Academic literature on the topic 'Business intelligence,data mining,utility'

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Journal articles on the topic "Business intelligence,data mining,utility"

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Rao, M. Venkata Krishna, Ch Suresh, K. Kamakshaiah, and M. Ravikanth. "Prototype Analysis for Business Intelligence Utilization in Data Mining Analysis." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (2017): 30. http://dx.doi.org/10.23956/ijarcsse.v7i7.93.

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Tremendous increase of high availability of more disparate data sources than ever before have raised difficulties in simplifying frequent utility report across multiple transaction systems apart from an integration of large historical data. It is main focusing concept in data exploration with high transactional data systems in real time data processing. This problem mainly occurs in data warehouses and other data storage proceedings in Business Intelligence (BI) for knowledge management and business resource planning. In this phenomenon, BI consists software construction of data warehouse quer
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Altuntas, Serkan. "A novel approach based on utility mining for store layout: a case study in a supermarket." Industrial Management & Data Systems 117, no. 2 (2017): 304–19. http://dx.doi.org/10.1108/imds-01-2016-0040.

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Purpose The purpose of this paper is to propose a novel approach based on utility mining for store layout. Design/methodology/approach A utility mining-based data mining algorithm is utilized in this paper. Findings A real-life case study in a supermarket is conducted to illustrate the proposed approach. The findings show that the proposed approach can be used easily and efficiently to arrange store layout. Research limitations/implications There are two limitations to this study. First, space allocation to each product family is not considered. Second, product placement in each product family
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Cheng, Haodong, Meng Han, Ni Zhang, Xiaojuan Li, and Le Wang. "A Survey of incremental high-utility pattern mining based on storage structure." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 841–66. http://dx.doi.org/10.3233/jifs-202745.

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Traditional association rule mining has been widely studied, but this is not applicable to practical applications that must consider factors such as the unit profit of the item and the purchase quantity. High-utility itemset mining (HUIM) aims to find high-utility patterns by considering the number of items purchased and the unit profit. However, most high-utility itemset mining algorithms are designed for static databases. In real-world applications (such as market analysis and business decisions), databases are usually updated by inserting new data dynamically. Some researchers have proposed
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Thompson, M. W., J. Hiestermann, and L. Moyo. "PROVING THE CAPABILITY FOR LARGE SCALE REGIONAL LAND-COVER DATA PRODUCTION BY SELF-FUNDED COMMERCIAL OPERATORS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W2 (November 16, 2017): 209–12. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w2-209-2017.

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For service providers developing commercial value-added data content based on remote sensing technologies, the focus is to typically create commercially appropriate geospatial information which has downstream business value. The primary aim being to link locational intelligence with business intelligence in order to better make informed decisions. From a geospatial perspective this locational information must be relevant, informative, and most importantly current; with the ability to maintain the information timeously into the future for change detection purposes. Aligned with this, GeoTerraIm
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Jamalpur, Bhavana, and S. S. V. N. Sharma. "Data Mining and Business Intelligence Dashboards." International Journal of Asian Business and Information Management 3, no. 4 (2012): 39–44. http://dx.doi.org/10.4018/jabim.2012100104.

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Today, the most successful companies are those that can respond quickly and flexibly to market changes and opportunities. A detailed understanding and knowledge is required to analyze the business environment at different levels. This can be achieved by implementing value added information throughout the organization. There has been an increased demand in the development of IT that performs data manipulation operations to report and analyze the data. While sifting through all that data, organizing it, and then using it to make the business run more profitably has become a bigger challenge. Dec
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Bayer, Harun, Mustafa Aksogan, Enes Celik, and Adil Kondiloglu. "Big data mining and business intelligence trends." Journal of Asian Business Strategy 7, no. 1 (2017): 23–33. http://dx.doi.org/10.18488/journal.1006/2017.7.1/1006.1.23.33.

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Boland, Giles W., James H. Thrall, and Richard Duszak. "Business Intelligence, Data Mining, and Future Trends." Journal of the American College of Radiology 12, no. 1 (2015): 9–11. http://dx.doi.org/10.1016/j.jacr.2014.10.003.

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Post, Gerald V., and Albert Kagan. "Business Intelligence." International Journal of Business Intelligence Research 3, no. 3 (2012): 16–28. http://dx.doi.org/10.4018/jbir.2012070102.

