Academic literature on the topic 'In-memory business intelligence'
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Journal articles on the topic "In-memory business intelligence"
IVAN, Mihaela-Laura. "Characteristics of In-Memory Business Intelligence." Informatica Economica 18, no. 3/2014 (September 30, 2014): 17–25. http://dx.doi.org/10.12948/issn14531305/18.3.2014.02.
Full textRantung, V. P., O. Kembuan, P. T. D. Rompas, A. Mewengkang, O. E. S. Liando, and J. Sumayku. "In-Memory Business Intelligence: Concepts and Performance." IOP Conference Series: Materials Science and Engineering 306 (February 2018): 012129. http://dx.doi.org/10.1088/1757-899x/306/1/012129.
Full textRoth, Jan A., Nicole Goebel, Thomas Sakoparnig, Simon Neubauer, Eleonore Kuenzel-Pawlik, Martin Gerber, Andreas F. Widmer, et al. "Secondary use of routine data in hospitals: description of a scalable analytical platform based on a business intelligence system." JAMIA Open 1, no. 2 (September 20, 2018): 172–77. http://dx.doi.org/10.1093/jamiaopen/ooy039.
Full textAl Omoush, Khaled Saleh. "Web-Based Collaborative Systems and Harvesting the Collective Intelligence in Business Organizations." International Journal on Semantic Web and Information Systems 14, no. 3 (July 2018): 31–52. http://dx.doi.org/10.4018/ijswis.2018070102.
Full textJOVIĆ, FRANJO, NINOSLAV SLAVEK, and DAMIR BLAŽEVIĆ. "REINFORCEMENT LEARNING IN NON-MARKOV CONSERVATIVE ENVIRONMENT USING AN INDUCTIVE QUALITATIVE MODEL." International Journal on Artificial Intelligence Tools 20, no. 05 (October 2011): 887–909. http://dx.doi.org/10.1142/s0218213011000425.
Full textMassaro, Alessandro, Antonio Panarese, Michele Gargaro, Costantino Vitale, and Angelo Maurizio Galiano. "Implementation of a Decision Support System and Business Intelligence Algorithms for the Automated Management of Insurance Agents Activities." International Journal of Artificial Intelligence & Applications 12, no. 03 (May 31, 2021): 01–13. http://dx.doi.org/10.5121/ijaia.2021.12301.
Full textRose, Dennis Michael, and Raymond Gordon. "Age-related cognitive changes and distributed leadership." Journal of Management Development 34, no. 3 (April 13, 2015): 330–39. http://dx.doi.org/10.1108/jmd-07-2013-0094.
Full textRouhani, Saeed, and Sogol Rabiee Savoji. "A Success Assessment Model for BI Tools Implementation." International Journal of Business Intelligence Research 7, no. 1 (January 2016): 25–44. http://dx.doi.org/10.4018/ijbir.2016010103.
Full textZeng, Qi, Liangchen Luo, Wenhao Huang, and Yang Tang. "Text Assisted Insight Ranking Using Context-Aware Memory Network." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 427–34. http://dx.doi.org/10.1609/aaai.v33i01.3301427.
Full textMollah, Ayatullah Faruk, Subhadip Basu, Mita Nasipuri, and Dipak Kumar Basu. "Handheld Mobile Device Based Text Region Extraction and Binarization of Image Embedded Text Documents." Journal of Intelligent Systems 22, no. 1 (March 1, 2013): 25–47. http://dx.doi.org/10.1515/jisys-2012-0019.
Full textDissertations / Theses on the topic "In-memory business intelligence"
Sakulsorn, Pattaravadee. "In-memory Business Intelligence : Verifying its Benefits against Conventional Approaches." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-128449.
Full textCígler, Lukáš. "Možnosti In-memory reportingových nástrojů." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-197493.
Full textKapitán, Lukáš. "Vliv vývojových trendů na řešení projektu BI." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-150006.
Full textSoukup, Petr. "High-Performance Analytics (HPA)." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-165252.
Full textMarinič, Štefan. "Posouzení informačního systému firmy a návrh změn." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2016. http://www.nusl.cz/ntk/nusl-241607.
