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3rd Nirma-University International Conference on Engineering (NUiCONE)

作者： Patel, Aditya B. Birla, Manashvi Nair, Ushma

ISBN: (纸本)9781467317191;9781467317207

The size of the databases used in today's enterprises has been growing at exponential rates day by day. Simultaneously, the need to process and analyze the large volumes of data for business decision making has also increased. In several business and scientific applications, there is a need to process terabytes of data in efficient manner on daily bases. This has contributed to the big data problem faced by the industry due to the inability of conventional database systems and software tools to manage or process the big data sets within tolerable time limits. Processing of data can include various operations depending on usage like culling, tagging, highlighting, indexing, searching, faceting, etc operations. It is not possible for single or few machines to store or process this huge amount of data in a finite time period. This paper reports the experimental work on big data problem and its optimal solution using Hadoop cluster, Hadoop Distributed File System (HDFS) for storage and using parallel processing to process large data sets using Map Reduce programming framework. We have done prototype implementation of Hadoop cluster, HDFS storage and Map Reduce framework for processing large data sets by considering prototype of big data application scenarios. The results obtained from various experiments indicate favorable results of above approach to address big data problem.

关键词： big data problem Hadoop cluster Hadoop Distributed File System Parallel Processing Map Reduce

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Minimizing big data problems using Cloud Computing Based on Hadoop Architecture 11

Minimizing Big Data Problems using Cloud Computing Based on ...

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11th Annual International Conference on High Capacity Optical Networks and Emerging/Enabling Technologies (Photonics for Energy)

作者： Adnan, Muhammad Afzal, Muhammad Aslam, Muhammad Jan, Roohl Martinez-Enriquez, A. M. UET Dept CS Lahore Pakistan Prince Sultan Univ Riyadh Saudi Arabia CINVESTAV IPN Dept CS Mexico City DF Mexico

ISBN: (纸本)9781479969401

This paper emphasizes importance and solution of big data problems through cloud computing. Knowledge embedded in big data generated by sensors, personal computers and mobile devices is compelling many companies to spend millions of dollars to solve problems of information and knowledge extraction to make intelligent decisions in time for the growth of their businesses. Google bigQuery, Rackspace big data Cloud, Amazon Web Services are some platforms that are providing limited solutions and infrastructures to deal with big data problems. However, our study motivates IT companies to use open source Hadoop architecture to develop cloud systems for reliable distributed computing to process their large data sets efficiently and effectively. Our main guideline is to resolve the big data through a company's own infrastructure and integrating various other big data infrastructures into their clouds. Also that, Hadoop reduce/map technique can be implemented on the clusters within and across the private and public clouds.

关键词： big data problem Hadoop Cloud Computing Map Reduce big data Infrastructure

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Cybersecurity in the Era of data Science: Examining New Adversarial Models

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IEEE SECURITY & PRIVACY 2019年第6期17卷 46-53页

作者： Yener, Bulent Gal, Tsvi Rensselaer Polytech Inst Data Sci Res Ctr Dept Comp Sci Troy NY 12180 USA Rensselaer Polytech Inst Data Sci Res Ctr Troy NY 12180 USA Morgan Stanley New York NY USA

The ever-increasing volume, variety, and velocity of threats dictates a big data problem in cybersecurity and necessitates deployment of AI and machine-learning (ML) algorithms. The limitations and vulnerabilities of AI/ML systems, combined with complexity of data, introduce a new adversarial model, which is defined and discussed in this article.

