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《Information processing & management》2022,59(6):103086
The quantity of electronic bank data grows exponentially with development of Information Technology (IT). The size of these data is impossible for traditional database and human analyst to come up with interesting information that will help in process of decision making. Management Information System (MIS) based Data warehouse (DW) and Data Mining (DM) techniques support the development of IT and process of management decision-making. But the traditional DW size make the query complex, which may cause unacceptable delay in decision support queries. Thus, in this paper an Efficient Electronic Bank MIS based DW and Mining Processing (EEBMIS-DWMP) was developed with cluster and non-cluster indexed view to provide decision-makers with both best response time and precise information. Also, analysis of the multilayer perception neural network, naïve Bayes, random forest, logistic regression, support vector machine and C5.0 on a real-world data of bank was done to improve effectiveness for campaign by analyzing the most useful features that influence campaign success. Results offer how the proposed EEBMIS-DWMP developed bank organizations by comparing performance of system with and without index view in terms of balance accuracy, accuracy, precision, recall, mean absolute error, root mean square error, F measure and running time. Conclusions from results offers that EEBMIS-DWMP can construct a database for each customer, a storage system that integrates data from a variety of sources into a single unified framework, decrease errors and time required to prepare financial reports, quickly access for information, analysis of data in multivariate, accurate prediction of competent, profitability segmentation. 相似文献
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《The Journal of High Technology Management Research》2023,34(2):100477
Increasing numbers of devices that output large amounts of geographically referenced data are being deployed as the Internet of Things (IoT) continues to expand. Partly as a result of the IoT's dynamic, decentralized, and heterogeneous architecture. These are all examples of the Internet of items (IoT), despite the fact that we might be thinking that one of these items is different from the others. The physical and digital worlds are connected by the Internet of Things (IoT). Nowadays, one of the key goals of the Internet is its own development. This paper provides an in-depth analysis of IoT-based data quality and data preparation strategies developed with multinational corporations in mind. The goal is to make IoT data more trustworthy and practical so that MNCs may use it to their advantage in making educated business decisions. The proposed structure consists of three distinct actions: gathering data, evaluating data quality, and cleaning up raw data. Data preprocessing research is essential since it decides and significantly affects the accuracy of predictions made in later stages. Thus, the recommendation for a special and useful combination in the framework of different data preprocessing task types, which includes the following four technical elements and is briefly justified, is made. The Internet of Things (IoT) is a design pattern in which commonplace items can be equipped with classification, sensing, networking, and processing capabilities that will enable them to communicate with one another over the Internet to fulfill a specific function. The Internet of Things will eventually change physical objects into virtual objects with intelligence. In addition to a detailed analysis of the IoT layer, this article gives an overview of the existing Internet of Things (IoT), technical specifics, and applications in this recently growing field. However, this publication will provide future scholars who desire to conduct study in this area of Internet of Things with a better knowledge. 相似文献
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数据仓库技术是基于信息系统业务发展的需要,基于数据库系统技术发展而来,并逐步独立的一系列新的应用技术。数据挖掘技术为应对信息爆炸、海量信息的处理提供了科学和有效的手段。本文简单介绍了关系数据仓库、数据挖掘的概念、结构、基本原理、技术和应用领域。 相似文献
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《Information processing & management》2022,59(1):102736
