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An approach toward integration of big data into decision making process

An approach toward integration of big data into decision mak...

引用

international conference on Decision Science and Management, ICDSM 2018

作者： Rani, Bindu Kant, Shri Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Research and Technology Development Centre Sharda University Greater Noida India

ISBN: (纸本)9789811393297

In today’s era of big data, large volume of data is generated from different sources having different qualities. It has set a path of rapid change and new discoveries in business decision making. Business organizations either big or small are yearning valuable and accurate information in decision making process. To achieve this, organizations are analyzing internal data in association with external data and producing big data value chain. Due to rapid growth of such data, efficient platform and appropriate data mining algorithms need to be developed extracting value from these datasets. This paper is a brief survey of approaches used for big data analytics in decision making process with current challenges and frameworks developed. Based on that, we are proposing an approach to increase the efficiency of decision making process in big data analytics. Some future research directions are also proposed to translate knowledge into improved decision making and performance. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Decision making

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The design for electric marketing service data middle-platform

The design for electric marketing service data middle-platfo...

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1st international conference on Big data analytics for Cyber-Physical System in smart City, BDCPS 2019

作者： Wang, Jinzhi Xu, Daoqiang Wang, Yuan Gao, Shijie Hu, Quangui Ding, Xiao State Grid Corporation of China Beijing100031 China State Grid Jiangsu Electric Power Co. Ltd. Nanjing210024 China China Realtime Database Co. Ltd. Nanjing210012 China Beijing China-Power Information Technology Co. Ltd. Beijing100192 China Beijing SGITG-Accenture Information Technology Co. Ltd. Beijing100031 China

ISBN: (纸本)9789811525674

In order to comprehensively improve the electric power marketing business data integration level, enhance the data value mining and innovation driving ability, and promote the business-oriented level of data, it is an effective way to build the electric power marketing service data middle-platform. Referring to the middle-platform design concept and engineering practices of e-business companies, combined with the characteristics of the electric power marketing, this paper analyzed the design principles of the data middle-platform, designed the overall structure of the marketing data middle-platform consisting of the gathering data center, the public data center, the data extraction center, the service ability center and the supporting system, and studied the key technology framework of the data middle-platform. In the end, taking the electric service applying timing limit analysis as an example, the application scene of the data middle-platform is introduced. © Springer Nature Singapore Pte Ltd. 2020.

关键词： data integration

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Severity Prediction of Software Vulnerabilities Using Textual data

Severity Prediction of Software Vulnerabilities Using Textua...

引用

international conference on Recent Trends in Machine Learning, IOT, smart Cities and Applications, ICMISC 2020

作者： Malhotra, Ruchika Vidushi Delhi Technological University Shahbad Daulatpur Main Bawana Road Delhi110042 India

ISBN: (纸本)9789811572333

Nowadays, all essential activities carried out by society are dependent on software systems. These systems with time have become more and more complex. Increase in complexity leads to introduction of vulnerabilities in the software system. Vulnerabilities are being reported every year and are increasing exponentially with time. These vulnerabilities required to be patched and fixed before getting exploited. To achieve this goal, textual data available on vulnerabilities is needed to be exploited to retrieve valuable information from it. Therefore, in this paper, we aim to develop a prediction model which will take textual description of Apache Tomcat vulnerabilities as input and will predict the severity of the vulnerabilities. Text mining techniques and machine learning algorithms are used to perform this task. The huge volume of textual data available on vulnerabilities has to be prioritized so that more severe vulnerabilities can be targeted first and limited resources can be put to its best use. Hence, it is essential to use text mining techniques for feature selection to reduce the volume of the data. The model being developed uses chi-square and information gain for feature selection, and among the machine learning algorithm, bagging technique, random forest, naïve Bayes, support vector machine are used for prediction. From results, it is observed that comparatively information gain gave better results among feature selection technique. Model based on naïve Bayes algorithm along with information gain as feature selection techniques performed good with the average values of 0.91, 90.90%, and 92.30%, respectively, for AUC, sensitivity, specificity, respectively. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

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smart Parking and Recommendation System Under Fog Calculation 8th

Smart Parking and Recommendation System Under Fog Calculatio...

