With the continuous development of the Internet, the structure of the network has also undergone great changes. The Internet is gradually flattening, and the links between autonomous systems are increasing. Many autonomous systems realize direct transmission through Internet switching centers or related links. In order to analyze the transmission changes of the core autonomous system in the Internet. Based on the historical data of actual network routing information, this paper studies and analyzes the development of the Internet and the transmission changes of core autonomous system in the Internet. For further research and analysis, this paper designs the experimental model and related algorithms, constructs the Internet topology relationship according to the actual network routing data, and carries out independent experiments on the core autonomous system. According to the experimental results, we can find that the hierarchical structure of the Internet gradually blurred, AS the core formed dense links between local network and can provide affordable for the Internet, while the core AS the transfer function of the Internet in weakening, but the core AS the stability of the transmission of the Internet is still very important, and even play a crucial role.
{"title":"An Experimental Study on the Core Autonomous System of Internet","authors":"Song Wen, Donghong Qin, Ting Lv, Lina Ge","doi":"10.1145/3456389.3456396","DOIUrl":"https://doi.org/10.1145/3456389.3456396","url":null,"abstract":"With the continuous development of the Internet, the structure of the network has also undergone great changes. The Internet is gradually flattening, and the links between autonomous systems are increasing. Many autonomous systems realize direct transmission through Internet switching centers or related links. In order to analyze the transmission changes of the core autonomous system in the Internet. Based on the historical data of actual network routing information, this paper studies and analyzes the development of the Internet and the transmission changes of core autonomous system in the Internet. For further research and analysis, this paper designs the experimental model and related algorithms, constructs the Internet topology relationship according to the actual network routing data, and carries out independent experiments on the core autonomous system. According to the experimental results, we can find that the hierarchical structure of the Internet gradually blurred, AS the core formed dense links between local network and can provide affordable for the Internet, while the core AS the transfer function of the Internet in weakening, but the core AS the stability of the transmission of the Internet is still very important, and even play a crucial role.","PeriodicalId":124603,"journal":{"name":"2021 Workshop on Algorithm and Big Data","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115624662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multimodal perception is not only an important ability of human intelligence, but also one of the essential differences between human intelligence and artificial intelligence. With the rapid development of sensor technology, artificial intelligence technology and the Internet, various modalities of sensor data are emerging rapidly, for example, vision sensors and voice sensors are widely used in target detection and speech interaction. Integrating multiple-modality sensor data could obtain more comprehensive and accurate information, and also enhance the reliability and fault tolerance of the system. Clarifying the principles and mechanisms of how human brain integrates multi-sensory information can provide a theoretical basis for the scientific development of artificial intelligence. The introduction of multi-modality integration modules in artificial intelligence can simulate human perception largely, thus artificial intelligence will be infinitely similar to human intelligence.
{"title":"Integration of Multiple-Modality Sensor Data and Artificial Intelligence","authors":"Hong Lu, Jintao Liu","doi":"10.1145/3456389.3456398","DOIUrl":"https://doi.org/10.1145/3456389.3456398","url":null,"abstract":"Multimodal perception is not only an important ability of human intelligence, but also one of the essential differences between human intelligence and artificial intelligence. With the rapid development of sensor technology, artificial intelligence technology and the Internet, various modalities of sensor data are emerging rapidly, for example, vision sensors and voice sensors are widely used in target detection and speech interaction. Integrating multiple-modality sensor data could obtain more comprehensive and accurate information, and also enhance the reliability and fault tolerance of the system. Clarifying the principles and mechanisms of how human brain integrates multi-sensory information can provide a theoretical basis for the scientific development of artificial intelligence. The introduction of multi-modality integration modules in artificial intelligence can simulate human perception largely, thus artificial intelligence will be infinitely similar to human intelligence.","PeriodicalId":124603,"journal":{"name":"2021 Workshop on Algorithm and Big Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130852539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To promote the information sharing of orders, production capacity and channels, and support core enterprises to play the synergistic linkage role of information flow on upstream and downstream, production, supply and marketing, with the advocation and support on digital supply chain construction from the Chinese government, this paper focuses on the guidance of market information data to the formulation of precision marketing strategy of featured agricultural products. Taking the questionnaire data of 927 respondents in Shenfu, Liaoning Province as an example, a statistical model was used to analyze the characteristics of consumers’ demand of agricultural products with calcium fruit characteristics, and the important factors that affect their purchase intention. In this way, targeted precision marketing suggestions are put forward. In addition to demonstrating the role of data in precision marketing of featured agricultural products, this research helps to accelerate the digital transformation of the industry and strengthen the new driving force of the real economy.
