247 results match your criteria Big Data[Journal]


Fuzzy Inspired Deep Belief Network for the Traffic Flow Prediction in Intelligent Transportation System Using Flow Strength Indicators.

Big Data 2020 Jul 6. Epub 2020 Jul 6.

School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

Intelligent transportation system (ITS) is an advance leading edge technology that aims to deliver innovative services to different modes of transport and traffic management. Traffic flow prediction (TFP) is one of the key macroscopic parameters of traffic that supports traffic management in ITS. Growth of the real-time data in transportation from various modern equipments, technology, and other resources has led to generate big data, posing a huge concern to deal with. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.0007DOI Listing

Soft Computing Models for Big Data and Internet of Things.

Big Data 2020 Jun 19. Epub 2020 Jun 19.

Delhi Technological University, New Delhi, India.

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2020.29035.cfp2DOI Listing

Internet of Things Data Visualization for Business Intelligence.

Authors:
Neeraj Kumar

Big Data 2020 Jun 19. Epub 2020 Jun 19.

Thapar Institute of Engineering, Thapar University, India.

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2020.29036.cfp2DOI Listing

FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media.

Big Data 2020 Jun;8(3):171-188

Department of Computer Science and Engineering, Arizona State University, Tempe, Arizona, USA.

Social media has become a popular means for people to consume and share the news. At the same time, however, it has also enabled the wide dissemination of , that is, news with intentionally false information, causing significant negative effects on society. To mitigate this problem, the research of fake news detection has recently received a lot of attention. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2020.0062DOI Listing

A Novel Oppositional Chaotic Flower Pollination Optimization Algorithm for Automatic Tuning of Hadoop Configuration Parameters.

Big Data 2020 Jun 19;8(3):218-234. Epub 2020 May 19.

Department of Computer Science and Engineering, KCG College of Technology, Chennai, India.

At present, due to the introduction of the big data era, numerous numbers of data are generated consistently. Many applications utilize big data platforms, namely Spark, Hadoop, Amazon web services, and so on, since these platforms use several parameters for tuning that further enhance the operating performances. It requires a long duration of time to tune the parameters because of the complex relationship and large quantity of parameters. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.0111DOI Listing

Moth-Flame Optimization-Bat Optimization: Map-Reduce Framework for Big Data Clustering Using the Moth-Flame Bat Optimization and Sparse Fuzzy C-Means.

Big Data 2020 Jun 19;8(3):203-217. Epub 2020 May 19.

Department of CSE and NSS Coordinator, JNTUA University, Ananthapuramu, India.

The technical advancements in big data have become popular and most desirable among users for storing, processing, and handling huge data sets. However, clustering using these big data sets has become a major challenge in big data analysis. The conventional clustering algorithms used scalable solutions for managing huge data sets. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.0125DOI Listing

Configuring Parallelism for Hybrid Layouts Using Multi-Objective Optimization.

Big Data 2020 Jun 12;8(3):235-247. Epub 2020 May 12.

Faculty of Computer Science, Technische Universität Dresden, Dresden, Germany.

Modern organizations typically store their data in a raw format in data lakes. These data are then processed and usually stored under hybrid layouts, because they allow projection and selection operations. Thus, they allow (when required) to read less data from the disk. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.0068DOI Listing

Community Detection in Social Networks Using Affinity Propagation with Adaptive Similarity Matrix.

Big Data 2020 Jun 12;8(3):189-202. Epub 2020 May 12.

Department of Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.

Community detection problem is a projection of data clustering where the network's topological properties are only considered for measuring similarities among nodes. Also, finding communities' kernel nodes and expanding a community from kernel will certainly help us to find optimal communities. Among the existing community detection approaches, the affinity propagation (AP)-based method has been showing promising results and does not require any predefined information such as the number of clusters (communities). Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.0143DOI Listing

Internet of Things Data Visualization for Business Intelligence.

Authors:
Neeraj Kumar

Big Data 2020 Jun 5;8(3):167-168. Epub 2020 May 5.

Thapar Institute of Engineering, Thapar University, India.

