domain to support R&D in the area of ‘advanced semiconductor materials and devices’ and provide information services on Intranet. Study found that concept based pattern matching on the datasets downloaded yielded better results as compared to the results by using the controlled vocabulary of the source database .</p>
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Tangwannawit, Panana, and Sakchai Tangwannawit. "Feature extraction to predict quality of segregating sweet tamarind using image processing." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 1 (January 1, 2022): 339. http://dx.doi.org/10.11591/ijeecs.v25.i1.pp339-346.
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<p>In this modern age, several new methods have been developed, especially in image processing for agriculture business, which consists of technologies derived from artificial intelligence (AI) capabilities called machine learning. Classify is a widely used method to analyze patterns, trends, as well as the body of knowledge from the data visualization. Image classification application improves discrimination and prediction efficiency. The objective of this research was to feature extraction of sweet tamarind and compare the algorithm for classification. This research used images from golden sweet tamarind species with the use of MATLAB and Python language. The steps of this research consisted of 1) preprocessing step for finding the distance to appropriate of the image quality, 2) feature extracting for finding the number of black pixels and the number of white pixels, perimeter, diameter, and centroid, and 3) classifying for algorithms' comparison. The results showed that the camera's distance to the image was 60 cm. The coefficient of determination was at 0.9956, and the Standard Error of Estimate was 7,424.736 pixels. The conclusion of classification found that the random forest had the highest accuracy at 92.00%, SD. = 8.06, precision = 90.12, recall = 92.86, and F1-score = 91.36.</p>
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Hegland, Markus. "Data mining techniques." Acta Numerica 10 (May 2001): 313–55. http://dx.doi.org/10.1017/s0962492901000058.
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Methods for knowledge discovery in data bases (KDD) have been studied for more than a decade. New methods are required owing to the size and complexity of data collections in administration, business and science. They include procedures for data query and extraction, for data cleaning, data analysis, and methods of knowledge representation. The part of KDD dealing with the analysis of the data has been termed data mining. Common data mining tasks include the induction of association rules, the discovery of functional relationships (classification and regression) and the exploration of groups of similar data objects in clustering. This review provides a discussion of and pointers to efficient algorithms for the common data mining tasks in a mathematical framework. Because of the size and complexity of the data sets, efficient algorithms and often crude approximations play an important role.
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Osesina, O. Isaac, and John Talburt. "A Data-Intensive Approach to Named Entity Recognition Combining Contextual and Intrinsic Indicators." International Journal of Business Intelligence Research 3, no. 1 (January 2012): 55–71. http://dx.doi.org/10.4018/jbir.2012010104.
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Over the past decade, huge volumes of valuable information have become available to organizations. However, the existence of a substantial part of the information in unstructured form makes the automated extraction of business intelligence and decision support information from it difficult. By identifying the entities and their roles within unstructured text in a process known as semantic named entity recognition, unstructured text can be made more readily available for traditional business processes. The authors present a novel NER approach that is independent of the text language and subject domain making it applicable within different organizations. It departs from the natural language and machine learning methods in that it leverages the wide availability of huge amounts of data as well as high-performance computing to provide a data-intensive solution. Also, it does not rely on external resources such as dictionaries and gazettes for the language or domain knowledge.
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Ahmad, Jawad, Abdur Rehman, Hafiz Tayyab Rauf, Kashif Javed, Maram Abdullah Alkhayyal, and Abeer Ali Alnuaim. "Service Recommendations Using a Hybrid Approach in Knowledge Graph with Keyword Acceptance Criteria." Applied Sciences 12, no. 7 (March 31, 2022): 3544. http://dx.doi.org/10.3390/app12073544.
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Businesses are overgrowing worldwide; people struggle for their businesses and startups in almost every field of life, whether industrial or academic. The businesses or services have multiple income streams with which they generate revenue. Most companies use different marketing and advertisement strategies to engage their customers and spread their services worldwide. Service recommendation systems are gaining popularity to recommend the best services and products to customers. In recent years, the development of service-oriented computing has had a significant impact on the growth of businesses. Knowledge graphs are commonly used data structures to describe the relations among data entities in recommendation systems. Domain-oriented user and service interaction knowledge graph (DUSKG) is a framework for keyword extraction in recommendation systems. This paper proposes a novel method of chunking-based keyword extractions for hybrid recommendations to extract domain-specific keywords in DUSKG. We further show that the performance of the hybrid approach is better than other techniques. The proposed chunking method for keyword extraction outperforms the existing value feature entity extraction (VF2E) by extracting fewer keywords.