关键词： big data Learning Artificial Intelligence Security Of data Cybersecurity data Science big data problem Machine Learning Adversarial Model AI ML Systems data Models Malware Artificial Intelligence Computer Security big data Real Time Systems

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Radar emitter classification based on unidimensional convolutional neural network

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IET RADAR SONAR AND NAVIGATION 2018年第8期12卷 862-867页

作者： Sun, Jun Xu, Guangluan Ren, Wenjuan Yan, Zhiyuan Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Elect Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Technol Geospatial Informat Proc & Applic Beijing 100190 Peoples R China

Radar emitter classification (REC) is an essential part of electronic warfare (EW) systems. In REC tasks, after deinterleaving, the intercepted radar signals are classified into specific radar types. With new radar types arising and the electromagnetism environment getting complicated, REC has become a big data problem. Meanwhile, there exist inconsistent features among samples. These two problems can affect the performance of classification. In this work, first, the authors designed a novel encoding method to deal with the inconsistent features. High-dimension sequences of equal length are generated as new features. Then a deep learning model named unidimensional convolutional neural network (U-CNN) is proposed to classify the encoded high-dimension sequences with big data. A large and complex radar emitter's dataset is used to evaluate the performance of the U-CNN model with the encoding method. Experiments show that the authors' proposal gains an improvement of 2-3% in accuracy compared with the state-of-the-art methods, while the time consumed for identifying 45,509 emitters is only 1.95s using a GPU. Specifically, for 12 indistinguishable radars, the classification accuracy is improved about 15%.

关键词： electronic warfare signal classification neural nets radar signal processing electromagnetism big data learning (artificial intelligence) sequences electrical engineering computing radar emitter classification unidimensional convolutional neural network REC electronic warfare system EW system radar signal classification electromagnetism environment big data problem encoding method high-dimension equal length sequence deep learning model U-CNN model

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Dynamic Taxonomy Composition via Keyqueries 14

Dynamic Taxonomy Composition via Keyqueries

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14th IEEE/ACM Joint Conference on Digital Libraries (JCDL) / 18th International Conference on Theory and Practice of Digital Libraries (TPDL)

作者： Gollub, Tim Voelske, Michael Hagen, Matthias Stein, Benno Bauhaus Univ Weimar D-99421 Weimar Germany

ISBN: (纸本)9781479955695

This paper presents an unsupervised framework for dynamic, subject-oriented taxonomy composition in digital libraries, which can naturally integrate existing library classification systems. The taxonomy classes in our approach correspond to so-called key queries that are run against the digital library's full-text retrieval system. Given a document, a keyquery is a set of few keywords for which the document achieves a high relevance score. Keyqueries can hence be viewed as a general and concise description of the returned retrieval results. The keyquery framework addresses important problems of static classification systems: overlarge classes and overly complex taxonomy structures. If, for instance, a leaf class grows to an indigestible size, keyqueries for the contained documents provide a suitable split mechanism. Since queries are well-known to library users from their daily web search experience, they increase the structural complexity in a transparent way. The paper presents also a strategy for taxonomy-based library exploration. Given a user's information need in the form of library documents, we synthesize a hierarchy of keyqueries that covers this library subset. We manage to solve this difficult set covering problem on-the-fly by combining inverted and reverted indexes along with heuristic search space pruning within a map-reduce application. An empirical evaluation with an ACM collection of scientific papers demonstrates the efficiency and effectiveness of our taxonomy composition framework.

关键词： dynamic taxonomy composition keyquery classification systems reverted index big data problem

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Challenges in Designing an Online Healthcare Platform for Personalised Patient Analytics

Challenges in Designing an Online Healthcare Platform for Pe...

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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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Frei-Chen bases based lossy digital image compression technique

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APPLIED COMPUTING AND INFORMATICS 2024年第1/2期20卷 105-118页

作者： Al-khassaweneh, Mahmood AlShorman, Omar Lewis Univ Dept Comp & Math Sci Romeoville IL 60446 USA Yarmouk Univ Dept Comp Engn Irbid Jordan Najran Univ Fac Engn Dept Elect Engn Najran Saudi Arabia Najran Univ AlShrouk Trading Co Najran Saudi Arabia