{"title":"Data Guidance to Precision Marketing of Featured Agricultural Products: Taking the Market Demand of Calcium Fruit in Shenfu Area as an Example","authors":"Qiaonan Zhu","doi":"10.1145/3456389.3456395","DOIUrl":"https://doi.org/10.1145/3456389.3456395","url":null,"abstract":"To promote the information sharing of orders, production capacity and channels, and support core enterprises to play the synergistic linkage role of information flow on upstream and downstream, production, supply and marketing, with the advocation and support on digital supply chain construction from the Chinese government, this paper focuses on the guidance of market information data to the formulation of precision marketing strategy of featured agricultural products. Taking the questionnaire data of 927 respondents in Shenfu, Liaoning Province as an example, a statistical model was used to analyze the characteristics of consumers’ demand of agricultural products with calcium fruit characteristics, and the important factors that affect their purchase intention. In this way, targeted precision marketing suggestions are put forward. In addition to demonstrating the role of data in precision marketing of featured agricultural products, this research helps to accelerate the digital transformation of the industry and strengthen the new driving force of the real economy.","PeriodicalId":124603,"journal":{"name":"2021 Workshop on Algorithm and Big Data","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134111273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid development of computer technology, human society is entering the era of knowledge society and information technology, so education is facing great challenges. The concept and practice of education need to be innovated, the knowledge construction in learning needs to be strengthened, and the focus of teaching should be shifted from “activity centered” to “thought centered”. The goal of collaborative knowledge construction is to form valuable public knowledge for learning groups, rather than simply improving the contents of learning individuals’ minds. It focuses on the construction and improvement of group knowledge. Through literature research, the topic form based on the Wiki network environment may help the construction of collaborative knowledge. On this basis, the topic collaborative knowledge construction model of the Internet + network environment is further discussed.
{"title":"Collaborative Knowledge Construction Process Model Based on Internet+","authors":"Shuangshuang Cao","doi":"10.1145/3456389.3456391","DOIUrl":"https://doi.org/10.1145/3456389.3456391","url":null,"abstract":"With the rapid development of computer technology, human society is entering the era of knowledge society and information technology, so education is facing great challenges. The concept and practice of education need to be innovated, the knowledge construction in learning needs to be strengthened, and the focus of teaching should be shifted from “activity centered” to “thought centered”. The goal of collaborative knowledge construction is to form valuable public knowledge for learning groups, rather than simply improving the contents of learning individuals’ minds. It focuses on the construction and improvement of group knowledge. Through literature research, the topic form based on the Wiki network environment may help the construction of collaborative knowledge. On this basis, the topic collaborative knowledge construction model of the Internet + network environment is further discussed.","PeriodicalId":124603,"journal":{"name":"2021 Workshop on Algorithm and Big Data","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133011046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the era of big data, under the conditions of rapid economic development in our country, various enterprises have also vigorously carried out marketing. In the context of big data, marketing research should be strengthened to effectively improve market. Market issues ensure that marketing has improved its status in the era of big data. This article has conducted a research and analysis on marketing in the context of big data. And then the opportunities and challenges of marketing in the context of big data has been explained, which gradually optimize the marketing implementation effect. The challenges faced by marketing has been understood which ensures that the big data model plays its best role in it.
