Physics-based simulations are often used to model and understand complex physical systems in domains such as fluid dynamics. Such simulations, although used frequently, often suffer from inaccurate or incomplete representations either due to their high computational costs or due to lack of complete physical knowledge of the system. In such situations, it is useful to employ machine learning (ML) to fill the gap by learning a model of the complex physical process directly from simulation data. However, as data generation through simulations is costly, we need to develop models being cognizant of data paucity issues. In such scenarios, it is helpful if the rich physical knowledge of the application domain is incorporated in the architectural design of ML models. We can also use information from physics-based simulations to guide the learning process using aggregate supervision to favorably constrain the learning process. In this article, we propose PhyNet, a deep learning model using physics-guided structural priors and physics-guided aggregate supervision for modeling the drag forces acting on each particle in a computational fluid dynamics-discrete element method. We conduct extensive experiments in the context of drag force prediction and showcase the usefulness of including physics knowledge in our deep learning formulation. PhyNet has been compared with several state-of-the-art models and achieves a significant performance improvement of 7.09% on average. The source code has been made available*.

Choosing the right parameter configurations for recurring jobs running on big data analytics platforms is difficult because there can be hundreds of possible parameter configurations to pick from. Even the selection of parameter configurations is based on different types of applications and user requirements. The difference between the best configuration and the worst configuration can have a performance impact of more than 10 times. However, parameters of big data platforms are not independent, which makes it a challenge to automatically identify the optimal configuration for a broad spectrum of applications. To alleviate these problems, we proposed MonkeyKing, a system that leverages past experience and collects new information to adjust parameter configurations of big data platforms. It can recommend key parameters, which have strong impact on performance according to job types, and then combine deep reinforcement learning (DRL) to optimize key parameters to improve job performance. We choose the current popular deep Q-network (DQN) structure and its four improved algorithms, including DQN, Double DQN, Dueling DQN, and the combined Double DQN and Dueling DQN, and finally found that the combined Double DQN and Dueling DQN has a better effect. Our experiments and evaluations on Spark show that performance can be improved by ∼25% under best conditions.

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. Recently, deep learning (DL) techniques have demonstrated the capability to extract comprehensive features efficiently, using multiple hidden layers, from such huge raw, unstructured, and nonlinear data. Nonlinearity in traffic data is the major cause of inaccuracy in TFP. In this article, we propose a flow strength indicator-based Chronological Dolphin Echolocation-Fuzzy, a bioinspired optimization method with fuzzy logic for incremental learning of deep belief network. Technical indicators provide flow strength features as an input to the model. Hidden layers of DL architecture consequently learn more features and propagate it as an input to next layer for supervised learning. The degree of membership to the features is identified by the membership functions, followed by weight optimization using Dolphin Echolocation algorithm to fit the model for the nonlinear data. Experiments performed on two different data sets, namely Traffic-major roads and performance measurement system-San Francisco (PEMS-SF), show good results for the proposed deep architecture. The analysis of the proposed method using log mean square error and log root mean square deviation acquires a minimum value of 2.4141 and 0.61 for the Traffic-major roads database taken for the time step duration of 1 year and a minimum value of 1.6691 and 0.5208 for PEMS-SF data set for the time step interval of 5 minutes, respectively. These positive results demonstrate key importance of our traffic flow model for the transportation system.

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 fake news, 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. Despite several existing computational solutions on the detection of fake news, the lack of comprehensive and community-driven fake news data sets has become one of major roadblocks. Not only existing data sets are scarce, they do not contain a myriad of features often required in the study such as news content, social context, and spatiotemporal information. Therefore, in this article, to facilitate fake news-related research, we present a fake news data repository FakeNewsNet, which contains two comprehensive data sets with diverse features in news content, social context, and spatiotemporal information. We present a comprehensive description of the FakeNewsNet, demonstrate an exploratory analysis of two data sets from different perspectives, and discuss the benefits of the FakeNewsNet for potential applications on fake news study on social media.

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. Thus, this study proposes a technique for big data clustering using the spark architecture. The proposed technique undergoes two steps for clustering the big data, involving feature selection and clustering, performed in the initial cluster nodes of spark architecture. At first, the initial cluster nodes read the big data from various distributed systems, and the optimal features are selected and placed in the feature vector based on the proposed moth-flame optimization-based bat (MFO-Bat) algorithm, which is designed by integrating MFO and Bat algorithms. Then, the selected features are fed to the final cluster nodes of spark, which uses the sparse-fuzzy C-means method for performing optimal clustering. The performance of proposed MFO-Bat outperformed other existing methods with a maximal classification accuracy of 95.806%, Dice coefficient of 99.181%, and Jaccard coefficient of 98.376%, respectively.

