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A convergent model for distributed processing of Big Sensor data in urban engineering networks

A convergent model for distributed processing of Big Sensor ...

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international conference on Information Technologies in Business and Industry (ITBI)

作者： Parygin, D. S. Finogeev, A. G. Kamaev, V. A. Finogeev, A. A. Gnedkova, E. P. Tyukov, A. P. Volgograd State Tech Univ 28 Lenina Ave Volgograd 400131 Russia Penza State Univ 40 Krasnaya Str Penza 440026 Russia

The problems of development and research of a convergent model of the grid, cloud, fog and mobile computing for analytical Big Sensor data processing are reviewed. The model is meant to create monitoring systems of spatially distributed objects of urban engineering networks and processes. The proposed approach is the convergence model of the distributed data processing organization. The fog computing model is used for the processing and aggregation of sensor data at the network nodes and/or industrial controllers. The program agents are loaded to perform computing tasks for the primary processing and data aggregation. The grid and the cloud computing models are used for integral indicators mining and accumulating. A computing cluster has a three-tier architecture, which includes the main server at the first level, a cluster of SCADA system servers at the second level, a lot of GPU video cards with the support for the Compute Unified Device Architecture at the third level. The mobile computing model is applied to visualize the results of intellectual analysis with the elements of augmented reality and geo-information technologies. The integrated indicators are transferred to the data center for accumulation in a multidimensional storage for the purpose of data mining and knowledge gaining.

关键词： Network architecture

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Co-Clustering with Side Information for Text mining

Co-Clustering with Side Information for Text Mining

引用

international conference on data mining and advanced computing (sapience)

作者： Thomas, Ramya Elizabeth Khan, Shamsuddin S. St Francis Inst Technol Dept Comp Engn Mumbai Maharashtra India

ISBN: (纸本)9781467385947

Many of the text mining applications contain a huge amount of information from document in the form of text. This text can be very helpful for Text Clustering. This text also includes various kind of other information known as Side Information or Metadata. Examples of this side information include links to other web pages, title of the document, author name or date of Publication which are present in the text document. Such metadata may possess a lot of information for the clustering purposes. But this Side information may be sometimes noisy. Using such Side Information for producing clusters without filtering it, can result to bad quality of Clusters. So we use an efficient Feature Selection method to perform the mining process to select that Side Information which is useful for Clustering so as to maximize the advantages from using it. The proposed technique, CCSI (Co-Clustering with Side Information) system makes use of the process of Co-Clustering or Two-mode clustering which is a data mining technique that allows concurrently clustering of the rows and columns of a matrix.

关键词： Co-Clustering data mining Text mining Side Information

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Heading Direction Estimation Using Deep Learning with Automatic Large-scale data Acquisition

Heading Direction Estimation Using Deep Learning with Automa...

引用

2018 international Joint conference on Neural Networks, IJCNN 2018

作者： Berriel, Rodrigo F. Torres, Lucas Tabelini Cardoso, Vinicius B. Guidolini, Rânik Badue, Claudine De Souza, Alberto F. Oliveira-Santos, Thiago Departamento de Informática Universidade Federal Do Espírito Santo Vitória Brazil

ISBN: (纸本)9781509060146

advanced Driver Assistance Systems (ADAS) have experienced major advances in the past few years. The main objective of ADAS includes keeping the vehicle in the correct road direction, and avoiding collision with other vehicles or obstacles around. In this paper, we address the problem of estimating the heading direction that keeps the vehicle aligned with the road direction. This information can be used in precise localization, road and lane keeping, lane departure warning, and others. To enable this approach, a large-scale database 1+ million images) was automatically acquired and annotated using publicly available platforms such as the Google Street View API and OpenStreetMap. After the acquisition of the database, a CNN model was trained to predict how much the heading direction of a car should change in order to align it to the road 4 meters ahead. To assess the performance of the model, experiments were performed using images from two different sources: A hidden test set from Google Street View (GSV) images and two datasets from our autonomous car (IARA). The model achieved a low mean average error of 2.359° and 2.524° for the GSV and IARA datasets, respectively;performing consistently across the different datasets. It is worth noting that the images from the IARA dataset are very different (camera, FOV, brightness, etc.) from the ones of the GSV dataset, which shows the robustness of the model. In conclusion, the model was trained effortlessly (using automatic processes) and showed promising results in real-world databases working in real-time (more than 75 frames per second). © 2018 IEEE.

