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ieee symposium SERIES ON computational intelligence

作者： Poh, Norman Tirunagari, Santosh Windridge, David Univ Surrey Dept Comp Guildford GU2 7XH Surrey England Univ Surrey CVSSP Guildford Surrey England

ISBN: (纸本)9781479945405

The growing number and size of clinical medical records (CMRs) represents new opportunities for finding meaningful patterns and patient treatment pathways while at the same time presenting a huge challenge for clinicians. Indeed, CMR repositories share many characteristics of the classical 'big data' problem, requiring specialised expertise for data management, extraction, and modelling. In order to help clinicians make better use of their time to process data, they will need more adequate data processing and analytical tools, beyond the capabilities offered by existing general purpose database management systems or database servers. One modelling technique that can readily benefit from the availability of big data, yet remains relatively unexplored is personalised analytics where a model is built for each patient. In this paper, we present a strategy for designing a secure healthcare platform for personalised analytics by focusing on three aspects: (1) data representation, (2) data privacy and security, and (3) personalised analytics enabled by machine learning algorithms.

关键词： big data data privacy data structures electronic health records health care learning (artificial intelligence) patient treatment security of data big data problem CMR repositories CMRs analytical tools clinical medical records data extraction data management data modelling data processing data representation data security healthcare platform security machine learning algorithms online healthcare platform patient treatment pathways personalised analytics personalised patient analytics Adaptation models Analytical models data models databases Medical services Security Servers data privacy data Security big data Machine learning algorithms Representation of data patient treatment physician services Servers data models data management Electronic Health Records extracting data Health Care ANALYSIS MODELS data structures

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A Scalable Machine Learning Online Service for big data Real-Time Analysis

A Scalable Machine Learning Online Service for Big Data Real...

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ieee symposium SERIES ON computational intelligence

作者： Baldominos, Alejandro Albacete, Esperanza Saez, Yago Isasi, Pedro Univ Carlos III Madrid Dept Comp Sci Madrid Spain

ISBN: (纸本)9781479945405

This work describes a proposal for developing and testing a scalable machine learning architecture able to provide real-time predictions or analytics as a service over domain-independent big data, working on top of the Hadoop ecosystem and providing real-time analytics as a service through a RESTful API. Systems implementing this architecture could provide companies with on-demand tools facilitating the tasks of storing, analyzing, understanding and reacting to their data, either in batch or stream fashion;and could turn into a valuable asset for improving the business performance and be a key market differentiator in this fast pace environment. In order to validate the proposed architecture, two systems are developed, each one providing classical machine-learning services in different domains: the first one involves a recommender system for web advertising, while the second consists in a prediction system which learns from gamers' behavior and tries to predict future events such as purchases or churning. An evaluation is carried out on these systems, and results show how both services are able to provide fast responses even when a number of concurrent requests are made, and in the particular case of the second system, results clearly prove that computed predictions significantly outperform those obtained if random guess was used.

关键词： big data Internet advertising data processing business data processing learning (artificial intelligence) purchasing recommender systems Hadoop ecosystem RESTful API Web advertising big data real-time analysis business performance churning classical machine-learning services domain-independent big data fast pace environment gamers behavior key market differentiator on-demand tools prediction system purchases real-time analytics real-time predictions recommender system scalable machine learning architecture scalable machine learning online service valuable asset Batch production systems big data Computer architecture data models Distributed databases Google Real-time systems big data Recommender systems Online advertising Business performance Purchasing advertising data processing Churn business data processing distributed databases data models Computer Architecture real-time systems PREDICTION SYSTEM Batch production systems Internets

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High Level High Performance Computing for Multitask Learning of Time-varying Models

High Level High Performance Computing for Multitask Learning...

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ieee symposium SERIES ON computational intelligence

作者： Signoretto, Marco Frandi, Emanuele Karevan, Zahra Suykens, Johan A. K. Katholieke Univ Leuven ESAT STADIUS Kasteelpk Arenberg 10 B-3001 Leuven Belgium

ISBN: (纸本)9781479945405

We propose an approach suitable to learn multiple time-varying models jointly and discuss an application in data-driven weather forecasting. The methodology relies on spectral regularization and encodes the typical multi-task learning assumption that models lie near a common low dimensional subspace. The arising optimization problem amounts to estimating a matrix from noisy linear measurements within a trace norm ball. Depending on the problem, the matrix dimensions as well as the number of measurements can be large. We discuss an algorithm that can handle large-scale problems and is amenable to parallelization. We then compare high level high performance implementation strategies that rely on Just-in-Time (JIT) decorators. The approach enables, in particular, to offload computations to a GPU without hard-coding computationally intensive operations via a low-level language. As such, it allows for fast prototyping and therefore it is of general interest for developing and testing novel computational models.

