oalib
Search Results: 1 - 10 of 100 matches for " "
All listed articles are free for downloading (OA Articles)
Page 1 /100
Display every page Item
Challenges of Big Data Analysis  [PDF]
Jianqing Fan,Fang Han,Han Liu
Statistics , 2013, DOI: 10.1093/nsr/nwt032
Abstract: Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This article give overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasis on the viability of the sparsest solution in high-confidence set and point out that exogeneous assumptions in most statistical methods for Big Data can not be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.
Big Data: Opportunities and Privacy Challenges  [PDF]
Hervais Simo Fhom
Computer Science , 2015,
Abstract: Recent advances in data collection and computational statistics coupled with increases in computer processing power, along with the plunging costs of storage are making technologies to effectively analyze large sets of heterogeneous data ubiquitous. Applying such technologies (often referred to as big data technologies) to an ever growing number and variety of internal and external data sources, businesses and institutions can discover hidden correlations between data items, and extract actionable insights needed for innovation and economic growth. While on one hand big data technologies yield great promises, on the other hand, they raise critical security, privacy, and ethical issues, which if left unaddressed may become significant barriers to the fulfillment of expected opportunities and long-term success of big data. In this paper, we discuss the benefits of big data to individuals and society at large, focusing on seven key use cases: Big data for business optimization and customer analytics, big data and science, big data and health care, big data and finance, big data and the emerging energy distribution systems, big/open data as enablers of openness and efficiency in government, and big data security. In addition to benefits and opportunities, we discuss the security, privacy, and ethical issues at stake.
Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges  [PDF]
S. Li,S. Dragicevic,F. Anton,M. Sester,S. Winter,A. Coltekin,C. Pettit,B. Jiang,J. Haworth,A. Stein,T. Cheng
Computer Science , 2015,
Abstract: Big data has now become a strong focus of global interest that is increasingly attracting the attention of academia, industry, government and other organizations. Big data can be situated in the disciplinary area of traditional geospatial data handling theory and methods. The increasing volume and varying format of collected geospatial big data presents challenges in storing, managing, processing, analyzing, visualizing and verifying the quality of data. This has implications for the quality of decisions made with big data. Consequently, this position paper of the International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisits the existing geospatial data handling methods and theories to determine if they are still capable of handling emerging geospatial big data. Further, the paper synthesises problems, major issues and challenges with current developments as well as recommending what needs to be developed further in the near future. Keywords: Big data, Geospatial, Data handling, Analytics, Spatial Modeling, Review
Research on Personal Privacy Protection of China in the Era of Big Data  [PDF]
Hui Zhao, Haoxin Dong
Open Journal of Social Sciences (JSS) , 2017, DOI: 10.4236/jss.2017.56012
Abstract: The purpose of this essay is to investigate the privacy concerns of Chinese, and to develop relevant protective measures. The groups are divided into two parts by gender and six parts by ages to analyze the different gender and different age groups of privacy concerns. The significance of this study is protecting personal data property. The data of personal information after finishing processing have economic value. These data once disclosed, will be not reversible, so it is important to study the personal privacy in the era of big data and to initiate and enforce legal and regulatory protection measures. Results show that Chinese’s privacy in public places for Internet records, friends dynamic and age’s awareness is insufficient; most people especially female lack privacy protection skills. Educators need to improve the relevant laws and regulations, promote privacy protection skills and strengthen the conception of privacy.
The potential and challenges of Big data - Recommendation systems next level application  [PDF]
Fatima El Jamiy,Abderrahmane Daif,Mohamed Azouazi,Abdelaziz Marzak
Computer Science , 2015,
Abstract: The continuous increase of data generated provides enormous possibilities of both public and private companies. The management of this mass of data or big data will play a crucial role in the society of the future, as it finds applications in different fields. There are so much potential and extremely useful insights hidden in the huge volume of data. The advanced analysis techniques available including predictive analytics, text mining, semantic analysis are needed to enable organizations to create a competitive advantage through data analyzed with different levels of sophistication, speed and accuracy previously unavailable. Therefore, is it still possible to have that level of sophistication with the ubiquitous numeric ocean that accompanies use every day via connected devices that invade our lives? However, development of big data requires a good understanding of the issues associated with it. And this is the purpose of this paper, which focuses on giving a close-up view of big data analysis, opportunities and challenges.
Personal Data v. Big Data in the EU: Control Lost, Discrimination Found  [PDF]
Maria Bottis, George Bouchagiar
Open Journal of Philosophy (OJPP) , 2018, DOI: 10.4236/ojpp.2018.83014
Abstract: We live in the Big Data age. Firms process an enormous amount of raw, unstructured and personal data derived from innumerous sources. Users consent to this processing by ticking boxes when using movable or immovable devices and things. The users’ control over the processing of their data appears today mostly lost. As algorithms sort people into groups for various causes, both legitimate and illegitimate, fundamental rights are endangered. This article examines the lawfulness of the data subject’s consent to the processing of their data under the new EU General Data Protection Regulation. It also explores the possible inability to fully anonymize personal data and provides an overview of specific “private networks of knowledge”, which firms may construct, in violation of people’s fundamental rights to data protection and to non-discrimination. As the Big Data age is here to stay, both law and technology must together reinforce, in the future, the beneficent use of Big Data, to promote the public good, but also, people’s control on their personal data, the foundation of their individual right to privacy.