Full textDulabh, Harshila Ravjee. "In-memory business intelligence: a Wits context." Thesis, 2014. http://hdl.handle.net/10539/18082.
Full textNguyen, Quang Dang. "The role of business intelligence in organizational memory supporti." Master's thesis, 2012. http://hdl.handle.net/1822/26319.
Full textNowadays, in all organizations the major challenge issue facing managers is that they must give the appropriate decisions in a fluctuating environment while the information seems very hard to recognize whether it is good or bad. However, the actions that result of the decisions made will lead the organization to be in a thriving or declining position. That is why the leaders of organization really do not want to take wrong decisions. In order to minimize the risks, the managers should use the collective knowledge and experiences sharing through the Organizational Memory effectively to reduce the rate of unsuccessful decision making. Moreover, the BI systems are also a managerial concept and tools to allow their business to improve the effectiveness of decision making and problem solving. In the light of these motivations, the aim of this dissertation is to comprehend the role of the BI systems in supporting the system of Organizational Memories more effectively in the real context of crowdsourcing initiative called CrowdUM.
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Idris, Muhammad. "Real-time Business Intelligence through Compact and Efficient Query Processing Under Updates." 2018. https://tud.qucosa.de/id/qucosa%3A33726.
Full textMazáčová, Markéta. "Možnosti analytických nástrojů v prostředí MS SQL Serveru." Master's thesis, 2017. http://www.nusl.cz/ntk/nusl-431661.
Full textMuwawa, Jean Nestor Dahj. "Data mining and predictive analytics application on cellular networks to monitor and optimize quality of service and customer experience." Diss., 2018. http://hdl.handle.net/10500/25875.
Full textCellular networks have evolved and are still evolving, from traditional GSM (Global System for Mobile Communication) Circuit switched which only supported voice services and extremely low data rate, to LTE all Packet networks accommodating high speed data used for various service applications such as video streaming, video conferencing, heavy torrent download; and for say in a near future the roll-out of the Fifth generation (5G) cellular networks, intended to support complex technologies such as IoT (Internet of Things), High Definition video streaming and projected to cater massive amount of data. With high demand on network services and easy access to mobile phones, billions of transactions are performed by subscribers. The transactions appear in the form of SMSs, Handovers, voice calls, web browsing activities, video and audio streaming, heavy downloads and uploads. Nevertheless, the stormy growth in data traffic and the high requirements of new services introduce bigger challenges to Mobile Network Operators (NMOs) in analysing the big data traffic flowing in the network. Therefore, Quality of Service (QoS) and Quality of Experience (QoE) turn in to a challenge. Inefficiency in mining, analysing data and applying predictive intelligence on network traffic can produce high rate of unhappy customers or subscribers, loss on revenue and negative services’ perspective. Researchers and Service Providers are investing in Data mining, Machine Learning and AI (Artificial Intelligence) methods to manage services and experience. This research study focuses on the application models of Data Mining and Machine Learning covering network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms will be applied on cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: R-Studio for Machine Learning, Apache Spark, SparkSQL for data processing and clicData for Visualization.
Electrical and Mining Engineering
M. Tech (Electrical Engineering)
Book chapters on the topic "In-memory business intelligence"
Maarouf, Otman, and Rachid El Ayachi. "Part-of-Speech Tagging Using Long Short Term Memory (LSTM): Amazigh Text Written in Tifinaghe Characters." In Business Intelligence, 3–17. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76508-8_1.
Full textPlattner, Hasso, and Alexander Zeier. "Finally, A Real Business Intelligence System Is at Hand." In In-Memory Data Management, 195–217. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29575-1_7.
Full textPlattner, Hasso, and Alexander Zeier. "Finally, a Real Business Intelligence System Is at Hand." In In-Memory Data Management, 171–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19363-7_8.
Full textSchapranow, Matthieu-P., Cindy Perscheid, Alf Wachsmann, Martin Siegert, Cornelius Bock, Friedrich Horschig, Franz Liedke, Janos Brauer, and Hasso Plattner. "A Federated In-memory Database System for Life Sciences." In Real-Time Business Intelligence and Analytics, 19–34. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24124-7_2.
