Academic literature on the topic 'In-memory business intelligence'

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Journal articles on the topic "In-memory business intelligence"

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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.

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Rantung, 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.

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Roth, 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.

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Abstract We describe a scalable platform for research-oriented analyses of routine data in hospitals, which evolved from a state-of-the-art business intelligence architecture for enterprise resource planning. This platform involves an in-memory database management system for data modeling and analytics and a high-performance cluster for more computing-intensive analytical tasks. Setting up platforms for research-oriented analyses is a highly dynamic, time-consuming, and costly process. In some health care institutions, effective research platforms may be derived from existing business intelligence systems.
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Al 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.

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The major purpose of this article is to empirically explore the role of web-based collaborative systems in harvesting the dimensions of collective intelligence and the expected outcomes. A questionnaire survey was developed to collect data from 29 firms across all industries with a sample of 239 respondents. Structural Equation Modeling, using Smart PLS was conducted to analyze the data. The results indicated that web-based collaborative systems play a significant role in harvesting the dimensions of collective intelligence, including collective cognition, shared memory, collective problem solving, knowledge sharing, and collective learning. The results also revealed the significant impact of web-based collective intelligence on the sense and response capability and on the quality and morality of organizations' decisions. In addition, the article reveals the significant impacts of BI tools and relationship quality on the role of web-based CI in achieving the expected outcomes.
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JOVIĆ, 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.

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The majority of real-world processes, such as power plants, banking and retail businesses, are non-Markov processes, being conservative systems with stochastic supply and demand. As an example, a retail process possesses long-term memory of the customer's experience and market price drift that deviates from the Markov property. Modeling the reward in this process is directed towards actions that have to be executed daily in order to support it. These actions are further severely distracted by the hidden periodicity of customer behavior on a monthly and weekly basis. Alternative solutions in the retail business are achieved using a retail potential market model and a pricing policy based on demography. The policy of non-Markov behavior has not been intensively studied, although the literature indicates the non-Markov nature of many real process models, such as bank rating migrations. A solution is proposed, based on day-to-day data collection from point-of-sale (POS) locations, synthesizing the reward function from separate sale component rewards using qualitative models, and indicating the most outstanding sale groups that form the reward model. The normalization of POS data has been used for the elimination of periodicities and of non-Markov features of the process data. Reinforcement learning has been additionally supported by artificial corrections of the normalized reward function, and thus the obtained models used for recognition of the most promising and most defective hidden retail product groups. Model data were analyzed for the statistical significance of the obtained results, comparing normalized and non-normalized sales data distributions. The method is simple and effective, being applicable to each POS separately, for a complex retail business network, as well as for other conservative environments. The obtained qualitative correlations of model and reward function lie between 0.72 and 0.95, even for the simple cases presented.
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Massaro, 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.

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Data processing is crucial in the insurance industry, due to the important information that is contained in the data. Business Intelligence (BI) allows to better manage the various activities as for companies working in the insurance sector. Business Intelligence based on the Decision Support System (DSS), makes it possible to improve the efficiency of decisions and processes, by improving them to the individual characteristics of the agents. In this direction, Key Performance Indicators (KPIs) are valid tools that help insurance companies to understand the current market and to anticipate future trends. The purpose of the present paper is to discuss a case study, which was developed within the research project "DSS / BI HUMAN RESOURCES", related to the implementation of an intelligent platform for the automated management of agents' activities. The platform includes BI, DSS, and KPIs. Specifically, the platform integrates Data Mining (DM) algorithms for agent scoring, K-means algorithms for customer clustering, and a Long Short-Term Memory (LSTM) artificial neural network for the prediction of agents KPIs. The LSTM model is validated by the Artificial Records (AR) approach, which allows to feed the training dataset in data-poor situations as in many practical cases using Artificial Intelligence (AI) algorithms. Using the LSTM-AR method, an analysis of the performance of the artificial neural network is carried out by changing the number of records in the dataset. More precisely, as the number of records increases, the accuracy increases up to a value equal to 0.9987.
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Rose, 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.