{"title":"Opportunities and Challenges of Marketing in the Context of Big Data","authors":"Shuangshuang Cao","doi":"10.1145/3456389.3456390","DOIUrl":"https://doi.org/10.1145/3456389.3456390","url":null,"abstract":"In the era of big data, under the conditions of rapid economic development in our country, various enterprises have also vigorously carried out marketing. In the context of big data, marketing research should be strengthened to effectively improve market. Market issues ensure that marketing has improved its status in the era of big data. This article has conducted a research and analysis on marketing in the context of big data. And then the opportunities and challenges of marketing in the context of big data has been explained, which gradually optimize the marketing implementation effect. The challenges faced by marketing has been understood which ensures that the big data model plays its best role in it.","PeriodicalId":124603,"journal":{"name":"2021 Workshop on Algorithm and Big Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121039946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiahui Wang, Pengcheng Han, Jinchao Chen, Chenglie Du
Nowadays, the vast majority of workflow applications are deploying on clouds for fast execution. Meanwhile, the market-oriented and price-driven characteristics of cloud computing make cost become a factor that cannot be ignored and challenge traditional workflow scheduling algorithms which focus only on the optimization of finish time (a.k.a makespan). A general way to consider cost and makespan at the same time is to model the problem as a constrained optimization problem. In this paper, we study the deadline-constrained and cost-minimization workflow scheduling problem, and propose the cost-efficient scheduling with deadline constraint (CESDC) algorithm. CESDC is a typical list scheduling algorithm, and contains three scheduling phases: deadline distribution, task prioritization and service selection. CESDC firstly distributes deadline to tasks by their levels and workloads, then prioritizes tasks according to a modified upward rank, and finally assigns services to tasks which meets the sub-deadline and minimizes the cost. Experiment results demonstrate that CESDC performs better in terms of success ratio and cost than those of several state-of-the-art approaches.
{"title":"Cost-Efficient Scheduling of Workflow Applications with Deadline Constraint on IaaS Clouds","authors":"Jiahui Wang, Pengcheng Han, Jinchao Chen, Chenglie Du","doi":"10.1145/3456389.3456401","DOIUrl":"https://doi.org/10.1145/3456389.3456401","url":null,"abstract":"Nowadays, the vast majority of workflow applications are deploying on clouds for fast execution. Meanwhile, the market-oriented and price-driven characteristics of cloud computing make cost become a factor that cannot be ignored and challenge traditional workflow scheduling algorithms which focus only on the optimization of finish time (a.k.a makespan). A general way to consider cost and makespan at the same time is to model the problem as a constrained optimization problem. In this paper, we study the deadline-constrained and cost-minimization workflow scheduling problem, and propose the cost-efficient scheduling with deadline constraint (CESDC) algorithm. CESDC is a typical list scheduling algorithm, and contains three scheduling phases: deadline distribution, task prioritization and service selection. CESDC firstly distributes deadline to tasks by their levels and workloads, then prioritizes tasks according to a modified upward rank, and finally assigns services to tasks which meets the sub-deadline and minimizes the cost. Experiment results demonstrate that CESDC performs better in terms of success ratio and cost than those of several state-of-the-art approaches.","PeriodicalId":124603,"journal":{"name":"2021 Workshop on Algorithm and Big Data","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121425460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Infectious diseases have troubled farmers continuously by spreading throughout crops. Thus, a proper identification of such disease is obligatory for the timely treatment which can save the money and efforts of small-scale farmers. The recent advancement in deep learning has provided a way to contribute to the sector of agriculture. In this paper deep learning based MobileNet architecture is employed to identify potato plant lesion characteristic. The application of transfer learning is accomplished by freezing the base layers and training only top 23 layers containing the added classifier layer. The model is then trained further to improve performance. The frozen layer weights of this pretrained model remained constant during training while the top layer weights are constrained by fine tuning to quit generalize feature map and get associated with specific features of new dataset. This enhances the model performances and gives 99.83 % accuracy in the image classification on the leaves of potato plant into the categories of infected disease. The experimental results demonstrate the feasibility of this procedure on portable devices.