关键词： geophysics computing learning (artificial intelligence) optimisation parallel processing time series weather forecasting data-driven weather forecasting high level high performance computing just-in-time decorators matrix dimensions multiple time-varying models multitask learning optimization problem spectral regularization time series analysis computational modeling Forecasting Graphics processing units Kernel Optimization Predictive models Vectors PARALLEL PROCESSING (COMPUTERS) weather forecasting geophysics computing computational modeling Prediction models time series analysis Graphics Processing Unit time varying Kernel Time series

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The Emerging "big Dimensionality"

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ieee computational intelligence MAGAZINE 2014年第3期9卷 14-26页

作者： Zhai, Yiteng Ong, Yew-Soon Tsang, Ivor W. Nanyang Technol Univ NTU Ctr Computat Intelligence C2i Sch Comp Engn SCE Singapore Singapore Univ Technol Sydney Ctr Quantum Computat & Intelligent Syst Sydney NSW 2007 Australia

The world continues to generate quintillion bytes of data daily, leading to the pressing needs for new efforts in dealing with the grand challenges brought by big data. Today, there is a growing consensus among the computational intelligence communities that data volume presents an immediate challenge pertaining to the scalability issue. However, when addressing volume in big data analytics, researchers in the data analytics community have largely taken a one-sided study of volume, which is the "big Instance Size" factor of the data. The flip side of volume which is the dimensionality factor of big data, on the other hand, has received much lesser attention. This article thus represents an attempt to fill in this gap and places special focus on this relatively under-explored topic of "big Dimensionality", wherein the explosion of features (variables) brings about new challenges to computational intelligence. We begin with an analysis on the origins of big Dimensionality. The evolution of feature dimensionality in the last two decades is then studied using popular data repositories considered in the data analytics and computational intelligence research communities. Subsequently, the state-of-the-art feature selection schemes reported in the field of computational intelligence are reviewed to reveal the inadequacies of existing approaches in keeping pace with the emerging phenomenon of big Dimensionality. Last but not least, the "curse and blessing of big Dimensionality" are delineated and deliberated.

关键词： big data computational intelligence data processing information processing Cellular phones Feature extraction

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Possibilistic Bayes Modelling for Predictive Analytics 15

Possibilistic Bayes Modelling for Predictive Analytics

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15th ieee International symposium on computational intelligence and Informatics

作者： Carlsson, Christer Heikkila, Markku Mezei, Jozsef Abo Akad Univ IAMSR Turku Finland

ISBN: (纸本)9781479953370

Studies in the process industry (and also common sense) show that the most cost effective way to keep production processes running is through predictive maintenance, i.e. to carry out optimal maintenance actions just in time before a process fails. Modern processes are highly automated;data is collected with sensor technology that forms a big data context and offers challenges to identify coming failures from very large sets of data. Modern analytics develops algorithms that are fast and effective enough to create possibilities for optimal JIT (Just -in Time) maintenance decisions.

关键词： Bayes methods big data just-in-time maintenance engineering big data context just-in time maintenance decisions optimal JIT optimal maintenance actions possibilistic Bayes modelling predictive analytics predictive maintenance process industry production processes sensor technology Analytical models big data computational modeling data models Industries Maintenance engineering Predictive models

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ieee SSCI 2014 general committee

IEEE SSCI 2014 general committee

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ieee symposium on computational intelligence in big data (cibd)

Provides a listing of current committee members and society officers.

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Renumber Strategy Enhanced Particle Swarm Optimization for Cloud Computing Resource Scheduling

Renumber Strategy Enhanced Particle Swarm Optimization for C...

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ieee Congress on Evolutionary Computation (CEC)

作者： Li, Hai-Hao Fu, Yu-Wen Zhan, Zhi-Hui Li, Jing-Jing Sun Yat Sen Univ Dept Comp Sci Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Sch Adv Comp Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Key Lab Machine Intelligence & Adv Comp Minist Educ Guangzhou Guangdong Peoples R China Sun Yat Sen Univ Engn Res Ctr Supercomp Engn Software Minist Educ Guangzhou Guangdong Peoples R China Educ Dept Guangdong Prov Key Lab Software Technol Guangzhou Guangdong Peoples R China State Key Lab Math Engn & Adv Comp Guangzhou Guangdong Peoples R China South China Normal Univ Sch Comp Sci Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781479974924

Cloud computing offers unprecedented capacity to execute large-scale workflows in the "era of big data". In 2014, a cost-minimization and deadline-constrained workflow scheduling (CMDCWS) model is firstly proposed by Rodriguez and Buyya, which is applicable for the business need of cloud computing that a workflow task should be finished by minimizing the execute cost within a deadline constraint. As scheduling cloud computing resources for workflow is an NP-hard problem, Rodriguez and Buyya proposed to use particle swarm optimization (PSO) to solve the CMDCWS problem. In traditional PSO for CMDCWS, each dimension in the particle position stands for each task and the value of the corresponding dimension stands for the index of the cloud resource that executes this task. However, this may have drawback because the value of each dimension does not relate to the resource characteristic but is only a meaningless index number. Therefore the learning behaviors among the particles do not make sense because learning from index number may not lead to better position. In this paper, we present a resource renumber strategy to encode the particle position and design a renumber PSO (RNPSO) for CMDCWS. In RNPSO, all the resources are re-ordered and re-numbered according to their computational ability, i.e., the cost per unit time. By this, the values of particle position can make sense and the positions difference between the well-performed and poorly-performed particles can guide poorly-performed particle to promising region. We conduct experiments on test cases with small, middle, and large scales to compare the performance of PSO and RNPSO. The results show that the resource renumber strategy is promising for enhancing the PSO performance.

