Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365879
H. Haugerud, J. Svensson, A. Yazidi
Cloud computing and its popularity has boomed over the last decade, enabling anyone to rent computing power on demand. Cloud providers such as Amazon and Google rent out surplus computing power for a discounted price according to demand in their data centers, but with the trade off that it is revocable and can only be rented for a short amount of time.This paper investigates the use of surplus computing power in order to reduce the cost of batch computing. We rely on a simple economical principle, the most cost-efficient Virtual Machine (VM) in a public cloud is the one that offers the highest performance per dollar. Therefore by rescheduling the workloads to the most cost-efficient location in terms of performance per dollar our solution dynamically provisions preemptible VMs in Google Cloud while continuously monitoring the performance per dollar of all available resources in every region. The algorithm automatically relocates workloads to a less expensive location if any appears and handles revoked access of the resources. Our algorithm views the cost reduction problem as a linear optimization problem with constraints and solves it using a greedy procedure. In the experiment we spawn Docker containers to mine cryptocurrency. The experimental results show that 67% of the cost is saved compared to renting on-demand VMs. The system can readily be extended to containers processing similar types of workloads and more generally to applications where the performance per dollar is easy to measure.
{"title":"Autonomous Provisioning of Preemptive Instances in Google Cloud for Maximum Performance Per Dollar","authors":"H. Haugerud, J. Svensson, A. Yazidi","doi":"10.1109/CloudTech49835.2020.9365879","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365879","url":null,"abstract":"Cloud computing and its popularity has boomed over the last decade, enabling anyone to rent computing power on demand. Cloud providers such as Amazon and Google rent out surplus computing power for a discounted price according to demand in their data centers, but with the trade off that it is revocable and can only be rented for a short amount of time.This paper investigates the use of surplus computing power in order to reduce the cost of batch computing. We rely on a simple economical principle, the most cost-efficient Virtual Machine (VM) in a public cloud is the one that offers the highest performance per dollar. Therefore by rescheduling the workloads to the most cost-efficient location in terms of performance per dollar our solution dynamically provisions preemptible VMs in Google Cloud while continuously monitoring the performance per dollar of all available resources in every region. The algorithm automatically relocates workloads to a less expensive location if any appears and handles revoked access of the resources. Our algorithm views the cost reduction problem as a linear optimization problem with constraints and solves it using a greedy procedure. In the experiment we spawn Docker containers to mine cryptocurrency. The experimental results show that 67% of the cost is saved compared to renting on-demand VMs. The system can readily be extended to containers processing similar types of workloads and more generally to applications where the performance per dollar is easy to measure.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122148921","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}
Pub Date : 2020-11-24DOI: 10.1109/cloudtech49835.2020.9365882
{"title":"CloudTech 2020 Authors Index","authors":"","doi":"10.1109/cloudtech49835.2020.9365882","DOIUrl":"https://doi.org/10.1109/cloudtech49835.2020.9365882","url":null,"abstract":"","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123923839","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}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365886
Jemai Bornia, A. Frihida, Olivier Debauche, S. Mahmoudi, P. Manneback
Everyday, a large amount of data is produced thanks to technological advances in the field of multimedia, associated with the generalization of their use in many applications. The need to keep control over this content, in terms of data analysis, classification, accurate AI (Artificial Intelligence) algorithms are required to perform this task efficiently and quickly. In this article, we propose an approach using deep learning technologies for the analysis of movement in video sequences. The suggested approach uses images from video splitting to detect objects / entities present and store their descriptions in a standard XML file. As result, we provide a Deep Learning algorithm using TensorFlow for tracking motion and animated entities in video sequences.
