Research in Big Data News

Research in Big Data


Research in Big Data provides information about the research on applications of big data in a variety of industries including education, transportation, government, and commercial. Research in Big Data examines some of the new technologies available to improve existing systems. Some of these new technologies include cloud computing, new forms of database management systems, and developments in machine learning, artificial intelligence, data mining, and data analysis.

Data Analysis

Data Analysis

Technologies for analyzing data.

Big Data

Big Data

The management of large amounts and large variety of data.


Visualization and Reporting

Dashboards and reporting of data.

Research in Big Data News

Research in Big Data

Jun 1, 2019 Issue 11

Digital Media

Kasera, O'Neill, and Bidwell (2016) promote novel techniques for improving ride sharing platforms including improving sociability, collaboration, and competition for ride sharing drivers. Boyd and Crawford (2012) describe the challenge of access to big data as an opportunity for improving digital media. Zhou and Nath (2013) propose a technique for improving flash memory storage with three index data-partitioning for flash storage devices.

Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon.

Kasera, J., O'Neill, J., & Bidwell, N. (2016). Sociality, tempo & flow: Learning from Namibian ride-sharing. 36-47.

Zhou, X., & Nath, S. (2013). Generic and efficient framework for search trees on flash memory storage systems. GeoInformatica, 17, 417-448.

Data Analytics

Zeng, Yang, and and Shao (2013) propose querying data as a directed graph to increase the capabilities of web scale data for resource description frameworks. Huang, Wang, Ding, and Chaudhuri (2019) compare classification tasks for machine learning in large datasets. Manolescu, Karanasos, Vassalos, and Zoupanos (2011) explore multiple views to increase performance optimization of XQuery algorithms.

Hohman, Head, Caruana, DeLine, and Drucker (2019) construct a visualization tool for understanding machine learning models. Wu, Drucker, Philipose, and Sivalingam (2019) develop a video querying language based on deep neural networks. Larson, Tower, Hadfield, Edge, and White (2018) combine image analysis with text analysis for digital forensics.

Hohman, F., Head, A., Caruana, R., DeLine, R., & Drucker, S. (2019). Gamut: A design probe to understand how data scientists understand machine learning models.

Huang, S., Wang, C., Ding, B., & Chaudhuri, S. (2019). Efficient identification of approximate best configuration of training in large datasets.

Larson, J., Tower, B., Hadfield, D., Edge, D., & White, C. (2018). Using web-scale graph analytics to counter technical support scams.

Manolescu, I., Karanasos, K., Vassalos, V., & Zoupanos, S. (2011). Efficient XQuery rewriting using multiple views. 972-983.

Wu, Y., Drucker, S., Philipose, M., & Sivalingam, L. R. (2019). Querying videos using DNN generated labels.

Zeng, K., Yang, J., & and Shao, B. A. (2013). A distributed graph engine for web scale RDF data.

Technology Regulation

Hiniker, Lee, Sobel, and Choe (2017) research how children are able to regulate their own usage of technology. Luger, Urquhart, Rodden, and Golembewski (2015) explore how to develop the data protection regulations for technology designers. Luger et al. (2015) explains that the General Data Protection Regulation, GDPR, creates challenges for technology designers for data breach notification, consent, privacy and rights to be forgotten.

Hiniker, A., Lee, B., Sobel, K., & Choe, E. K. (2017). Plan & play: Supporting intentional media use in early childhood. 85-95.

Luger, E., Urquhart, L., Rodden, T., & Golembewski, M. (2015). Playing the legal card: Using ideation cards to raise data protection issues within the design process.

Cloud Computing and Data Brokers

The advance of cloud computing presents new opportunities for data brokers in the health care industry (Elhoseny et al., 2018). Elhoseny et al. (2018) propose that the cloud broker acts as an intermediary between the stakeholder devices and the cloud. The stakeholder devices include smartphones for doctors and personal computers for patients (Elhoseny et al., 2018). Gao, Thiebes, and Sunyaev (2018) attempt to develop taxonomy for cloud computing for healthcare. Abdulkadri, Evans, and Ash (2016) discuss opportunities for data brokers for big data analytics in the Caribbean.

Abdulkadri, A., Evans, A., & Ash, T. (2016). An assessment of big data for official statistics in the Caribbean: Challenges and opportunities.

Elhoseny, M., Abdelaziz, A., Salama, A. S., Riad, A. M., Muhammad, K., & Sangaiah, A. K. (2018). A hybrid model of internet of things and cloud computing to manage big data in health services applications. Future Generation Computer Systems, 86, 1383-1394.

Gao, F., Thiebes, S., & Sunyaev, A. (2018). Rethinking the meaning of cloud computing for health care: A taxonomic perspective and future research directions. Journal of Medical Internet Research, 20(7).

Machine Learning and Data in Healthcare

Machine learning can be implemented to prevent fraudulent claims in big data analytics in the healthcare industry (Mehta & Pandit, 2018). Mehta and Pandit (2018) explain how big data analytics can be implemented for fraud detection in health care such as for detecting fraudulent claims. Mehta and Pandit (2018) also explain that the veracity in big data analytics in healthcare refers to the accuracy in finding fraudulent data, duplicates, inconsistencies, missing values, and ambiguous data. Waghade and Karandikar (2018) describe the challenges of manual detection of fraud in healthcare systems.

Another implementation of analyzing fraudulent data in healthcare systems can be for the purpose of securing healthcare systems (Mehta & Pandit, 2018). Mehta and Pandit (2018) explain that data breaches are a significant vulnerability for technological systems in health care. Michelle and Mello (2018) suggest an expansion of penalties and civil litigation awards for the misuse of healthcare information data. Young, Borgetti, and Clapham (2018) explain that fines of $50,000 per data breach and a maximum fine of $1.5 million dollars can be imposed as per the Healthcare Information Portability and Accountability Act.