关键词： cloud computing particle swarm optimization renumber resource scheduling

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Optimization of Relational database Usage Involving big data A Model Architecture for big data applications 5

Optimization of Relational Database Usage Involving Big Data...

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ieee symposium on computational intelligence and data Mining (CIDM)

作者： Durham, Erin-Elizabeth A. Rosen, Andrew Harrison, Robert W. Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

ISBN: (纸本)9781479945184

Effective big data applications dynamically handle the retrieval of decisioned results based on stored large datasets efficiently. One effective method of requesting decisioned results, or querying, large datasets is the use of SQL and database management systems such as MySQL. But a problem with using relational databases to store huge datasets is the decisioned result retrieval time, which is often slow largely due to poorly written queries / decision requests. This work presents a model to re-architect big data applications in order to efficiently present decisioned results: lowering the volume of data being handled by the application itself, and significantly decreasing response wait times while allowing the flexibility and permanence of a standard relational SQL database, supplying optimal user satisfaction in today's data Analytics world. We experimentally demonstrate the effectiveness of our approach.

关键词： Relational database SQL query query optimization materialized view data Mining Business intelligence big data analysis

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big data and the SP Theory of intelligence

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ieee ACCESS 2014年 2卷 301-315页

作者： Wolff, J. Gerard CognitionResearch Org Menai Bridge Gwynedd Wales

This paper is about how the SP theory of intelligence and its realization in the SP machine may, with advantage, be applied to the management and analysis of big data. The SP system-introduced in this paper and fully described elsewhere-may help to overcome the problem of variety in big data;it has potential as a universal framework for the representation and processing of diverse kinds of knowledge, helping to reduce the diversity of formalisms and formats for knowledge, and the different ways in which they are processed. It has strengths in the unsupervised learning or discovery of structure in data, in pattern recognition, in the parsing and production of natural language, in several kinds of reasoning, and more. It lends itself to the analysis of streaming data, helping to overcome the problem of velocity in big data. Central in the workings of the system is lossless compression of information: making big data smaller and reducing problems of storage and management. There is potential for substantial economies in the transmission of data, for big cuts in the use of energy in computing, for faster processing, and for smaller and lighter computers. The system provides a handle on the problem of veracity in big data, with potential to assist in the management of errors and uncertainties in data. It lends itself to the visualization of knowledge structures and inferential processes. A high-parallel, open-source version of the SP machine would provide a means for researchers everywhere to explore what can be done with the system and to create new versions of it.

关键词： Artificial intelligence big data cognitive science computational efficiency data compression data-centric computing energy efficiency pattern recognition uncertainty unsupervised learning

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An Anarchy of Methods: Current Trends in How intelligence Is Abstracted in AI

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ieee INTELLIGENT SYSTEMS 2014年第6期29卷 56-62页

作者： Lehman, Joel Clune, Jeff Risi, Sebastian Univ Texas Austin Austin TX 78712 USA Univ Wyoming Dept Comp Sci Laramie WY 82071 USA IT Univ Copenhagen Copenhagen Denmark

Artificial intelligence (AI) is a sprawling field encompassing a diversity of approaches to machine intelligence and disparate perspectives on how intelligence should be viewed. Because researchers often engage only within their own specialized area of AI, there are many interesting broad questions about AI as a whole that often go unanswered. How should intelligence be abstracted in AI research? Which subfields, techniques, and abstractions are most promising? Why do researchers bet their careers on the particular abstractions and techniques of their chosen subfield of AI? Should AI research be "bio-inspired" and remain faithful to the process that produced intelligence (evolution) or the biological substrate that enables it (networks of neurons)? Discussing these big-picture questions motivated us to organize an AAAI Fall symposium, which gathered participants across AI subfields to present and debate their views. This article distills the resulting insights.

关键词： artificial intelligence AI abstraction AI research AI subfields AI techniques artificial intelligence bio-inspired research machine intelligence Adaptive systems Artificial intelligence Biological system modeling Brain modeling computational modeling Design methodology Neural networks Neuroscience Robots AI adaptive systems artificial intelligence cognitive science computational neuroscience deep learning design automation developmental robotics evolving neural networks intelligent systems neuroevolution

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