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Mesbahi, Nadjib, Okba Kazar, Saber Benharzallah, and Merouane Zoubeidi. "A Cooperative Multi-Agent Approach-Based Clustering in Enterprise Resource Planning." International Journal of Knowledge and Systems Science 6, no. 1 (January 2015): 34–45. http://dx.doi.org/10.4018/ijkss.2015010103.
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With the rapid development of information technology and the gradual extension of information technology to enterprise, enterprise resource planning system has become a tool that enables uniform and consistent management of information system (IS) of the company with a large single database. In addition, knowledge discovery is a technology whose purpose is to promote information and knowledge extraction from a large database. This paper proposes a cooperative multi-agent approach based clustering in enterprise resource planning for extract unknown knowledge in the enterprise resource planning database. To achieve this, the authors call the paradigm of multi-agent system to distribute the complexity of several autonomous entities called agents, whose goal is to group records or observations on similar objects classes using the clustering technique. This will help business decision-makers to take good decisions and provide a very good response time by the use of multi-agent system. To implement the proposed architecture, it is more convenient to use the JADE platform while providing a complete set of services and agents comply with the specifications FIPA.
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Neykova, Melina, and Boyanka Zhelyazova. "THE ROLE OF BUSINESS INTELLIGENT SYSTEMS IN MONITORING AND ANALYSIS OF UNIVERSITY DATA." Proceedings of CBU in Natural Sciences and ICT 1 (November 16, 2020): 54–59. http://dx.doi.org/10.12955/pns.v1.121.
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Nowadays, it has become clear that the success of different sized organizations depends on the speed at which they adapt to the dynamic changes and challenges of competitive market structures. At the same time, it is considered that information is a key element for identifying the strengths and weaknesses of the business activities of organizations and the trends for their future development and market consolidation.
Modern universities face major challenges related to the processing of large amounts of data, which are continuously generated by different systems and units, but in most cases, the information flow is not analysed effectively enough. Namely the efficient extraction of educational data is an important aspect for the analysis of the state of the university as well as the effective planning of its future development.
Therefore, the main purpose of this study is to consider the capabilities of intelligent business analysis information systems to monitor and control the large volumes of data generated at the University of Forestry, Bulgaria. Implementing such a system will help transform data into valuable information and knowledge that will assist academic leadership in taking timely, informed, reasoned managerial decisions and actions, taking into account the dynamic and competitive educational environment and rapidly changing educational needs in higher education.
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Manicketh, Tintu Jose, and Mannancheril Sebastian Francis. "Extraction of natural colorants from Araucaria columnaris, Macaranga peltata and Averrhoa bilimbi for textile coloration." International Journal of Clothing Science and Technology 32, no. 6 (May 5, 2020): 789–801. http://dx.doi.org/10.1108/ijcst-06-2019-0075.
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PurposeThe paper aims to investigate the feasibility of developing natural dyes from the barks of Araucaria columnaris and leaves of Macaranga peltata, Averrhoa bilimbi. The paper also deals with the application of natural dyes in textile coloration.Design/methodology/approachDye extraction was carried out using the aqueous method. The dyeability of the aqueous extract was assessed on cotton, silk and polyester yarns using different mordants (alum, acetic acid, CuSO4, lemon juice) and without mordant. UV–Visible spectral analysis and pH of different natural dyes were determined. Percent absorption, K/S values, CIELab values and fastness properties of the selected dyed yarns were also assessed.FindingsThe percentage values for dye exhaustion differed with various mordants. The K/S values were found to be influenced by the addition of mordants. Different hues were obtained with the usage of different mordants. Fastness results exhibited good to very good grades.Research limitations/implicationsThe effective application of aqueous method of dye extraction in the study avoids solvent toxicity. The current results proved that the dyeing could be achieved at room temperature for different yarns (cotton, silk, polyester). At present, no report exists in the literature of research work on the extraction of natural dyes from the leaves of M. peltata, A. bilimbi and their dyeing potential on cotton, silk and polyester.Practical implicationsThe present work offers new environment-friendly dye as well as simple dyeing method. Barks and leaves are promising sources of dye. Enormous availability of barks and leaves avoids the exploitation of the plant parts for the extraction of natural dyes.Originality/valueThe important feature of this study was the effective dyeing of natural and synthetic fibers at room temperature. The novel sources of natural dyes would contribute significantly to the existing knowledge of dyeing, and the natural dyes reduce the environmental impact of synthetic dyes.
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Seethapathy, Senthil Kumar, and C. Naveeth Babu. "ENHANCED APPROACH FOR SOIL CLASSIFICATION USING BOOSTED C5.0 DECISION TREE ALGORITHM." BSSS journal of computer 12, no. 1 (June 30, 2021): 11–21. http://dx.doi.org/10.51767/jc1202.
