scholarly journals SDN helps Big Data to optimize access to data

2018 ◽  
pp. 297-317
Keyword(s):  
Big Data ◽  
Access To Data

Big Data and Privacy State of the Art

2019 ◽  
pp. 104-158
Author(s):  
Amine Rahmani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Exponential Growth ◽  
Scientific Community ◽  
Ethical Issues ◽  
Massive Data ◽  
Medium Term ◽  
Access To Data ◽  
Fast Access ◽  
The Web

The phenomenon of big data (massive data mining) refers to the exponential growth of the volume of data available on the web. This new concept has become widely used in recent years, enabling scalable, efficient, and fast access to data anytime, anywhere, helping the scientific community and companies identify the most subtle behaviors of users. However, big data has its share of the limits of ethical issues and risks that cannot be ignored. Indeed, new risks in terms of privacy are just beginning to be perceived. Sometimes simply annoying, these risks can be really harmful. In the medium term, the issue of privacy could become one of the biggest obstacles to the growth of big data solutions. It is in this context that a great deal of research is under way to enhance security and develop mechanisms for the protection of privacy of users. Although this area is still in its infancy, the list of possibilities continues to grow.

Big Data and Privacy State of the Art

2021 ◽  
pp. 947-991
Author(s):  
Amine Rahmani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Exponential Growth ◽  
Scientific Community ◽  
Ethical Issues ◽  
Massive Data ◽  
Medium Term ◽  
Access To Data ◽  
Fast Access ◽  
The Web

The phenomenon of big data (massive data mining) refers to the exponential growth of the volume of data available on the web. This new concept has become widely used in recent years, enabling scalable, efficient, and fast access to data anytime, anywhere, helping the scientific community and companies identify the most subtle behaviors of users. However, big data has its share of the limits of ethical issues and risks that cannot be ignored. Indeed, new risks in terms of privacy are just beginning to be perceived. Sometimes simply annoying, these risks can be really harmful. In the medium term, the issue of privacy could become one of the biggest obstacles to the growth of big data solutions. It is in this context that a great deal of research is under way to enhance security and develop mechanisms for the protection of privacy of users. Although this area is still in its infancy, the list of possibilities continues to grow.

Applying the K-Means Algorithm in Big Raw Data Sets with Hadoop and MapReduce

2016 ◽  
pp. 1220-1243
Author(s):  
Ilias K. Savvas ◽  
Georgia N. Sofianidou ◽  
M-Tahar Kechadi
Keyword(s):  
Big Data ◽  
Clustering Algorithm ◽  
File System ◽  
Large Data ◽  
Large Data Sets ◽  
Distributed File System ◽  
Data Sets ◽  
Raw Data ◽  
Hadoop Distributed File System ◽  
Access To Data

Big data refers to data sets whose size is beyond the capabilities of most current hardware and software technologies. The Apache Hadoop software library is a framework for distributed processing of large data sets, while HDFS is a distributed file system that provides high-throughput access to data-driven applications, and MapReduce is software framework for distributed computing of large data sets. Huge collections of raw data require fast and accurate mining processes in order to extract useful knowledge. One of the most popular techniques of data mining is the K-means clustering algorithm. In this study, the authors develop a distributed version of the K-means algorithm using the MapReduce framework on the Hadoop Distributed File System. The theoretical and experimental results of the technique prove its efficiency; thus, HDFS and MapReduce can apply to big data with very promising results.

scholarly journals Improving the data access control using blockchain for healthcare domain

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 901
Author(s):  
Olaosebikan Tahir Yinka ◽  
Su-Cheng Haw ◽  
Timothy Tzen Vun Yap ◽  
Samini Subramaniam
Keyword(s):  
Big Data ◽  
Identity Management ◽  
Performance Metrics ◽  
Data Access ◽  
Data Access Control ◽  
Proposed Model ◽  
Big Data Technologies ◽  
Access To Data ◽  
Authorization Control ◽  
Privacy Issues