引用

8th international conference on Communications, Signal Processing, and Systems, CSPS 2019

作者： Mu, Jiasong Wei, Yunna Yang, Xiaorun College of Physical and Electronic Information Tianjin Normal University Tianjin China College of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9789811394089

Parking is becoming a major problem in urban traffic, and intelligent parking system is an effective way to solve this problem. intelligent parking system requires high accuracy of local information and strong real-time feedback, which have brought challenges to traditional cloud computing. In this case, a fog computing framework based on the idea of "layered processing" is proposed. The fog layer is close to the data source to ensure that requests and information from users and terminals can be quickly processed and responded by system. This paper designs a smart parking system with parking space and road condition information collection, road condition analysis, parking space reservation, road navigation and parking space guidance functions, and introduces two fog calculations in the intelligent parking system that based on the fog calculation framework, and introduces two typical application scenarios: video detection of license plates and parking spaces, driver parking space recommendations based on big data. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Fog

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Design Strategy of Wearable Equipment Based on smart Home Pension System 3rd

Design Strategy of Wearable Equipment Based on Smart Home Pe...

引用

3rd international conference on Innovative computing, IC 2020

作者： Xia, Ping Guangzhou College of Technology and Business Guangdong China

ISBN: (纸本)9789811559587

Wearable device is a new type of IoT product that has the function of directly contacting the human body and collecting physiological physical data and can provide critical intelligent services for the home care system. This paper analyzes the inevitable trend of the development of the smart home pension model, expounds the entry point and function of the wearable equipment applied to the smart home pension system, and puts forward the hardware and software design strategies and challenges for the future development of wearable equipment. The practical application of wearable equipment in the smart home pension system can provide specialized old-age pension services for the elderly, contribute to solving the problems of social livelihood, and provide a new idea for the construction of smart home pension service system. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Software design

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Edge computing and artificial intelligence for real-time poultry monitoring 17

Edge computing and artificial intelligence for real-time pou...

引用

17th international conference on Mobile Systems and Pervasive computing, MobiSPC 2020

作者： Debauche, Olivier Mahmoudi, Saïd Mahmoudi, Sidi Ahmed Manneback, Pierre Bindelle, Jérôme Lebeau, Frédéric University of Mons Faculty of Engineering - ILIA Infortech 20 Place du Parc Mons7000 Belgium University of Liège - GxABT TERRA Passage des déportés 2 Gembloux5030 Belgium University of Liège - GxABT Precision Livestock and Nutrition Passage des déportés 2 Gembloux5030 Belgium University of Liège - GxABT BioDynE Passage des déportés 2 Gembloux5030 Belgium

smart Poultry acquires data from aviaries by means of sensor network at reduced intervals of time (every minute) that generate hundred thousands of data. The conjunction of Internet of Things and Artificial Intelligence open the field of the real-time monitoring of poultry and,advance analytics and automation if data is from high quality. In this paper, we propose a scalable monitoring of a poultry achieved with open hardware wireless sensors network and software. We use a Gated Recurrent Unit, an artificial intelligence algorithm to validate and predicate environmental parameters. © 2020 The Authors.

关键词： Edge computing

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A Flexible and Interpretable Framework for Predicting Anomalous Behavior in Industry 4.0 Environments 34th

A Flexible and Interpretable Framework for Predicting Anomal...

引用

34th international conference on Advanced Information Networking and Applications, AINA 2020

作者： Langone, Rocco Cuzzocrea, Alfredo Skantzos, Nikolaos Deloitte Advanced Analytics Zaventem Belgium iDEA Lab University of Calabria Rende Italy

ISBN: (纸本)9783030440404

A method called interpretable anomaly prediction is introduced in this paper. The proposed methodology allows us to handle prediction issues in industry 4.0 settings by using regularized logistic regression as core model. In particular, in contrast to anomaly detection algorithms which permits to identify if the current data are anomalous or not, the proposed technique is able to predict the probability that future data will be abnormal. Furthermore, feature extraction and selection mechanisms give insights on the possible root causes leading to failures. © 2020, Springer Nature Switzerland AG.

关键词： Industry 4.0

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IoT and Cloud computing based smart Water Metering System

IoT and Cloud Computing based Smart Water Metering System

引用

2020 international conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2020

作者： Ray, Aritra Goswami, Shreemoyee University of Calcutta A. K. C. School of Information Technology Kolkata India

