Big Data-Survey

By P. Aruna Sri, Anusha M. 2016

1,578 citations recorded by the source corpus.

Big data is the term for any gathering of information sets, so expensive and complex, that it gets to be hard to process for utilizing customary information handling applications. The difficulties incorporate investigation, catch, duration, inquiry, sharing, stockpiling, Exchange, perception, and protection infringement. To reduce spot business patterns, anticipate diseases, conflict etc., we require bigger data sets when compared with the smaller data sets. Enormous information is hard to work with utilizing most social database administration frameworks and desktop measurements and perception bundles, needing rather enormously parallel programming running on tens, hundreds, or even a large number of servers. In this paper there was an observation on Hadoop architecture, different tools used for big data and its security issues.

Subfields: Management Information Systems

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Citations in the corpus

A Survey on Cleaning Dirty Data Using Machine Learning Paradigm for Big Data Analytics (2018, background): “There are different tools available in market to handle the big data but these tools concernts with few issues [3].”
Digital twin-based smart production management and control framework for the complex product assembly shop-floor (2018, background): “The assembly shop-floor data presents the typical “ 3V ” characteristics of big data [38], i.e., volume (large amount of data), variety (various types and forms of data), and velocity (high data generation speed).”
Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison (2018, background): “In order to extract the value from massive data, big data uses advanced tools and algorithms different from traditional ones [13].”
A Review of Passive RFID Tag Antenna-Based Sensors and Systems for Structural Health Monitoring Applications (2017, background): “However, to enable large-scale pervasive sensor networks which collect big data [9], the sensing platform has to be reliable, energy efﬁcient, and extremely low cost to become a viable long-term solution [10].”
When things matter: A survey on data-centric internet of things (2016, background): “There are three factors or requirements to be considered when designing a distributed storage system (Chen et al., 2014): Consistency: Consistency means to ensure that multiple copies of the same data are identical since server failures and parallel storage may cause inconsistency.”
Can we trust Big Data? Applying philosophy of science to software (2016, methodology): “Here, we follow the analysis provided by Chen et al. (2014) about the uses of the term in commercial contexts.”
Social big data: Recent achievements and new challenges (2015, background): “Social media [9] has become one of the most representative and relevant data sources for big data.”
The role of big data in smart city (2016, result): “Certainly, the characteristics of such data mostly include unstructured features compared with big data collected by other means (M. Chen et al., 2014).”
5G wireless backhaul networks: challenges and research advances (2014, background): “2 I. INTRODUCTION To meet the challenges of the expected traffic volume increase in wireless communications (say that of 2020 as compared to the 2010 level [1]), research on the ext generation cellular networks (or 5G networks), is highly anticipated in the next decade.”
Big Data in Smart Farming – A review (2017, background): “In big data applications, the value chain refers to the sequence of activities from data capture to decision making and data marketing (Chen et al., 2014; Miller and Mork, 2013).”
Smart Clothing: Connecting Human with Clouds and Big Data for Sustainable Health Monitoring (2016, background): “– Beyond-SmartClothing (BSC) on Clouds: Many technologies can be integrated in BSC [65], such as cloud computing, big data [12], machine learning, etc.”
Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges (2015, background): “Other modern approaches have also been proposed in literature taking advantage of technological developments such as cloud computing , parallel processing , indexing and querying for real time utilization (e.g., Chen et al. 2014, Hoffman 2012, Liu et al. 2013, Lu et al. 2013).”
Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges (2016, methodology): “[35] and the real-time analytics applied on Big Data in cloud systems, enables the implementation of these techniques to extract new insights from the data.”
Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey (2018, background): “There are four main types of NoSQL databases: 1) key-value; 2) column-oriented; 3) document-oriented; and 4) graph-based databases [37].”
Disease Prediction by Machine Learning Over Big Data From Healthcare Communities (2017, background): “With the growth in medical data [2], collecting electronic health records (EHR) is increasingly convenient [3].”
Optimized IoT service placement in the fog (2017, background): “, data prefiltering inBigData scenarios [8], preprocessing of data streams from sensor nodes [19], or data processing in smart systems [26].”
Implementing Smart Factory of Industrie 4.0: An Outlook (2016, background): “, Internet of Things (IoT) [1–3], wireless sensor networks [4, 5], big data [6], cloud computing [7–9], embedded system [10], and mobile Internet [11]) are being introduced into the manufacturing environment, which ushers in a fourth industrial revolution.”
MeDShare: Trust-Less Medical Data Sharing Among Cloud Service Providers via Blockchain (2017, background): “With the advent of the technology age and the subsequent collection of huge volumes of data that have ushered in the big data era, sharing data offers attractive value on prospects of which are still uncovering [3].”