More Like this

1. Cybersecurity in Big Data Era: From Securing Big Data to Data-Driven Security

   https://doi.org/10.1109/TSC.2019.2907247  

   Rawat, Danda B.; Doku, Ronald; Garuba, Moses (March 2019, IEEE Transactions on Services Computing)

   "Knowledge is power" is an old adage that has been found to be true in today's information age. Knowledge is derived from having access to information. The ability to gather information from large volumes of data has become an issue of relative importance. Big Data Analytics (BDA) is the term coined by researchers to describe the art of processing, storing and gathering large amounts of data for future examination. Data is being produced at an alarming rate. The rapid growth of the Internet, Internet of Things (IoT) and other technological advances are the main culprits behind this sustained growth. The data generated is a reflection of the environment it is produced out of, thus we can use the data we get out of systems to figure out the inner workings of that system. This has become an important feature in cybersecurity where the goal is to protect assets. Furthermore, the growing value of data has made big data a high value target. In this paper, we explore recent research works in cybersecurity in relation to big data. We highlight how big data is protected and how big data can also be used as a tool for cybersecurity. We summarize recent works in the form of tables and have presented trends, open research challenges and problems. With this paper, readers can have a more thorough understanding of cybersecurity in the big data era, as well as research trends and open challenges in this active research area. 

   more » « less
2. Big Data in Finance

   Goldstein, Itay; Spatt, Chester; Ye, Mao (April 2021, The Review of financial studies)

   null (Ed.)
3. Profiling Hyperscale Big Data Processing

   https://doi.org/10.1145/3579371.3589082  

   Gonzalez, Abraham; Kolli, Aasheesh; Khan, Samira; Liu, Sihang; Dadu, Vidushi; Karandikar, Sagar; Chang, Jichuan; Asanovic, Krste; Ranganathan, Parthasarathy (January 2023, Proceedings of the 50th Annual International Symposium on Computer Architecture)
4. The Personification of Big Data

   https://doi.org/10.1017/dsi.2019.409  

   Stevenson, Phillip Douglas; Mattson, Christopher Andrew (July 2019, Proceedings of the Design Society: International Conference on Engineering Design)

   Abstract Organizations all over the world, both national and international, gather demographic data so that the progress of nations and peoples can be tracked. This data is often made available to the public in the form of aggregated national level data or individual responses (microdata). Product designers likewise conduct surveys to better understand their customer and create personas. Personas are archetypes of the individuals who will use, maintain, sell or otherwise be affected by the products created by designers. Personas help designers better understand the person the product is designed for. Unfortunately, the process of collecting customer information and creating personas is often a slow and expensive process. In this paper, we introduce a new method of creating personas, leveraging publicly available databanks of both aggregated national level and information on individuals in the population. A computational persona generator is introduced that creates a population of personas that mirrors a real population in terms of size and statistics. Realistic individual personas are filtered from this population for use in product development. 

   more » « less
5. Supervised compression of big data

   https://doi.org/10.1002/sam.11508  

   Joseph, V. Roshan; Mak, Simon (June 2021, Statistical Analysis and Data Mining: The ASA Data Science Journal)

   null (Ed.)