Business intelligence (BI) incorporates business research, data mining, data visualization, data tools,infrastructure, and best practices to help businesses make more data-driven choices.Business intelligence's challenging characteristics include data breaches, difficulty in analyzing different data sources, and poor data quality is consideredessential factors. In this paper, IoT-based Efficient Data Visualization Framework (IoT- EDVF) has been proposed to strengthen leaks' risk, analyze multiple data sources, and data quality management for business intelligence in corporate finance.Corporate analytics management is introduced to enhance the data analysis system's risk, and the complexity of different sources can allow accessing Business Intelligence. Financial risk analysis is implemented to improve data quality management initiative helps use main metrics of success, which are essential to the individual needs and objectives. The statistical outcomes of the simulation analysis show the increasedperformance with a lower delay response of 5ms and improved revenue analysis with the improvement of 29.42% over existing models proving the proposed framework's reliability. 相似文献
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《Information processing & management》2022,59(5):103036
The development of Management Information Systems (MIS) is impossible without the use of machine learning (ML). It's a type of Artificial Intelligence (AI) that makes predictions using statistical models. When it comes to financial analysis, there are numerous risk-related concerns to contend with today (FI). In the financial sector, machine learning algorithms are used to detect fraud, automate trading, and provide financial advice to investors. To better serve its customers, the financial sector can now save borrower data according to specific criteria thanks to MIS. In fact, there is a large amount of data about debtors, making load management a difficult task. ML can examine millions of data sets in a short period of time without being explicitly programmed to improve the results. This type of algorithm can aid financial institutions in making grant selections for their clients. For the objective of classifying FI in terms of fraud or not, the Intelligent Information System for Financial Institutions (IISFI) relying on Supervised ML (SML) Algorithms has been created in this work. Bayesian Belief Network, Neural Network, Decision trees, Naïve Bayes, and Nearest Neighbor has been compared for the purpose of classifying FI risks using the performance measures asfalse positive rate, true positive rate, true negative rate, false negative rate, accuracy, F-Measure, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Med AE, Receiver Operating Characteristic (ROC) area,Precision Recall Characteristic (PRC) area, and measures of PC. 相似文献
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随着信息技术的不断发展,应用商业智能技术进行数据挖掘与分析对商家来说也越来越重要,分类回归树和神经网络算法是数据挖掘的经典算法,其广泛运用在数据分析、预测和评估等方面。文章分别运用分类回归树和神经网络算法对零售商品采取促销方案后收入变化的数据进行分析,并建立相应的模型对促销方案效果进行预测。 相似文献
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《International Journal of Information Management》2016,36(4):645-652
The internet of things (IoT) is potentially interconnecting unprecedented amounts of raw data, opening countless possibilities by two main logical layers: become data into information, then turn information into knowledge. The former is about filtering the significance in the appropriate format, while the latter provides emerging categories of the whole domain. This path of the data is a bottom-up flow. On the other hand, the path of the process is a top-down flow, starting at the strategic level of business and scientific institutions. Today, the path of the process treasures a sizeable amount of well-known methods, architectures and technologies: the so called Big Data. On the top, Big Data analytics aims variable association (e-commerce), data mining (predictive behaviour) or clustering (marketing segmentation). Digging the Big Data architecture there are a myriad of enabling technologies for data taking, storage and management. However the strategic aim is to enhance knowledge with the appropriate information, which does need of data, but not vice versa. In the way, the magnitude of upcoming data from the IoT will disrupt the data centres. To cope with the extreme scale is a matter of moving the computing services towards the data sources. This paper explores the possibilities of providing many of the IoT services which are currently hosted in monolithic cloud centres, moving these computing services into nano data centres (NaDa). Particularly, data-information processes, which usually are performing at sub-problem domains. NaDa distributes computing power over the already present machines of the IP provides, like gateways or wireless routers to overcome latency, storage cost and alleviate transmissions. Large scale questionnaires have been taken for 300 IT professionals to validate the points of view for IoT adoption. Considering IoT is by definition connected to the Internet, NaDa may be used to implement the logical low layer architecture of the services. Obviously, such distributed NaDa send results on a logical high layer in charge of the information-knowledge turn. This layer requires the whole picture of the domain to enable those processes of Big Data analytics on the top. 相似文献