{"title":"Application of MobileNet-v1 for Potato Plant Disease Detection Using Transfer Learning","authors":"Sumita Mishra, Anshuman Singh, Vineet Singh","doi":"10.1145/3456389.3456403","DOIUrl":"https://doi.org/10.1145/3456389.3456403","url":null,"abstract":"Infectious diseases have troubled farmers continuously by spreading throughout crops. Thus, a proper identification of such disease is obligatory for the timely treatment which can save the money and efforts of small-scale farmers. The recent advancement in deep learning has provided a way to contribute to the sector of agriculture. In this paper deep learning based MobileNet architecture is employed to identify potato plant lesion characteristic. The application of transfer learning is accomplished by freezing the base layers and training only top 23 layers containing the added classifier layer. The model is then trained further to improve performance. The frozen layer weights of this pretrained model remained constant during training while the top layer weights are constrained by fine tuning to quit generalize feature map and get associated with specific features of new dataset. This enhances the model performances and gives 99.83 % accuracy in the image classification on the leaves of potato plant into the categories of infected disease. The experimental results demonstrate the feasibility of this procedure on portable devices.","PeriodicalId":124603,"journal":{"name":"2021 Workshop on Algorithm and Big Data","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120965068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the multiuser multiple-input multiple-output (MU-MIMO) system, to reduce the influence of channel correlation on system performance, the base station (BS) should select the appropriate subset of user equipments (UEs) according to their channel state information (CSI). Due to a lack of channel reciprocity, the downlink CSI needs to be fed back to the BS in frequency division duplexing (FDD) mode. Some scholars have exploited kinds of deep neural networks (DNNs) for sensing and recovering CSI. However, user selection after all the CSI is reconstructed by DNNs will bring a great time delay. In this paper, we propose a deep learning-based CSI feedback scheme called US-CsiNet. Based on adversarial autoencoder (AAE), US-CsiNet can explicitly cover user schedule information while representing CSI. At the UE side, the encoder of US-CsiNet maps the CSI into codewords of which part are feature information for user schedule. Then the BS applies these partial codewords to separate the UEs into different groups and select active UEs. Finally, the decoder of AAE reconstructs the CSI of these active UEs. US-CsiNet can not only simplify the user selection process but also guarantee the accuracy of CSI reconstruction. The simulation results show that the proposed approach outperforms maximum channel gain (MCG) user selection algorithms and achieves the nearly same performance with semiorthogonal user selection (SUS) which needs full CSI of all users at the BS.
{"title":"DL-Based Joint CSI Feedback and User Selection in FDD Massive MIMO","authors":"Yuanshang Mao, Xin Liang, Xinyu Gu","doi":"10.1145/3456389.3456399","DOIUrl":"https://doi.org/10.1145/3456389.3456399","url":null,"abstract":"In the multiuser multiple-input multiple-output (MU-MIMO) system, to reduce the influence of channel correlation on system performance, the base station (BS) should select the appropriate subset of user equipments (UEs) according to their channel state information (CSI). Due to a lack of channel reciprocity, the downlink CSI needs to be fed back to the BS in frequency division duplexing (FDD) mode. Some scholars have exploited kinds of deep neural networks (DNNs) for sensing and recovering CSI. However, user selection after all the CSI is reconstructed by DNNs will bring a great time delay. In this paper, we propose a deep learning-based CSI feedback scheme called US-CsiNet. Based on adversarial autoencoder (AAE), US-CsiNet can explicitly cover user schedule information while representing CSI. At the UE side, the encoder of US-CsiNet maps the CSI into codewords of which part are feature information for user schedule. Then the BS applies these partial codewords to separate the UEs into different groups and select active UEs. Finally, the decoder of AAE reconstructs the CSI of these active UEs. US-CsiNet can not only simplify the user selection process but also guarantee the accuracy of CSI reconstruction. The simulation results show that the proposed approach outperforms maximum channel gain (MCG) user selection algorithms and achieves the nearly same performance with semiorthogonal user selection (SUS) which needs full CSI of all users at the BS.","PeriodicalId":124603,"journal":{"name":"2021 Workshop on Algorithm and Big Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130779725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shuqin Dong, Chengqing Yu, Guangxi Yan, Jintian Zhu, Hui Hu
Traffic speed forecasting is one of the important issues in the intelligent transportation system, which is related to traffic management planning. The existing studies tend to use single models to forecast the traffic speed, and cannot completely extract the complex information of the traffic speed sequence. This research proposes a new hybrid model based on reinforcement learning for the accurate forecasting of traffic speed. The model contains the LSTM network and the GRU network as predictors for in-depth mining of the characteristics of traffic speed data and uses reinforcement learning to integrate the results of the two predictors, combining the advantages of multiple predictors to achieve stable and accurate forecasting results of traffic speed. This paper uses two sets of measured traffic data from Guangzhou to test the effectiveness, and five other traffic speed forecasting models are also established for comparison. Experimental results show that the hybrid model applied in the article has the best performance on both data sets, and the MAPEs are 5.02% and 3.25%.