{"title":"Deep Learning and Approach for Tracking People’s Movements in a Video","authors":"Jemai Bornia, A. Frihida, Olivier Debauche, S. Mahmoudi, P. Manneback","doi":"10.1109/CloudTech49835.2020.9365886","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365886","url":null,"abstract":"Everyday, a large amount of data is produced thanks to technological advances in the field of multimedia, associated with the generalization of their use in many applications. The need to keep control over this content, in terms of data analysis, classification, accurate AI (Artificial Intelligence) algorithms are required to perform this task efficiently and quickly. In this article, we propose an approach using deep learning technologies for the analysis of movement in video sequences. The suggested approach uses images from video splitting to detect objects / entities present and store their descriptions in a standard XML file. As result, we provide a Deep Learning algorithm using TensorFlow for tracking motion and animated entities in video sequences.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125315451","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}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365900
A. Ouarhim, Jihane Lakhrouit, Karim Baïna
the need of real-time enterprises increases according to our life style and customers’ satisfaction goal. Real-time business process is one of the important components of a real-time enterprise. For that, a formal definition of real-time concept and real-time process was indispensable. This work presents a formal definition of real-time concept, real-time ontology and a proposition of a real-time component with real-time attributes. This new dimension of time contains three components: latency time, acceptance interval and theoretical time. This definition gives a new vision of time, not just an ordinary vision as periods and calendar but as a response to our needs in real-time enterprises: real-time. Before the proposition of a real-time component for BPMN language, which we applicate to Novel Coronavirus (2019-nCoV) management process, as case study.
{"title":"Business Process Modeling Notation Extension for Real Time Handling - Application to Novel Coronavirus (2019-nCoV) management process","authors":"A. Ouarhim, Jihane Lakhrouit, Karim Baïna","doi":"10.1109/CloudTech49835.2020.9365900","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365900","url":null,"abstract":"the need of real-time enterprises increases according to our life style and customers’ satisfaction goal. Real-time business process is one of the important components of a real-time enterprise. For that, a formal definition of real-time concept and real-time process was indispensable. This work presents a formal definition of real-time concept, real-time ontology and a proposition of a real-time component with real-time attributes. This new dimension of time contains three components: latency time, acceptance interval and theoretical time. This definition gives a new vision of time, not just an ordinary vision as periods and calendar but as a response to our needs in real-time enterprises: real-time. Before the proposition of a real-time component for BPMN language, which we applicate to Novel Coronavirus (2019-nCoV) management process, as case study.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122512736","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}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365866
Hamid Naceur Benkhaled, Djamel Berrabah, F. Boufarès
Record Linkage (RL) is the process of detecting duplicates in one or several datasets. The main important phase during the RL process is blocking, it reduces the quadratic complexity of the RL process by dividing the data into several blocks, in which, matching between the records is done. Several blocking techniques were proposed in the literature, but most of them do not have a mechanism of controlling the generated block sizes, which is a very important condition in the field of real-time RL or privacy-preserving RL. In this paper, we propose a mechanism to control the block sizes generated by the K-Modes based Record Linkage. The experiments done on three real-world datasets show satisfying results where most of the duplicates records were detected while respecting the specified block sizes.
{"title":"Block Sizes Control For an Efficient Real Time Record Linkage","authors":"Hamid Naceur Benkhaled, Djamel Berrabah, F. Boufarès","doi":"10.1109/CloudTech49835.2020.9365866","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365866","url":null,"abstract":"Record Linkage (RL) is the process of detecting duplicates in one or several datasets. The main important phase during the RL process is blocking, it reduces the quadratic complexity of the RL process by dividing the data into several blocks, in which, matching between the records is done. Several blocking techniques were proposed in the literature, but most of them do not have a mechanism of controlling the generated block sizes, which is a very important condition in the field of real-time RL or privacy-preserving RL. In this paper, we propose a mechanism to control the block sizes generated by the K-Modes based Record Linkage. The experiments done on three real-world datasets show satisfying results where most of the duplicates records were detected while respecting the specified block sizes.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130425479","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}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365917
Hammou Djalal Rafik, S. Mahmoudi, A. Reda, Mechab Boubaker
Biometric iris recognition is a very advanced technology for the data protection and identification of individuals. This technology is widely used by multi-national society in terms of data protection and security. A biometric iris recognition system requires an adapted architecture and specific because it generally recommends 5 steps. The acquisition step consists of getting a good quality iris image by digital cameras of high resolution. The segmentation can use an algorithm and mathematical methods such as John Daugman’s Interro Differential Operator [3] or Richard Paul Wildes’s Hough Transform [4]. The normalization phase projects to transform the relevant information from the circular iris image into the rectangular shape. The feature extraction step requires the use of specific filters (1-D Log-Gabor). The end step is the matching that allows us to compare the descriptor of the user with that of the database to determine if the person is authentic or not and this is done using Hamming Distance. The objective of this article is the use of our approach to improving results. The experiments were tested on the Casia V1 [16], MMU1 [17] iris biometric database, which gave very good and encouraging results. We found an accuracy rate of 99.9263 % for Casia V1 and 99.4168 % for MMU1.
