International Journal of Data Analysis Techniques and Strategies最新文献_第6页

Semantic integration of traditional and heterogeneous data sources (UML, XML and RDB) in OWL2 triplestore OWL2三层存储中传统数据源和异构数据源(UML、XML和RDB)的语义集成

International Journal of Data Analysis Techniques and Strategies Pub Date : 2021-01-01 DOI: 10.1504/IJDATS.2021.114667

Oussama El Hajjamy, Hajar Khallouki, L. Alaoui, M. Bahaj

{"title":"Semantic integration of traditional and heterogeneous data sources (UML, XML and RDB) in OWL2 triplestore","authors":"Oussama El Hajjamy, Hajar Khallouki, L. Alaoui, M. Bahaj","doi":"10.1504/IJDATS.2021.114667","DOIUrl":"https://doi.org/10.1504/IJDATS.2021.114667","url":null,"abstract":": With the success of the internet and the expansion of the amount of data in the web, the exchange of information from various heterogeneous and classical data sources becomes a critical need. In this context, researchers must propose integration solutions that allow applications to simultaneously access several data sources. In this perspective, we propose a semi-automatic integration approach of classical data sources via a global schema located in database management systems of RDF or OWL data, called triplestore. Our contribution is subdivided into three axes: 1) an automatic mapping solution that converts classical data sources such as UML, XML and RDB to local ontologies based on OWL2 language; 2) an alignment system of local ontologies based on syntactic, semantic and structural similarity measurement techniques in order to increase the probability of having real correspondences and real differences; 3) a fusion system of pre-existing local ontologies into a global ontology based on the alignment found in the previous step. integration of heterogeneous classical data sources ontological","PeriodicalId":38582,"journal":{"name":"International Journal of Data Analysis Techniques and Strategies","volume":"36 1","pages":"36-58"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73200000","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

Combining planning and learning for context aware service composition 结合计划和学习来实现上下文感知的服务组合

International Journal of Data Analysis Techniques and Strategies Pub Date : 2021-01-01 DOI: 10.1504/IJDATS.2021.114673

Tarik Fissaa, M. Hamlaoui, Hatim Guermah, H. Hafiddi, M. Nassar

{"title":"Combining planning and learning for context aware service composition","authors":"Tarik Fissaa, M. Hamlaoui, Hatim Guermah, H. Hafiddi, M. Nassar","doi":"10.1504/IJDATS.2021.114673","DOIUrl":"https://doi.org/10.1504/IJDATS.2021.114673","url":null,"abstract":": Computing vision introduced by Mark Weiser in the early ‘90s has defined the basis of what is called now ubiquitous computing. This new discipline results from the convergence of powerful, small and affordable computing devices with networking technologies that connect them all together. Thus, ubiquitous computing has brought a new generation of service-oriented architectures (SOA) based on context-aware services. These architectures provide users with personalised and adapted behaviours by composing multiple services according to their contexts. In this context, the objective of this paper is to propose an approach for context-aware semantic-based services composition. Our contributions are built around following axes: 1) a semantic-based context modelling and context-aware semantic composite service specification; 2) an architecture for context-aware semantic-based services composition using artificial intelligence planning; 3) an intelligent mechanism based on reinforcement learning for context-aware selection in order to deal with dynamicity and uncertain character of modern ubiquitous environment.","PeriodicalId":38582,"journal":{"name":"International Journal of Data Analysis Techniques and Strategies","volume":"1 1","pages":"151-169"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78672984","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: 0

Sentiment classification of review data using sentence significance score optimisation 基于句子显著性评分优化的评论数据情感分类

International Journal of Data Analysis Techniques and Strategies Pub Date : 2021-01-01 DOI: 10.1504/IJDATS.2021.114670