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Purpose – The purpose of this paper is to examine the evidence for age-related changes in cognition and the implications for leadership styles. In particular, a case is argued for distributed forms of leadership that encourage contribution across the age spectrum and hierarchical levels. Design/methodology/approach – This paper takes a conceptual approach, combining the psychology and management literatures in arguing the case for newer leadership forms, appropriate to an ageing workforce. Findings – Three principal components of intelligence (fluid, and crystallised intelligence and working memory) are considered and it is argued that high levels of fluid intelligence, generally higher in younger employees, should be accessed while being balanced by crystallised intelligence (experience). Distributed leadership has been mainly applied in educational settings. This paper argues for distributed leadership to maximise creativity and innovation. Practical implications – Leadership forms that maximise creative input from staff across all age levels are likely to contribute to firm innovation and sustainability. Additionally, job satisfaction and turnover among junior staff may be positively influenced through opportunities for greater participation. Social implications – The elements discussed in this paper address important leadership issues for managing a multigenerational workforce. Originality/value – Distributed leadership has been discussed in educational and health literatures for some time; however it is only recently that this approach to leadership has appeared in mainstream management literature. The discussion of age-related changes and distributed leadership introduces and important topic for further research in newer forms of leadership.
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Rouhani, 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.

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In today's rapidly-changing business environment, the need for useful business analytics is vital for organizations, not only to succeed, but also to survive. Traditional enterprise systems have disabilities to meet the expectations of organizational decision makers in the competitive area. In this regard, it is necessary to evaluate the success of BI tools in organizations, and there is a need to provide a model for this assessment. Hence, in this study, a model for assessing the success of business intelligence is presented by identifying and introducing the most important and effective factors in evaluating the success of BI tools. This study is an applied study in terms of purpose and a survey-descriptive, empirical study in terms of methodology. According to statistical methods, importance of the success factors was evaluated and the results show that 24 factors were identified consequential in research model based on four areas such as organizational memory, information integration, knowledge creation, and presentation.
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Zeng, 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.

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Extracting valuable facts or informative summaries from multi-dimensional tables, i.e. insight mining, is an important task in data analysis and business intelligence. However, ranking the importance of insights remains a challenging and unexplored task. The main challenge is that explicitly scoring an insight or giving it a rank requires a thorough understanding of the tables and costs a lot of manual efforts, which leads to the lack of available training data for the insight ranking problem. In this paper, we propose an insight ranking model that consists of two parts: A neural ranking model explores the data characteristics, such as the header semantics and the data statistical features, and a memory network model introduces table structure and context information into the ranking process. We also build a dataset with text assistance. Experimental results show that our approach largely improves the ranking precision as reported in multi evaluation metrics.
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Mollah, 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.

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Abstract.Effective text region extraction and binarization of image embedded text documents on mobile devices having limited computational resources is an open research problem. In this paper, we present one such technique for preprocessing images captured with built-in cameras of handheld devices with an aim of developing an efficient Business Card Reader. At first, the card image is processed for isolating foreground components. These foreground components are classified as either text or non-text using different feature descriptors of texts and images. The non-text components are removed and the textual ones are binarized with a fast adaptive algorithm. Specifically, we propose new techniques (targeted to mobile devices) for (i) foreground component isolation, (ii) text extraction and (iii) binarization of text regions from camera captured business card images. Experiments with business card images of various resolutions show that the present technique yields better accuracy and involves low computational overhead in comparison with the state-of-the-art. We achieve optimum text/non-text separation performance with images of resolution 800×600 pixels with an average recall rate of 93.90% and a precision rate of 96.84%. It involves a peak memory consumption of 0.68 MB and processing time of 0.102 seconds on a moderately powerful notebook, and 4 seconds of processing time on a PDA.
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Dissertations / Theses on the topic "In-memory business intelligence"

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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.

