Physical Sciences and Mathematics Commons^™

Development Opportunities And Application Prospects Of Aero-Engine Simulation Technology Under Digital Transformation, Jianguo Cao

Journal of System Simulation

Abstract: The development of China's social economy and the improvement of its national defense capability in the new era put forward higher requirements for the development of aero-engines. It is urgent to promote the digital transformation of aero-engines in order to achieve coordinated, agile and efficient aero-engine development. Based on the current research and development of aero-engine in China, this paper clarifies the new connotation of "speediness and efficiency, accurate mapping, comprehensive coverage, and dynamic prediction" given by the development of emerging cutting-edge technologies to aero-engine simulation technology, as well as the new technical features of "spatio-temporal ubiquity, data driven, …

Lightweight Distributed Computing Framework For Orchestrating High Performance Computing And Big Data, Muhammed Numan İnce, Meli̇h Günay, Joseph Ledet

Turkish Journal of Electrical Engineering and Computer Sciences

In recent years, the need for the ability to work remotely and subsequently the need for the availability of remote computer-based systems has increased substantially. This trend has seen a dramatic increase with the onset of the 2020 pandemic. Often local data is produced, stored, and processed in the cloud to remedy this flood of computation and storage needs. Historically, HPC (high performance computing) and the concept of big data have been utilized for the storage and processing of large data. However, both HPC and Hadoop can be utilized as solutions for analytical work, though the differences between these may …

Big Data With Cloud Computing: Discussions And Challenges, Amanpreet Kaur Sandhu

Big Data Mining and Analytics

With the recent advancements in computer technologies, the amount of data available is increasing day by day. However, excessive amounts of data create great challenges for users. Meanwhile, cloud computing services provide a powerful environment to store large volumes of data. They eliminate various requirements, such as dedicated space and maintenance of expensive computer hardware and software. Handling big data is a time-consuming task that requires large computational clusters to ensure successful data storage and processing. In this work, the definition, classification, and characteristics of big data are discussed, along with various cloud services, such as Microsoft Azure, Google Cloud, …

Big Issues For Big Data: Challenges For Critical Spatial Data Analytics, Chris Brunsdon, Alexis Comber

Journal of Spatial Information Science

In this paper we consider some of the issues of working with big data and big spatial data and highlight the need for an open and critical framework. We focus on a set of challenges underlying the collection and analysis of big data. In particular, we consider 1) inference when working with usually biased big data, challenging the assumed inferential superiority of data with observations, n, approaching N, the population n -> N. We also emphasise 2) the need for analyses that answer questions of practical significance or with greater emphasis on the size of the effect, rather than the …

Geoai: Where Machine Learning And Big Data Converge In Giscience, Wenwen Li

Journal of Spatial Information Science

In this paper GeoAI is introduced as an emergent spatial analytical framework for data-intensive GIScience. As the new fuel of geospatial research, GeoAI leverages recent breakthroughs in machine learning and advanced computing to achieve scalable processing and intelligent analysis of geospatial big data. The three-pillar view of GeoAI, its two methodological threads (data-driven and knowledge-driven), as well as their geospatial applications are highlighted. The paper concludes with discussion of remaining challenges and future research directions of GeoAI.

Spatio-Temporal Visual Analytics: A Vision For 2020s, Natalia Andrienko, Gennady Andrienko

Journal of Spatial Information Science

Visual analytics is a research discipline that is based on acknowledging the power and the necessity of the human vision, understanding, and reasoning in data analysis and problem solving. Visual analytics develops methods, analytical workflows, and software tools for analysing data of various types, particularly, spatio-temporal data, which can describe the processes going on in the environment, society, and economy. We briefly overview the achievements of the visual analytics research concerning spatio-temporal data analysis and discuss the major open problems.

On The Semantics Of Big Earth Observation Data For Land Classification, Gilberto Camara

Journal of Spatial Information Science

This paper discusses the challenges of using big Earth observation data for land classification. The approach taken is to consider pure data-driven methods to be insufficient to represent continuous change. I argue for sound theories when working with big data. After revising existing classification schemes such as FAO's Land Cover Classification System (LCCS), I conclude that LCCS and similar proposals cannot capture the complexity of landscape dynamics. I then investigate concepts that are being used for analyzing satellite image time series; I show these concepts to be instances of events. Therefore, for continuous monitoring of land change, event recognition needs …

To Thine Own Self Be True? Incentive Problems In Personalized Law, Jordan M. Barry, John William Hatfield, Scott Duke Kominers

William & Mary Law Review

Recent years have seen an explosion of scholarship on “personalized law.” Commentators foresee a world in which regulators armed with big data and machine learning techniques determine the optimal legal rule for every regulated party, then instantaneously disseminate their decisions via smartphones and other “smart” devices. They envision a legal utopia in which every fact pattern is assigned society’s preferred legal treatment in real time.

