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共 3354 条记录，以下是411-420 订阅

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Processing missing information in big data environment 3rd

Processing missing information in big data environment

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3rd international conference on data mining and Big data, DMBD 2018 held in conjunction with the 9th international conference on Swarm Intelligence, ICSI 2018

作者： Chen, Yuxin Li, Shun Yao, Jiahui School of EECS Peking University Beijing China School of Information Science and Technology University of International Relations Beijing China

ISBN: (纸本)9783319938028

How to handle missing information is essential for system efficiency and robustness in the field of the database. Missing information in big data environment tends to have richer semantics, leading to more complex computational logic, as well as affecting operations and implement. The existing methods either have limited semantic expression ability or do not consider the influence of big data environment. To solve these problems, this paper proposes a novel missing information processing method. Combining the practical case of the big data environment, we summary the missing information into two types: unknown and nonexistent value, and define four-valued logic to support the logic operation. The relational algebra is extended systematically to describe the data operations. We implement our approach on the dynamic table model in the self-developed big data management system Muldas. Experimental results on real large-scale sparse data sets show the proposed approach has the good ability of semantic expression and computational efficiency. © Springer international Publishing AG, part of Springer Nature 2018.

关键词： Big data

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Analytical Study of Intruder Detection System in Big data Environment 1st

引用

1st international conference on Soft computing: Theories and Applications, SoCTA 2016

作者： Aldubai, Ahmed Faud Humbe, Vikas T. Chowhan, Santosh S. School of Computational Sciences S.R.T.M University NandedMaharashtra India School of Technology S.R.T.M University Sub-Campus LaturMaharashtra India

ISBN: (纸本)9789811056864

This paper presents analytical study of intruder detection system in big data environment. In recent years, the size of data increases at high speed, through daily increasing in number of people and online applications such as online airline ticket booking, online banking, online payment system, etc., using the Internet and network services, and this leads to a huge amount of data from terabyte to petabytes which are called as the big data. Therefore, predict and analysis of network traffic from a possible intrusion attack through permanent gathering of network traffic data and learning of their characteristics on the fly are the critical aspects. Various experiments are conducted and summed up, to be able to identify different problems in existing network applications and traffics. Analysis of intruder detection over network traffic includes support vector machine (SVM) approach, distance-based classifiers, genetic algorithm, and fuzzy neural network classifiers based on data mining techniques. Similarly, several interesting hybrid techniques are implemented to attain efficient and effective results of intruder detection system analysis over network. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Big data

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Clinical application of decision support system for treatment of migraine 5th

Clinical application of decision support system for treatmen...

引用

5th international conference on Frontier computing, FC 2016

作者： Hui, Lin Keh, Huan Chao Chiang, Meng Chu Liu, Zhen Yao Department of Innovative Information and Technology Tamkang University Yilan County26247 Taiwan Department of Computer Science and Information Engineering Tamkang University New Taipei City Taiwan

ISBN: (纸本)9789811031861

In this modern society, migraine belongs to a kind of common disease. The hospitals begin to use clinical decision support system a lot to improve the accuracy of diagnosis, this kind of system can help the physicians make the decisions and give a treatment by entering the information of the patients in advance. In the treatment of migraine, the patients need to keep diaries of headache, the physicians can make a diagnosis and trace according to the diaries. In this paper, we come up with constructing a clinical decision support system for treatment of migraine. We use the headache diaries as data, in order to store the data easily, we transform paper diaries to electronic diaries first, store the data in the databases of the servers or the mobile platforms (e.g. smartphones, tablet computers), and the data can be shown on the front-end interface. We also make use of data mining to analyze the factors, medicines and associations of migraine, and the result will be recorded in the system to improve the efficiency of the physicians’ inspection. © Springer Nature Singapore Pte Ltd. 2018.