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Business intelligence project failures in organizations derive from various causes. Technological aspects regarding the use of business intelligence tools expose the problem of too complicated tool for operational users, lack of system scalability, dissatisfied software performance, and hard coded business requirements on the tools. This study was conducted in order to validate in-memory business intelligence advantages towards functionality, flexibility, performance, ease of use, and ease of development criteria. A case study research method had been applied to achieve the goals in this thesis. Primarily, a pilot study was carried out to collect the data both from literatures and interviews. Therefore, the design of test case had been developed. Types of testing can be divided into 2 categories: BI functionality test and performance test. The test results reveal that in-memory business intelligence enhances conventional business intelligence performance by improving the software’s loading time and response time. At the meantime, it was proved to be flexible than rule-based, query-based, and OLAP tools, whereas its functionality and ease of development were justified to be better than query-based system. Moreover, in-memory business intelligence provides a better ease of use over query-based and rule-based business intelligence tools. Pair wise comparisons and analyses between selected in-memory business intelligence tool, QlikView, and conventional business intelligence software, Cognos, SAS, and STB Reporter, from 3 banks were made in this study based on the aforementioned test results.
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Cígler, Lukáš. "Možnosti In-memory reportingových nástrojů." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-197493.

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Diploma thesis focuses on in-memory data processing, its use in reporting and Business Intelligence (BI) in general. The main goal of the theoretical part is to introduce the in memory principles, highlight the differences from hard drive data processing and overview possible implementations of in-memory technology in BI solution. The output of this section is an analysis of advantages and disadvantages of in-memory solutions in various perspectives. The practical part of the thesis consists of the performance benchmark that compares the performance of data processing using the in-memory principles and conventional hard drive methods. The performance comparison is realized in the reporting tools environment, QlikView for in-memory approach and Reporting Services for hard drive based method. Several data sets are used for testing in both mentioned tools. End of the chapter provides the assessment of testing results and discusses the strengths and weaknesses of both principles of data processing. The conclusion of this work discusses the advantages and disadvantages of in-memory data processing and defines the key questions that company management should ask before investing in innovation of the present BI solution. Moreover the conclusion contains recommendations for possible further follow-up work.
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Kapitán, Lukáš. "Vliv vývojových trendů na řešení projektu BI." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-150006.

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The aim of this these is to analyse the trends occurring in Business intelligence. It does examine, summarise and judge each of the trends from the point of their usability in the real world, their influence and modification of each phase of the implementation of Bussiness intelligence. It is clear that each of these trends has its positives and negatives which can influence the statements in the evaluation. These factors are taken into consideration and analysed as well. The advantages and disadvantages of the trends are occurring especially in the areas of economical demand and technical difficultness. The main aim is to compare the methods of implementation of Bussiness intelligence with actual trends in BI. In order to achieve this a few crucial points were set: to investigate recent trends in the BI and to define the methods of implementation in the broadest terms. The awaited benefit of this these is already mentioned investigation and analysis of trends in the area of Bussiness intelligence and its use in implementation.
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Soukup, Petr. "High-Performance Analytics (HPA)." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-165252.

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The aim of the thesis on the topic of High-Performance Analytics is to gain a structured overview of solutions of high performance methods for data analysis. The thesis introduction concerns with definitions of primary and secondary data analysis, and with the primary systems which are not appropriate for analytical data analysis. The usage of mobile devices, modern information technologies and other factors caused a rapid change of the character of data. The major part of this thesis is devoted particularly to the historical turn in the new approaches towards analytical data analysis, which was caused by Big Data, a very frequent term these days. Towards the end of the thesis there are discussed the system sources which greatly participate in the new approaches to the analytical data analysis as well as in the technological solutions of High Performance Analytics themselves. The second, practical part of the thesis is aimed at a comparison of the performance in conventional methods for data analysis and in one of the high performance methods of High Performance Analytics (more precisely, with In-Memory Analytics). Comparison of individual solutions is performed in identical environment of High Performance Analytics server. The methods are applied to a certain sample whose volume is increased after every round of executed measurement. The conclusion evaluates the tests results and discusses the possibility of usage of the individual High Performance Analytics methods.
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Marinič, 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.

