206 results match your criteria Big Data[Journal]


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

Big Data 2019 Apr 17. 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

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https://www.liebertpub.com/doi/10.1089/big.2018.0093
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http://dx.doi.org/10.1089/big.2018.0093DOI Listing
April 2019
1 Read

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

Big Data 2019 Mar 20. 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

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http://dx.doi.org/10.1089/big.2018.0130DOI Listing

Seasonal Linear Predictivity in National Football Championships.

Authors:
Giuseppe Jurman

Big Data 2019 Mar;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

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http://dx.doi.org/10.1089/big.2018.0076DOI Listing

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

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

2 University of Pittsburgh, Pittsburgh, Pennsylvania.

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http://dx.doi.org/10.1089/big.2019.29029.ediDOI Listing

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

Big Data 2019 Mar 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

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http://dx.doi.org/10.1089/big.2018.0094DOI Listing

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

Big Data 2019 Mar 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

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

Learning to Rate Player Positioning in Soccer.

Big Data 2019 Mar 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

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http://dx.doi.org/10.1089/big.2018.0054DOI Listing

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

Big Data 2018 Dec 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

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

Big Data, Efficient Markets, and the End of Daily Fantasy Sports As We Know It?

Big Data 2018 Nov 16. Epub 2018 Nov 16.

Department of Business Information and Analytics, Daniels College of Business, University of Denver , Denver, Colorado.

Fantasy sports are a popular way for individuals to add another layer of enjoyment to their interest in sports. While fantasy sports have been around for many years, access to big data sets and computer power to process them is a relatively new phenomenon, as well as the ability to compete in daily competitions and not just season-long campaigns. We posit that access to new and yet unforeseen data, models, and computing power to manage it, when viewed through the lens of efficient market hypothesis, will cause the daily fantasy sports market to change dramatically. Read More

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http://dx.doi.org/10.1089/big.2018.0057DOI Listing
November 2018

Overreaction in Football Wagers.

Big Data 2018 Nov 14. Epub 2018 Nov 14.

Department of Economics, Pomona College , Claremont, California.

Football scores are an imperfect measure of a team's ability, and consequently exaggerate differences in abilities. Those teams that perform the best and the worst are not really so far from average in their ability; thus their future performances regress to the mean. Betting data indicate that gamblers do not fully account for this regression. Read More

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https://www.liebertpub.com/doi/10.1089/big.2018.0036
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http://dx.doi.org/10.1089/big.2018.0036DOI Listing
November 2018
21 Reads

Effects of Pacing Properties on Performance in Long-Distance Running.

Big Data 2018 Nov 13. Epub 2018 Nov 13.

Leiden Institute of Advanced Computer Science (LIACS), Leiden University , Leiden, The Netherlands .

This article focuses on the performance of runners in official races. Based on extensive public data from participants of races organized by the Boston Athletic Association, we demonstrate how different pacing profiles can affect the performance in a race. An athlete's pacing profile refers to the running speed at various stages of the race. Read More

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http://dx.doi.org/10.1089/big.2018.0070DOI Listing
November 2018

Not Every Pass Can Be an Assist: A Data-Driven Model to Measure Pass Effectiveness in Professional Soccer Matches.

Big Data 2019 Mar 21;7(1):57-70. Epub 2018 Sep 21.

1 Center for Human Movement Sciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands.

In professional soccer, nowadays almost every team employs tracking technology to monitor performance during trainings and matches. Over the recent years, there has been a rapid increase in both the quality and quantity of data collected in soccer resulting in large amounts of data collected by teams every single day. The sheer amount of available data provides opportunities as well as challenges to both science and practice. Read More

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http://dx.doi.org/10.1089/big.2018.0067DOI Listing

Electronic Health Record-Based Screening for Substance Abuse.

Big Data 2018 09 19;6(3):214-224. Epub 2018 Sep 19.

Department of Population Health, New York University School of Medicine, New York, New York.

Existing methods of screening for substance abuse (standardized questionnaires or clinician's simply asking) have proven difficult to initiate and maintain in primary care settings. This article reports on how predictive modeling can be used to screen for substance abuse using extant data in electronic health records (EHRs). We relied on data available through Veterans Affairs Informatics and Computing Infrastructure (VINCI) for the years 2006 through 2016. Read More

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http://dx.doi.org/10.1089/big.2018.0002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6154440PMC
September 2018
1 Read

Data-Driven Investment Strategies for Peer-to-Peer Lending: A Case Study for Teaching Data Science.