生物特征虹膜识别是一项非常先进的个人数据保护和身份识别技术。该技术在数据保护和安全方面被多国社会广泛使用。生物特征虹膜识别系统需要一个适应的架构和特定的，因为它通常建议5个步骤。采集步骤是通过高分辨率的数码相机获得高质量的虹膜图像。分割可以使用John Daugman的Interro Differential Operator[3]或Richard Paul Wildes的Hough Transform[4]等算法和数学方法。归一化阶段是将圆形虹膜图像的相关信息转化为矩形虹膜图像。特征提取步骤需要使用特定的过滤器(1-D Log-Gabor)。最后一步是匹配，它允许我们将用户的描述符与数据库的描述符进行比较，以确定该人是否真实，这是使用汉明距离完成的。本文的目的是使用我们的方法来改进结果。实验在Casia V1[16]、MMU1[17]虹膜生物特征数据库上进行了测试，得到了非常好的令人鼓舞的结果。我们发现Casia V1和MMU1的准确率分别为99.9263%和99.4168%。
{"title":"A Model Of A Biometric Recognition System Based On The Hough Transform Of Libor Masek and 1-D Log-Gabor Filter","authors":"Hammou Djalal Rafik, S. Mahmoudi, A. Reda, Mechab Boubaker","doi":"10.1109/CloudTech49835.2020.9365917","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365917","url":null,"abstract":"Biometric iris recognition is a very advanced technology for the data protection and identification of individuals. This technology is widely used by multi-national society in terms of data protection and security. A biometric iris recognition system requires an adapted architecture and specific because it generally recommends 5 steps. The acquisition step consists of getting a good quality iris image by digital cameras of high resolution. The segmentation can use an algorithm and mathematical methods such as John Daugman’s Interro Differential Operator [3] or Richard Paul Wildes’s Hough Transform [4]. The normalization phase projects to transform the relevant information from the circular iris image into the rectangular shape. The feature extraction step requires the use of specific filters (1-D Log-Gabor). The end step is the matching that allows us to compare the descriptor of the user with that of the database to determine if the person is authentic or not and this is done using Hamming Distance. The objective of this article is the use of our approach to improving results. The experiments were tested on the Casia V1 [16], MMU1 [17] iris biometric database, which gave very good and encouraging results. We found an accuracy rate of 99.9263 % for Casia V1 and 99.4168 % for MMU1.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121330558","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}
Pub Date : 2020-11-24DOI: 10.1109/cloudtech49835.2020.9365880
{"title":"[CloudTech 2020 Front cover]","authors":"","doi":"10.1109/cloudtech49835.2020.9365880","DOIUrl":"https://doi.org/10.1109/cloudtech49835.2020.9365880","url":null,"abstract":"","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128659914","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}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365888
Fatima Zahra Guerrouj, R. Latif, A. Saddik
Artificial intelligence is a field in full development, from facial recognition to autonomous vehicles and referral systems for online shopping, passing by smart farming, these new technologies are invading our daily lives.Nowadays, agricultural applications require more and more computer vision technologies for continuous monitoring and analysis of crop health and yield. That is why machine learning has become one of the mechanisms that make farming more efficient by using high-precision algorithms. This article deals with the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), which are the most widely used indices in precision agriculture. In this work, we adopt GPU-based heterogeneous architecture using parallel programming with the CUDA language. The algorithm is evaluated on several platforms: NVIDIA Jetson TX1, DELL-desktop, and XU4 board. It has been discovered that the execution time of the two NDVI and NDWI indices on the embedded TX1 card is more optimized and improved compared to the execution time on the XU4 card and the Desktop.