{"title":"A Novel Ensemble Reinforcement Learning Gated Recursive Network for Traffic Speed Forecasting","authors":"Shuqin Dong, Chengqing Yu, Guangxi Yan, Jintian Zhu, Hui Hu","doi":"10.1145/3456389.3456397","DOIUrl":"https://doi.org/10.1145/3456389.3456397","url":null,"abstract":"Traffic speed forecasting is one of the important issues in the intelligent transportation system, which is related to traffic management planning. The existing studies tend to use single models to forecast the traffic speed, and cannot completely extract the complex information of the traffic speed sequence. This research proposes a new hybrid model based on reinforcement learning for the accurate forecasting of traffic speed. The model contains the LSTM network and the GRU network as predictors for in-depth mining of the characteristics of traffic speed data and uses reinforcement learning to integrate the results of the two predictors, combining the advantages of multiple predictors to achieve stable and accurate forecasting results of traffic speed. This paper uses two sets of measured traffic data from Guangzhou to test the effectiveness, and five other traffic speed forecasting models are also established for comparison. Experimental results show that the hybrid model applied in the article has the best performance on both data sets, and the MAPEs are 5.02% and 3.25%.","PeriodicalId":124603,"journal":{"name":"2021 Workshop on Algorithm and Big Data","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127415784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The terms “data science” and “big data” become very popular these days. Importance of these concepts are popularly recognized mainly due to the success of AI technologies. Especially, machine learning (ML) technologies such as deep learning have been applied practically in these years and equipment using these ICT technologies becomes very sophisticated. Thus, our life becomes more convenient. Huge amount of data is required in order to apply ML technologies into practical use. As a result, “big data” and “big data analysis” are recognized quite important. Even with such an environment, “small data (or non-big data)” and “small data analysis” remain important. Small data and small data analysis have advantages such as ease of data collection, ease of data analysis/mining, and appropriateness for experimental analysis in the style of trial and error, especially for domain-specific exploratory analysis. In this paper, we discuss advantages of small data analysis in comparison with big data analysis based on our experience of analysis mainly of those data obtained in our educational practices. We conclude that it is an efficient and effective method for developing data analysis methods to start from small data and expanding them in their size and variety.
{"title":"Small Data Analysis for Bigger Data Analysis","authors":"Toshiro Minami, Y. Ohura","doi":"10.1145/3456389.3456404","DOIUrl":"https://doi.org/10.1145/3456389.3456404","url":null,"abstract":"The terms “data science” and “big data” become very popular these days. Importance of these concepts are popularly recognized mainly due to the success of AI technologies. Especially, machine learning (ML) technologies such as deep learning have been applied practically in these years and equipment using these ICT technologies becomes very sophisticated. Thus, our life becomes more convenient. Huge amount of data is required in order to apply ML technologies into practical use. As a result, “big data” and “big data analysis” are recognized quite important. Even with such an environment, “small data (or non-big data)” and “small data analysis” remain important. Small data and small data analysis have advantages such as ease of data collection, ease of data analysis/mining, and appropriateness for experimental analysis in the style of trial and error, especially for domain-specific exploratory analysis. In this paper, we discuss advantages of small data analysis in comparison with big data analysis based on our experience of analysis mainly of those data obtained in our educational practices. We conclude that it is an efficient and effective method for developing data analysis methods to start from small data and expanding them in their size and variety.","PeriodicalId":124603,"journal":{"name":"2021 Workshop on Algorithm and Big Data","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129828739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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