But regulation is a dynamic process; regulated parties react to law. They change their behavior to pursue their preferred outcomes— which often diverge from society’s—and they will continue to do so under personalized law: They …

Smt Bounded Constrained Non Centralized Automaton Web Service Model Checking, Wei Rong, Xibing Shen, Yang Yi

Journal of System Simulation

Abstract: In model checking for web services applications, the combination of traditional finite state machine cannot guarantee the correctness of web service composition, a method of non centralized automaton model for web service detection algorithm was put forward,based on which could meet of mode theory (satisfiability modulo of the nanocomposite, SMT). The SMT was used to detect the bounded model of timed automata, and the time automaton was directly converted into SMT identifiable logic formula and was solved; using the SMT timed automata theory, implementation of employee travel arrangements for composite web service was modeled and verified. Through …

Qos-Aware Scheduling For Data Intensive Workflow, Wan Cong, Cuirong Wang, Wang Cong

Journal of System Simulation

Abstract: The development of technology enables people to access resources from different data centers. Resource management and scheduling of applications, such as workflow, that are deployed on the cloud computing environment have already become a hot spot. A QoS-aware scheduling algorithm for data intensive workflow on multiple data center environment was proposed. Scheduling data intensive workflow on multiple data center environment has two characteristics: A large amount of data is distributed in different geographical locations, the process of data migration will consume a large amount of time and bandwidth; secondly, the data centers have different price and resources. Data migration …

Approach To Process Smart Grid Time-Serial Big Data Based On Hbase, Wang Yuan, Tao Ye, Yuan Jun, He Wei

Journal of System Simulation

Abstract: With the development of critical theories and technologies in Internet of things (IOT), more and more attentions have been focused on the IOT applications. Smart Grid is one of the typical IOT applications on which a huge number of sensors have been deployed to gather and generate time-serial data to make sense of the running states of the key devices. How to apply these data to make smart grid running secure and stable is a hot research topic. By considering the fact that smart grid is characterized by a huge number of devices, a huge amount of data and …

Research On Technology Of Data Storage And Access In High-Throughput Simulation, Zishuo Wang, Yanlong Zhai, Wenjun Tao, Yang Hao, Zhang Han, Duzheng Qing

Journal of System Simulation

Abstract: Aiming at the massive data processing requirements of simulation application in the big data environment, the concept and reference structure of high-throughput simulation were proposed and defined in accordance with the architecture and technical characteristics of high-throughput big data computing. For the problem of data access bottleneck in high-throughput simulation, a high-throughput simulation data storage and access system was designed based on distributed memory file system, and the non-volatile memory was integrated to improve the throughput of data access. The experimental results of typical simulation applications show that the high-throughput simulation storage system with distributed memory file system and …

Parallel Method For Extracting Pulses From Multi-Source Massive Partial Discharge Signals, Liuwang Wang, Yongli Zhu, Yafei Jia

Journal of System Simulation

Abstract: Aiming at the issue of discharge pulse extraction for multi-source and massive PD signals, a novel parallel method based on Message Passing Interface was proposed. The proposed method applied a parallel mode called manager-worker-writer. In this method, a manager dynamically assigned task to several workers, and these workers executed tasks in parallel and a writer received results from workers in real time, so data management was separated from task execution. In addition, the manager identified sources of PD signals and sent them to workers as the keys for analyzing different data files and setting algorithm parameters, so multi-source and …

Optimization Of Real-Time Wireless Sensor Based Big Data With Deep Autoencoder Network: A Tourism Sector Application With Distributed Computing, Beki̇r Aksoy, Utku Kose

Turkish Journal of Electrical Engineering and Computer Sciences

Internet usage has increased rapidly with the development of information communication technologies. The increase in internet usage led to the growth of data volumes on the internet and the emergence of the big data concept. Therefore, it has become even more important to analyze the data and make it meaningful. In this study, 690 million queries and approximately 5.9 quadrillion data collected daily from different servers were recorded on the Redis servers by using real-time big data analysis method and load balance structure for a company operating in the tourism sector. Here, wireless networks were used as a triggering factor …

“Where’S The I-O?” Artificial Intelligence And Machine Learning In Talent Management Systems, Manuel F. Gonzalez, John F. Capman, Frederick L. Oswald, Evan R. Theys, David L. Tomczak

Personnel Assessment and Decisions

Artificial intelligence (AI) and machine learning (ML) have seen widespread adoption by organizations seeking to identify and hire high-quality job applicants. Yet the volume, variety, and velocity of professional involvement among I-O psychologists remains relatively limited when it comes to developing and evaluating AI/ML applications for talent assessment and selection. Furthermore, there is a paucity of empirical research that investigates the reliability, validity, and fairness of AI/ML tools in organizational contexts. To stimulate future involvement and research, we share our review and perspective on the current state of AI/ML in talent assessment as well as its benefits and potential pitfalls; …