Full textRout, Minakhi, Dhiraj Bhattarai, and Ajay Kumar Jena. "Recurrent Neural Network-Based Long Short-Term Memory Deep Neural Network Model for Forex Prediction." In Artificial Intelligence and Machine Learning in Business Management, 205–21. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003125129-13.
Full textChu, Mei-Tai, and Rajiv Khosla. "Alignment of Knowledge Sharing Mechanism and Knowledge Node Positioning." In Business Intelligence, 318–37. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9562-7.ch017.
Full textGovender, Cookie M. "Creative Accelerated Problem Solving (CAPS) for Advancing Business Performance." In Advances in Religious and Cultural Studies, 84–109. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2385-8.ch005.
Full textJames, A. P. "Machine Intelligence Using Hierarchical Memory Networks." In Handbook of Research on Computational Intelligence for Engineering, Science, and Business, 62–74. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2518-1.ch003.
Full textRamos, Isabel, and Jorge Oliveira e Sá. "Organizational Memory." In Advances in Business Information Systems and Analytics, 206–23. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5970-4.ch010.
Full textGhosh, Pramit, Debotosh Bhattacharjee, Mita Nasipuri, and Dipak Kumar Basu. "Computer Intelligence in Healthcare." In Handbook of Research on Computational Intelligence for Engineering, Science, and Business, 716–15. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2518-1.ch028.
Full textConference papers on the topic "In-memory business intelligence"
Rantung, Vivi Peggie, Julyeta Paulina Amelia Runtuwene, Cindy Pamela C. Munaiseche, Ferdinan Ivan Sangkop, Gladly Caren Rorimpandey, and Parabelem Tinno Dolf Rompas. "In-Memory Business Intelligence for Study Program Accreditation in Indonesia." In The 7th Engineering International Conference (EIC), Engineering International Conference on Education, Concept and Application on Green Technology. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0009010203030306.
Full textRantung, Vivi Peggie, Julyeta Paulina Amelia Runtuwene, Cindy Pamela C. Munaiseche, Ferdinan Ivan Sangkop, Gladly Caren Rorimpandey, and Parabelem Tinno Dolf Rompas. "In-Memory Business Intelligence for Study Program Accreditation in Indonesia." In The 7th Engineering International Conference (EIC), Engineering International Conference on Education, Concept and Application on Green Technology. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0009010203090312.
Full textCao, Shi-nan, Han-dong Li, and Yan Wang. "Long-Term Memory in Realized Volatility: Evidence from Chinese Stock Market." In 2010 3rd International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2010. http://dx.doi.org/10.1109/bife.2010.82.
Full textHardt, Alexandre Keunecke, Christian Pinto de Souza, Felipe Chagas Rabello, Gustavo Vieira Machado, and Roberto Resque de Freitas. "BUSINESS INTELLIGENCE 4.0: MANIPULANDO ALTO VOLUME DE DADOS DE MANUFATURA DE FORMA DISTRIBUÍDA E IN-MEMORY." In 20º Seminário de Automação & TI. São Paulo: Editora Blucher, 2017. http://dx.doi.org/10.5151/2237-0234-28096.
Full textMaknickienė, Nijolė, and Darius Sabaliauskas. "Investment portfolio analysis by using neural networks." In Contemporary Issues in Business, Management and Economics Engineering. Vilnius Gediminas Technical University, 2019. http://dx.doi.org/10.3846/cibmee.2019.028.
Full textOgudo, Kingsley A., and Dahj Muwawa Jean Nestor. "Modeling of an Efficient Low Cost, Tree Based Data Service Quality Management for Mobile Operators Using in-Memory Big Data Processing and Business Intelligence use Cases." In 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD). IEEE, 2018. http://dx.doi.org/10.1109/icabcd.2018.8465410.
Full textRathmann, Christian, Alexander Czechowicz, and Horst Meier. "An Investigation of Service-Oriented Shape Memory Actuator Systems for Resource Efficiency." In ASME 2013 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/smasis2013-3065.
Full textBraden, Paul, and Kaitlyn Gainer. "Application of the Shape Memory Effect to Restore Smoothness." In ASME 2015 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/smasis2015-8827.
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