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Data mining and business intelligence tools have been adding features and gaining uses, and statistical tools developed for data mining tasks often require advanced knowledge and training to apply. Development of these selected tools requires tradeoffs in ease of use and power. This study asks users to evaluate the various tools and attributes to identify the relative value of the various components and provide direction for improvements and new tools. Evaluating multi-attribute software is a challenging task, and this study provides a method of evaluating the data and analyzing tradeoffs. A s
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Azevedo, Ana, and Manuel Filipe Santos. "QMBE: A data mining language for business intelligence." International Journal Of Data Mining And Emerging Technologies 1, no. 1 (2011): 22. http://dx.doi.org/10.5958/j.2249-3212.1.1.4.

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C. H. Chen, Jason. "Research on Business Intelligence with Data Mining Applications." International Journal of Business and Economics Research 6, no. 2 (2017): 19. http://dx.doi.org/10.11648/j.ijber.20170602.11.

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Dissertations / Theses on the topic "Business intelligence,data mining,utility"

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Corain, Elisabetta. "Ottimizzazione Percorsi Logistica, modello analitico a supporto delle simulazioni degli impatti di modifica della dislocazione degli impianti." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Il contesto della tesi è quello di un'iniziativa progettuale per una importante realtà italiana dell'ambito utility. In particolare prevede l'analisi, la progettazione e l'implementazione di un modello analitico a supporto delle simulazioni degli impatti di modifica della dislocazione degli impianti utilizzati per il conferimento di rifiuti. La realizzazione di questo modello consentirà di effettuare simulazioni 'self-service' sui possibili impatti economici derivanti da una diversa dislocazione (o una diversa attribuzione) dei punti di conferimento dei rifiuti, attraverso la selezione di alcu
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Flory, Long Mrs. "A WEB PERSONALIZATION ARTIFACT FOR UTILITY-SENSITIVE REVIEW ANALYSIS." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3739.

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Online customer reviews are web content voluntarily posted by the users of a product (e.g. camera) or service (e.g. hotel) to express their opinions about the product or service. Online reviews are important resources for businesses and consumers. This dissertation focuses on the important consumer concern of review utility, i.e., the helpfulness or usefulness of online reviews to inform consumer purchase decisions. Review utility concerns consumers since not all online reviews are useful or helpful. And, the quantity of the online reviews of a product/service tends to be very large. Manual as
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Dzimko, Miroslav. "Řešení Business Intelligence." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2017. http://www.nusl.cz/ntk/nusl-318585.

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Diploma thesis presents an evaluation of the current state of the company system, identification of critical areas and areas suitable for improvement. Based on the theoretical knowledge and analysis results, commercial Business Intelligence software is designed to enhance the quality and efficiency of the company's decision-support system and the introduction of an advanced Quality Culture system. The thesis reveals critical locations in the corporate environment and opens up space to design improvements to the system.
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Eggert, Sandy, and Juliane Meier. "Business Intelligence : Lösungen im Überblick." Universität Potsdam, 2010. http://opus.kobv.de/ubp/volltexte/2010/4449/.

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Schieber, Andreas, and Andreas Hilbert. "Status Quo der Textanalyse im Rahmen der Business Intelligence." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-138447.

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Vor dem Hintergrund der Zunahme unstrukturierter Daten für Unternehmen befasst sich dieser Beitrag mit den Möglichkeiten, die durch den Einsatz der Business Intelligence für Unternehmen bestehen, wenn durch gezielte Analyse die Bedeutung dieser Daten erfasst, gefiltert und ausgewertet werden können. Allgemein ist das Ziel der Business Intelligence die Unterstützung von Entscheidungen, die im Unternehmen (auf Basis strukturierter Daten) getroffen werden. Die zusätzliche Auswertung von unstrukturierten Daten, d.h. unternehmensinternen Dokumenten oder Texten aus dem Web 2.0, führt zu einer Vergrö
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Kříž, Jan. "Business Intelligence řešení pro společnost 1188." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2015. http://www.nusl.cz/ntk/nusl-224859.

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Cílem této diplomové práce je vytvoření Business Intelligence řešení pro společnost 1188. Na základě výsledného Business Intelligence řešení bude umožněno managementu společnosti vykonávat přesnější rozhodnutí, která se budou shodovat se strategií společnosti.
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Štefke, Martin. "Využitie techník a princípov data miningu v rámci business intelligence riešenia." Master's thesis, Vysoká škola ekonomická v Praze, 2017. http://www.nusl.cz/ntk/nusl-359131.