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The diploma thesis deals with migration of SAP Business Warehouse system to the new in-memory technology SAP HANA. Theoretical basis defines basic terms, usage in-memory technology in Business Intelligence and migration options. In the practical part, the best option of migration and best practices for the company was designed.
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Dulabh, Harshila Ravjee. "In-memory business intelligence: a Wits context." Thesis, 2014. http://hdl.handle.net/10539/18082.

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The organisational demand for real-time, flexible and cheaper approaches to Business Intelligence is impacting the Business Intelligence ecosystem. In-memory databases, in-memory analytics, the availability of 64 bit computing power, as well as the reduced costs of memory, are enabling technologies to meet this demand. This research report examines whether these technologies will have an evolutionary or a revolutionary impact on traditional Business Intelligence implementations. An in-memory analytic solution was developed for University of the Witwatersrand Procurement Office, to evaluate the benefits claimed for the in-memory approach for Business intelligence, in the development, reporting and analysis processes. A survey was used to collect data on the users' experience when using an in-memory solution. The results indicate that the in-memory solution offers a fast, flexible and visually rich user experience. However, there are certain key steps of the traditional BI approach that cannot be omitted. The conclusion reached is that the in-memory approach to Business Intelligence can co-exist with the traditional Business Intelligence approach, so that the merits of both approaches can be leveraged to enhance value for an organisation.
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Nguyen, Quang Dang. "The role of business intelligence in organizational memory supporti." Master's thesis, 2012. http://hdl.handle.net/1822/26319.

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Dissertation Report Master in Information System
Nowadays, 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.

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Responsive analytics are rapidly taking over the traditional data analytics dominated by the post-fact approaches in traditional data warehousing. Recent advancements in analytics demand placing analytical engines at the forefront of the system to react to updates occurring at high speed and detect patterns, trends and anomalies. These kinds of solutions find applications in Financial Systems, Industrial Control Systems, Business Intelligence and on-line Machine Learning among others. These applications are usually associated with Big Data and require the ability to react to constantly changing data in order to obtain timely insights and take proactive measures. Generally, these systems specify the analytical results or their basic elements in a query language, where the main task then is to maintain these results under frequent updates efficiently. The task of reacting to updates and analyzing changing data has been addressed in two ways in the literature: traditional business intelligence (BI) solutions focus on historical data analysis where the data is refreshed periodically and in batches, and stream processing solutions process streams of data from transient sources as flow (or set of flows) of data items. Both kinds of systems share the niche of reacting to updates (known as dynamic evaluation); however, they differ in architecture, query languages, and processing mechanisms. In this thesis, we investigate the possibility of a reactive and unified framework to model queries that appear in both kinds of systems. In traditional BI solutions, evaluating queries under updates has been studied under the umbrella of incremental evaluation of updates that is based on relational incremental view maintenance model and mostly focus on queries that feature equi-joins. Streaming systems, in contrast, generally follow the automaton based models to evaluate queries under updates, and they generally process queries that mostly feature comparisons of temporal attributes (e.g., timestamp attributes) along-with comparisons of non-temporal attributes over streams of bounded sizes. Temporal comparisons constitute inequality constraints, while non-temporal comparisons can either be equality or inequality constraints, hence these systems mostly process inequality joins. As starting point, we postulate the thesis that queries in streaming systems can also be evaluated efficiently based on the paradigm of incremental evaluation just like in BI systems in a main-memory model. The efficiency of such a model is measured in terms of runtime memory footprint and the update processing cost. To this end, the existing approaches of dynamic evaluation in both kind of systems present a trade-off between memory footprint and the update processing cost. More specifically, systems that avoid materialization of query (sub) results incur high update latency and systems that materialize (sub) results incur high memory footprint. We are interested in investigating the possibility to build a model that can address this trade-off. In particular, we overcome this trade-off by investigating the possibility of practical dynamic evaluation algorithm for queries that appear in both kinds of systems, and present a main-memory data representation that allows to enumerate query (sub) results without materialization and can be maintained efficiently under updates. We call this representation the Dynamic Constant Delay Linear Representation (DCLR). We devise DCLRs with the following properties: 1) they allow, without materialization, enumeration of query results with bounded-delay (and with constant delay for a sub-class of queries); 2) they allow tuple lookup in query results with logarithmic delay (and with constant delay for conjunctive queries with equi-joins only); 3) they take space linear in the size of the database; 4) they can be maintained efficiently under updates. We first study the DCLRs with the above-described properties for the class of acyclic conjunctive queries featuring equi-joins with projections and present the dynamic evaluation algorithm. Then, we present the generalization of thiw algorithm to the class of acyclic queries featuring multi-way theta-joins with projections. We devise DCLRs with the above properties for acyclic conjunctive queries, and the working of dynamic algorithms over DCLRs is based on a particular variant of join trees, called the Generalized Join Trees (GJTs) that guarantee the above-described properties of DCLRs. We define GJTs and present the algorithms to test a conjunctive query featuring theta-joins for acyclicity and to generate GJTs for such queries. To do this, we extend the classical GYO algorithm from testing a conjunctive query with equalities for acyclicity to test a conjunctive query featuring multi-way theta-joins with projections for acyclicity. We further extend the GYO algorithm to generate GJTs for queries that are acyclic. We implemented our algorithms in a query compiler that takes as input the SQL queries and generates Scala executable code – a trigger program to process queries and maintain under updates. We tested our approach against state of the art main-memory BI and CEP systems. Our evaluation results have shown that our DCLRs based approach is over an order of magnitude efficient than existing systems for both memory footprint and update processing cost. We have also shown that the enumeration of query results without materialization in DCLRs is comparable (and in some cases efficient) as compared to enumerating from materialized query results.
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Mazáčová, Markéta. "Možnosti analytických nástrojů v prostředí MS SQL Serveru." Master's thesis, 2017. http://www.nusl.cz/ntk/nusl-431661.