关键词： data mining

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data prefetching on in-order processors 16

Data prefetching on in-order processors

引用

international conference on High Performance computing & Simulation (HPCS)

作者： Ortega, Cristobal Garcia, Victor Moreto, Miquel Casas, Marc Rusitoru, Roxana Univ Politecn Cataluna Barcelona Spain Barcelona Supercomp Ctr BSC CNS Barcelona Spain ARM Ltd Cambridge England

ISBN: (纸本)9781538678794

Low-power processors have attracted attention due to their energy-efficiency. A large market, such as the mobile one, relies on these processors for this very reason. Even High Performance computing (HPC) systems are starting to consider low-power processors as a way to achieve exascale performance within 20MW, however, they must meet the right performance/Watt balance. Current low-power processors contain in-order cores, which cannot re-order instructions to avoid data dependency-induced stalls. Whilst this is useful to reduce the chip's total power consumption, it brings several challenges. Due to the evolving performance gap between memory and processor, memory is a significant bottleneck. In-order cores cannot reorder instructions and are memory latency bound, something data prefetching can help alleviate by ensuring data is readily available. In this work, we do an exhaustive analysis of available data prefetching techniques in state-of-the-art in-order cores. We analyze 5 static prefetchers and 2 dynamic aggressiveness and destination mechanisms applied to 3 data prefetchers on a set of HPC mini- and proxy-applications, whilst running on in-order processors. We show that next-line prefetching can achieve nearly top performance with a reasonable bandwidth consumption when throttled, whilst neighbor prefetchers have been found to be best, overall.

关键词： data prefetching in-order processor High Perfomance computing

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12th international conference on advanced data mining and Applications, ADMA 2016

12th International Conference on Advanced Data Mining and Ap...

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12th international conference on advanced data mining and Applications, ADMA 2016

ISBN: (纸本)9783319495859

The proceedings contain 60 papers. The special focus in this conference is on advanced data mining and Applications. The topics include: Effective monotone knowledge integration in kernel support vector machines;textual cues for online depression in community and personal settings;confidence-weighted bipartite ranking;mining distinguishing customer focus sets for online shopping decision support;community detection in networks with less significant community structure;prediction-based, prioritized market-share insight extraction;interrelationships of service orchestrations;outlier detection on mixed-type data;an energy-based approach;low-rank feature reduction and sample selection for multi-output regression;biologically inspired pattern recognition for E-nose sensors;improving cytogenetic search with GPUs using different string matching schemes;a framework for interlinking smart things in the internet of things;efficient mining of pan-correlation patterns from time course data;dynamic reverse furthest neighbor querying algorithm of moving objects;an ensemble approach for better truth discovery;single classifier selection for ensemble learning;community detection in dynamic attributed graphs;secure computation of skyline query in mapreduce;recommending features of mobile applications for developer;causality-guided feature selection;temporal interaction biased community detection in social networks;extracting key challenges in achieving sobriety through shared subspace learning;unified weighted label propagation algorithm using connection factor;metric learning for cold-start recommendations;time series forecasting on engineering systems using recurrent neural networks;effective traffic flow forecasting using taxi and weather data and discovering trip hot routes using large scale taxi trajectory data.

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Application of data science and machine learning for well completion optimization

Application of data science and machine learning for well co...

引用

Offshore Technology conference, OTC 2018

作者： Pankaj, Piyush Geetan, Steve MacDonald, Richard Shukla, Priyavrat Sharma, Abhishek Menasria, Samir Xue, Han Judd, Tobias Schlumberger United States EP Energy Corporation United States

ISBN: (纸本)9781510862531

In today's data-driven economy, operators that integrate vast stores of fundamental reservoir and production data with the highperformance predictive analytics solutions can emerge as winners in the contest of maximizing estimated ultimate recovery (EUR). The scope of this study is to demonstrate a new workflow coupling earth sciences with data analytics to operationalize well completion optimization. The workflow aims to build a robust predictive model that allows users to perform sensitivity analysis on completion designs within a few hours. Current workflows for well completion and production optimization in unconventional reservoirs require extensive earth modeling, fracture simulation, and production simulations. With considerable effort and wide scale of sensitivity, studies could enable optimized well completion design parameters such as optimal cluster spacing, optimal proppant loading, optimal well spacing, etc. Yet, today, less than 5% of the wells fractured in North America are designed using advanced simulation due to the required level of data, skillset, and long computing times. Breaking these limitations through parallel fracture and reservoir simulations in the cloud and combining such simulation with data analytics and artificial intelligence algorithms helped in the development of a powerful solution that creates models for fast, yet effective, completion design. The approach was executed on Eagle Ford wells as a case study in 2016. Over 2000 data points were collected with completion sensitivity performed on a multithreaded cluster environment on these wells. advanced machine learning and data mining algorithms of data analytics such as random forest, gradient boost, linear regression, etc. were applied on the data points to create a proxy model for the fracturing and numerical production simulator. With the gradient boost technique, over 90% accuracy was achieved between the proxy model and the actual results. Hence, the proxy model could predict

关键词： Sensitivity analysis

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Big data analytics and security: A big choice and challenge for the generation 1st

Big data analytics and security: A big choice and challenge ...