关键词： Roads and streets

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Analysis of Human Mobility Based on Cellular data 1

Analysis of Human Mobility Based on Cellular Data

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1st international conference on computing and Applied Informatics (ICCAI) - Applied Informatics toward Smart Environment, People, and Society

作者： Arifiansyah, F. Saptawati, G. A. P. Inst Teknol Bandung Sch Elect Engn & Informat Ganesha 10 Bandung West Java Indonesia

Nowadays not only adult but even teenager and children have their own mobile phones. This phenomena indicates that the mobile phone becomes an important part of everyday's life. Based on these indication, the amount of cellular data also increased rapidly. Cellular data defined as the data that records communication among mobile phone users. Cellular data is easy to obtain because the telecommunications company had made a record of the data for the billing system of the company. Billing data keeps a log of the users cellular data usage each time. We can obtained information from the data about communication between users. Through data visualization process, an interesting pattern can be seen in the raw cellular data, so that users can obtain prior knowledge to perform data analysis. Cellular data processing can be done using data mining to find out human mobility patterns and on the existing data. In this paper, we use frequent pattern mining and finding association rules to observe the relation between attributes in cellular data and then visualize them. We used weka tools for finding the rules in stage of data mining. Generally, the utilization of cellular data can provide supporting information for the decision making process and become a data support to provide solutions and information needed by the decision makers.

关键词： Cellular telephones

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A Review on data mining techniques and factors used in Educational data mining to predict student amelioration

A Review on Data Mining techniques and factors used in Educa...

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2016 international conference on data mining and advanced computing, sapience 2016

作者： Anoopkumar, M. Md Zubair Rahman, A.M.J. Bharathiar University Coimbatore India Department Of Computer Applications SNG College of Engineering Kadayiruppu India Al-Ameen Engineering College Karundevan Palayam Nanjai Uthukuli Post ErodeTamilnadu India

ISBN: (纸本)9781467385947

Educational data mining (EDM) is an interdisciplinary ingenuous research area that handles the development of methods to explore data arising in a scholastic fields. Computational approaches used by EDM is to examine scholastic data in order to study educational questions. As a result, it provides intrinsic knowledge of teaching and learning process for effective education planning. This paper conducts a comprehensive study on the recent and relevant studies put through in this field to date. The study focuses on methods of analysing educational data to develop models for improving academic performances and improving institutional effectiveness. This paper accumulates and relegates literature, identifies consequential work and mediates it to computing educators and professional bodies. We identify research that gives well-fortified advise to amend edifying and invigorate the more impuissant segment students in the institution. The results of these studies give insight into techniques for ameliorating pedagogical process, presaging student performance, compare the precision of data mining algorithms, and demonstrate the maturity of open source implements. © 2016 IEEE.

关键词： Teaching

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Clustering Algorithms and data Streams for Supervised Control of data and Prevention of Threats in Mobile Application Systems 37th

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37th international conference on Information Systems Architecture and Technology (ISAT)

作者： Majchrzycka, Aneta Poniszewska-Maranda, Aneta Lodz Univ Technol Inst Informat Technol Lodz Poland

ISBN: (纸本)9783319465838;9783319465821

The paper aims to present the possibilities of application of various data mining techniques in order to increase the security level of mobile application systems. The scope of work incorporates the usage of clustering algorithms-particularly Density-Based Spatial Clustering of Applications with Noise (DBScan)-as well as other mechanisms connected with data streams. The proposed solution is based on the process of monitoring the incoming server requests obtained from mobile devices which use server application to connect to the data.

关键词： Mobile systems Security of mobile applications data exploration data mining Clustering data streams

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A Systematic Mapping of Software Engineering Approaches to Develop Big data Systems

A Systematic Mapping of Software Engineering Approaches to D...

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Euromicro conference on Software Engineering and advanced Applications (SEAA)

作者： Rodrigo Laigner Marcos Kalinowski Sérgio Lifschitz Rodrigo Salvador Monteiro Daniel de Oliveira Informatics Department Pontifical Catholic University of Rio de Janeiro (PUC-Rio) Rio de Janeiro Brazil Computing Institute Fluminense Federal University (UFF) Niterói Brazil

ISBN: (纸本)9781538673843

[Context] data is being collected at an unprecedented scale. data sets are becoming so large and complex that traditionally engineered systems may be inadequate to deal with them. While software engineering comprises a large set of approaches to support engineering robust software systems, there is no comprehensive overview of approaches that have been proposed and/or applied in the context of engineering big data systems. [Goal] This study aims at surveying existing research on big data software engineering to unveil and characterize the development approaches and major contributions. [Method] We conducted a systematic mapping study, identifying 52 related research papers, dated from 2011 to 2016. We classified and analyzed the identified approaches, their objectives, application domains, development lifecycle phase, and type of contribution. [Results] As a result, we outline the current state of the art and gaps on employing software engineering approaches to develop big data systems. For instance, we observed that the major challenges are in the area of software architecture and that more experimentation is needed to assess the classified approaches. [Conclusion] The results of this systematic mapping provide an overview on existing approaches to support building big data systems and helps to steer future research based on the identified gaps.