System Analysis Method Based On Simulation Big Data, Guangya Si, Wang Fei, Liu Yang

Journal of System Simulation

Abstract: Wargaming and exploratory simulation with large-scale simulation systems produce massive simulation data. These data contain many complexity patterns of war, and are significant samples for studying the mechanism of war. Based on the definition of simulation big data, an analysis framework based on simulation big data is proposed, which is divided into three levels: simulation environment and data planning, big data acquisition and storage, and analysis and mining. The simulation data planning and analysis and mining are briefly introduced.

Data Mining And Machine Learning To Improve Northern Florida’S Foster Care System, Daniel Oldham, Nathan Foster, Mihhail Berezovski

Beyond: Undergraduate Research Journal

The purpose of this research project is to use statistical analysis, data mining, and machine learning techniques to determine identifiable factors in child welfare service records that could lead to a child entering the foster care system multiple times. This would allow us the capability of accurately predicting a case’s outcome based on these factors. We were provided with eight years of data in the form of multiple spreadsheets from Partnership for Strong Families (PSF), a child welfare services organization based in Gainesville, Florida, who is contracted by the Florida Department for Children and Families (DCF). This data contained a …

What To Do When Privacy Is Gone, James Brusseau

Computer Ethics - Philosophical Enquiry (CEPE) Proceedings

Today’s ethics of privacy is largely dedicated to defending personal information from big data technologies. This essay goes in the other direction. It considers the struggle to be lost, and explores two strategies for living after privacy is gone. First, total exposure embraces privacy’s decline, and then contributes to the process with transparency. All personal information is shared without reservation. The resulting ethics is explored through a big data version of Robert Nozick’s Experience Machine thought experiment. Second, transient existence responds to privacy’s loss by ceaselessly generating new personal identities, which translates into constantly producing temporarily unviolated private information. The …

Parallel Pattern Recognition Of Leak Current Data Using Spark-Knn, Li Li, Yongli Zhu, Yaqi Song

Journal of System Simulation

Abstract: With the rapid development of smart grid, the status monitoring data of power grid equipment increase exponentially and gradually form the big data. Traditional computing architectures are no longer to meet the demand of computing performance. This paper explores how Spark and Cloud computing can accelerate performance of missive insulator leak current data pattern recognition. The Parallel KNN (k-Nearest Neighbor) algorithm is designed and implemented by using Spark and Aliyun E-MapReduce cloud computing platform. The results from experiments show that the performance of Spark-KNN is 2.97 times of MapReduce-KNN and gains acceleration of 8.8 times. The experimental results confirm …

Association Rules Analysis Method Of Spatial Data Under Mapreduce Framework, Mingzhi Zhang, Li Yi

Journal of System Simulation

Abstract: Spatial data has the characteristic of extensity, timeliness, multidimensional, large amount of data and complex relations. Some non-conventional data screening tool for analysis and mining is required to find out the patterns, rules and characteristics knowledge in the spatial big data for battlefield situation awareness and battle space management. In view that the existing Apriori algorithm scans the database too frequently, the Apriori algorithm is improved on the basis of working principle of Map Reduce .The fast analysis ideas and technologyframework of spatial data is proposed. An elementary validate prototype is built for the key technology experimentation.Experimental results …

Data Analysis Through Social Media According To The Classified Crime, Serkan Savaş, Nuretti̇n Topaloğlu

Turkish Journal of Electrical Engineering and Computer Sciences

The amount and variety of data generated through social media sites has increased along with the widespread use of social media sites. In addition, the data production rate has increased in the same way. The inclusion of personal information within these data makes it important to process the data and reach meaningful information within it. This process can be called intelligence and this meaningful information may be for commercial, academic, or security purposes. An example application is developed in this study for intelligence on Twitter. Crimes in Turkey are classified according to Turkish Statistical Institute criminal data and keywords are …

Web Personalization Issues In Big Data And Semantic Web: Challenges Andopportunities, Bujar Raufi, Florije Ismaili, Jaumin Ajdari, Xhemal Zenuni

Turkish Journal of Electrical Engineering and Computer Sciences

Web personalization is a process that utilizes a set of methods, techniques, and actions for adapting the linking structure of an information space or its content or both to user interaction preferences. The aim of personalization is to enhance the user experience by retrieving relevant resources and presenting them in a meaningful fashion. The advent of big data introduced new challenges that locate user modeling and personalization community in a new research setting. In this paper, we introduce the research challenges related to Web personalization analyzed in the context of big data and the Semantic Web. This paper also introduces …