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2020.29036.cfpDOI Listing

Soft Computing Models for Big Data and Internet of Things.

Big Data 2020 Jun 30;8(3):169-170. Epub 2020 Apr 30.

Delhi Technological University, New Delhi, India.

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2020.29035.cfpDOI Listing

Multimedia Big Data Analytics for Engineering Education.

Big Data 2020 Jun 28;8(3):165-166. Epub 2020 Apr 28.

Brandon University, Canada.

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2020.29034.cfp2DOI Listing

The Evolution of Publication Hotspots in Electronic Health Records from 1957 to 2016 and Differences Among Six Countries.

Big Data 2020 Apr;8(2):89-106

Academic Department, Shanxi Health Education Center, Taiyuan, China.

This study aims to reveal the evolution of publication hotspots in the field of electronic health records (EHRs) and differences among countries. We applied keyword frequency analysis, keyword co-occurrence analysis, principal component analysis, multidimensional scaling analysis, and visualization technology to compare the high-frequency Medical Subject Heading (MeSH) terms in six countries during the periods 1957-2008 and 2009-2016. After 2009, the number of MeSH terms reflecting information exchange and information mining increased, and various types of evaluations based on EHRs and cohort studies significantly increased. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.0024DOI Listing

CDNB: CAVIAR-Dragonfly Optimization with Naive Bayes for the Sentiment and Affect Analysis in Social Media.

Big Data 2020 Apr;8(2):107-124

Department of Computer Science & Engineering, Sant Gadge Baba Amravati University, Amravati, India.

With the advent of the new information technologies, the growth of online reviews regarding an organization or a company or any other sector has been playing a vital role in improving the sector plans and decisions. The vast significance of the online reviews that determine the sentiment polarity is the hectic challenge of the current scenario. Sentiment classification is a process of classifying the text according to the sentimental polarities of opinions, which has positive or negative. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.0130DOI Listing

SecDedoop: Secure Deduplication with Access Control of Big Data in the HDFS/Hadoop Environment.

Authors:
P Ramya C Sundar

Big Data 2020 Apr;8(2):147-163

Department of Computer Science and Engineering, Christian College of Engineering and Technology, Oddanchatram, Dindigul, India.

With the rapid growth of storage providers, data deduplication is an essential storage optimization technique that greatly minimizes data storage costs by storing a unique copy of duplicate data. Nowadays, deduplication introduces various new challenges such as security and insufficient space issue. Hence, in this article, we propose a secure data deduplication with access control of big data over HDFS (Hadoop )/Hadoop environment, called SecDedoop. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.0120DOI Listing

Optimal Feature Selection for Big Data Classification: Firefly with Lion-Assisted Model.

Big Data 2020 Apr;8(2):125-146

Dr. Mahalingam College of Engineering and Technology, Pollachi, India.

In this article, the proposed method develops a big data classification model with the aid of intelligent techniques. Here, the Parallel Pool Map reduce Framework is used for handling big data. The model involves three main phases, namely (1) feature extraction, (2) optimal feature selection, and (3) classification. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.0022DOI Listing

Big Data in Business.

Authors:
Haitham Nobanee

Big Data 2020 02;8(1)

The University of Oxford, The University of Liverpool, Abu Dhabi University.

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.29032.cfp2DOI Listing
February 2020

Certification or Advanced Degrees.

Authors:
Dan Holle

Big Data 2020 02;8(1):2-4

Pfizer Global Research and Development, Groton, Connecticut.

The value of training for a data sciences professional is in the eye of the beholder. And dependent on the scope and breadth of that training and the cost and time frame of that training. Value for the employee may differ from value for the employer. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.0172DOI Listing
February 2020

Stock Market Prediction Using Optimized Deep-ConvLSTM Model.

Big Data 2020 02;8(1):5-24

Jaypee University of Engineering & Technology, Raghogarh, Guna, Madhya Pradesh, India.

Stock market prediction acts as a challenging area for the investors for obtaining the profits in the financial markets. A greater number of models used in stock market forecasting is not capable of providing an accurate prediction. This article proposes a stock market prediction system that effectively predicts the state of the stock market. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2018.0143DOI Listing
February 2020

A Web Application for Interactive Visualization of European Basketball Data.