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The thesis compares the development time and performance of two types of Microsoft analytical models created in Visual Studio that are deployed on the local analytics servers and the Azure cloud server. Theoretical research will be summarizing business intelligence and data warehouses components, Microsoft tools for business intelligence realization and features using in-memory database technologies. The practical part introduces the AdventureWorks demonstration database,describes the implementation of both analytical models and compares both models with respect to the time-consuming development, performance and suitability of deployment.
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Muwawa, 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.

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This research study focuses on the application models of Data Mining and Machine Learning covering cellular 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 have been applied on real 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: RStudio for Machine Learning and process visualization, Apache Spark, SparkSQL for data and big data processing and clicData for service Visualization. Two use cases have been studied during this research. In the first study, the process of Data and predictive Analytics are fully applied in the field of Telecommunications to efficiently address users’ experience, in the goal of increasing customer loyalty and decreasing churn or customer attrition. Using real cellular network transactions, prediction analytics are used to predict customers who are likely to churn, which can result in revenue loss. Prediction algorithms and models including Classification Tree, Random Forest, Neural Networks and Gradient boosting have been used with an exploratory Data Analysis, determining relationship between predicting variables. The data is segmented in to two, a training set to train the model and a testing set to test the model. The evaluation of the best performing model is based on the prediction accuracy, sensitivity, specificity and the Confusion Matrix on the test set. The second use case analyses Service Quality Management using modern data mining techniques and the advantages of in-memory big data processing with Apache Spark and SparkSQL to save cost on tool investment; thus, a low-cost Service Quality Management model is proposed and analyzed. With increase in Smart phone adoption, access to mobile internet services, applications such as streaming, interactive chats require a certain service level to ensure customer satisfaction. As a result, an SQM framework is developed with Service Quality Index (SQI) and Key Performance Index (KPI). The research concludes with recommendations and future studies around modern technology applications in Telecommunications including Internet of Things (IoT), Cloud and recommender systems.
Cellular 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)
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Book chapters on the topic "In-memory business intelligence"

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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.

