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Massively Parallel Processing Database for Sequence and Graph Data Structures Applied to Rapid-Response Drug Repurposing 8

Massively Parallel Processing Database for Sequence and Grap...

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8th IEEE international conference on Big Data (Big Data)

作者： Rickett, Christopher D. Maschhoff, Kristyn J. Sukumar, Sreenivas R. Hewlett Packard Enterprise Spring TX 77389 USA

ISBN: (纸本)9781728162515

In this paper, we present the application of a massively parallel-processing graph database for rapid-response drug repurposing. the novelty of our approach is that the scalable graph database is able to host a knowledge graph of medically relevant facts integrated from multiple knowledge sources and also act as a computational engine capable of in-database protein sequence analytics. We demonstrate the performance of the graph database on a real-world use-case to hypothesize cures for COVID-19, leveraging its built-in accelerated protein-sequence matching capabilities at unprecedented scale (to simultaneously handle data size and query latency requirements for interactive research). Based on supporting evidence from medical literature, we show that results generated by computing similarity of COVID-19 virus proteins across 4 million other open-science sequences and intelligently traversing over a 150 billion facts from open-science medical knowledge produces biologically insightful results. By presenting sample queries and extending application to use-cases beyond COVID-19, we demonstrate the use and value of the novel database for hypotheses generation in reducing the time-to-insight and increasing researcher productivity with interactivity.

关键词： graph database graph analytics in-database analytics distributed processing parallel processing sequence analytics

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Improving Join Reordering for Large Scale distributed computing 8

Improving Join Reordering for Large Scale Distributed Comput...

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8th IEEE international conference on Big Data (Big Data)

作者： Margoor, Amogh Bhosale, Mayur Qubole Inc Santa Clara CA 95050 USA Uber Technol Inc Bangalore Karnataka India

ISBN: (纸本)9781728162515

SQL Join is one of the most commonly used operators for workloads running upon SQL Engines built for Big Data like Apache Spark SQL, Apache Hive and Presto. As Joins on Big Data can be expensive, Join optimizations can significantly improve the efficiency and performance of it's workloads. In this work, we would address the problem of finding optimal order for SQL Joins, which is a well researched NP-Hard problem for traditional workloads. Traditional Cost Based Optimization in Query processing, including Join Reordering algorithms are not effective on Big Data due to lack of statistics. Statistics are mostly absent in Big Data as they are expensive to compute and needs frequent recompute due to velocity of data change. Hence, ensuring optimal order of Joins for Big Data is still largely a manual process done via trial and error. We are proposing a novel greedy algorithm for Join Reordering which can work in the absence of statistics. Proposed technique is of linear complexity in terms of number of joins. We have observed that on TPC-DS benchmarks it can reorder 39 queries and improve query performance upto 51%.

关键词： SQL on Big Data Query Processing Join Order Data Management Systems

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distributed de novo assembler for large-scale long-read datasets 8

Distributed de novo assembler for large-scale long-read data...

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8th IEEE international conference on Big Data (Big Data)

作者： Goswami, Sayan Lee, Kisung Park, Seung-Jong Louisiana State Univ Dept Comp Sci Shreveport LA 71105 USA Louisiana State Univ Sch Elect Engn & Comp Sci Shreveport LA 71105 USA Louisiana State Univ Sch Elect Engn & Comp Sci Ctr Computat & Technol Shreveport LA 71105 USA

ISBN: (纸本)9781728162515

third-generation DNA sequencing technologies such as single-molecule real-time sequencing (SMRT) and nanopore sequencing have the potential to fill the gaps in the existing genome databases since the raw sequences produced by these machines are much longer than those of previous generations and therefore result in more contiguous assemblies. However, these long reads have a high error rate, which makes the assembly process computationally challenging. Moreover, since existing long-read assemblers are designed to run on a single machine, they either take days to complete or run out of memory on even moderate-sized datasets. In this paper, we present a distributed long-read assembler that can assemble large-scale noisy sequence datasets on thousands of cores, resulting in orders of magnitude faster assembly times. By effectively using the map-reduce computation model with a distributed hash-map, both built using a high-performance active messaging middleware, we can assemble a PacBio human genome dataset with 139 billion base-pairs (about 130 GB) in about 33 minutes (using 2,560 cores) compared to more than 38 hours (using 28 cores) with the current state-of-the-art assembler.