引用

1st international conference on Smart computing and Informatics, SCI 2016

作者： Girmay, Gebremichael Bhaskari, D. Lalitha Andhra University Visakhapatnam India

ISBN: (纸本)9789811055461

The concept of big data comes into concern when challenges have been identified in digital capacity, velocity, and type of the data gathered. The domain of data source is diverse: social networking, mobile phones, sensors, satellites, and different types of organizations. The data is collected/generated at high rate, and its type is complex (structured, unstructured, and semi-structured). Big data analytics, which is the process to reveal hidden patterns and secret correlations that is to predict and capture insights from this overwhelming data, forced standard technologies to be upgraded or replaced by big data technology, and has a multitude benefits and applications. In this paper, the benefits and particularly the challenges of privacy and security in big data are dealt and solutions are suggested. Parallel to capturing insights from such massive and mixed data which also is sourced from huge varieties of IoT devices, the principles of big data pose advanced security solutions. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Big data

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You tube data analytics

You tube data analytics

引用

2018 international conference on Social computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, BRiMS 2018

作者： Tokdemir, Serpil Agarwal, Nitin University of Arkansas at Little Rock Little RockAR72204 United States

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Traffic Accidents Classification and Injury Severity Prediction 3

Traffic Accidents Classification and Injury Severity Predict...

引用

3rd IEEE international conference on Intelligent Transportation Engineering, ICITE 2018

作者： Cuenca, Laura Garcia Puertas, Enrique Aliane, Nourdine Andres, Javier Fernandez Department of Science Computing and Technology Universidad Europea de Madrid Spain Department of Industrial and Aeroespace Engineering Universidad Europea de Madrid Spain

ISBN: (纸本)9781538678312

Traffic accidents constitutes the first cause of death and injury in many developed countries. However, traffic accidents information and data provided by public organisms can be exploited to classify these accidents according to their type and severity, and consequently try to build predictive model. Detecting and identifying injury severity in traffic accidents in real time is primordial for speeding post-accidents protocols as well as developing general road safety policies. This article presents a case study of traffic accidents classification and severity prediction in Spain. Raw data are from Spanish traffic agency covering a period of six years ranging from 2011 to 2015. To this end, are compared three different machine learning classification techniques, such as Gradient Boosting Trees, Deep Learning and Naïve Bayes. © 2018 IEEE.

关键词： data mining

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Proceedings - 3rd IEEE international conference on data Science and advanced Analytics, DSAA 2016

Proceedings - 3rd IEEE International Conference on Data Scie...

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3rd IEEE international conference on data Science and advanced Analytics, DSAA 2016

ISBN: (纸本)9781509052066

The proceedings contain 86 papers. The topics discussed include: active semi-supervised classification based on multiple clustering hierarchies;combining static and dynamic features for multivariate sequence classification;harvester: influence optimization in symmetric interaction networks;a framework for description and analysis of sampling-based approximate triangle counting algorithms;limiting the diffusion of information by a selective PageRank-preserving approach;anomaly detection in automobile control network data with long short-term memory networks;infinite Langevin mixture modeling and feature selection;efficient identification of Tanimoto nearest neighbors;a parallel framework for grid-based bottom-up subspace clustering;impact of query sample selection bias on information retrieval system ranking;learning multifaceted latent activities from heterogeneous mobile data;the semantic knowledge graph: a compact, auto-generated model for real-time traversal and ranking of any relationship within a domain;what would a data scientist ask? automatically formulating and solving predictive problems;behavior-oriented time segmentation for mining individualized rules of mobile phone users;and online experimentation diagnosis and troubleshooting beyond AA validation.

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