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Plattner, 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.

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Plattner, 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.

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Schapranow, 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.

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Rout, 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.

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Chu, 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.

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As the organizational memory in terms of collective knowledge evolves, how to construct an effective knowledge sharing mechanism to covert individual knowledge into collective knowledge becomes fairly demanding. CoPs approach is widely accepted as effective mechanism to facilitate knowledge sharing. Knowledge nodes in the context of knowledge flow, unlike workflow, can often transcend organizational boundaries and are distinct and different than workflow models. This paper aims to develop, implement, and analyze a CoPs Centered knowledge flow model in a multinational organization. This model is underpinned in a CoPs framework built around four expected performance including four dimensions and sixteen criteria as a comprehensive mechanism to intensify knowledge sharing effect. Next, this study clusters knowledge workers/nodes with common criteria (attitudes and beliefs) towards this model. These common attitudes and beliefs between two knowledge workers/nodes imply that knowledge sharing among them is likely to be more effective than between knowledge workers/nodes with dissimilar attitudes and beliefs. Fuzzy Multi-Criteria Decision Making MCDM) and cluster analysis techniques are adopted as research methods. A Dynamic knowledge flow activity analysis model is also defined as part of future work.
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Govender, 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.

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Businesses are automated, complex, unpredictable, operating in a global marketplace with limited collective creative problem-solving intelligence for advancing performance. This chapter explores how business advance and succeed, by maximising individual creativity and problem-solving abilities. Literature reviewed in the last two decades revealed the latest evolving business trends, allowing for compared human versus automation performance, compared ROI and risk of innovative business factors, compared business beneficiary intelligence to consciousness levels, and correlated co-creative intelligence elements of psychomotor, cognitive and affective intelligences to engagement, awareness, and changed behaviour skills. A CAPS model is a management cascaded solution for co-creating business intelligence by enhancing individual creativity using these 15 elements: consciousness, know yourself, brain knowledge, imagination, problem solving, creative thinking, speed reading, mind maps, mind management, memory skills, responsibility, goal setting, stress, success, and accelerated learning.
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James, 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.

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This chapter presents the fundamentals of a hardware based memory network that can perform complex cognitive tasks. The network is designed to provide space dimensionality reduction, which enables desired functionality in a random environment. Complex network functionality is achieved by simple network cells that minimize the needed chip area for hardware implementation. Functionality of this network is demonstrated by automatic character recognition with various input deformations. In the character recognition, the network is trained to recognize characters deformed by random noise, rotation, scaling, and shifting. This example demonstrates how cognitive functionality of a hardware network can be achieved through an evolutionary process, as distinct from design based on mathematical formalism.
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Ramos, 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.

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Nowadays, the major challenge to organizations managers is that they must make appropriate decisions in a turbulent environment while it is hard to recognize whether information is good or bad, because actions resulting from wrong decisions may place the organization at risk of survival. That is why organizations managers try to avoid making wrong decisions. In order to improve this, managers should use collective knowledge and experiences shared through Organizational Memory (OM) effectively to reduce the rate of unsuccessful decision making. In this sense, Business Intelligence (BI) tools allow managers to improve the effectiveness of decision making and problem solving. In the light of these motivations, the aim of this chapter is to comprehend the role of BI systems in supporting OM effectively in the real context of a crowdsourcing academic initiative called CrowdUM.
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Ghosh, 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.

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Low cost solutions for the development of intelligent bio-medical devices that not only assist people to live in a better way but also assist physicians for better diagnosis are presented in this chapter. Two such devices are discussed here, which are helpful for prevention and diagnosis of diseases. Statistical analysis reveals that cold and fever are the main culprits for the loss of man-hours throughout the world, and early pathological investigation can reduce the vulnerability of disease and the sick period. To reduce this cold and fever problem a household cooling system controller, which is adaptive and intelligent in nature, is designed. It is able to control the speed of a household cooling fan or an air conditioner based on the real time data, namely room temperature, humidity, and time for which system is active, which are collected from environment. To control the speed in an adaptive and intelligent manner, an associative memory neural network (Kramer) has been used. This embedded system is able to learn from training set; i.e., the user can teach the system about his/her feelings through training data sets. When the system starts up, it allows the fan to run freely at full speed, and after certain interval, it takes the environmental parameters like room temperature, humidity, and time as inputs. After that, the system takes the decision and controls the speed of the fan.
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Conference papers on the topic "In-memory business intelligence"

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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.