The Impacts Of Cloud Computing And Big Data Applications On Developing World-Based Smallholder Farmers, Nir Kshetri

International Journal of Business and Technology

Cloud computing and big data applications are likely to have far-reaching and profound impacts on developing world-based smallholder farmers. Especially, the use of mobile devices to access cloudbased applications is a promising approach to deliver value to smallholder farmers in developing countries since according to the International Telecommunication Union, mobile-cellular penetration in developing countries is expected to reach 90% by the end of 2014. This article examines the contexts, mechanisms, processes and consequences associated with cloud computing and big data deployments in farming activities that could affect the lives of developing world-based smallholder farmers. We analyze the roles of big …

The Billion Object Platform (Bop): A System To Lower Barriers To Support Big, Streaming, Spatio-Temporal Data Sources, Devika Kakkar, Ben Lewis, David Smiley, Ariel Nunez

Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings

With funding from the Sloan Foundation and Harvard Dataverse, the Harvard Center for Geographic Analysis (CGA) has developed a big spatio-temporal data visualization platform called the Billion Object Platform or "BOP". The goal of the project is to lower barriers for scholars who wish to access large, streaming, spatio-temporal datasets. Since once archived, streaming data gets big fast, and since most GIS systems don't support interactive visualization of millions of objects, a new platform was needed. The BOP is loaded with the latest billion geo-tweets and is fed a real-time stream of about 1 million tweets per day. The CGA …

Optimizing Spatiotemporal Analysis Using Multidimensional Indexing With Geowave, Richard Fecher, Michael A. Whitby

Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings

The open source software GeoWave bridges the gap between geographic information systems and distributed computing. This is done by preserving locality of multidimensional data when indexing it into a single-dimensional key-value store, using space filling curves. This means that like values in each dimension are stored physically close together in the datastore. We demonstrate the efficiencies and benefits of the GeoWave indexing algorithm to store and query billions of spatiotemporal data points. We show how this indexing strategy can be used to reduce query and processing times by multiple orders of magnitude using publicly available taxi trip data published by …

Analysis Of Security In Big Data Related To Healthcare, Isabel De La Torre, Begoña García-Zapirain, Miguel López-Coronado

Journal of Digital Forensics, Security and Law

Big data facilitates the processing and management of huge amounts of data. In health, the main information source is the electronic health record with others being the Internet and social media. Health-related data refers to storage in big data based on and shared via electronic means. Why are criminal organisations interested in this data? These organisations can blackmail people with information related to their health condition or sell the information to marketing companies, etc. This article analyses healthcare-related big data security and proposes different solutions. There are different techniques available to help preserve privacy such as data modification techniques, cryptographic …

Security And The Transnational Information Polity, Michael M. Losavio, Adel Said Elmaghraby

Journal of Digital Forensics, Security and Law

Global information and communications technologies create criminal opportunities in which criminal violation and physical proximity are decoupled. As in all our endeavors, the good become the prey of the bad. Murderous and venal exploitation of ICT has followed from the inception of the Internet, threatening all the good it brings and the trust we need so badly as a people. As the work continues to expand the implementation of Smart Cities and the Internet of Things, there will be more opportunities for exploitation of these technologies. We examine the social and liberty risks our data and technology-driven responses may entail.

Hadoop Framework Implementation And Performance Analysis On A Cloud, Göksu Zeki̇ye Özen, Mehmet Tekerek, Rayi̇mbek Sultanov

Turkish Journal of Electrical Engineering and Computer Sciences

The Hadoop framework uses the MapReduce programming paradigm to process big data by distributing data across a cluster and aggregating. MapReduce is one of the methods used to process big data hosted on large clusters. In this method, jobs are processed by dividing into small pieces and distributing over nodes. Parameters such as distributing method over nodes, the number of jobs held in a parallel fashion, and the number of nodes in the cluster affect the execution time of jobs. The aim of this paper is to determine how the numbers of nodes, maps, and reduces affect the performance of …

An Automated Approach For Digital Forensic Analysis Of Heterogeneous Big Data, Hussam Mohammed, Nathan Clarke, Fudong Li

Journal of Digital Forensics, Security and Law

The major challenges with big data examination and analysis are volume, complex interdependence across content, and heterogeneity. The examination and analysis phases are considered essential to a digital forensics process. However, traditional techniques for the forensic investigation use one or more forensic tools to examine and analyse each resource. In addition, when multiple resources are included in one case, there is an inability to cross-correlate findings which often leads to inefficiencies in processing and identifying evidence. Furthermore, most current forensics tools cannot cope with large volumes of data. This paper develops a novel framework for digital forensic analysis of heterogeneous …