Proceedings of the 12th International Conference on Management of Digital EcoSystems最新文献_第2页

A Meta Learning Approach for Automating Model Selection in Big Data Environments using Microservice and Container Virtualization Technologies 使用微服务和容器虚拟化技术在大数据环境中自动化模型选择的元学习方法

Proceedings of the 12th International Conference on Management of Digital EcoSystems Pub Date : 2020-11-02 DOI: 10.1145/3415958.3433072

Shadi Shahoud, Hatem Khalloof, Moritz Winter, Clemens Düpmeier, V. Hagenmeyer

{"title":"A Meta Learning Approach for Automating Model Selection in Big Data Environments using Microservice and Container Virtualization Technologies","authors":"Shadi Shahoud, Hatem Khalloof, Moritz Winter, Clemens Düpmeier, V. Hagenmeyer","doi":"10.1145/3415958.3433072","DOIUrl":"https://doi.org/10.1145/3415958.3433072","url":null,"abstract":"For a given specific machine learning task, very often several machine learning algorithms and their right configurations are tested in a trial-and-error approach, until an adequate solution is found. This wastes human resources for constructing multiple models, requires a data analytics expert and is time-consuming, since a variety of learning algorithms are proposed in literature and the non-expert users do not know which one to use in order to obtain good performance results. Meta learning addresses these problems and supports non-expert users by recommending a promising learning algorithm based on meta features computed from a given dataset. In the present paper, a new generic microservice-based framework for realizing the concept of meta learning in Big Data environments is introduced. This framework makes use of a powerful Big Data software stack, container visualization, modern web technologies and a microservice architecture for a fully manageable and highly scalable solution. In this demonstration and for evaluation purpose, time series model selection is taken into account. The performance and usability of the new framework is evaluated on state-of-the-art machine learning algorithms for time series forecasting: it is shown that the proposed microservice-based meta learning framework introduces an excellent performance in assigning the adequate forecasting model for the chosen time series datasets. Moreover, the recommendation of the most appropriate forecasting model results in a well acceptable low overhead demonstrating that the framework can provide an efficient approach to solve the problem of model selection in context of Big Data.","PeriodicalId":198419,"journal":{"name":"Proceedings of the 12th International Conference on Management of Digital EcoSystems","volume":"184 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124914520","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}

引用次数: 5

Towards a Roadmap for the Internet of Things Software Systems Engineering 面向物联网软件系统工程的路线图

Proceedings of the 12th International Conference on Management of Digital EcoSystems Pub Date : 2020-11-02 DOI: 10.1145/3415958.3433100

R. Motta, K. Oliveira, G. Travassos

引用次数: 1

LEOnto

Proceedings of the 12th International Conference on Management of Digital EcoSystems Pub Date : 2020-11-02 DOI: 10.1145/3415958.3433076

Anis Tissaoui, S. Sassi, R. Chbeir

引用次数: 1

A Partitioning GPU-based Algorithm for Processing the k Nearest-Neighbor Query 基于分区gpu的k近邻查询处理算法

Proceedings of the 12th International Conference on Management of Digital EcoSystems Pub Date : 2020-11-02 DOI: 10.1145/3415958.3433071

Polychronis Velentzas, M. Vassilakopoulos, A. Corral

引用次数: 4

A Secure Tracing Method in Fog Computing Network for the IoT Devices 物联网设备雾计算网络中的安全跟踪方法

Proceedings of the 12th International Conference on Management of Digital EcoSystems Pub Date : 2020-11-02 DOI: 10.1145/3415958.3433074

A. Alamer, Sultan Basudan, P. Hung

引用次数: 6

Selection of Information Streams in Social Sensing: an Interdependence- and Cost-aware Ranking Method 社会感知中的信息流选择:一种相互依赖和成本意识排序方法

Proceedings of the 12th International Conference on Management of Digital EcoSystems Pub Date : 2020-11-02 DOI: 10.1145/3415958.3433099

G. Gianini, C. Mio, F. Viola, Jianyi Lin, Nawaf I. Almoosa

引用次数: 1

A Retrospective on ASPires: An Advanced System for the Prevention and Early Detection of Forest Fires 回顾:一种先进的森林火灾预防和早期检测系统

Proceedings of the 12th International Conference on Management of Digital EcoSystems Pub Date : 2020-11-02 DOI: 10.1145/3415958.3433039

P. Peinl

引用次数: 4

A new conceptual framework for enhancing legal information retrieval at the Brazilian Superior Court of Justice 加强巴西高等法院法律信息检索的新概念框架

Proceedings of the 12th International Conference on Management of Digital EcoSystems Pub Date : 2020-11-02 DOI: 10.1145/3415958.3433087

Thiago Gomes, M. Ladeira

引用次数: 8

A Novel Framework for Event Interpretation in a Heterogeneous Information System 异构信息系统中一种新的事件解释框架

Proceedings of the 12th International Conference on Management of Digital EcoSystems Pub Date : 2020-11-02 DOI: 10.1145/3415958.3433073

Nabila Guennouni, C. Sallaberry, Sébastien Laborie, R. Chbeir

{"title":"A Novel Framework for Event Interpretation in a Heterogeneous Information System","authors":"Nabila Guennouni, C. Sallaberry, Sébastien Laborie, R. Chbeir","doi":"10.1145/3415958.3433073","DOIUrl":"https://doi.org/10.1145/3415958.3433073","url":null,"abstract":"Over the last decade, the number of research and development projects on sensor network technology has grown exponentially. Events detection is among these research fields, it allows the monitoring of the environment. To build an interpretation to these events, the combination of sensor network and document corpus data is essential since document corpus provide significant amounts of important and valuable information (e.g., technical data sheets, maintenance reports, customer sheets). However, most information systems in connected environments do not support the interconnection of sensor network and document corpus data, hence, user has to look for an explanation by himself through multiple queries on both data sources which is indeed very tedious, time consuming and requires a huge compilation effort. In this paper, we show that recent researches on 5W1H question-answering (\"What? Who? Where? When? Why? How?\") are an interesting issue to facilitate tunnelling through heterogeneous data sources (sensor networks and document corpus) and the identification of relevant data for the purpose of explaining an event. Consequently, we propose ISEE (an Information System for Event Explanation), a framework for event interpretation based on (i) the semantic representation of a heterogeneous information system, (ii) the cross-analysis of both sensor network and document corpus data and (iii) 5W1H question-answering techniques.","PeriodicalId":198419,"journal":{"name":"Proceedings of the 12th International Conference on Management of Digital EcoSystems","volume":"331 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115769998","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}

引用次数: 1

A Methodology for Non-Functional Property Evaluation of Machine Learning Models 机器学习模型的非功能属性评价方法

Proceedings of the 12th International Conference on Management of Digital EcoSystems Pub Date : 2020-11-02 DOI: 10.1145/3415958.3433101

M. Anisetti, C. Ardagna, E. Damiani, Paolo G. Panero

引用次数: 6