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Data mining includes the utilization of erudite data analysis tools to discover previously unidentified, suitable patterns and relationships in enormous data sets. Data mining tools can incorporate statistical models, machine learning methods such as neural networks or decision trees, and mathematical algorithms. As a result data mining comprises of more process. This performs analysis and prediction than collecting and managing data. The main objective of data mining is to identify valid, potentially useful, novel and understandable correlations and patterns in existing data. Finding and analyzing useful patterns in data is known by different names (e.g., knowledge extraction, information discovery, information harvesting, data archaeology, and data pattern processing). The term data mining is basically utilized by statisticians, database researchers, and the business communities.
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Sabando-Vera, David, Marcela Yonfa-Medranda, Néstor Montalván-Burbano, Jose Albors-Garrigos, and Katherine Parrales-Guerrero. "Worldwide Research on Open Innovation in SMEs." Journal of Open Innovation: Technology, Market, and Complexity 8, no. 1 (January 13, 2022): 20. http://dx.doi.org/10.3390/joitmc8010020.
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Research on open innovation (OI) has increased in recent years, showing its potential in various areas of knowledge. Its relation to small and medium-sized enterprises has attracted the attention of academics. This article aims to evaluate the intellectual structure of the scientific study of OI, and its close relationship with various scientific fields, through a bibliometric analysis of this academic field using the Scopus database and the application of the VOSviewer software. The methodology comprises a rigorous systematic and transparent process divided into four phases: (i) the establishment of search criteria for the research field, through a literature review for its selection; (ii) the selection of the database, the establishment of the search equation and extraction of information; (iii) the application of inclusion and exclusion criteria for the selected documents and an explanation of the usefulness of the software; and (iv) the analysis of the results through the approaches of scientific output performance and bibliometric mapping. The results show an increasing trend of IO publications in SMEs, consolidated in 396 articles with contributions from 65 countries and 947 authors. The intellectual structure shows seven themes related to firm performance, R&D networks, business management, business models, capabilities and knowledge transfer. This study contributes to the field by providing an overview of IO in SME contexts. It also provides insightful information to policymakers for developing policies for firm economic growth.
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Liu, Xiaowei, and Hongjin Liu. "Design and Application of English Grammar Intelligent Question Bank System." Scientific Programming 2021 (October 29, 2021): 1–10. http://dx.doi.org/10.1155/2021/9483719.
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The English grammar question bank can reserve a large number of special knowledge points and special categories of questions, which is helpful for English learners. Therefore, we design and verify the application effect of an intelligent question bank system. We construct an English grammar knowledge map, which is used to design the system architecture, including basic layer, data layer, service layer, business layer, and user layer. On the basis of the system structure, the index system of the item bank is established, and the functional modules of the system are designed comprehensively. Finally, we design a system database based on MySQL and give the table of knowledge points of English grammar. In order to verify the performance of the designed English grammar intelligent test bank system, a verification experiment is carried out. The experiment selects the accuracy rate of English grammatical feature extraction, recall rate, convergence, and system configuration accuracy. The experimental results show that the proposed system can extract English grammatical features with higher accuracy, and the recall rate is more satisfactory. Compared with the reference systems, the designed system configuration accuracy is higher.
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Petrulyte, Salvinija, Deimante Plascinskiene, and Donatas Petrulis. "Testing and predicting of yarn pull-out in aroma-textile." International Journal of Clothing Science and Technology 29, no. 4 (August 7, 2017): 566–77. http://dx.doi.org/10.1108/ijcst-10-2016-0113.
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Purpose The purpose of this paper is to predict the pull-out force of loop pile of ramie/cotton terry woven fabrics treated with aroma-microcapsules as well as to understand and to interpret the pull-out behaviour developing the mathematical model. Design/methodology/approach The displacements and forces associated with pulling a yarn from different structures of fabrics were determined. Regression analysis and factorial designs were performed. Findings The yarn pull-out behaviour of terry fabric is highly dependent on the applied treating and demonstrated various extents of variability under the different pulling distances. The character of yarn pull-out is periodic and depends on fabric construction. The difference between the resistance to pile loop extraction for the grey and modified terry fabrics depends on the changed fabric’s structure. The existence of good relation between binder’s concentration and resistance to pile loop extraction of terry fabric was proved. Practical implications The study enables to forecast important loop feature for terry aroma-textiles: to be securely held in the place preventing loop pulling. Originality/value The assessment of the influence of fabric’s weft density and binder’s concentration for the yarn pull-out of terry aroma-textile was proposed. The research developed analysis and empiric mathematical equations suitable for predicting of displacements and forces related to pulling phenomenon as well as designing new multifunctional terry fabrics with resistance to pile loop extraction required. The received knowledge could enlarge the base of information needful for design of new products for clothing, home textile and healthcare/well-being applications as well.
