International journal of database theory and application最新文献_第5页

SQL-to-MapReduce Translation for Efficient OLAP Query Processing with MapReduce 使用MapReduce进行高效OLAP查询处理的sql到MapReduce转换

International journal of database theory and application Pub Date : 2017-06-30 DOI: 10.14257/IJDTA.2017.10.6.05

Hyeon Gyu Kim

引用次数: 2

Capital Markets Prediction: Multi-Faceted Sentiment Analysis using Supervised Machine Learning 资本市场预测:使用监督机器学习的多方面情绪分析

International journal of database theory and application Pub Date : 2017-06-30 DOI: 10.14257/IJDTA.2017.10.6.07

Kushatha Kelebeng, H. Hlomani

{"title":"Capital Markets Prediction: Multi-Faceted Sentiment Analysis using Supervised Machine Learning","authors":"Kushatha Kelebeng, H. Hlomani","doi":"10.14257/IJDTA.2017.10.6.07","DOIUrl":"https://doi.org/10.14257/IJDTA.2017.10.6.07","url":null,"abstract":"Over the years the stock market has proved to be very difficult to predict due to its unpredictable activities. Data mining techniques such as clustering, decision trees, genetic algorithms and artificial neural networks have been used in order to predict the stock market. Although there has been a significant amount of research done in this area, there are still many issues that have not been explored yet. The impact of fundamental analysis in the prediction of the stock market has been ignored though it can play a vital role in the prediction of the stock market. In this research, the problem of how a social data sentiment correlates to stock price is studied. A stock price prediction model was built using social data sentiments to predict the stock market. Sentiments analysis principles were applied to machine learning techniques in order to find the correlation between the stock market and public sentiments. This study particularly intended to assess the predictability of prices on the Botswana Stock Exchange through the application of Facebook sentiments classification. Three classification models were created that depicted news polarity as happy, calm, alert and vital. Results show that Naïve Bayes and Support vector machine performed well in both types of testing as compared to Random Forest. Naïve Bayes gave good results in terms of error margins with an accuracy of 83.3% making it the best classifier for our data set. When plotting the time series plot of sentiment scores and comparing it to the actual stock price graph, a conclusion can be reached that sentiments and stock prices are related and thus stock prices can be predicted using sentiments.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"1 1","pages":"87-102"},"PeriodicalIF":0.0,"publicationDate":"2017-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75605592","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 Topology-concerned Spatial Vector Data Model for Column-oriented Databases 面向列数据库的拓扑空间矢量数据模型

International journal of database theory and application Pub Date : 2017-05-31 DOI: 10.14257/IJDTA.2017.10.5.04

Kun Zheng, M. Kwan, Falin Fang, Junjun Yin, D. Gu, Yanli Fu

{"title":"A Topology-concerned Spatial Vector Data Model for Column-oriented Databases","authors":"Kun Zheng, M. Kwan, Falin Fang, Junjun Yin, D. Gu, Yanli Fu","doi":"10.14257/IJDTA.2017.10.5.04","DOIUrl":"https://doi.org/10.14257/IJDTA.2017.10.5.04","url":null,"abstract":"In today’s “Big Data” era, the volume of spatial data grows rapidly. Addressing the challenges in efficient spatial Big Data storage and management becomes urgent. However, conventional row-based spatial databases have many limitations, such a slow data I/O efficiency, low data retrieval performance, poor scalability, and high maintenance costs. These conventional spatial databases are no longer suitable for today’s spatial Big Data. On the other hand, column-oriented databases have several superior features, such as high reliability, scalability and fault tolerance. More importantly, they have better I/O efficiency for query processing. This paper presents a topology-concerned spatial vector data model for column-oriented databases and designed the physical storage model, which is a unified model for storing and managing information of geometry, attribute and topology of spatial objects. For the storage characteristics of column-oriented databases, the model designed a new Rowkey encoding schema with the Z-order filling curve approach. This encoding schema of Rowkey considering spatial proximity optimizes the organizational structure of spatial data models. It means nearby spatial objects are also closer to each other in the physical storage, which can further improve the efficiency of spatial data storage and enable spatial query capability in column-oriented databases. Three experiments were conducted including data storing, range query and K-NN query to analyze the efficiency and spatial query capability of the data model. The results of the experiments show that the data model has good scalability and efficiency on the vector data storage and spatial query. It is suitable for large-scale spatial vector data storage and management in column-oriented databases.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"66 1","pages":"33-46"},"PeriodicalIF":0.0,"publicationDate":"2017-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75993915","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