Introduction: Unauthorized access to data is one of the most significant privacy issues that hinder most industries from adopting big data technologies. Even though specific processes and structures have been put in place to deal with access authorization and identity management for large databases nonetheless, the scalability criteria are far beyond the capabilities of traditional databases. Hence, most researchers are looking into other solutions, such as big data management. Methods: In this paper, we firstly study the strengths and weaknesses of implementing cryptography and blockchain for identity management and authorization control in big data, focusing on the healthcare domain. Subsequently, we propose a decentralized data access and sharing system that preserves privacy to ensure adequate data access management under the blockchain. In addition, we designed a blockchain framework to resolve the decentralized data access and sharing system privacy issues, by implementing a public key infrastructure model, which utilizes a signature cryptography algorithm (elliptic curve and signcryption). Lastly, we compared the proposed blockchain model to previous techniques to see how well it performed. Results: We evaluated the blockchain on four performance metrics which include throughput, latency, scalability, and security. The proposed blockchain model was tested using a sample of 5000 patients and 500,000 observations. The performance evaluation results further showed that the proposed model achieves higher throughput and lower latency compared to existing approaches when the workload varies up to 10,000 transactions. Discussion: This research reviews the importance of blockchains as they provide infinite possibilities to individuals, companies, and governments.

scholarly journals Salmon, sensors, and translation: The agency of Big Data in environmental governance

2018 ◽  
Vol 36 (5) ◽  
pp. 905-925 ◽  
Author(s):  
Francisco Ascui ◽  
Marcus Haward ◽  
Heather Lovell
Keyword(s):  
Big Data ◽  
Environmental Governance ◽  
Actor Network Theory ◽  
Environmental Data ◽  
Time Data ◽  
Salmon Aquaculture ◽  
Environmental Interactions ◽  
Aquaculture Management ◽  
Access To Data

This paper explores the emerging role of Big Data in environmental governance. We focus on the case of salmon aquaculture management from 2011 to 2017 in Macquarie Harbour, Australia, and compare this with the foundational case that inspired the development of the concept of ‘translation’ in actor-network theory, that of scallop domestication in St Brieuc Bay, France, in the 1970s. A key difference is the salience of environmental data in the contemporary case. Recent dramatic events in the environmental governance of Macquarie Harbour have been driven by increasing spatial and temporal resolution of environmental monitoring, including real-time data collection from sensors mounted on the fish themselves. The resulting environmental data now takes centre stage in increasingly heated debates over how the harbour should be managed: overturning long-held assumptions about environmental interactions, inducing changes in regulatory practices and institutions, fracturing historical alliances and shaping the on-going legitimacy of the industry. Environmental Big Data is now a key actor within the networks that constitute and enact environmental governance. Given its new and unpredictable agency, control over access to data is likely to become critical in future power struggles over environmental resources and their governance.

The Northeast Big Data Innovation Hub: "Enabling Seamless Data Sharing in Industry and Academia" Workshop Report

2017 ◽  
Author(s):  
Jane Greenberg ◽  
◽  
Samantha Grabus ◽  
Florence Hudson ◽  
Tim Kraska ◽  
...  
Keyword(s):  
Big Data ◽  
Data Sharing ◽  
Technology Policy ◽  
Data Sets ◽  
Real World Data ◽  
Academic Impact ◽  
Analysis Technique ◽  
Challenges And Opportunities ◽  
Technological Barriers ◽  
Access To Data

Increasingly, both industry and academia, in fields ranging from biology and social sciences to computing and engineering, are driven by data (Provost & Fawcett, 2013; Wixom, et al, 2014); and both commercial success and academic impact are dependent on having access to data. Many organizations collecting data lack the expertise required to process it (Hazen, et al, 2014), and, thus, pursue data sharing with researchers who can extract more value from data they own. For example, a biosciences company may benefit from a specific analysis technique a researcher has developed. At the same time, researchers are always on the search for real-world data sets to demonstrate the effectiveness of their methods. Unfortunately, many data sharing attempts fail, for reasons ranging from legal restrictions on how data can be used—to privacy policies, different cultural norms, and technological barriers. In fact, many data sharing partnerships that are vital to addressing pressing societal challenges in cities, health, energy, and the environment are not being pursued due to such obstacles. Addressing these data sharing challenges requires open, supportive dialogue across many sectors, including technology, policy, industry, and academia. Further, there is a crucial need for well-defined agreements that can be shared among key stakeholders, including researchers, technologists, legal representatives, and technology transfer officers. The Northeast Big Data Innovation Hub (NEBDIH) took an important step in this area with the recent "Enabling Seamless Data Sharing in Industry and Academia" workshop, held at Drexel University September 29-30, 2016. The workshop brought together representatives from these critical stakeholder communities to launch a national dialogue on challenges and opportunities in this complex space.