ISBN: (纸本)9781728165752

This paper focuses on the developmental and implementation methodology of smart water meter based on Internet of Things (IoT) and Cloud computing equipped with machine learning algorithms, to differentiate between normal and excessive water usage at industrial, domestic and all other sectors having an abundance of water usage, both for Indian and worldwide context. Recognizing that intelligent metering of water has the potential to alter customer engagement of water usage in urban and rural water supplies, this paper fosters for sustainable water management, a need of the present. With shrinking reserves of clean water resources worldwide, it is becoming cumbersome to cater for this resource to masses in the coming years on a consistent basis. Using our smart water meter, water resources can be managed efficiently and an optimum use could save water for the future generations. Sensors will provide for real time monitoring of hydraulic data, automated control and alarming from Cloud platform in case of events such as water leakages, excessive usage, etc. Analysis of the same will help in taking meaningful actions. Thus we do propose for a smart water metering technology that can be utilized by Indian citizens, and worldwide, to curb wastage of water. With an ease of monitoring and visualization of the data through the Cloud platform combined with machine learning based tools to detect excess water consumption, the server-less architecture we propose can be easily adopted and implemented in a large scale. © 2020 IEEE.

关键词： Machine learning

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A Reinforcement Learning-Based Virtual Machine Placement Strategy in Cloud data Centers 22

A Reinforcement Learning-Based Virtual Machine Placement Str...

引用

22nd IEEE international conference on High Performance computing and Communications, 18th IEEE international conference on smart City and 6th IEEE international conference on data Science and Systems, HPCC-smartCity-DSS 2020

作者： Long, Saiqin Li, Zhetao Xing, Yun Tian, Shujuan Li, Dongsheng Yu, Rong School of Computer Science Xiangtan University Key Lab. of Hunan Prov. for Internet of Things and Info. Secty. Hunan Intl. Sci. and Technol. Coop. Base of Intelligent Netwk. Xiangtan China Xiangtan University School of Computer Science Xiangtan China National University of Defense Technology College of Computer Changsha China

ISBN: (纸本)9781728176499

With the widespread use of cloud computing, energy consumption of cloud data centers is increasing which mainly comes from IT equipment and cooling equipment. This paper argues that once the number of virtual machines on the physical machines reaches a certain level, resource competition occurs, resulting in a performance loss of the virtual machines. Unlike most papers, we do not impose placement constraints on virtual machines by giving a CPU cap to achieve the purpose of energy savings in cloud data centers. Instead, we use the measure of performance loss to weigh. We propose a reinforcement learning-based virtual machine placement strategy(RLVMP) for energy savings in cloud data centers. The strategy considers the weight of virtual machine performance loss and energy consumption, which is finally solved with the greedy strategy. Simulation experiments show that our strategy has a certain improvement in energy savings compared with the other algorithms. © 2020 IEEE.

关键词： Energy conservation

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A Vision of smart Traffic Infrastructure for Traditional, Connected, and Autonomous Vehicles

A Vision of Smart Traffic Infrastructure for Traditional, Co...

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international conference on Connected and Autonomous Driving (MetroCAD)

作者： Ranka, Sanjay Rangarajan, Anand Elefteriadou, Lily Srinivasan, Siva Poasadas, Emmanuel Hoffman, Dan Ponnulari, Raj Dilmore, Jeremy Byron, Tom Univ Florida Gainesville FL 32611 USA City Gainesville Gainesville FL USA Florida Dept Transportat Tallahasee FL USA

ISBN: (数字)9781728160597

ISBN: (纸本)9781728160597

This smart city traffic management approach seeks to use edge-based video-stream processing (using multicore and GPU processors) at intersections and in public vehicles (city buses, fire trucks, ambulances, school buses) to convert video data into space-time trajectories of individual vehicles and pedestrians that are transmitted to a cloud-based system. Key information is then synthesized in the cloud from them to create a real-time city-wide traffic palette. Real-time or offline processing both at the edge and the cloud will then be leveraged to optimize intersection operations, manage network traffic, identify near-collisions between various units of traffic, provide street parking information, and a host of other applications. Additional information such as weather and environment will also be leveraged. The use of edge-based real-time machine learning (ML) techniques and videostream processing has several significant advantages. (1) Because there is no need to store copious amounts of video (few minutes typically suffice for edge-based processing), it automatically addresses concerns of public agencies who do not want person-identifiable information to be stored for reasons of citizen privacy and legality. (2) The processing of the video stream at the edge will allow for the use of low bandwidth communication using wireline and wireless networks to a central system such as a cloud, resulting in a compressed and holistic picture of the entire city. (3) The real-time nature of processing enables a wide variety of novel transportation applications at the intersection, street, and system levels that were not possible hitherto, significantly impacting safety and mobility.

关键词： video processing sensor data machine learning big data analytics intelligent transportation smart city

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