Solving Big Data Challenges for Enterprise Application Performance Management  [PDF]
Tilmann Rabl,Mohammad Sadoghi,Hans-Arno Jacobsen,Sergio Gómez-Villamor,Victor Muntés-Mulero,Serge Mankowskii
Computer Science , 2012,
Abstract: As the complexity of enterprise systems increases, the need for monitoring and analyzing such systems also grows. A number of companies have built sophisticated monitoring tools that go far beyond simple resource utilization reports. For example, based on instrumentation and specialized APIs, it is now possible to monitor single method invocations and trace individual transactions across geographically distributed systems. This high-level of detail enables more precise forms of analysis and prediction but comes at the price of high data rates (i.e., big data). To maximize the benefit of data monitoring, the data has to be stored for an extended period of time for ulterior analysis. This new wave of big data analytics imposes new challenges especially for the application performance monitoring systems. The monitoring data has to be stored in a system that can sustain the high data rates and at the same time enable an up-to-date view of the underlying infrastructure. With the advent of modern key-value stores, a variety of data storage systems have emerged that are built with a focus on scalability and high data rates as predominant in this monitoring use case. In this work, we present our experience and a comprehensive performance evaluation of six modern (open-source) data stores in the context of application performance monitoring as part of CA Technologies initiative. We evaluated these systems with data and workloads that can be found in application performance monitoring, as well as, on-line advertisement, power monitoring, and many other use cases. We present our insights not only as performance results but also as lessons learned and our experience relating to the setup and configuration complexity of these data stores in an industry setting.
Mobile, Cloud, and Big Data Computing: Contributions, Challenges, and New Directions in Telecardiology  [PDF]
Jui-Chien Hsieh,Ai-Hsien Li,Chung-Chi Yang
International Journal of Environmental Research and Public Health , 2013, DOI: 10.3390/ijerph10116131
Abstract: Many studies have indicated that computing technology can enable off-site cardiologists to read patients’ electrocardiograph (ECG), echocardiography (ECHO), and relevant images via smart phones during pre-hospital, in-hospital, and post-hospital teleconsultation, which not only identifies emergency cases in need of immediate treatment, but also prevents the unnecessary re-hospitalizations. Meanwhile, several studies have combined cloud computing and mobile computing to facilitate better storage, delivery, retrieval, and management of medical files for telecardiology. In the future, the aggregated ECG and images from hospitals worldwide will become big data, which should be used to develop an e-consultation program helping on-site practitioners deliver appropriate treatment. With information technology, real-time tele-consultation and tele-diagnosis of ECG and images can be practiced via an e-platform for clinical, research, and educational purposes. While being devoted to promote the application of information technology onto telecardiology, we need to resolve several issues: (1) data confidentiality in the cloud, (2) data interoperability among hospitals, and (3) network latency and accessibility. If these challenges are overcome, tele-consultation will be ubiquitous, easy to perform, inexpensive, and beneficial. Most importantly, these services will increase global collaboration and advance clinical practice, education, and scientific research in cardiology.
Genomics and Biological Big Data: Facing Current and Future Challenges around Data and Software Sharing and Reproducibility  [PDF]
Sandra Gesing,Thomas Richard Connor,Ian Taylor
Computer Science , 2015,
Abstract: Novel technologies in genomics allow creating data in exascale dimension with relatively minor effort of human and laboratory and thus monetary resources compared to capabilities only a decade ago. While the availability of this data salvage to find answers for research questions, which would not have been feasible before, maybe even not feasible to ask before, the amount of data creates new challenges, which obviously need new software and data management systems. Such new solutions have to consider integrative approaches, which are not only considering the effectiveness and efficiency of data processing but improve reusability, reproducibility and usability especially tailored to the target user communities of genomic big data. In our opinion, current solutions tackle part of the challenges and have each their strengths but lack to provide a complete solution. We present in this paper the key challenges and the characteristics cutting-edge developments should possess for fulfilling the needs of the user communities to allow for seamless sharing and data analysis on a large scale.
Big Data in Critical Infrastructures Security Monitoring: Challenges and Opportunities  [PDF]
L. Aniello,A. Bondavalli,A. Ceccarelli,C. Ciccotelli,M. Cinque,F. Frattini,A. Guzzo,A. Pecchia,A. Pugliese,L. Querzoni,S. Russo
Computer Science , 2014,
Abstract: Critical Infrastructures (CIs), such as smart power grids, transport systems, and financial infrastructures, are more and more vulnerable to cyber threats, due to the adoption of commodity computing facilities. Despite the use of several monitoring tools, recent attacks have proven that current defensive mechanisms for CIs are not effective enough against most advanced threats. In this paper we explore the idea of a framework leveraging multiple data sources to improve protection capabilities of CIs. Challenges and opportunities are discussed along three main research directions: i) use of distinct and heterogeneous data sources, ii) monitoring with adaptive granularity, and iii) attack modeling and runtime combination of multiple data analysis techniques.
Page 1 /100
Display every page Item


Home
Copyright © 2008-2017 Open Access Library. All rights reserved.