Ketan Kumar Todi, S. Muralikrishna, B. A. Rao

{"title":"Sentiment classification of review data using sentence significance score optimisation","authors":"Ketan Kumar Todi, S. Muralikrishna, B. A. Rao","doi":"10.1504/IJDATS.2021.114670","DOIUrl":"https://doi.org/10.1504/IJDATS.2021.114670","url":null,"abstract":": A significant amount of work has been done in the field of sentiment analysis in textual data using the concepts and techniques of natural language processing (NLP). In this work, unlike the existing techniques, we present a novel method wherein we consider the significance of the sentences in formulating the opinion. Often in any review, the sentences in the review may correspond to different aspects which are often irrelevant in deciding whether the sentiment is positive or negative on a topic. Thus, we assign a sentence significance score to evaluate the overall sentiment of the review. We employ a clustering mechanism followed by the neural network approach to determine the optimal significance score for the review. The proposed supervised method shows a higher accuracy than the state-of-the-art techniques. We further determine the subjectivity of sentences and establish a relationship between subjectivity of sentences and the significance score. We experimentally show that the significance scores found in the proposed method correspond to identifying the subjective sentences and objective sentences in reviews. The sentences with low significance score corresponds to objective sentences and the sentences with high significance score corresponds to subjective sentences.","PeriodicalId":38582,"journal":{"name":"International Journal of Data Analysis Techniques and Strategies","volume":"15 1","pages":"59-71"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90603965","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

Rough set-based attribute reduction and decision rule formulation for marketing data 基于粗糙集的营销数据属性约简与决策规则制定

International Journal of Data Analysis Techniques and Strategies Pub Date : 2021-01-01 DOI: 10.1504/ijdats.2021.118016

Murchhana Tripathy, Anita Panda, Santilata Champati

引用次数: 0

Improving the predictive ability of multivariate calibration models using support vector data description 利用支持向量数据描述提高多元校正模型的预测能力

International Journal of Data Analysis Techniques and Strategies Pub Date : 2021-01-01 DOI: 10.1504/ijdats.2021.10033412

Walid Gani

引用次数: 0

Bayesian Consensus Clustering with LIME for Security in Big Data 基于LIME的大数据安全贝叶斯共识聚类

International Journal of Data Analysis Techniques and Strategies Pub Date : 2021-01-01 DOI: 10.1504/ijdats.2021.10023272

Balamurugan Selvarathinam

引用次数: 0

Bayesian consensus clustering with LIME for security in big data 基于LIME的大数据安全贝叶斯共识聚类

International Journal of Data Analysis Techniques and Strategies Pub Date : 2021-01-01 DOI: 10.1504/IJDATS.2021.114665

S. Balamurugan, M. Thangaraj

引用次数: 0

Sentiment analysis: a review and framework foundations 情感分析:综述与框架基础

International Journal of Data Analysis Techniques and Strategies Pub Date : 2021-01-01 DOI: 10.1504/ijdats.2021.10043775

Bousselham EL HADDAOUI, R. Chiheb, R. Faizi, A. E. Afia

引用次数: 1

Spam filtering based on PV-DBOW model 基于PV-DBOW模型的垃圾邮件过滤

International Journal of Data Analysis Techniques and Strategies Pub Date : 2021-01-01 DOI: 10.1504/ijdats.2021.10043782

Ghizlane Hnini, Anass Fahfouh, J. Riffi, Mohamed Adnane Mahraz, Ali Yahyaouy, H. Tairi

引用次数: 1

Efficient data clustering algorithm designed using a heuristic approach 采用启发式方法设计了高效的数据聚类算法

International Journal of Data Analysis Techniques and Strategies Pub Date : 2021-01-01 DOI: 10.1504/IJDATS.2021.114666

P. Nandal, Deepa Bura, Dr.Meeta Singh

{"title":"Efficient data clustering algorithm designed using a heuristic approach","authors":"P. Nandal, Deepa Bura, Dr.Meeta Singh","doi":"10.1504/IJDATS.2021.114666","DOIUrl":"https://doi.org/10.1504/IJDATS.2021.114666","url":null,"abstract":": Information retrieval from a large amount of information available in a database is a major issue these days. The relevant information extraction from the voluminous information available on the web is being done using various techniques like natural language processing, lexical analysis, clustering, categorisation, etc. In this paper, we have discussed the clustering methods used for clustering of large amount of data using different features to classify the data. In today’s era, various problem solving techniques makes the use of a heuristic approach for designing and developing various efficient algorithms. In this paper, we have proposed a clustering technique using a heuristic function to select the centroid so that the clusters formed are as per the need of the user. The heuristic function designed in this paper is based on the conceptually similar data points so that they are grouped into accurate clusters. k -means clustering algorithm is majorly used to cluster the data which is also focussed in this paper. It has been empirically found that the clusters formed and the data points which belong to a cluster are close to human analysis as compared to existing clustering algorithms.","PeriodicalId":38582,"journal":{"name":"International Journal of Data Analysis Techniques and Strategies","volume":"54 1","pages":"3-14"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81808690","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: 3