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EL-SAID, ASMAA M., ALI I. ELDESOKY, and HESHAM A. ARAFAT. "AN EFFICIENT OBJECT ORIENTED TEXT ANALYSIS (OOTA) APPROACH TO CONSTRUCT STATIC STRUCTURE WITH DYNAMIC BEHAVIOR." International Journal of Information Acquisition 09, no. 01 (March 2013): 1350006. http://dx.doi.org/10.1142/s021987891350006x.
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In many fields of science, IT applications and business environments successfully evolved systems to receive vast amount of electronic data and information. Due to increasing electronic data and information, most recent researches have tried to find a solution to resolve the crisis of information overload. These solutions include a combination of techniques of data mining, machine learning, natural language processing and information retrieval, information extraction, and knowledge management. A great challenge is how to exploit those information and knowledge resources and turn them into useful knowledge available to concerned people. The value of knowledge increases when people can share and capitalize on it. Thus, approaches that can help researchers to benefit from existing hidden knowledge are needed. For this, tools that can analyze, extract and explore relevant and useful information with relations are required. So, the main contribution of this paper is to integrate the technology of XML with text analysis for introducing an efficient concept-based structure model, where this model can represent the text in a form that can be easily understood, shared, managed and mined. This paper describes an efficient object oriented text analysis (OOTA) approach by generating an object oriented model that transforms unstructured text to a specific structured form and stored in XML format. The experimental results show that this approach has a good promotion on results.
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Premji, Zahra, K. Alix Hayden, and Shauna Rutherford. "Teaching Knowledge Synthesis Methodologies in a Higher Education Setting: A Scoping Review of Face-to-Face Instructional Programs." Evidence Based Library and Information Practice 16, no. 2 (June 15, 2021): 111–44. http://dx.doi.org/10.18438/eblip29895.
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Abstract Background – Knowledge synthesis (KS) reviews are increasingly being conducted and published. Librarians are frequently taking a role in training colleagues, faculty, graduate students, and others on aspects of knowledge syntheses methods. Objective – In order to inform the design of a workshop series, the authors undertook a scoping review to identify what and how knowledge synthesis methods are being taught in higher education settings, and to identify particularly challenging concepts or aspects of KS methods. Methods – The following databases were searched: MEDLINE, EMBASE & APA PsycInfo (via Ovid); LISA (via ProQuest); ERIC, Education Research Complete, Business Source Complete, Academic Search Complete, CINAHL, Library & Information Science Source, and SocIndex (via EBSCO); and Web of Science core collection. Comprehensive searches in each database were conducted on May 31, 2019 and updated on September 13, 2020. Relevant conferences and journals were hand searched, and forward and backward searching of the included articles was also done. Study selection was conducted by two independent reviews first by title/abstract and then using the full-text articles. Data extraction was completed by one individual and verified independently by a second individual. Discrepancies in study selection and data extraction were resolved by a third individual. Results – The authors identified 2,597 unique records, of which 48 full-text articles were evaluated for inclusion, leading to 17 included articles. 12 articles reported on credit courses and 5 articles focused on stand-alone workshops or workshop series. The courses/workshops were from a variety of disciplines, at institutions located in North America, Europe, New Zealand, and Africa. They were most often taught by faculty, followed by librarians, and sometimes involved teaching assistants. Conclusions – The instructional content and methods varied across the courses and workshops, as did the level of detail reported in the articles. Hands-on activities and active learning strategies were heavily encouraged by the authors. More research on the effectiveness of specific teaching strategies is needed in order to determine the optimal ways to teach KS methods.
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Jones, Robert T., Lucy S. Tusting, Hugh M. P. Smith, Sylvester Segbaya, Michael B. Macdonald, Michael J. Bangs, and James G. Logan. "The Role of the Private Sector in Supporting Malaria Control in Resource Development Settings." Journal of Infectious Diseases 222, Supplement_8 (October 29, 2020): S701—S708. http://dx.doi.org/10.1093/infdis/jiaa488.
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Abstract Industrial operations of the private sector, such as extraction, agriculture, and construction, can bring large numbers of people into new settlement areas and cause environmental change that promotes the transmission of vector-borne diseases. Industry-related workers and communities unduly exposed to infection risk typically lack the knowledge and means to protect themselves. However, there is a strong business rationale for protecting local resident employees through integrated vector control programs, as well as an ethical responsibility to care for these individuals and the affected communities. We discuss the role and challenges of the private sector in developing malaria control programs, which can include extensive collaborations with the public sector that go on to form the basis of national vector control programs or more broadly support local healthcare systems.