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This thesis is focused on using data mining in Business Intelligence solution. The goal of this thesis is implementation of data mining in Business Intelligence solution. Integration of these technologies is posible to obtain synergic effect. The thesis is made in context of academic project Farfalia and results are used in the project. The goal of project is to create component for a new form of reporting and that is using data mining as a tool for data analysis. The benefit of the implementation is automatic browsing of multidimensional data cube / multidimensional data model to find interes
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Bergvall, Jacob. "Business Intelligence : En inblick i relationen mellan tillgänglig data och hur den används inom marknadsföring." Thesis, Linköpings universitet, Filosofiska fakulteten, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139450.

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Oavsett vilken bransch ett företag tillhör finns idag stora möjligheter att samla in data relaterad till företagets kunder, processer, varor och tjänster. Om man har resurser och intresse finns oanade möjligheter att ta tillvara på all data, analysera den och sedan använda den för att vidareutveckla och optimera företaget. Syftet med denna uppsats är att undersöka vilka utmaningar som finns när det kommer till att använda lagrad data inom Business Intelligence, samt att undersöka vilka faktorer som påverkar processen att omvandla lagrad data till användbar information som sedan kan användas vi
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Ahmad, Yasmeen. "Management, visualisation & mining of quantitative proteomics data." Thesis, University of Dundee, 2012. https://discovery.dundee.ac.uk/en/studentTheses/6ed071fc-e43b-410c-898d-50529dc298ce.

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Exponential data growth in life sciences demands cross discipline work that brings together computing and life sciences in a usable manner that can enhance knowledge and understanding in both fields. High throughput approaches, advances in instrumentation and overall complexity of mass spectrometry data have made it impossible for researchers to manually analyse data using existing market tools. By applying a user-centred approach to effectively capture domain knowledge and experience of biologists, this thesis has bridged the gap between computation and biology through software, PepTracker (h
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Schmied, Marten. "Business Intelligence in Healthcare - Data Mining Techniques as a Possible Hospital Management Tool in Austria." Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-264283.

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Public healthcare provision is under increasing economic restraints making an efficient and sustainable managerial planning a necessity in the hospital sector. Business Intelligence is the extraction of business relevant knowledge in order to adjust and refine executive operations. On the free market industries have applied the according methods and software dedicated to the generation of Business Intelligence is offered by variety of companies. Data Mining, furthermore, describes the facilitation of algorithms in order to train programs to detect unseen patterns from huge amounts of data. The
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Books on the topic "Business intelligence,data mining,utility"

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Azevedo, Ana, and Manuel Filipe Santos. Integration of data mining in business intelligence systems. Business Science Reference, 2015.

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S, Yu Philip, Zhang Chengqi, Zhang Huaifeng, and SpringerLink (Online service), eds. Data Mining for Business Applications. Springer US, 2009.

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Business intelligence: Data mining and optimization for decision making. Wiley, 2009.

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Richard, Hoptroff, ed. Data mining and business intelligence: A guide to productivity. Idea Group Pub., 2001.

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Big data and business analytics. CRC Press, 2013.

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Thuraisingham, Bhavani. Web Data Mining and Applications in Business Intelligence and Counter-Terrorism. Taylor and Francis, 2003.

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Inmon, William H. Tapping into unstructured data: Integrating unstructured data and textual analytics into business intelligence. Prentice Hall, 2008.

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T, Kreulen Jeffrey, ed. Mining the talk: Unlocking the business value in unstructured information. IBM Press, 2007.

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service), SpringerLink (Online, ed. Business Analytics for Managers. Springer Science+Business Media, LLC, 2011.

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Michele, Chambers, and Dhiraj Ambiga 1975-, eds. Big data, big analytics: Emerging business intelligence and analytic trends for today's businesses. John Wiley & Sons, Inc., 2013.

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Book chapters on the topic "Business intelligence,data mining,utility"

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Müller, Roland M., and Hans-Joachim Lenz. "Data Mining." In Business Intelligence. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35560-8_3.

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Azalmad, Mohamed, and Youssef Fakir. "Data Mining Approach for Intrusion Detection." In Business Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76508-8_15.