关键词： genome assembly third-generation sequences long reads big data map-reduce high-performance computing

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Centrality and Scalability Analysis on distributed Graph of Large-Scale E-mail Dataset for Digital Forensics 8

Centrality and Scalability Analysis on Distributed Graph of ...

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8th IEEE international conference on Big Data (Big Data)

作者： Ozcan, Selim Astekin, Merve Shashidhar, Narasimha K. Zhou, Bing Sam Houston State Univ Dept Comp Sci Huntsville TX 77340 USA TUBITAK BILGEM Informat Technol Inst Kocaeli Turkey Ozyegin Univ Dept Comp Sci Istanbul Turkey

ISBN: (纸本)9781728162515

Today's digital forensics software tools mostly do not offer automatic analysis methods to reveal evidences among huge amounts of digital files within hard disk images. It is important that finding evidence in digital and cyber forensics investigations as soon as possible by examining hard disk images. E-mails constitute a rich source of information in hard disk images, and they are the most possible data source to obtain an evidence. the analyzers search e-mail files by manually or using traditional methods in order to find an evidence. However, this operation could take a long time due to the size of the e-mail data which can contain a huge number of files and a huge volume of data. this study introduces an end-to-end distributed graph analysis framework for large-scale digital forensic datasets, and evaluates the accuracy of the centrality algorithms and the scalability of the proposed framework in terms of running time performance. the framework is comprised of specific processes to perform pre-processing, graph building, and algorithm activities. An architecture is introduced based on distributed big data techniques. three different centrality algorithms are implemented to analyze the accuracy of our framework. Further, three implementations are provided to demonstrate the running time performance of our framework. Experiments are performed on Enron e-mail dataset to analyze the centrality algorithms, to evaluate the performance of the framework, and to compare the running times between the traditional approach and our approach. Moreover, the running time performance of the framework is evaluated under various parallelization level. the accuracy of the results is also evaluated and compared between the centrality algorithms. the comparison shows that some certain algorithms provide more accurate results and it is possible to improve the running time by orders of magnitude utilizing our end-to-end distributed graph analysis approach.

关键词： graph analysis centrality measurement runtime comparison scalability distributed systems big data digital forensics e-mail forensics

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NeuroKube: An Automated and Autoscaling Neuroimaging Reconstruction Framework using Cloud Native computing and A.I 8

NeuroKube: An Automated and Autoscaling Neuroimaging Reconst...

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8th IEEE international conference on Big Data (Big Data)

作者： Madany, Matthew Marcus, Kyle Peltier, Steven Ellisman, Mark H. Altintas, Ilkay Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9781728162515

the Neuroscience domain stands out from the field of sciences for its dependence on the study and characterization of complex, intertwining structures. Understanding the complexity of the brain has led to widespread advances in the structure of large-scale computing resources and the design of artificially intelligent analysis systems. However, the scale of problems and data generated continues to grow and outpace the standards and practices of neuroscience. In this paper, we present an automated neuroscience reconstruction framework, called NeuraKube, for large-scale processing and labeling of neuroimage volumes. Automated labels are generated through a machine-learning (ML) workflow, with data-intensive steps feeding through multiple GPU stages and distributed data locations leveraging autoscalable cloud-native deployments on a multi-institution Kubernetes system. Leading-edge hardware and storage empower multiple stages of machine-learning, GPU-accelerated solutions. this demonstrates an abstract approach to allocating the resources and algorithms needed to elucidate the highly complex structures of the brain. We summarize an integrated gateway architecture, and a scalable workflow driven segmentation and reconstruction environment that brings together image big data with state-of-the-art, extensible machine learning methods.