Big Data 2018 09 17;6(3):191-213. Epub 2018 Sep 17.

Information, Operations, and Management Sciences, NYU Stern School of Business, New York, New York.

We develop a number of data-driven investment strategies that demonstrate how machine learning and data analytics can be used to guide investments in peer-to-peer loans. We detail the process starting with the acquisition of (real) data from a peer-to-peer lending platform all the way to the development and evaluation of investment strategies based on a variety of approaches. We focus heavily on how to apply and evaluate the data science methods, and resulting strategies, in a real-world business setting. Read More

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https://www.liebertpub.com/doi/10.1089/big.2018.0092
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http://dx.doi.org/10.1089/big.2018.0092DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6154448PMC
September 2018
1 Read

Aspects of Data Ethics in a Changing World: Where Are We Now?

Authors:
David J Hand

Big Data 2018 09 17;6(3):176-190. Epub 2018 Sep 17.

Department of Mathematics, Imperial College, London, United Kingdom.

Ready data availability, cheap storage capacity, and powerful tools for extracting information from data have the potential to significantly enhance the human condition. However, as with all advanced technologies, this comes with the potential for misuse. Ethical oversight and constraints are needed to ensure that an appropriate balance is reached. Read More

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http://dx.doi.org/10.1089/big.2018.0083DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6154451PMC
September 2018

On Building Online Visualization Maps for News Data Streams by Means of Mathematical Optimization.

Big Data 2018 06;6(2):139-158

4 Department of Economics, Copenhagen Business School , Frederiksberg, Denmark .

In this article we develop a novel online framework to visualize news data over a time horizon. First, we perform a Natural Language Processing analysis, wherein the words are extracted, and their attributes, namely the importance and the relatedness, are calculated. Second, we present a Mathematical Optimization model for the visualization problem and a numerical optimization approach. Read More

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http://dx.doi.org/10.1089/big.2018.0017DOI Listing

Big-BOE: Fusing Spanish Official Gazette with Big Data Technology.

Big Data 2018 06;6(2):124-138

Departamento Ingeniería Telemática, UC3M-BS, Institute of Financial Big Data, Universidad Carlos III de Madrid , Madrid, Spain .

The proliferation of new data sources, stemmed from the adoption of open-data schemes, in combination with an increasing computing capacity causes the inception of new type of analytics that process Internet of things with low-cost engines to speed up data processing using parallel computing. In this context, the article presents an initiative, called BIG-Boletín Oficial del Estado (BOE), designed to process the Spanish official government gazette (BOE) with state-of-the-art processing engines, to reduce computation time and to offer additional speed up for big data analysts. The goal of including a big data infrastructure is to be able to process different BOE documents in parallel with specific analytics, to search for several issues in different documents. Read More

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http://dx.doi.org/10.1089/big.2018.0007DOI Listing

An Interview with Bart Baesens, One of the Authors of Principles of Database Management.

Big Data 2018 06;6(2):69-71

2 Southampton Business School, University of Southampton , Southampton, United Kingdom .

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http://dx.doi.org/10.1089/big.2018.0044DOI Listing

Deep Learning Method for Denial of Service Attack Detection Based on Restricted Boltzmann Machine.

Big Data 2018 06;6(2):159-169

Institute of Information Technology , Azerbaijan National Academy of Sciences, Baku, Azerbaijan .

In this article, the application of the deep learning method based on Gaussian-Bernoulli type restricted Boltzmann machine (RBM) to the detection of denial of service (DoS) attacks is considered. To increase the DoS attack detection accuracy, seven additional layers are added between the visible and the hidden layers of the RBM. Accurate results in DoS attack detection are obtained by optimization of the hyperparameters of the proposed deep RBM model. Read More

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http://dx.doi.org/10.1089/big.2018.0023DOI Listing

Collective Behavior of Social Bots Is Encoded in Their Temporal Twitter Activity.