In the big data era, image compression is of significant importance in today's world. Importantly, compression of large sized images is required for everyday tasks;including electronic data communications and internet transactions. However, two important measures should be considered for any compression algorithm: the compression factor and the quality of the decompressed image. In this paper, we use Frei-Chen bases technique and the Modified Run Length Encoding (RLE) to compress images. The Frei-Chen bases technique is applied at the first stage in which the average subspace is applied to each 3 x 3 block. Those blocks with the highest energy are replaced by a single value that represents the average value of the pixels in the corresponding block. Even though Frei-Chen bases technique provides lossy compression, it maintains the main characteristics of the image. Additionally, the Frei-Chen bases technique enhances the compression factor, making it advantageous to use. In the second stage, RLE is applied to further increase the compression factor. The goal of using RLE is to enhance the compression factor without adding any distortion to the resultant decompressed image. Integrating RLE with Frei-Chen bases technique, as described in the proposed algorithm, ensures high quality decompressed images and high compression rate. The results of the proposed algorithms are shown to be comparable in quality and performance with other existing methods.

关键词： big data problem Compression factor Frei-Chen basis Run Length Encoding

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PULP: Scalable Multi-Objective Multi-Constraint Partitioning for Small-World Networks 2

PULP: Scalable Multi-Objective Multi-Constraint Partitioning...

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IEEE International Conference on big data

作者： Slota, George M. Madduri, Kamesh Rajamanickam, Sivasankaran Penn State Univ Dept Comp Sci & Engn University Pk PA 16802 USA Sandia Natl Labs Scalable Algorithms Dept Albuquerque NM 87185 USA

ISBN: (纸本)9781479956661

We present PULP, a parallel and memory-efficient graph partitioning method specifically designed to partition low-diameter networks with skewed degree distributions. Graph partitioning is an important big data problem because it impacts the execution time and energy efficiency of graph analytics on distributed-memory platforms. Partitioning determines the in-memory layout of a graph, which affects locality, intertask load balance, communication time, and overall memory utilization of graph analytics. A novel feature of our method PULP (Partitioning using Label Propagation) is that it optimizes for multiple objective metrics simultaneously, while satisfying multiple partitioning constraints. Using our method, we are able to partition a web crawl with billions of edges on a single compute server in under a minute. For a collection of test graphs, we show that PULP uses 8-39x less memory than state-of-the-art partitioners and is up to 14.5x faster, on average, than alternate approaches (with 16-way parallelism). We also achieve better partitioning quality results for the multi-objective scenario.

关键词： big data graph theory parallel processing resource allocation search engines small-world networks big data problem PULP Web crawl partitioning quality communication time distributed-memory platforms energy efficiency execution time graph analytics graph locality in-memory graph layout intertask load balancing low-diameter network partitioning multiple objective metrics optimization multiple partitioning constraints overall memory graph analytics utilization parallel-memory-efficient graph partitioning method parallelism partitioning-using-label propagation scalable multiobjective multiconstraint partitioning skewed degree distributions Clustering algorithms Communities Heuristic algorithms Layout Measurement Memory management Partitioning algorithms

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Two-Step Estimation for Modeling the Earthquake Occurrences in Sumatra by Neyman-Scott Cox Point Processes 6th

Two-Step Estimation for Modeling the Earthquake Occurrences ...

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6th International Conference on Soft Computing in data Science (SCDS)

作者： Choiruddin, Achmad Susanto, Tabita Yuni Metrikasari, Rahma Inst Teknol Sepuluh Nopember ITS Dept Stat Surabaya Indonesia

ISBN: (纸本)9789811673344;9789811673337

The Cox point process is highly considered for earthquake modeling. However, the complex earthquake data which involve a large number of occurrences and geological variables often require expensive computation. This study aims to propose an efficient algorithm based on the two-step procedure by constructing the first and second order composite likelihoods. We consider four Neyman-Scott Cox process models and apply them to fit the earthquake distribution in Sumatra. We conclude that the Cauchy cluster process performs best.

关键词： big data problem Cluster point process Disaster risk reduction Earthquake modeling Spatial point pattern

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