关键词： Neuroscience Segmentation Superresolution Machine Learning Workflow G ateway B ig D ata Kubernetes

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A Merge Collision Prediction TDMA-MAC Protocol in distributed VANET

A Merge Collision Prediction TDMA-MAC Protocol in Distribute...

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international conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Chang-Yue Zhang Shi-Yuan Han Jin Zhou Bao-Zhu Li Xiao-Jie Yu Kang Yao Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China Internet of Things and Smart City Innovation Platform Zhuhai Fudan Innovation Institute Zhuhai China Shandong Provincial Network Security and Infomationization Technology Center Jinan China

ISBN: (纸本)9781665402460

the communication between vehicles has attracted more and more attention in intelligent transportation system(ITS). Due to the rapid movement of vehicles, the topology of network nodes changes rapidly, resulting in collisions between vehicles. therefore, the design of efficient and reliable media access control protocol is very necessary to solve the communication problem between vehicles. In this paper, a merge collision prediction TDMA-MAC in distributed VANET(MCPMAC) is proposed to solve the merging collision problem. In MCPMAC, when multiple vehicles occupying the same time slot meet within the two hop communication range, one vehicle continues to use the time slot by comparing the time stamp of the vehicle access time slot, and the other vehicles sequentially select the idle time slot to reduce the probability of merging collision between vehicles. Simulation results show MCPMAC protocol has higher reliability and lower number of collisions than the existing MAC protocols.

关键词： Protocols Network topology Simulation Merging Vehicular ad hoc networks Transportation Media Access Protocol

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8th international conference on Soft computing for Problem Solving, SocProS 2018

8th International Conference on Soft Computing for Problem S...

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8th international conference on Soft computing for Problem Solving, SocProS 2018

ISBN: (纸本)9789811500343

the proceedings contain 163 papers. the special focus in this conference is on Soft computing for Problem Solving. the topics include: Optimization of Target Oriented Network Intelligence Collection for the Social Web by Using k-Beam Search;compressed Air Energy Storage Driven by Wind Power Plant for Water Desalination through Reverse Osmosis Process;fully Fuzzy Semi-linear Dynamical System Solved by Fuzzy Laplace Transform Under Modified Hukuhara Derivative;comparison of Performance of Four-Element Microstrip Array Antenna Using Electromagnetic Bandgap Structures;model Development for Strength Properties of Laterized Concrete Using Artificial Neural Network Principles;wind Power Forecasting Using Parallel Random Forest Algorithm;leukemia Cell Segmentation from Microscopic Blood Smear Image Using C-Mode;Implementation of Exploration in TONIC Using Non-stationary Volatile Multi-arm Bandits;fuzzy Logic-Based Model for Predicting Surface Roughness of Friction Drilled Holes;load Bearing Capacity for a Ferrofluid Squeeze Film in Double Layered Porous Rough Conical Plates;Face Recognition and Classification Using GoogleNET Architecture;Enhancing Public Health Surveillance by Measurement of Similarity Using Rough Sets and GIS;data Analytics Implemented over E-commerce Data to Evaluate Performance of Supervised Learning Approaches in Relation to Customer Behavior;optimal Renewable Energy Resource Based distributed Generation Allocation in a Radial Distribution System;PV Module Temperature Estimation by Using ANFIS;modified Artificial Potential Field Approaches for Mobile Robot Navigation in Unknown Environments;Analysis of BASNs Battery Performance at Different Temperature Conditions Using Artificial Neural Networks (ANN);ASIC Implementation of Fixed-Point Iterative, Parallel, and Pipeline CORDIC Algorithm;adaptive Sensor Ranking Based on Utility Using Logistic Regression.

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Research on Data Protection Architecture Based on Block Chain 8th

Research on Data Protection Architecture Based on Block Chai...