Big Data 2018 06;6(2):113-123

1 Percipio, Ltd. , Maribor, Slovenia .

Computational propaganda deploys social or political bots to try to shape, steer, and manipulate online public discussions and influence decisions. Collective behavior of populations of social bots has not been yet widely studied, although understanding of collective patterns arising from interactions between bots would aid social bot detection. In this study, we show that there are significant differences in collective behavior between population of bots and population of humans as detected from their Twitter activity. Read More

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http://dx.doi.org/10.1089/big.2017.0041DOI Listing

How Heterogeneity Affects the Design of Hadoop MapReduce Schedulers: A State-of-the-Art Survey and Challenges.

Big Data 2018 06;6(2):72-95

Department of Computer Science & Engineering, Punjab Engineering College , Chandigarh, India .

MapReduce (MR) computing paradigm and its open source implementation Hadoop have become a de facto standard to process big data in a distributed environment. Initially, the Hadoop system was homogeneous in three significant aspects, namely, user, workload, and cluster (hardware). However, with growing variety of MR jobs and inclusion of different configurations of nodes in the existing cluster, heterogeneity has become an essential part of Hadoop systems. Read More

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http://dx.doi.org/10.1089/big.2018.0013DOI Listing

Editorial Message.

Authors:
Zoran Obradovic

Big Data 2018 06;6(2):67-68

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

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http://dx.doi.org/10.1089/big.2018.29026.zobDOI Listing
June 2018
1 Read

A Data Snapshot Approach for Making Real-Time Predictions in Basketball.

Big Data 2018 06 8;6(2):96-112. Epub 2018 Jun 8.

Kate Tiedemann College of Business, University of South Florida St. Petersburg , St. Petersburg, Florida.

This article proposes a novel approach, called data snapshots, to generate real-time probabilities of winning for National Basketball Association (NBA) teams while games are being played. The approach takes a snapshot from a live game, identifies historical games that have the same snapshot, and uses the outcomes of these games to calculate the winning probabilities of the teams in this game as the game is underway. Using data obtained from 20 seasons worth of NBA games, we build three models and compare their accuracies to a baseline accuracy. Read More

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http://dx.doi.org/10.1089/big.2017.0054DOI Listing
June 2018
2 Reads

FraudBuster: Reducing Fraud in an Auto Insurance Market.

Big Data 2018 03;6(1):3-12

Department of Computer Science and Engineering, iCeNSA, University of Notre Dame , Notre Dame, Indiana.

Nonstandard insurers suffer from a peculiar variant of fraud wherein an overwhelming majority of claims have the semblance of fraud. We show that state-of-the-art fraud detection performs poorly when deployed at underwriting. Our proposed framework "FraudBuster" represents a new paradigm in predicting segments of fraud at underwriting in an interpretable and regulation compliant manner. Read More

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http://dx.doi.org/10.1089/big.2017.0083DOI Listing
March 2018
1 Read

A Literature Survey and Experimental Evaluation of the State-of-the-Art in Uplift Modeling: A Stepping Stone Toward the Development of Prescriptive Analytics.

Big Data 2018 03;6(1):13-41

1 Faculty of Economic and Social Sciences and Solvay Business School, Vrije Universiteit Brussel , Brussels, Belgium .

Prescriptive analytics extends on predictive analytics by allowing to estimate an outcome in function of control variables, allowing as such to establish the required level of control variables for realizing a desired outcome. Uplift modeling is at the heart of prescriptive analytics and aims at estimating the net difference in an outcome resulting from a specific action or treatment that is applied. In this article, a structured and detailed literature survey on uplift modeling is provided by identifying and contrasting various groups of approaches. Read More

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http://dx.doi.org/10.1089/big.2017.0104DOI Listing

Geospatial Analytics in Retail Site Selection and Sales Prediction.

Big Data 2018 03;6(1):42-52

2 TM Geomatics, Telekom Malaysia, Kuala Lumpur, Malaysia .

Studies have shown that certain features from geography, demography, trade area, and environment can play a vital role in retail site selection, largely due to the impact they asserted on retail performance. Although the relevant features could be elicited by domain experts, determining the optimal feature set can be intractable and labor-intensive exercise. The challenges center around (1) how to determine features that are important to a particular retail business and (2) how to estimate retail sales performance given a new location? The challenges become apparent when the features vary across time. Read More

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http://dx.doi.org/10.1089/big.2017.0085DOI Listing

Special Issue on Profit-Driven Analytics.