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. However, this is not very well exploited by distributed processing frameworks (e.g., Hadoop, Spark) when analytical queries are posed. These frameworks divide the data into multiple partitions and then process each partition in a separate task, consequently creating tasks based on the total file size and not the actual size of the data to be read. This typically leads to launching more tasks than needed, which, in turn, increases the query execution time and induces significant waste of computing resources. To allow a more efficient use of resources and reduce the query execution time, we propose a method that decides the number of tasks based on the data being read. To this end, we first propose a cost-based model for estimating the size of data read in hybrid layouts. Next, we use the estimated reading size in a multi-objective optimization method to decide the number of tasks and computational resources to be used. We prototyped our solution for Apache Parquet and Spark and found that our estimations are highly correlated (0.96) with the real executions. Further, using TPC-H we show that our recommended configurations are only 5.6% away from the Pareto front and provide 2.1 × speedup compared with default solutions.

Big data implies every action is recorded – everything that moves, every production line, every transaction, every breath – is recorded. Complete information is captured by sensor-networks, social networks, and millions of ‘Internet-of-Things’ devices. But more importantly, progress in machine learning and data mining enables the synthesis of patterns and knowledge from such vast data sets. Processes are controlled by artificial decision makers equipped with all the wisdom of the new information age. What is left for people to do?

Big DataVol. 8, No. 1 Call for PapersFree AccessCall for Special Issue Papers: Big Data in BusinessDeadline for Manuscript Submission: March 20, 2020Guest Editor: Haitham NobaneeGuest Editor: Haitham NobaneeThe University of Oxford, The University of Liverpool, Abu Dhabi UniversitySearch for more papers by this authorPublished Online:14 Feb 2020https://doi.org/10.1089/big.2019.29032.cfp2AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail Big data is a fast-growing field that describes and analyzes huge amounts of structured, semi-structured, or unstructured data that comes from various sources that are vast and complex. It requires advanced analytics applications and sophisticated data-processing software. Big data has revolutionized the way businesses and institutions operate. Business entities from several sizes and sectors are benefiting from big data applications. Business entities revealed such benefits could improve efficiency and increase profitability and growth. Many business entities have several plans for using and implementing big data tools to improve operations, customer satisfaction and loyalty, enhancing corporate governance practices, fighting against fraud, cyber-attacks, money laundry, and other financial crimes. Big data has many other applications in business. This special issue is devoted to publishing high-quality papers that involve theoretical and practical aspects related to big data in business. Reviews on this topic are also welcome.Topics of interest for this special issue include, but are not limited to:The use of big data in business, marketing, finance, and accountingBig data and machine learning in business decision-making and performance evaluationBig data and enterprise risk managementBig data and corporate governanceBig data in marketing, promotions, and social networkingBig data in supply chain managementBig data and predictive algorithms in business, marketing, finance, and accountingBig data and business ethicsBig data and investment decisionsThe use of big data in financial institutions and marketsThe uses of big data in anti-financial crimes such as fraud and money laundry detection.Big data and online shopping.Monetizing big data for dynamic advertisementBig data and customer engagementBig data, machine-to-machine (M2M) analytics to improve product life-cycle managementBig data, automation, and internal efficiencyThe use of big data to compete, capture, innovate, and improve competitive advantageThe use of big data in creating new revenue streamsBig data and credit analysisBig data and insuranceBig data, business lending, and FinTechAll manuscripts should be submitted via the online submission system of Big Data. The Instructions for Authors can be found at www.liebertpub.com/big. The reviewing and selection of papers will be carried out in accordance with the standards of the journal.Submit your paper for peer review online: https://mc.manuscriptcentral.com/bigFiguresReferencesRelatedDetails Volume 8Issue 1Feb 2020 InformationCopyright 2020, Mary Ann Liebert, Inc., publishersTo cite this article:Guest Editor: Haitham Nobanee.Call for Special Issue Papers: Big Data in Business.Big Data.Feb 2020.1-1.http://doi.org/10.1089/big.2019.29032.cfp2Published in Volume: 8 Issue 1: February 14, 2020PDF download