{"title":"Evaluation of NDVI and NDWI parameters in CPU-GPU Heterogeneous Platforms based CUDA","authors":"Fatima Zahra Guerrouj, R. Latif, A. Saddik","doi":"10.1109/CloudTech49835.2020.9365888","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365888","url":null,"abstract":"Artificial intelligence is a field in full development, from facial recognition to autonomous vehicles and referral systems for online shopping, passing by smart farming, these new technologies are invading our daily lives.Nowadays, agricultural applications require more and more computer vision technologies for continuous monitoring and analysis of crop health and yield. That is why machine learning has become one of the mechanisms that make farming more efficient by using high-precision algorithms. This article deals with the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), which are the most widely used indices in precision agriculture. In this work, we adopt GPU-based heterogeneous architecture using parallel programming with the CUDA language. The algorithm is evaluated on several platforms: NVIDIA Jetson TX1, DELL-desktop, and XU4 board. It has been discovered that the execution time of the two NDVI and NDWI indices on the embedded TX1 card is more optimized and improved compared to the execution time on the XU4 card and the Desktop.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116010186","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}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365869
Fabrice Nolack Fote, Saïd Mahmoudi, Amine Roukh, S. Mahmoudi
Smart Farming has always been referred to as agriculture, but nowadays, that is no longer the case. Today, Smart farming is made up of Precision Agriculture (PA) and Precision Livestock Farming (PLF). Big Data technologies and algorithms can be relevant for managing and monitoring data related to any farm. Precision livestock farming concerns genetics, animal welfare, animal nutrition, reproduction, species protection and animal health. This paper presents a general overview of Big Data tools that can be applied in a smart farming application. New Technologies are offering many tools used to facilitate the management of data collection, risk minimization, climate change anticipation, secure storage and analysis, etc. The main purpose of Big Data tools is to increase productions in order to offer higher quantities while ensuring higher quality products. However, they remain some issues that need to be accomplished.
{"title":"Big Data Storage and Analysis for Smart Farming","authors":"Fabrice Nolack Fote, Saïd Mahmoudi, Amine Roukh, S. Mahmoudi","doi":"10.1109/CloudTech49835.2020.9365869","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365869","url":null,"abstract":"Smart Farming has always been referred to as agriculture, but nowadays, that is no longer the case. Today, Smart farming is made up of Precision Agriculture (PA) and Precision Livestock Farming (PLF). Big Data technologies and algorithms can be relevant for managing and monitoring data related to any farm. Precision livestock farming concerns genetics, animal welfare, animal nutrition, reproduction, species protection and animal health. This paper presents a general overview of Big Data tools that can be applied in a smart farming application. New Technologies are offering many tools used to facilitate the management of data collection, risk minimization, climate change anticipation, secure storage and analysis, etc. The main purpose of Big Data tools is to increase productions in order to offer higher quantities while ensuring higher quality products. However, they remain some issues that need to be accomplished.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116055661","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}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365876
F. Ezzaki, N. Abghour, A. Elomri, K. Moussaid, M. Rida
The bloom filter is a probabilistic data model used to test the existence of an element in a set, i.e., for any given item, the bloom filter could test the membership query on this candidate. The bloom filter has many advantages due to its simplicity and efficiency in highly solving the issue of data representation in many fields and to support membership queries, it has been known as space and time-efficient randomized data structure, by filtering out redundant data and optimizing the memory consumption. However, bloom filters are limited to membership tests and don’t support the deletion of elements. They also generate the false positive probability as they are based on a probabilistic model, this error rate is generated when an element that doesn’t belong to a set is considered as a member of this set by the bloom filter. Our goal is to compare a number of well- existed algorithms related to the boom filter for future work on the optimization of the join’s algorithms in MapReduce. This paper provides an overview of the different variants of the bloom filter and analyses the studies that have been interested in this area of research.
{"title":"Bloom filter and its variants for the optimization of MapReduce’s algorithms: A review","authors":"F. Ezzaki, N. Abghour, A. Elomri, K. Moussaid, M. Rida","doi":"10.1109/CloudTech49835.2020.9365876","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365876","url":null,"abstract":"The bloom filter is a probabilistic data model used to test the existence of an element in a set, i.e., for any given item, the bloom filter could test the membership query on this candidate. The bloom filter has many advantages due to its simplicity and efficiency in highly solving the issue of data representation in many fields and to support membership queries, it has been known as space and time-efficient randomized data structure, by filtering out redundant data and optimizing the memory consumption. However, bloom filters are limited to membership tests and don’t support the deletion of elements. They also generate the false positive probability as they are based on a probabilistic model, this error rate is generated when an element that doesn’t belong to a set is considered as a member of this set by the bloom filter. Our goal is to compare a number of well- existed algorithms related to the boom filter for future work on the optimization of the join’s algorithms in MapReduce. This paper provides an overview of the different variants of the bloom filter and analyses the studies that have been interested in this area of research.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"93 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122065462","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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