Big Data 2018 03;6(1):1-2

3 Department of Decision Analytics and Risk, Southampton Business School, University of Southampton , Southampton, United Kingdom .

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http://dx.doi.org/10.1089/big.2018.29025.bbaDOI Listing
March 2018
1 Read

Profit-Based Model Selection for Customer Retention Using Individual Customer Lifetime Values.

Big Data 2018 03;6(1):53-65

1 Department of Decision Sciences and Information Management, KU Leuven , Leuven, Belgium .

The goal of customer retention campaigns, by design, is to add value and enhance the operational efficiency of businesses. For organizations that strive to retain their customers in saturated, and sometimes fast moving, markets such as the telecommunication and banking industries, implementing customer churn prediction models that perform well and in accordance with the business goals is vital. The expected maximum profit (EMP) measure is tailored toward this problem by taking into account the costs and benefits of a retention campaign and estimating its worth for the organization. Read More

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http://dx.doi.org/10.1089/big.2018.0015DOI Listing
March 2018
1 Read

Fake News: A Technological Approach to Proving the Origins of Content, Using Blockchains.

Big Data 2017 12;5(4):356-371

Department of Informatics, School of Engineering and Informatics, University of Sussex , Brighton, United Kingdom .

In this article, we introduce a prototype of an innovative technology for proving the origins of captured digital media. In an era of fake news, when someone shows us a video or picture of some event, how can we trust its authenticity? It seems that the public no longer believe that traditional media is a reliable reference of fact, perhaps due, in part, to the onset of many diverse sources of conflicting information, via social media. Indeed, the issue of "fake" reached a crescendo during the 2016 U. Read More

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http://dx.doi.org/10.1089/big.2017.0071DOI Listing
December 2017
4 Reads

Detecting Bots on Russian Political Twitter.

Big Data 2017 12;5(4):310-324

1 Department of Politics, New York University , New York, New York.

Automated and semiautomated Twitter accounts, bots, have recently gained significant public attention due to their potential interference in the political realm. In this study, we develop a methodology for detecting bots on Twitter using an ensemble of classifiers and apply it to study bot activity within political discussions in the Russian Twittersphere. We focus on the interval from February 2014 to December 2015, an especially consequential period in Russian politics. Read More

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http://dx.doi.org/10.1089/big.2017.0038DOI Listing
December 2017
3 Reads

Computational Propaganda and Political Big Data: Moving Toward a More Critical Research Agenda.

Big Data 2017 12;5(4):273-276

Oxford Internet Institute, University of Oxford , Oxford, United Kingdom .

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http://dx.doi.org/10.1089/big.2017.29024.cprDOI Listing
December 2017

Harvesting Social Signals to Inform Peace Processes Implementation and Monitoring.

Big Data 2017 12;5(4):337-355

1 Department of Computer Science and Engineering, University of Notre Dame , Notre Dame, Indiana.

Peace processes are complex, protracted, and contentious involving significant bargaining and compromising among various societal and political stakeholders. In civil war terminations, it is pertinent to measure the pulse of the nation to ensure that the peace process is responsive to citizens' concerns. Social media yields tremendous power as a tool for dialogue, debate, organization, and mobilization, thereby adding more complexity to the peace process. Read More

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http://dx.doi.org/10.1089/big.2017.0055DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5734239PMC
December 2017
2 Reads

Social Bots: Human-Like by Means of Human Control?

Big Data 2017 12;5(4):279-293

Department of Information Systems, University of Münster , Münster, Germany .

Social bots are currently regarded an influential but also somewhat mysterious factor in public discourse and opinion making. They are considered to be capable of massively distributing propaganda in social and online media, and their application is even suspected to be partly responsible for recent election results. Astonishingly, the term social bot is not well defined and different scientific disciplines use divergent definitions. Read More

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http://www.liebertpub.com/doi/10.1089/big.2017.0044
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http://dx.doi.org/10.1089/big.2017.0044DOI Listing
December 2017
2 Reads

Improving Predictive Accuracy in Elections.