Authors:
Guillermo Vinué

Big Data 2020 02 31;8(1):70-86. Epub 2020 Jan 31.

Faculty of Mathematics, University of Valencia, Valencia, Spain.

The statistical analysis of basketball games is a fast-growing field. Certainly, basketball data are scientifically relevant because an appropriate analysis provides a great deal of information about the performance of both players and teams. The number of games played each season generates a large amount of data worth analyzing. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2018.0124DOI Listing
February 2020

SOOM: Sort-Based Optimizer for Big Data Multi-Query.

Big Data 2020 02 30;8(1):38-61. Epub 2020 Jan 30.

Faculty of Computers and Information, Cairo University, Cairo, Egypt.

Mostly, sorting of data is a common operation in many applications, which causes the consumption of resources and thus leads to computation overheads. Regarding the context of Big Data multi-query, the shared sort operations are fairly large, which incur high-cost I/Os whether explicit or implicit. In particular, Big Data multi-query, including aggregation and sort operations, takes long execution time due to reshuffle of the same data multiple times using similar tasks. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.0023DOI Listing
February 2020

STDADS: An Efficient Slow Task Detection Algorithm for Deadline Schedulers.

Big Data 2020 02 29;8(1):62-69. Epub 2020 Jan 29.

National Institute of Technology, Jalandhar, India.

The MapReduce programming model was designed and developed for Google File System to efficiently process large-scale distributed data sets. The open source implementation of this Google project was called the Apache Hadoop. Hadoop architecture includes Hadoop MapReduce and Hadoop Distributed File System (HDFS). Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.0039DOI Listing
February 2020

Using Behavioral Analytics to Predict Customer Invoice Payment.

Big Data 2020 02 23;8(1):25-37. Epub 2020 Jan 23.

Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey.

Experiences from various industries show that companies may have problems collecting customer invoice payments. Studies report that almost half of the small- and medium-sized enterprise and business-to-business invoices in the United States and United Kingdom are paid late. In this study, our aim is to understand customer behavior regarding invoice payments, and propose an analytical approach to learning and predicting payment behavior. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2018.0116DOI Listing
February 2020

Mining the Thin Air-for Understanding of Urban Society.

Big Data 2019 12;7(4):262-275

School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.

We explore the potential of crowd-sourced information on human mobility and activities in an urban population drawn from a significant fraction of smartphones in the Los Angeles basin during February-May 2015. The raw dataset was collected by WeFi, a smartphone app provider. The dataset is noisy, irregular, and lean; however, it is large scale (over a billion events), cheap to collect, and arguably unbiased. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.0026DOI Listing
December 2019

Editorial Message.

Authors:
Zoran Obradovic

Big Data 2019 12;7(4):216-217

Department of Computer and Information Sciences, Temple University, Philadelphia, Pennsylvania.

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.29033.zobDOI Listing
December 2019

An Experience-Centered Approach to Training Effective Data Scientists.

Big Data 2019 12;7(4):249-261

Center for Data Science and Public Policy, Computer Science Department and Harris School of Public Policy, University of Chicago, Chicago, Illinois.

Like medicine, psychology, or education, data science is fundamentally an applied discipline, with most students who receive advanced degrees in the field going on to work on practical problems. Unlike these disciplines, however, data science education remains heavily focused on theory and methods, and practical coursework typically revolves around cleaned or simplified data sets that have little analog in professional applications. We believe that the environment in which new data scientists are trained should more accurately reflect that in which they will eventually practice, and we propose here a data science master's degree program that takes inspiration from the residency model used in medicine. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.0100DOI Listing
December 2019

Deep Learning on Big, Sparse, Behavioral Data.

Big Data 2019 12;7(4):286-307

Technology, Operations & Statistics Department, NYU Stern School of Business, New York, New York.