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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/10.5220/0009010203090312.

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Cao, 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.

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Hardt, 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.

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Maknickienė, 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.

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Purpose – the purpose of the article is to compare the formation of portfolios and to make predictions about how it will change. Research methodology – for analysis, optimization and predictions use the neural network models that are created using a neural recurrent long short-term memory cell architecture network and Markowitz’s modern portfolio theory Findings – this article compares the portfolios of IT field with different instruments and level of optimization. Research limitations – the main limit of the article is that only historical data is used. The real-time investment would check the performance of the portfolio creation methodology under uncertain conditions. Practical implications – the results of the article give opportunities for investors and speculators in the finance market by using neural networks for forming investment portfolios, as well as analysing and predicting their changes. Originality/Value – the growing high-tech use in financial markets changes our habits and our understanding of the surrounding world. The financial sphere has also had several changes, and it has undergone major changes that will change the approach to producing financial forecasts and analysis. Including Artificial Intelligence in these processes brings new innovative opportunities.
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Ogudo, 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.

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Rathmann, 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.

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Nowadays companies working in the field of industrial applications realize that traditional electromagnets reach their technical limits. Additionally, there is an increasing awareness for resource-efficiency and safety, due to shortages in resources and rise in legal requirements. Furthermore customers demand more and more system solution, because of increased complexity in technical applications. Service-oriented shape memory actuator systems are an innovative approach that can help companies meeting these challenges and thus not only keep but enhance their competitiveness. Therefore, this paper is focusing on the presentation of a service-oriented smart memory actuator system based on condition monitoring or reconfiguration by heat treatment. Hence an experimental actuator system is designed. This system is used to evaluate feasibility of condition monitoring to predict lifetime and the ability to reconfigure properties in use by heat treatment. Finally, an appropriate business model for service-oriented shape memory actuator systems is discussed. First results of the project are presented within the paper as well as a business model draft for a service-oriented shape memory actuator system. Further studies should be done investigating the feasibility of remote heat treatment as well as developing applicable business models for shape memory actuator systems.
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Braden, 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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A major worldwide industry is the display and preservation of historical and rare documents, paintings, canvases, tapestries and other works of art. Many private collectors and museums pay large amounts, such as the $23 million for the U.S. National Gallery and $8 million for the U.S. National Archives. There is an even greater demand for many consumers who desire an affordable way to safely maintain their images in top condition for viewing and enjoyment. Another industry where the smoothness of the paper documents is important is in the shipping and delivery business. Here, many shipments are done with cylindrical tubes that cause the paper to appear bent and not flat. In some cases, this can pose a major problem for scanning and electronic devices which need a flat surface for optimal performance. A novel new alternative to traditional conservation methods is the use of Shape Memory Alloys (SMA’s) to remove wrinkles and other surface anomalies. SMA’s use a thermoelastic property called the Shape Memory Effect (SME) to recover large strains by phase transformation. In this process, the SMA is stretched until the polycrystalline microstructure is detwinned Martensite. Then, energy in the form of heat is applied to the SMA which causes the phase transformation to the more compact Austenite. Thus, a reverse method is the proposed solution for the complex problem faced by art preservation experts. Instead of using large clamps and having to wait for results, we demonstrate how embedded SMA wires in a robust picture frame can provide a continuous restorative force that maintains the picture’s smoothness. Using proper simple wiring from the SMA wires to the picture, it is possible to remove the strains in the paper and hold the picture to the proper smoothness long term. We provide experimental results and offer suggestions for the future use of SMA’s in this new field of art restoration.
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