Big Data 2017 12;5(4):325-336

2 NOAA Southern Regional Climate Center, Louisiana State University , Baton Rouge, Louisiana.

The problem of accurately predicting vote counts in elections is considered in this article. Typically, small-sample polls are used to estimate or predict election outcomes. In this study, a machine-learning hybrid approach is proposed. Read More

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http://dx.doi.org/10.1089/big.2017.0047DOI Listing
December 2017

Should We Regulate Digital Platforms?

Authors:
Vasant Dhar

Big Data 2017 12;5(4):277-278

Stern School of Business, New York University , New York, New York.

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http://dx.doi.org/10.1089/big.2017.29023.vdcDOI Listing
December 2017
1 Read

Japan's 2014 General Election: Political Bots, Right-Wing Internet Activism, and Prime Minister Shinzō Abe's Hidden Nationalist Agenda.

Big Data 2017 12 28;5(4):294-309. Epub 2017 Nov 28.

Friedrich-Alexander-Universität Erlangen-Nürnberg , Erlangen, Germany .

In this article, we present results on the identification and behavioral analysis of social bots in a sample of 542,584 Tweets, collected before and after Japan's 2014 general election. Typical forms of bot activity include massive Retweeting and repeated posting of (nearly) the same message, sometimes used in combination. We focus on the second method and present (1) a case study on several patterns of bot activity, (2) methodological considerations on the automatic identification of such patterns and the prerequisite near-duplicate detection, and (3) we give qualitative insights into the purposes behind the usage of social/political bots. Read More

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http://dx.doi.org/10.1089/big.2017.0049DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5733662PMC
December 2017

On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products.

Big Data 2017 09;5(3):246-255

2 Department of Electrical and Computer Engineering, University of Virginia , Charlottesville, Virginia.

Machine learning algorithms increasingly influence our decisions and interact with us in all parts of our daily lives. Therefore, just as we consider the safety of power plants, highways, and a variety of other engineered socio-technical systems, we must also take into account the safety of systems involving machine learning. Heretofore, the definition of safety has not been formalized in a machine learning context. Read More

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September 2017
7 Reads

Detecting Spatial Patterns of Disease in Large Collections of Electronic Medical Records Using Neighbor-Based Bootstrapping.

Big Data 2017 09;5(3):213-224

1 Center for Data Intensive Science, University of Chicago , Chicago, Illinois.

We introduce a method called neighbor-based bootstrapping (NB2) that can be used to quantify the geospatial variation of a variable. We applied this method to an analysis of the incidence rates of disease from electronic medical record data (International Classification of Diseases, Ninth Revision codes) for ∼100 million individuals in the United States over a period of 8 years. We considered the incidence rate of disease in each county and its geospatially contiguous neighbors and rank ordered diseases in terms of their degree of geospatial variation as quantified by the NB2 method. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5647508PMC
September 2017
5 Reads

A Message from the Editor-in-Chief of Big Data.

Authors:

Big Data 2017 09;5(3):175-176

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September 2017
1 Read

DISCRN: A Distributed Storytelling Framework for Intelligence Analysis.

Big Data 2017 09;5(3):225-245

1 Virginia Tech, Falls Church, Virginia.

Storytelling connects entities (people, organizations) using their observed relationships to establish meaningful storylines. This can be extended to spatiotemporal storytelling that incorporates locations, time, and graph computations to enhance coherence and meaning. But when performed sequentially these computations become a bottleneck because the massive number of entities make space and time complexity untenable. Read More

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September 2017
7 Reads

What Is the Role of Artificial Intelligence in Sports?

Authors:
Vasant Dhar

Big Data 2017 09;5(3):173-174

Stern School of Business, New York University , New York, New York.

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September 2017
7 Reads

Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals.

Big Data 2017 09;5(3):197-212

3 Stern School of Business, New York University , New York, New York.

Recent studies show the remarkable power of fine-grained information disclosed by users on social network sites to infer users' personal characteristics via predictive modeling. Similar fine-grained data are being used successfully in other commercial applications. In response, attention is turning increasingly to the transparency that organizations provide to users as to what inferences are drawn and why, as well as to what sort of control users can be given over inferences that are drawn about them. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5647518PMC
September 2017
1 Read

Strength in Numbers: Using Big Data to Simplify Sentiment Classification.