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8th international conference on Communications, Signal Processing, and Systems, CSPS 2019

作者： Zhang, Jianbiao Liu, Yanhui Zhang, Zhaoqian Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Trusted Computing Beijing100124 China

ISBN: (纸本)9789811394089

In recent years, blockchain has emerged as a new decentralized infrastructure and distributed computing paradigm with the increasing popularity of digital cryptocurrencies such as Bitcoin. Blockchain technology is a decentralized, de-trusted, open and transparent distributed data storage technology that can reduce the costs of trust and achieve secure and reliable data interaction. Big data has been widely concerned and studied because it contains great value, however, because big data sets usually contain a lot of privacy-sensitive information, extensive and in-depth research on privacy protection of big data has not been realized. Under the premise, reasonable storage of big data is an urgent problem to be solved. Under the premise of analyzing blockchain structure and access control, this paper proposes data security storage protection architecture based on blockchain technology and access control technology, and analyzes the architecture in detail, and finally summarizes the paper. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Access control

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8th international conference on Software Process Improvement, CIMPS 2019

8th International Conference on Software Process Improvement...

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8th international conference on Software Process Improvement, CIMPS 2019

ISBN: (纸本)9783030335465

the proceedings contain 21 papers. the special focus in this conference is on international conference on Software Process Improvement . the topics include: Gamification in Software Engineering: A Tertiary Study;knowledge Transfer in Software Companies Based on Machine Learning;linked Data and Musical Information to Improvement the Cultural and Heritage Knowledge Management;distributed System Based on Deep Learning for Vehicular Re-routing and Congestion Avoidance;from a Conceptual to a Computational Model of Cognitive Emotional Process for Engineering Students;Algorithm Proposal to Control a Robotic Arm for Physically Disable People Using the LCD Touch Screen;multithreading Programming for Feature Extraction in Digital Images;selection and Fusion of Color Channels for Ripeness Classification of Cape Gooseberry Fruits;model Driven Automatic Code Generation: An Evolutionary Approach to Disruptive Innovation Benefits;cluster Monitoring and Integration in Technology Company;objectives Patterns Applied to the Business Model of a Public Education System;a Data Driven Platform for Improving Performance Assessment of Software Defined Storage Solutions;teaching Approach for the Development of Virtual Reality Videogames;requirements Validation in the Information Systems Software Development: An Empirical Evaluation of Its Benefits for a Public Institution in Lima;towards a Social and Human Factor Classification Related to Productivity in Software Development Teams;software Product Quality in DevOps Contexts: A Systematic Literature Review;Reinforcing DevOps Generic Process with a Guidance Based on the Basic Profile of ISO/IEC 29110;a Selection Process of Graph Databases Based on Business Requirements.

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IoMT Security: SHA3-512, AES-256, RSA and LSB Steganography

IoMT Security: SHA3-512, AES-256, RSA and LSB Steganography

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National Foundation for Science and Technology Development conference on Information and Computer Science (NICS)

作者： Wassim Alexan Ahmed Ashraf Eyad Mamdouh Sarah Mohamed Mohamed Moustafa Faculty of IET The German University in Cairo Cairo Egypt Faculty of Informatics and Computer Science The German International University in Cairo Cairo Egypt

ISBN: (纸本)9781665410021

the Internet of Medical things (IoMT) has been witnessing huge leaps in its development due to the advancements of neighboring technologies. those include 5G, big data and cloud storage. While IoMT provides a rich environment for the ultra-fast share and transfer of pathological analyses and disease diagnoses, it also presents networking and security engineers with unprecedented challenges. the need to protect the transmission of the sensitive information in relation to patients’ identities and diagnoses has always been a priority. this security problem is only exaggerated by the sheer volume of medical data to be transferred through a network in real time. this paper proposes an information security scheme for IoMT that utilizes AES-256, RSA, SHA3-512 and LSB embedding in medical scans or images. the proposed scheme not only guarantees the secure transmission of medical data through a network, but it also satisfies the conditions of user authentication and confidentiality. the numerical results showcase the superiority of the proposed scheme.

关键词： Computer science Steganography Pathology Cloud computing Information security Internet of Medical things Big Data

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