Data brokers in the healthcare industry have a role of managing the data between the stakeholders in the health care industry (Rothstein, 2017). The stakeholders in health care include patients and clinicians (Elhoseny et al., 2018). Patient data can include data from hospital devices, Internet of Things devices, and electronic health records (Elhoseny et al., 2018). Clinician data can include clinical records and medical information (Elhoseny et al., 2018). Data brokers can act as an intermediary of the data between patients and clinicians (Elhoseny et al., 2018).

Elhoseny, M., Abdelaziz, A., Salama, A. S., Riad, A. M., Muhammad, K., & Sangaiah, A. K. (2018). A hybrid model of internet of things and cloud computing to manage big data in health services applications. Future Generation Computer Systems, 86, 1383-1394.

Mehta, N., & Pandit, A. (2018). Concurrence of big data analytics and healthcare: A systematic review. International Journal of Medical Informatics, 114, 57-65.

Rothstein, M. A. (2017). Structural challenges of precision medicine: Currents in contemporary bioethics. Journal of Law, Medicine & Ethics, 45(2), 274-279. doi:10.1177/1073110517720655

Waghade, S. S., & Karandikar, A. M. (2018). A comprehensive study of healthcare fraud detection based on machine learning. International Journal of Applied Engineering Research, 13(6), 4175-4178.

Young, J. D., Borgetti, S. A., & Clapham, P. J. (2018). Telehealth: Exploring the ethical issues. DePaul Journal of Health Care Law, 19(3), 2.

Resource Description Frameworks

Goasdoue et al. (2013) develop an XRQ language that combines features of XQuery and SPARQL to for a combination of XML and RDF data management. Curino et al. (2019) expand resource managers to develop a large scale big data resource management framework. Hausenblas, Kerrin, Pizzo, Viegas, and Wilson (2013) describe open metadata as a step towards developing open access data and develop a tool for mapping resource description framework data to OData.

Curino, C., and Karanasos, K., and Fumarola, G. M., Huang, B., Chaliparambil, K., Suresh, A., . . . Ramakrishnan, R. (2019). Hydra: a federated resource manager for data-center scale analytics.

Goasdoue, F., Karanasos, K., Katsis, Y., Leblay, J., Manolescu, I., & Zampetakis, S. (2013). Growing Triples on Trees: an XML-RDF Hybrid Model for Annotated Documents. VLDB J., 22, 589-613.

Hausenblas, M., Kerrin, M., Pizzo, M., Viegas, E., & Wilson, N. (2013). Linking Structured Data.

IoT and Data Brokers

The Internet of Things provides opportunities to access more data for patients, thus creating the need to broker the data between patients and clinicians (Elhoseny et al., 2018). Elhoseny et al. (2018) propose a hybrid data model of Internet of Things and cloud computing for health care applications. Banerjee, Hemphill, and Longstreet (2018) discuss the challenges of implementing wearable technology in healthcare with personal health information being shared with third party data brokers. Cheng, Zhang, Hancke, Karnouskos, and Colombo (2018) describe how brokers in publish and subscribe messaging systems can deliver messages for Internet of Things devices for healthcare systems. Mishra, Kumari, Sajit, and Pandey (2018) demonstrate how data brokers can communicate between protocols for effective network transmissions of healthcare devices.

Elhoseny, M., Abdelaziz, A., Salama, A. S., Riad, A. M., Muhammad, K., & Sangaiah, A. K. (2018). A hybrid model of internet of things and cloud computing to manage big data in health services applications. Future Generation Computer Systems, 86, 1383-1394.

Banerjee, S., Hemphill, T., & Longstreet, P. (2018). Wearable devices and healthcare: Data sharing and privacy. The Information Society, 34(1), 49-57.

Cheng, B., Zhang, J., Hancke, G. P., Karnouskos, S., & Colombo, A. W. (2018). Industrial cyberphysical systems: Realizing cloud-based big data infrastructures. IEEE Industrial Electronics Magazine, 12(1), 25-35.

Mishra, A., Kumari, A., Sajit, P., & Pandey, P. (2018). Remote web based ECG monitoring using MQTT protocol for IOT in healthcare. Development, 5(04).

Artificial Intelligence

Horvitz (2016) develop safety practices for artificial intelligence and highlight disclosure, transparency, explainability, self-monitoring, reporting, developing standard protocols, and adherence to best practices. Johnson, Hofmann, Hutton, Bignell, and Hofmann (2016) develop a flexible platform for artificial intelligence that can support research such as multi-agent systems, robotics, and reinforcement learning. Fast and Horvitz (2016) describes some of the ethical considerations that have developed in the advance of artificial intelligence that indicate public perceptions of artificial intelligence becoming dangerous.

Horvitz (2014) describes some of the potential challenges for artificial intelligence such as the ability to influence the outcome of political systems, the need for laws designed for autonomous and semi-autonomous systems, and disclosure of when artificial intelligence systems are communicating with people. Morrison et al. (2017) explore how artificial intelligence can be developed for people with visual disabilities. Crawford and Calo (2016) propose a social systems analysis that reinforces artificial intelligence systems with human feedback to monitor if approaches are successful.

Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature, 538, 311-313.

Fast, E., & Horvitz, E. (2016). Long-term trends in the public perception of artificial intelligence.

Horvitz, E. (2016). Reflections on safety and artificial intelligence.

Horvitz, E. (2014). One hundred year study on artificial intelligence: Reflections and framing.

Morrison, C., Cutrell, E., Dhareshwar, A., Doherty, K., Thieme, A., & Taylor, A. (2017). Imagining artificial intelligence applications with people with visual disabilities using tactile ideation.