2017 Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI)最新文献_第4页

Optimizing the Enterprise Search 优化企业搜索

2017 Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI) Pub Date : 2017-08-01 DOI: 10.1109/MCSI.2017.20

Juris Rats

{"title":"Optimizing the Enterprise Search","authors":"Juris Rats","doi":"10.1109/MCSI.2017.20","DOIUrl":"https://doi.org/10.1109/MCSI.2017.20","url":null,"abstract":"NoSQL solutions are used for search in large data volumes of unstructured data, mostly for the search of the public data. The problem is how to use the technology for effective search of enterprise data where users access rights apply. The search mechanism has to be organized in a way to work fast on a large volume of data to return only the results a user has access to. An objective of our research is to create such a search mechanism. We evaluate several types and brands of NoSQL databases and select the most appropriate for the solution of the problem on hand. We use a polyglot persistence model that advocates to use several technologies inside a solution to profit from the strength of each. The solution we created uses an SQL database for the input and maintenance of the current data and a NoSQL Elasticsearch database for the search and retrieval of data. We created a methodology as well to evaluate the performance of the solution (and a search mechanism in particular). The evaluation results on a 1 million document database show that the data requests of a flow generated by 2000 user base are processed in 0.14 seconds in average. The evaluation tests are performed on a one node Elasticsearch database on a commodity server hence the proposed solution should be regarded as an effective way to handle search of enterprise data.","PeriodicalId":113351,"journal":{"name":"2017 Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130395019","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

Local Fractional Operator on Quadratic Riccati Differential Equation with Variable Coefficients 变系数二次Riccati微分方程的局部分数算子

2017 Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI) Pub Date : 2017-08-01 DOI: 10.1109/MCSI.2017.38

S. Edeki, G. O. Akinlabi, R. Borkor

引用次数: 0

Image Analysis by Discrete Relative Vector Field 离散相对矢量场的图像分析

2017 Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI) Pub Date : 2017-08-01 DOI: 10.1109/MCSI.2017.15

X. Zhuang, N. Mastorakis, S. Kaminaris, Jieru Chi, Hanping Wang

引用次数: 0

Designing Model for Calculating the Amount of Cyber Risk Insurance 网络风险保险金额计算模型设计

2017 Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI) Pub Date : 2017-08-01 DOI: 10.1109/MCSI.2017.41

K. Piromsopa, Tomás Klíma, Lukáš Pavlík

引用次数: 6

Prediction of the Popularity from Google Trends Using Stationary Control: The Case of STM Publishers 使用平稳控制从Google趋势预测流行度:以STM出版商为例

2017 Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI) Pub Date : 2017-08-01 DOI: 10.1109/MCSI.2017.62

M. Poulos, S. Papavlasopoulos, Petros A Kostagiolas, S. Kapidakis

引用次数: 2

RBF Approximation of Big Data Sets with Large Span of Data 大数据跨度大数据集的RBF逼近

2017 Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI) Pub Date : 2017-08-01 DOI: 10.1109/MCSI.2017.44

V. Skala

引用次数: 7

Mathematical Modeling of Drill Strings Nonlinear Vibrations Taking into Account a Friction Forces 考虑摩擦力的钻柱非线性振动数学建模

2017 Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI) Pub Date : 2017-08-01 DOI: 10.1109/MCSI.2017.59

L. Khajiyeva, Aliya Umbetkulova

引用次数: 0

Expression Recognition Using Sparse Selection of log-Gabor Facial Features 基于log-Gabor面部特征稀疏选择的表情识别

2017 Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI) Pub Date : 2017-08-01 DOI: 10.1109/MCSI.2017.11

Krasimir Tonchev, N. Neshov, A. Manolova, V. Poulkov

引用次数: 1

The Architectural Software Concepts Implemented in Distributed High Resolution Constructive Simulation Environment SymSG Border Tactics, Supporting Polish Border Guard Computer Assisted Exercises 在分布式高分辨率建设性模拟环境SymSG边界战术中实现的建筑软件概念，支持波兰边防部队计算机辅助演习

2017 Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI) Pub Date : 2017-08-01 DOI: 10.1109/MCSI.2017.50

M. Chmielewski, Damian Frąszczak, Marcin Kukielka, Dawid Bugajewski

{"title":"The Architectural Software Concepts Implemented in Distributed High Resolution Constructive Simulation Environment SymSG Border Tactics, Supporting Polish Border Guard Computer Assisted Exercises","authors":"M. Chmielewski, Damian Frąszczak, Marcin Kukielka, Dawid Bugajewski","doi":"10.1109/MCSI.2017.50","DOIUrl":"https://doi.org/10.1109/MCSI.2017.50","url":null,"abstract":"This paper presents main concepts of SymSG Border Tactics simulation software facilitating specialised tool for operational training of Polish Border Guard officers. The constructive simulator integrates two separate environments a simulator with supplemented GIS module and imitated SWK system which serves as Command&Control application used by Polish Border Guard. Each and every system’s training station hosts a constructive simulator integrated with SWK imitation serving as a reporting console supplementing trainee with realworld UI for registering and managing operational data. Simulation environment delivers a set of applications (duty reporting, communication, map modules) and a constructive simulator which performs realistic scenario gaming process. The architecture of the system integrates many implementation technologies JavaSE, .NET and integration methods involving inmemory data grid solution, message queues, webservices depending on the component integration requirements. The paper is a critical analysis and a case study of project’s achievements describing the evolution of the architecture, it’s refinement process and finally the summary of lessons learned. The work describes key components of the constructive simulator environment aiming at GIS, simulation algorithms, event-based simulation engine, operational graphics mechanisms, distributed data sharing components, speech and messaging module. Implemented and deployed simulation software is currently undergoing operational tests.","PeriodicalId":113351,"journal":{"name":"2017 Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128507422","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: 4

DeepMBS: Prediction of Protein Metal Binding-Site Using Deep Learning Networks DeepMBS:利用深度学习网络预测蛋白质金属结合位点

2017 Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI) Pub Date : 2017-08-01 DOI: 10.1109/MCSI.2017.13

Ismail Haberal, H. Oğul

{"title":"DeepMBS: Prediction of Protein Metal Binding-Site Using Deep Learning Networks","authors":"Ismail Haberal, H. Oğul","doi":"10.1109/MCSI.2017.13","DOIUrl":"https://doi.org/10.1109/MCSI.2017.13","url":null,"abstract":"The tertiary structure of a protein indicates what vital function that protein fulfills in the cell. Prediction of the metal binding comformation of a protein from its sequence is a crucial step in predicting its tertiary structure. In this study, a computational method was developed for predicting the binding of Histidine and Cysteine to metals. We propose a deep convolutional neural network architecture, DeepMBS, to predict protein metal binding sites. To our knowledge, this study is the first realization of deep learning idea for the problem of predicting metal binding site. The method allows automatic extraction of complex interactions between important features using only sequence information by utilizing PAM120 scoring matrix. Features were extracted from protein sequences obtained from the Protein Data Bank and deep convolutional neural network was applied to these features. According to experimental results on a benchmark dataset, metal binding states can be predicted with 82% recall and 79% precision. These results show that a better performance can be achived with deep learning approach compared with previous studies on the same dataset.","PeriodicalId":113351,"journal":{"name":"2017 Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122274271","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: 10