Research on Mobile Database Model of Ad Hoc Network Based on Multi-parameter Weighted Clustering 基于多参数加权聚类的Ad Hoc网络移动数据库模型研究

International journal of database theory and application Pub Date : 2017-05-31 DOI: 10.14257/IJDTA.2017.10.5.02

Tao Zhan, Lei Wang

引用次数: 1

A Comparative Study of Different Dimensionality Reduction Methods with Naïve Bayes Classifier for Mapping Customer Requirements to Product Configurations 基于Naïve贝叶斯分类器的不同降维方法在客户需求到产品配置映射中的比较研究

International journal of database theory and application Pub Date : 2017-05-31 DOI: 10.14257/ijdta.2017.10.5.05

Yao Jiao, Yu Yang

{"title":"A Comparative Study of Different Dimensionality Reduction Methods with Naïve Bayes Classifier for Mapping Customer Requirements to Product Configurations","authors":"Yao Jiao, Yu Yang","doi":"10.14257/ijdta.2017.10.5.05","DOIUrl":"https://doi.org/10.14257/ijdta.2017.10.5.05","url":null,"abstract":"Mapping customer requirements to product configurations are difficult due to the uncertainty and ambiguity of customers’ expression. The Naïve Bayes Classifier (NBC) is suitable to quantify the expression of customers, and to map their requirements to configurations with good performance. However, the prerequisite of manually independent of product attributes for NBC require preprocess. Dimensionality reduction methods are effective for simplifying the data complexity while separating the correlations between data Against the background, this paper conducts a comparative study of 7 dimensionality reduction methods as preprocess procedure for integrating with NBC to map customer requirements to product configurations. Two realistic design cases are illustrated for the comparison, and the outcomes are measured by the accuracy and F-measure. The results of this study imply several findings that the loss of information has great impact on all methods, and linear methods are more sensitive to the loss of information, and several nonlinear methods are more capable in handling the loss of information than other methods, and local linear methods are suggested compared with global nonlinear methods.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"50 1","pages":"47-58"},"PeriodicalIF":0.0,"publicationDate":"2017-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75009311","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

Dynamic QoS evaluation for Web Services Using Data Envelopment Analysis on Real-time Status 基于实时状态数据包络分析的Web服务动态QoS评价

International journal of database theory and application Pub Date : 2017-05-31 DOI: 10.14257/ijdta.2017.10.5.06

Luda Wang, Peng Zhang

引用次数: 0

Internet of Things in Cloud Environment: Services and Challenges 云环境下的物联网:服务与挑战

International journal of database theory and application Pub Date : 2017-05-31 DOI: 10.14257/IJDTA.2017.10.5.03

Karuna Lochab, D. Yadav, Mayank Singh, A. Sharmab

引用次数: 2

Public Cloud Storage for the Seismic Big Data Based on Amazon EC2 Cluster and Hadoop 基于Amazon EC2集群和Hadoop的地震大数据公有云存储

International journal of database theory and application Pub Date : 2017-05-31 DOI: 10.14257/ijdta.2017.10.5.01

Jie Xiong, Song Zhang

引用次数: 0

Grid-based k-Nearest Neighbor Queries over Moving Object Trajectories with MapReduce 基于网格的移动对象轨迹k近邻查询

International journal of database theory and application Pub Date : 2017-04-30 DOI: 10.14257/IJDTA.2017.10.4.01

Ying Xia, Ruidi Wang, Xu Zhang, Hae-Young Bae

引用次数: 2

Current Situation and Application of Graph Data Mining Technology 图数据挖掘技术的现状及应用

International journal of database theory and application Pub Date : 2017-03-31 DOI: 10.14257/ijdta.2017.10.3.01

Meng Zhang, Pingping Wei, Suzhi Zhang, Jiaxing Xu

引用次数: 0