The outstanding performance of deep learning (DL) for computer vision and natural language processing has fueled increased interest in applying these algorithms more broadly in both research and practice. This study investigates the application of DL techniques to classification of large sparse behavioral data-which has become ubiquitous in the age of big data collection. We report on an extensive search through DL architecture variants and compare the predictive performance of DL with that of carefully regularized logistic regression (LR), which previously (and repeatedly) has been found to be the most accurate machine learning technique generally for sparse behavioral data. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.0095DOI Listing
December 2019

Transforming Finance Into Vision: Concurrent Financial Time Series as Convolutional Nets.

Big Data 2019 12;7(4):276-285

Center for Data Science, Stern School of Business, SCT Capital Management, New York University, New York, New York.

We present a novel representation for multiple synchronized financial time series as images, motivated by deep learning methods in machine vision. The research pursues two related strands of inquiry. The first is to transform concurrent synchronized time series analysis-one that is prevalent in Finance and other domains-into a machine vision problem so that the standard deep learning machinery such as convolutional nets can be applied to the transformed problem. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.0139DOI Listing
December 2019

Interview with Dr. Silvio Carta, Author of the Book (Routledge 2019).

Authors:
Silvio Carta

Big Data 2019 12 1;7(4):218-220. Epub 2019 Nov 1.

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.0083DOI Listing
December 2019

Big Data in Business.

Authors:
Haitham Nobanee

Big Data 2019 12 7;7(4):215. Epub 2019 Oct 7.

The University of Oxford, The University of Liverpool, Abu Dhabi University.

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.29032.cfpDOI Listing
December 2019

Editorial Message.

Authors:
Zoran Obradovic

Big Data 2019 09;7(3):139

Department of Computer and Information Sciences, Temple University, Philadelphia, Pennsylvania.

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.29031.zobDOI Listing
September 2019
1 Read

Taylor and Gradient Descent-Based Actor Critic Neural Network for the Classification of Privacy Preserved Medical Data.

Big Data 2019 09;7(3):176-191

PDM University, Bahadurgarh (Delhi NCR), HR, India.

Classification of the privacy preserved medical data is the domain of the researchers as it stirs the importance behind hiding the sensitive data from the third-party authenticator. Ensuring the privacy of the medical records and using the disease prediction mechanisms played a remarkable role in peoples' lives such that the earlier detection of the diseases is required for earlier diagnosis. Accordingly, this article proposes a method, named Taylor gradient descent (TGD)-based actor critic neural network (ACNN), which concentrates on performing the medical data classification. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2018.0166DOI Listing
September 2019

Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review.

Big Data 2019 12 14;7(4):221-248. Epub 2019 Aug 14.

Department of Pediatrics, Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.

The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers, yet its performance competes with the most complex classifiers in the literature. The core of this classifier depends mainly on measuring the distance or similarity between the tested examples and the training examples. This raises a major question about which distance measures to be used for the KNN classifier among a large number of distance and similarity measures available? This review attempts to answer this question through evaluating the performance (measured by accuracy, precision, and recall) of the KNN using a large number of distance measures, tested on a number of real-world data sets, with and without adding different levels of noise. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2018.0175DOI Listing
December 2019

BART: BAckward Regression Trimming.

Authors:
Bart Baesens

Big Data 2019 09 2;7(3):207-213. Epub 2019 Aug 2.

Faculty of Economics and Business, KU Leuven, Belgium.

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.0081DOI Listing
September 2019

Improving Prediction of High-Cost Health Care Users with Medical Check-Up Data.

Big Data 2019 09 27;7(3):163-175. Epub 2019 Jun 27.

Technology Management, Economics and Policy Graduate Program, Department of Industrial Engineering, Seoul National University, Seoul, Republic of Korea.

Studies found that a small portion of the population spent the majority of health care resources, and they highlighted the importance of predicting high-cost users in the health care management and policy. Most prior research on high-cost user prediction models are based on diagnosis data with additional cost and health care utilization data to improve prediction accuracy. To further improve the prediction of high-cost users, researchers have been testing various new data sources such as self-reported health status data. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2018.0096DOI Listing
September 2019
1 Read

Artificial Intelligence in China.