Big Data 2017 09;5(3):256-271

2 School of Business, Stevens Institute of Technology , Hoboken, New Jersey.

Sentiment classification, the task of assigning a positive or negative label to a text segment, is a key component of mainstream applications such as reputation monitoring, sentiment summarization, and item recommendation. Even though the performance of sentiment classification methods has steadily improved over time, their ever-increasing complexity renders them comprehensible by only a shrinking minority of expert practitioners. For all others, such highly complex methods are black-box predictors that are hard to tune and even harder to justify to decision makers. Read More

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September 2017
3 Reads

Predictive Analytics for City Agencies: Lessons from Children's Services.

Authors:
Ravi Shroff

Big Data 2017 09 22;5(3):189-196. Epub 2017 Aug 22.

Center for Urban Science and Progress, New York University , Brooklyn, New York.

Many municipal agencies maintain detailed and comprehensive electronic records of their interactions with citizens. These data, in combination with machine learning and statistical techniques, offer the promise of better decision making, and more efficient and equitable service delivery. However, a data scientist employed by an agency to implement these techniques faces numerous and varied choices that cumulatively can have significant real-world consequences. Read More

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http://dx.doi.org/10.1089/big.2016.0052DOI Listing
September 2017
9 Reads

Research Challenges in Financial Data Modeling and Analysis.

Big Data 2017 09 17;5(3):177-188. Epub 2017 Aug 17.

5 Department of Computer Science, George Washington University , Washington, District of Columbia.

Significant research challenges must be addressed in the cleaning, transformation, integration, modeling, and analytics of Big Data sources for finance. This article surveys the progress made so far in this direction and obstacles yet to be overcome. These are issues that are of interest to data-driven financial institutions in both corporate finance and consumer finance. Read More

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http://dx.doi.org/10.1089/big.2016.0074DOI Listing
September 2017
12 Reads

Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science.

Big Data 2017 06;5(2):85-97

4 Department of Construction Management, University of Washington , Seattle, Washington.

What would data science look like if its key critics were engaged to help improve it, and how might critiques of data science improve with an approach that considers the day-to-day practices of data science? This article argues for scholars to bridge the conversations that seek to critique data science and those that seek to advance data science practice to identify and create the social and organizational arrangements necessary for a more ethical data science. We summarize four critiques that are commonly made in critical data studies: data are inherently interpretive, data are inextricable from context, data are mediated through the sociomaterial arrangements that produce them, and data serve as a medium for the negotiation and communication of values. We present qualitative research with academic data scientists, "data for good" projects, and specialized cross-disciplinary engineering teams to show evidence of these critiques in the day-to-day experience of data scientists as they acknowledge and grapple with the complexities of their work. Read More

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http://dx.doi.org/10.1089/big.2016.0050DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5515123PMC

The Structural Consequences of Big Data-Driven Education.

Authors:
Elana Zeide

Big Data 2017 06;5(2):164-172

1 Center for Information Technology Policy, Princeton University , Princeton, New Jersey.

Educators and commenters who evaluate big data-driven learning environments focus on specific questions: whether automated education platforms improve learning outcomes, invade student privacy, and promote equality. This article puts aside separate unresolved-and perhaps unresolvable-issues regarding the concrete effects of specific technologies. It instead examines how big data-driven tools alter the structure of schools' pedagogical decision-making, and, in doing so, change fundamental aspects of America's education enterprise. Read More

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http://dx.doi.org/10.1089/big.2016.0061DOI Listing
June 2017
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Diversity in Big Data: A Review.

Big Data 2017 06;5(2):73-84

3 Department of Computer Science, Drexel University , Philadelphia, Pennsylvania.

Big data technology offers unprecedented opportunities to society as a whole and also to its individual members. At the same time, this technology poses significant risks to those it overlooks. In this article, we give an overview of recent technical work on diversity, particularly in selection tasks, discuss connections between diversity and fairness, and identify promising directions for future work that will position diversity as an important component of a data-responsible society. Read More

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http://dx.doi.org/10.1089/big.2016.0054DOI Listing

Social and Technical Trade-Offs in Data Science.

Big Data 2017 06;5(2):71-72

4 Senior Researcher, Microsoft Research; Adjunct Associate Professor, UMass Amherst.

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http://dx.doi.org/10.1089/big.2017.29020.sttDOI Listing