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Informational privacy, data mining, and the Internet 总被引：3，自引：2，他引：1
Herman T. Tavani 《Ethics and Information Technology》1999,1(2):137-145
Privacy concerns involving data mining are examined in terms of four questions: (1) What exactly is data mining? (2) How does data mining raise concerns for personal privacy? (3) How do privacy concerns raised by data mining differ from those concerns introduced by ‘traditional’ information-retrieval techniques in computer databases? (4) How do privacy concerns raised by mining personal data from the Internet differ from those concerns introduced by mining such data from ‘data warehouses?’ It is argued that the practice of using data-mining techniques, whether on the Internet or in data warehouses, to gain information about persons raises privacy concerns that (a) go beyond concerns introduced in traditional information-retrieval techniques in computer databases and (b) are not covered by present data-protection guidelines and privacy laws. 相似文献
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《Information processing & management》2020,57(1):102122
Big data generated by social media stands for a valuable source of information, which offers an excellent opportunity to mine valuable insights. Particularly, User-generated contents such as reviews, recommendations, and users’ behavior data are useful for supporting several marketing activities of many companies. Knowing what users are saying about the products they bought or the services they used through reviews in social media represents a key factor for making decisions. Sentiment analysis is one of the fundamental tasks in Natural Language Processing. Although deep learning for sentiment analysis has achieved great success and allowed several firms to analyze and extract relevant information from their textual data, but as the volume of data grows, a model that runs in a traditional environment cannot be effective, which implies the importance of efficient distributed deep learning models for social Big Data analytics. Besides, it is known that social media analysis is a complex process, which involves a set of complex tasks. Therefore, it is important to address the challenges and issues of social big data analytics and enhance the performance of deep learning techniques in terms of classification accuracy to obtain better decisions.In this paper, we propose an approach for sentiment analysis, which is devoted to adopting fastText with Recurrent neural network variants to represent textual data efficiently. Then, it employs the new representations to perform the classification task. Its main objective is to enhance the performance of well-known Recurrent Neural Network (RNN) variants in terms of classification accuracy and handle large scale data. In addition, we propose a distributed intelligent system for real-time social big data analytics. It is designed to ingest, store, process, index, and visualize the huge amount of information in real-time. The proposed system adopts distributed machine learning with our proposed method for enhancing decision-making processes. Extensive experiments conducted on two benchmark data sets demonstrate that our proposal for sentiment analysis outperforms well-known distributed recurrent neural network variants (i.e., Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU)). Specifically, we tested the efficiency of our approach using the three different deep learning models. The results show that our proposed approach is able to enhance the performance of the three models. The current work can provide several benefits for researchers and practitioners who want to collect, handle, analyze and visualize several sources of information in real-time. Also, it can contribute to a better understanding of public opinion and user behaviors using our proposed system with the improved variants of the most powerful distributed deep learning and machine learning algorithms. Furthermore, it is able to increase the classification accuracy of several existing works based on RNN models for sentiment analysis. 相似文献