Big Data 2019 06;7(2):85-86

2 Department of Computers and Electrical Engineering, Rostock University, Germany.

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.29030.ediDOI Listing
June 2019
3 Reads

Midterm Power Load Forecasting Model Based on Kernel Principal Component Analysis and Back Propagation Neural Network with Particle Swarm Optimization.

Big Data 2019 06 8;7(2):130-138. Epub 2019 Apr 8.

School of Automation, Nanjing University of Science and Technology, Nanjing, China.

To improve the accuracy of midterm power load forecasting, a forecasting model is proposed by combing kernel principal component analysis (KPCA) with back propagation neural network. First, the dimension of the input space is reduced by KPCA, then input the data set to the neural network model, optimized by particle swarm optimization. The monthly average of daily peak loads is forecasted to modify the daily forecast values and output the daily peak load in the end. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2018.0118DOI Listing
June 2019
2 Reads

An Empirical Study on the Artificial Intelligence Writing Evaluation System in China CET.

Authors:
Xiaoxia Lu

Big Data 2019 06 10;7(2):121-129. Epub 2019 May 10.

School of Foreign Language Studies, Xi'an University, Shaanxi, China.

The Artificial Intelligence Writing Evaluation system is widely used in China College English writing. It provides for both teachers and the English learners services of automated composition evaluation on the net in order that teacher's working load can be reduced and they can learn directly about the students' English writing level and that the students' English writing will be improved. Juku automated writing evaluation (AWE) is one of the most used systems among colleges and universities in China. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2018.0151DOI Listing
June 2019
1 Read

Abnormal Data Region Discrimination and Cross-Monitoring Points Historical Correlation Repair of Water Intake Data.

Big Data 2019 06 10;7(2):99-113. Epub 2019 May 10.

3 Water Resources Management Center, Ministry of Water Resources, Beijing, China.

For the problems of abnormal values existing in the water intake monitoring data and centralized uploaded report, the abnormal data region discrimination (ADRD) algorithm and the cross-monitoring points historical correlation repair (CMHCR) method are proposed to discriminate and repair the abnormal data. The characteristics of abnormal data distribution are analyzed, and the ADRD algorithm is proposed. ADRD uses the relationship between 0 values and the abnormal large value, and the ratio of the abnormal large value to the expectation to distinguish the abnormal data region. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2018.0148DOI Listing
June 2019
1 Read

Multiple Targets Tracking with Big Data-Based Measurement for Extended Binary Phase Shift Keying Transceiver.

Big Data 2019 06 29;7(2):87-98. Epub 2019 Apr 29.

2 School of Information Science and Engineering, Southeast University, Nanjing, China.

Extended binary phase shift keying (EBPSK) transmit-receive system is considered as a high-resolution radar tracking system. The target kinematic states can be estimated from a time series of target range and velocity measurements. The measurements usually have a huge amount of data. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2018.0127DOI Listing
June 2019
1 Read

Adjusting to the GDPR: The Impact on Data Scientists and Behavioral Researchers.

Big Data 2019 09 27;7(3):140-162. Epub 2019 Apr 27.

Institute of Service Science, National Tsing Hua University, Hsinchu, Taiwan.

Rapid growth in the availability of behavioral big data (BBD) has outpaced the speed of updates to ethical research codes and regulation of data privacy and human subjects' data collection, storage, and use. The introduction of the European Union's (EU's) General Data Protection Regulation (GDPR) in May 2018 will have far-reaching effects on data scientists and researchers who use BBD, not only in the EU, but around the world. Consequently, many companies are struggling to comply with the Regulation. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2018.0176DOI Listing
September 2019
1 Read

Efficient State Management for Scaling Out Stateful Operators in Stream Processing Systems.

Big Data 2019 09 17;7(3):192-206. Epub 2019 Apr 17.

Beijing Engineering Research Center of Massive Language Information Processing and Cloud Computing Application, School of Computer Science, Beijing Institute of Technology, Beijing, China.