scholarly journals An Ecosystem Perspective On Asset Management Information

2017 ◽  
Vol 25 (3) ◽  
pp. 150-157
Author(s):  
Lasse Metso ◽  
Mirka Kans
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Sharing ◽  
Asset Management ◽  
Interview Study ◽  
Management Information ◽  
Management Actions ◽  
Railway Industry ◽  
Business Opportunities ◽  
Access To Data

AbstractBig Data and Internet of Things will increase the amount of data on asset management exceedingly. Data sharing with an increased number of partners in the area of asset management is important when developing business opportunities and new ecosystems. An asset management ecosystem is a complex set of relationships between parties taking part in asset management actions. In this paper, the current barriers and benefits of data sharing are identified based on the results of an interview study. The main benefits are transparency, access to data and reuse of data. New services can be created by taking advantage of data sharing. The main barriers to sharing data are an unclear view of the data sharing process and difficulties to recognize the benefits of data sharing. For overcoming the barriers in data sharing, this paper applies the ecosystem perspective on asset management information. The approach is explained by using the Swedish railway industry as an example.

scholarly journals Real-time immunization trends in under-2 children in Pakistan: insights from big data analysis

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
D A Siddiqi ◽  
A Mirza ◽  
S Abdullah ◽  
V K Dharma ◽  
M T Shah ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Retention Rate ◽  
Immunization Coverage ◽  
Qr Code ◽  
Catchment Population ◽  
Immunization Registry ◽  
Sindh Province ◽  
Access To Data ◽  
Coverage Rates

Abstract Background Despite the provision of free-of-cost vaccines in Pakistan, fully immunized child (FIC) coverage in Sindh province remains low at 49%. In 2012, we developed and piloted the Zindagi Mehfooz (Safe Life; ZM) Digital Immunization Registry, an Android-based platform that enables vaccinators to enroll and track child level immunization data of children in the catchment population. In 2017, ZM was scaled-up across Sindh province and is currently being used by 2,284 vaccinators across 1,526 facilities serving >48 million (m) population. Methods All children under-2 years of age visiting EPI centers are enrolled. At enrollment, caregiver and child bio-data and child immunization history are recorded, and the child is provided with a unique Quick Response (QR) code for identification. For follow-up immunization visits, 3 SMS reminders are sent to caregivers, and upon immunization, child history is retrieved by scanning the QR code and vaccination record updated. ZM allows real-time access to data and generation of monitoring reports. Data from ZM was used to calculate coverage rates, timeliness, and trends for immunization coverage in Sindh. Results From Oct'17 to Dec'19, more than 2.4m children and 0.8m women were enrolled in the Registry, while >17m immunizations were administered. The FIC coverage in 12-23 months old children has increased from 49% (at baseline) to 57% for children enrolled in ZM. Additionally, pentavalent-3 coverage increased from 59% to 68%. Discussion ZM demonstrates the potential of DIRs to improve immunization outcomes within low-resource settings by enabling better child tracking and a higher retention rate. Additionally, the big dataset provides the opportunity to identify real-time trends and provides actionable data for evidence-based decision making. Key messages ZM Immunization Registry has strengthened the current EPI program through increased FIC coverage and timeliness through better tracking of children and increased retention. Big Data from ZM can be used to analyze immunization trends of global relevance, and guide strategic policy decisions for improving immunization coverage and equity, based on actionable data insights.

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