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Fakir, Youssef, and Jihane El Iklil. "Clustering Techniques for Big Data Mining." In Business Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76508-8_14.

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Kolker, Alexander. "Business Intelligence and Data Mining." In Healthcare Management Engineering: What Does This Fancy Term Really Mean? Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-2068-2_5.

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Fouché, Guy, and Lynn Langit. "Data Mining with Excel." In Foundations of SQL Server 2008 R2 Business Intelligence. Apress, 2011. http://dx.doi.org/10.1007/978-1-4302-3325-1_11.

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Fouché, Guy, and Lynn Langit. "Introduction to Data Mining." In Foundations of SQL Server 2008 R2 Business Intelligence. Apress, 2011. http://dx.doi.org/10.1007/978-1-4302-3325-1_14.

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Kenny Kumar, Mathe John, and Dipti Rana. "High Average Utility Itemset Mining: A Survey." In Proceedings of International Conference on Computational Intelligence and Data Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8767-2_30.

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Moreno, María N., Joel P. Lucas, and Vivian F. López. "Fuzzy Data-Mining Hybrid Methods for Recommender Systems." In Soft Computing for Business Intelligence. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-53737-0_22.

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Mrazek, Jan. "Data Mining for Robust Business Intelligence Solutions." In Principles of Data Mining and Knowledge Discovery. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-540-48247-5_77.

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Hao, Ming, Umeshwar Dayal, and Meichun Hsu. "Visual Data Mining for Business Intelligence Applications." In Web-Age Information Management. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45151-x_1.

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Conference papers on the topic "Business intelligence,data mining,utility"

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Damasceno, Ernani, Ana Azevedo, and Manuel Pérez-Cota. "Data Mining, Business Intelligence, Grid and Utility Computing: A Bibliometric Review of the Literature from 2015 to 2020." In 23rd International Conference on Enterprise Information Systems. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010527303670373.

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Arora, Deepali, Stephen W. Neville, and Kin Fun Li. "Mining WiFi Data for Business Intelligence." In 2013 Eighth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC). IEEE, 2013. http://dx.doi.org/10.1109/3pgcic.2013.67.

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Mishra, Brojo Kishore, Deepannita Hazra, Kahkashan Tarannum, and Manas Kumar. "Business Intelligence using Data Mining techniques and Business Analytics." In 2016 International Conference System Modeling & Advancement in Research Trends (SMART). IEEE, 2016. http://dx.doi.org/10.1109/sysmart.2016.7894496.

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Khan, Mohiudddin A. H., Sateesh Kumar Pradhan, and Saleh Mohammed Al-Turki. "Data mining techniques for E-business intelligence." In 2015 2nd World Symposium on Web Applications and Networking (WSWAN). IEEE, 2015. http://dx.doi.org/10.1109/wswan.2015.7210313.

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"Workshop on Data Mining and Business Intelligence." In 2007 IEEE 23rd International Conference on Data Engineering Workshop. IEEE, 2007. http://dx.doi.org/10.1109/icdew.2007.4400962.

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Castelo-Branco, Francisca, Jose Luis Reis, Jose Carvalho Vieira, and Jose Paulo Marques dos Santos. "Business Intelligence in Sports Retail: Data Mining Application." In 2021 16th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, 2021. http://dx.doi.org/10.23919/cisti52073.2021.9476335.

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YILDIRIM, Irfan, and Mete CELIK. "FIMHAUI: Fast Incremental Mining of High Average-Utility Itemsets." In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). IEEE, 2018. http://dx.doi.org/10.1109/idap.2018.8620819.

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Hang, Yang, and Simon Fong. "A Framework of Business Intelligence-Driven Data Mining for E-business." In 2009 Fifth International Joint Conference on INC, IMS and IDC. IEEE, 2009. http://dx.doi.org/10.1109/ncm.2009.403.

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Martin, A., T. Miranda Lakshmi, and V. Prasanna Venkatesan. "A business intelligence framework for business performance using data mining techniques." In 2012 International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET). IEEE, 2012. http://dx.doi.org/10.1109/incoset.2012.6513936.

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Chung, Ping-Tsai, and Sarah H. Chung. "On data integration and data mining for developing business intelligence." In 2013 IEEE Long Island Systems, Applications and Technology Conference (LISAT). IEEE, 2013. http://dx.doi.org/10.1109/lisat.2013.6578235.

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