Many big data applications require real-time analysis of continuous data streams. Stream Processing Systems (SPSs) are designed to act on real-time streaming data using continuous queries consisting of interconnected operators. The dynamic nature of data streams, for example, fluctuation in data arrival rates and uneven data distribution, can cause an operator to be a bottleneck one. Read More

View Article

Download full-text PDF

Source
https://www.liebertpub.com/doi/10.1089/big.2018.0093
Publisher Site
http://dx.doi.org/10.1089/big.2018.0093DOI Listing
September 2019
6 Reads

An Improved Particle Filtering Algorithm Using Different Correlation Coefficients for Nonlinear System State Estimation.

Big Data 2019 06 20;7(2):114-120. Epub 2019 Mar 20.

State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China.

Particle filtering (PF) algorithm has found an increasingly wide utilization in many fields at present, especially in nonlinear and non-Gaussian situations. Because of the particle degeneracy limitation, various resampling methods have been researched. This article proposed an improved PF algorithm combining with different rank correlation coefficients to overcome the shortcomings of degeneracy. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2018.0130DOI Listing
June 2019
4 Reads

Seasonal Linear Predictivity in National Football Championships.

Authors:
Giuseppe Jurman

Big Data 2019 03;7(1):21-34

Fondazione Bruno Kessler, Povo, Italy.

Predicting the results of sport matches and competitions is a growing research field, benefiting from the increasing amount of available data and novel data analytics techniques. Excellent forecasts can be achieved by advanced statistical and machine learning methods applied to detailed historical data, especially in very popular sports such as football (soccer). Here, we show that despite the large number of confounding factors, the results of a football team in longer competitions (e. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2018.0076DOI Listing
March 2019
1 Read

Sports Analytics in the Era of Big Data: Moving Toward the Next Frontier.

Big Data 2019 03;7(1):1-2

2 University of Pittsburgh, Pittsburgh, Pennsylvania.

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2019.29029.ediDOI Listing
March 2019
1 Read

Keeping it 100: Social Media and Self-Presentation in College Football Recruiting.

Big Data 2019 03 13;7(1):3-20. Epub 2019 Mar 13.

Department of Management Sciences, University of Iowa, Iowa City, Iowa.

Social media provides a platform for individuals to craft personal brands and influence their perception by others, including potential employers. Yet there remains a need for more research investigating the relationship between individuals' online identities and offline outcomes. This study focuses on the context of college football recruiting, specifically on the relationship between recruits' Twitter activities and coaches' scholarship offer decisions. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2018.0094DOI Listing
March 2019
1 Read

Finding Roles of Players in Football Using Automatic Particle Swarm Optimization-Clustering Algorithm.

Big Data 2019 03 15;7(1):35-56. Epub 2019 Feb 15.

3 KDD Lab, ISTI-CNR, Pisa, Italy.

Recently, professional team sport organizations have invested their resources to analyze their own and opponents' performance. So, developing methods and algorithms for analyzing team sports has become one of the most popular topics among data scientists. Analyzing football is hard because of its complexity, number of events in each match, and constant flow of circulation of the ball. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2018.0069DOI Listing

Learning to Rate Player Positioning in Soccer.

Big Data 2019 03 23;7(1):71-82. Epub 2019 Jan 23.

Institute of Information Systems, Leuphana University, Lüneburg, Germany.

We investigate how to learn functions that rate game situations on a soccer pitch according to their potential to lead to successful attacks. We follow a purely data-driven approach using techniques from deep reinforcement learning to valuate multiplayer positionings based on positional data. Empirically, the predicted scores highly correlate with dangerousness of actual situations and show that rating of player positioning without expert knowledge is possible. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2018.0054DOI Listing
March 2019
2 Reads

Visualizing a Team's Goal Chances in Soccer from Attacking Events: A Bayesian Inference Approach.

Big Data 2018 12 13;6(4):271-290. Epub 2018 Dec 13.

Stratagem Technologies, London, United Kingdom.

We consider the task of determining the number of chances a soccer team creates, along with the composite nature of each chance-the players involved and the locations on the pitch of the assist and the chance. We infer this information using data consisting solely of attacking events, which the authors believe to be the first approach of its kind. We propose an interpretable Bayesian inference approach and implement a Poisson model to capture chance occurrences, from which we infer team abilities. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1089/big.2018.0071DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6306690PMC
December 2018
2 Reads