关键词： Big data Software engineering Systematics Software Libraries databases data mining

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A Heuristic Network for Predicting the Percentage of Gross Domestic Product Distribution

A Heuristic Network for Predicting the Percentage of Gross D...

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international Symposium on advanced Intelligent Informatics (SAIN)

作者： Emmilya Umma Aziza Gaffar Irwan Gani Haviluddin Achmad Fanany Onnilita Gaffar Rayner Alfred Department of Economic Study Mulawarman University East Kalimantan Indonesia Faculty of Computer Science and Information Technology Mulawarman University East Kalimantan Indonesia Associate Professor of Computer Science Faculty of Computing and Informatics University Malaysia Sabah East Kalimantan Indonesia

ISBN: (纸本)9781538652817;9781538652800

In a country, economic growth indicators are measured by GDP growth rates. In order to predict GDP growth then, economic situation and economic development strategy have always been used. In this study, GDP contribution at the current price by the industrial sector has been predicted by using heuristic network method. There were nine GDP growth variables including (1) Agriculture, Livestock, Forestry, Fishery, (2) mining & Quarrying, (3) Manufacturing Industry, (4) Electricity, Gas, Water supply, (5) Construction, (6) Trade, Hotel, Restaurant, (7) Transport& Communication, (8) Finance, Real Estate & Business Services, and (9) Services in the period 2001-2016 have been analyzed. Experimental results show that the error rate forecasting in 2017 is less than 10%. The results show that intelligent computing method (heuristic network) can be an alternative method for predicting the contribution of GDP. This method predicts fairly quickly, significantly and produces an acceptable error prediction.

关键词： Economic indicators Training data models Time series analysis Agriculture Informatics

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Analyzing the Impact of Software Design Patterns in data mining Application 1

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international conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems (ICAIECES)

作者： Prabhakar, Nair Puja Rani, Devika Narayanan, A. G. Hari Judy, M. V. Amrita Univ Amrita Viswa Vidyapeetham Amrita Sch Arts & Sci Dept CS & IT Kochi Kerala India

ISBN: (数字)9789811031748

ISBN: (纸本)9789811031748;9789811031731

data mining is all about analyzing the data gathered from different sources, based on different context and summarizing it into meaningful information. The belief of utilizing design pattern to improve the data mining application is relatively high. Design Pattern is a broad and general repeatable solution to a problem that occurs frequently in a software design. There are only a few number of researches illustrating their benefit. In this paper, we try to expose the relation between data mining architecture and design patterns. We have here taken a layered architecture for data mining environment and analyzed the impact of design pattern in every components of given architecture. This paper presents a survey on various design patterns used in data mining which is used to fulfill functional and nonfunctional requirements (quality attributes).

关键词： data mining Design patterns Layered architecture Quality attributes

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Smart health care solution—a data mining approach

Smart health care solution—a data mining approach

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international conference on Communication and computing Systems, ICCCS 2016

作者： Kavita Ruhil, Neelam AIM & ACT Banasthali University Jaipur India Department of Electronics and Computer Engineering Dronacharya College of Engineering Gurgaon India

ISBN: (纸本)9781138029521

Due to various environmental changes ailment is a common problem faced by humans these days. Diseases like cold and fever are very common in people these days. Immunity capacity of humans is reduced due to adverse changes in surroundings. In this work we propose a ‘Smart Health Care Solution’ via data mining application which recommends medicines to the user on the basis of disease and symptoms. In our experimental procedure we have considered 10 common diseases to form the database of symptoms and causes of diseases that are common in today’s time like: cold, fever, jaundice etc. We explore the ‘Classification’ technique on our dataset for our reference. Naïve Bayes algorithm is applied on the dataset. Weka is chosen for implementation of algorithms. We use Explorer application for prediction of Salts for our database on the basis of diseases and their respective symptoms. By applying the significant classification technique on particular dataset, rules set is engender which will help the researchers working in healthcare for intelligent decision making. © 2017 Taylor & Francis Group, London.

关键词： Health care

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