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本文介绍了一种针对化学数据分析的挖掘系统原型实现和设计理论。阐述从化学数据分析的角度开发一个联机分析数据挖掘系统原型的理论过程，研究过程采用数据仓库提供的OLAP技术进行关联规则挖掘，提供了一种数据项的二进制编码技术，对于提高数据信息的处理能力和可靠性有一定意义。预期实现从各种文献资料或数据库自动抽取有关化学反应的信息，发现新的有用化学成分，完成合成设计和反应预测等功能，从而对数据挖掘的实现进行了有益的尝试。 相似文献
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数据挖掘与知识发现关系探析 总被引：2，自引：0，他引：2
以数据挖掘与知识发现的分类为切入点,详细探讨数据挖掘与知识发现的关系.总结出关于数据挖掘与知识发现的关系问题有三种观点,即数据挖掘就是知识发现,数据挖掘是知识发现的一个步骤,数据挖掘与知识发现是完全不同的两个概念.三种观点各有道理,取决于研究者的研究背景、研究范畴与目标.最后对数据挖掘与知识发现的发展趋势进行探讨. 相似文献
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统计量化规则（SQ rule）在数据挖掘中拥有重要和有用的地位。尽管集中式挖掘SQ规则的算法已经存在,但是集中式算法不能简单应用到分布式环境中,尤其涉及到分布式环境中各方的私有信息保护的时候。考虑数据分布共享的多方,在不泄漏各自的私有信息的情况下,合作完成SQ规则的挖掘问题。该问题属于保护私有信息的数据挖掘（PPDM）研究领域的问题。基于3个PPDM的基本工具,包括安全求和、安全求平均和安全求频繁项集的集合等,提交2个算法,共同完成水平划分数据下的保护私有信息的SQ规则挖掘。其中,一个算法安全计算置信区间,该区间用来检验规则的重要性;另一个算法安全挖掘规则。最后,给出算法的正确性、安全性和复杂性分析。 相似文献
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《The Journal of High Technology Management Research》2023,34(2):100465
Businesses have begun using IT apps for a variety of reasons in recent years. The rapid advancement of new technologies has opened up vast prospects for businesses to digitise their operations, enhance their use of information systems, and compete more effectively in the global marketplace. Information technology (IT) businesses can benefit greatly from Big Data analytics due to the depth and breadth of their data analysis. Big data can be used to examine IT departments in the following ways: performance analysis, forecast maintenance, security analysis, and resource analysis. When it comes to boosting their business's dependability, speed, quality, and effectiveness, most companies rely on big data. Companies can gain a competitive edge thanks to the massive amounts of data that big data is able to collect, store, and manage. Big data analytics is being used by a growing number of businesses to make sense of their mountain of data. In this paper, we examine the ways in which IBM, TCS, and Cognizant use big data within their operations. Long-term planning strategies and business intelligence practises are also suggested in this research as means of protecting personal information. 相似文献
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由于人为因素,订单出错是常有的事。采用计算机订单管理系统,能充分发挥计算机精准的特性及擅长处理大数据量的能力,保证处理过程的正确性。该系统基于B/S架构,使用面向对象C#语言和.net框架开发,并充分利用了.net的OO特性,结合MS SQL SERVER,在开发过程中运用多层结构的设计思想（表现层、业务逻辑层、数据访问层）,是一个高内聚、低耦合的MIS系统,能有效的帮助企业实施企业信息化管理,节约运营成本。 相似文献
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《Information processing & management》2023,60(1):103121
Because of the big volume of marketing data, a human analyst would be unable to uncover any useful information for marketing that could aid in the process of making decision. Smart Data Mining (SDM), which is considered an important field from Artificial Intelligence (AI) is completely assisting in the performance business management analytics and marketing information. In this study, most reliable six algorithms in SDM are applied; Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), ID3, and C4.5 on actual data of marketing for bank that taken from Cloud Internet of Thing (CIoT). The objectives of this study are to build an efficient framework to increase campaign of marketing for banks by identifying main characteristics that affect a success and to test the performance of CIoT and SDM algorithms. This study is expected to enhance the scientific contributions to investigating the marketing information capacities by integrating SDM with CIoT. The performances of SDM algorithms are calculated by eight measures; accuracy, balance accuracy, precision, mean absolute error, root mean absolute error, recall, F1- Score and running time. The experimental findings show that the proposed framework is successful, with higher accuracies and good performance. Results revealed that customer service & marketing tactics are essential for a Company’ success & survival. Also, the C4.5 has accomplished better achievement than the SVM, RF, LR, NB, & ID3. At the end, CIoT Platform was evaluated by response time, request rate & processing of bank data. 相似文献
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数据挖掘在电子商务中应用问题研究 总被引：5，自引：0，他引：5
随着商业信息和商业数据的急剧增加，如何有效地分析和利用这些信息，找出其中的内在联系，为经营活动服务成为电子商务经营者共同关注的问题。本文论述了数据挖掘的由来、数据挖掘的基本功能、国内外数据挖掘技术的发展概况以及数据挖掘在电子商务中的应用等问题。 相似文献
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信息时代的战略管理模式研究 总被引：1，自引：0，他引：1
信息技术的发展和企业环境的变化,使企业战略管理面临准确性和及时性的挑战。共享数据和数据挖掘技术为企业战略管理带来了新手段。提出信息时代的战略制定、实施和评价模型,将定量和定性相结合,指导企业及时获得动态信息,在战略制定中获得广泛的信息和知识支持,在战略实施和评价中动态调整战略,并不断积累知识。 相似文献