Big data หรือ อภิมหาข้อมูล


Big data หรือ อภิมหาข้อมูล
ปัจจุบันข้อมูลถูกสร้างจากแหล่งต่าง ๆ มากมายมีแต่แต่เพิ่มขึ้นไม่มีวันลดลง และมีรูปแบบที่หลากหลาย เช่น ข้อมูลจากโลกโซเชียลหรือเว็บไซต์ต่าง ๆ หรือข้อมูลด้านธนาคาร สุขภาพ และการสื่อสารต่าง ๆ ข้อมูลขนาดใหญ่เหล่านี้จะถูกจัดการและนำไปใช้ประโยชน์สูงสุดได้อย่างไร
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ระบบการจัดการ การวิเคราะห์ และ การจัดเก็บข้อมูลขนาดใหญ่  และระบบความปลอดภัยของข้อมูล



 
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Books / Book Chapters

Big data : using smart big data, analytics and metrics to make better decisions and improve performance / Bernard Marr
Print Location: 658.40380285574 M358B 2015---Central Library
Smarter Business -- S = Start with strategy -- M = Measure metrics and data -- A = Apply analytics -- R = Report results -- T = Transform business.
ISBN: 9781118965832 (paperback)
          1118965833
note: http://library.car.chula.ac.th/record=b2041606
Big Data อภิมหาข้อมูล / เขียนโดย วิกเตอร์ เมเยอร์ ชอนเบอร์เกอร์, เคนเน็ต ชูเคียร์ ; กวี รุจีรัตน์, แปล
Print Location: 306.46 ช318บ, 306.46 ช318บ c.2---Central Library
ความเชื่อมโยงของฐานข้อมูล -- ดาต้าฟิเคชั่น -- ความเสี่ยง -- การควบคุม
ISBN: 9786163260277
note: http://library.car.chula.ac.th/record=b1950318
Cognitive Networked Sensing and Big Data [electronic resource] / by Robert Qiu, Michael Wicks
Cognitive Networked Sensing and Big Data defines high-dimensional data processing in the context of wireless distributed computing and cognitive sensing. This book presents the challenges that are unique to this area such as synchronization caused by the high mobility of the nodes. The authors discuss the integration of software defined radio implementation and testbed development. The book also bridges new research results and contextual reviews. Additionally, the authors provide an examination of large cognitive radio network; hardware testbed; distributed sensing; and distributed computing
ISBN: 9781461445449
ISBN/ISSN: 10.1007/978-1-4614-4544-9

Articles

Big Data and IT-Enabled Services: Ecosystem and Coevolution Some full text available
Big data goes beyond datasets and is more than an IT priority. This article positions big data in the context of business, technology, and innovation, and presents a service-oriented and evolutionary view of big data as a case of disruptive IT-enabled innovation. Big data services are IT-enabled services that emerge as a result of combining diverse data-focused resources from the ecosystem of technologies, market needs, social actors, and other institutional contexts. These services have already had far-reaching effects on business practices. The big data services ecosystem has changed over time through an evolutionary process of variation and selective retention. Businesses and governments should actively experiment with novel big data services by dynamically configuring heterogeneous resources from the ever-changing big data ecosystem.
Published in:IT Professional  (Volume:17 ,  Issue: 2 )Page(s): 20 - 25
ISSN :1520-9202
INSPEC Accession Number: 15056683
DOI: 10.1109/MITP.2015.17
Source: IEEE Journals & Magazines
 
Concurrent Bandwidth Reservation Strategies for Big Data Transfers in High-Performance Networks Some full text available
Because of the deployment of large-scale experimental and computational scientific applications, big data is being generated on a daily basis. Such large volumes of data usually need to be transferred from the data generating center to remotely located scientific sites for collaborative data analysis in a timely manner. Bandwidth reservation along paths provisioned by dedicated high-performance networks (HPNs) has proved to be a fast, reliable, and predictable way to satisfy the transfer requirements of massive time-sensitive data. In this paper, we study the problem of scheduling multiple bandwidth reservation requests (BRRs) concurrently within an HPN while achieving their best average transfer performance. Two common data transfer performance parameters are considered: the Earliest Completion Time (ECT) and the Shortest Duration (SD). Since not all BRRs in one batch can oftentimes be successfully scheduled, the problem of scheduling all BRRs in one batch while achieving their best average ECT and SD are converted into the problem of scheduling as many BRRs as possible while achieving the average ECT and SD of scheduled BRRs, respectively. The aforementioned two problems are proved to be NP-complete problems. Two fast and efficient heuristic algorithms with polynomial-time complexity are proposed. Extensive simulation experiments are conducted to compare their performance with two proposed naive algorithms in various performance metrics. Performance superiority of these two fast and efficient algorithms is verified.
Identifier: ISSN: 1932-4537 ; DOI: 10.1109/TNSM.2015.2430358
Source: IEEE Journals & Magazines
Deep learning applications and challenges in big data analytics Some full text available

Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Companies such as Google and Microsoft are analyzing large volumes of data for business analysis and decisions, impacting existing and future technology. Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process. Complex abstractions are learnt at a given level based on relatively simpler abstractions formulated in the preceding level in the hierarchy. A key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized. In the present study, we explore how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks. We also investigate some aspects of Deep Learning research that need further exploration to incorporate specific challenges introduced by Big Data Analytics, including streaming data, high-dimensional data, scalability of models, and distributed computing. We conclude by presenting insights into relevant future works by posing some questions, including defining data sampling criteria, domain adaptation modeling, defining criteria for obtaining useful data abstractions, improving semantic indexing, semi-supervised learning, and active learning.
Identifier: E-ISSN: 2196-1115 ; DOI: 10.1186/s40537-014-0007-7
Source: Springer Science & Business Media B.V.

How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study Some full text available
Big data has the potential to revolutionize the art of management. Despite the high operational and strategic impacts, there is a paucity of empirical research to assess the business value of big data. Drawing on a systematic review and case study findings, this paper presents an interpretive framework that analyzes the definitional perspectives and the applications of big data. The paper also provides a general taxonomy that helps broaden the understanding of big data and its role in capturing business value. The synthesis of the diverse concepts within the literature on big data provides deeper insights into achieving value through big data strategy and implementation.
Identifier: ISSN: 0925-5273 ; DOI: 10.1016/j.ijpe.2014.12.031
Source: ScienceDirect (Elsevier B.V.)
Managing a Big Data project: The case of Ramco Cements Limited Some full text available
Currently many organizations are in the process of implementing Big Data related projects in order to extract meaningful insights from their data for better decision making. Though there are various frameworks postulating the best practices which should be adopted while implementing analytics projects, they do not cater to the complexities associated with a Big Data project. In this paper our goal is two-fold: to develop a new framework that can provide organizations a holistic roadmap in conceptualizing, planning and successfully implementing Big Data projects and to validate this framework through our observation of a descriptive case study of an organization that has implemented such a project. Although the manufacturing sector has been slow in incorporating analytics in their strategic decision making, the situation is changing with increasing use of analytics for product development, operations and logistics. We explore the Big Data project at a manufacturing company, Ramco Cements Limited, India, describe the system developed by them and highlight the benefits accrued from it. We investigate the entire process by which the project is implemented using the lens of our proposed framework. Our results reveal that a clear understanding of the business problem, a detailed and well planned step-by-step project map, a cross functional project team, adoption of innovative visualization techniques, patronage and active involvement of top management and a culture of data driven decision making are essential for the success of a Big Data project.
Identifier: ISSN: 0925-5273 ; DOI: 10.1016/j.ijpe.2014.12.032
Source: ScienceDirect (Elsevier B.V.)
Obtaining a data quality index with respect to case bases Some full text available
Within case-based reasoning (CBR), terms concerning quality of a case base are mentioned in publications, but partially without clarifications of criteria. When developing a CBR system from scratch, an index for case base quality supports an assessment of the actual cases. In this approach, both theory and an application are demonstrated. An index was defined and subsequently applied within a proof of concept. In addition, various approaches concerning case base quality are demonstrated. Big data occur within a combination of high velocity, great volume and variety of incoming data. New cases are suitable if they are referring to an economic value. Defining an index to measure the case base quality copes with that. In this paper, an overview of the CBR-related index towards the big picture regarding data quality can be seen. To demonstrate weighting, concrete invocations of the defined subindices are itemized with respect to applied data sets. This paper depicts a generic easy-to-use index with respect to case bases.
Language: English
Identifier: ISSN: 2196-8888 ; E-ISSN: 2196-8896 ; DOI: 10.1007/s40595-014-0030-9
Source: Springer Science & Business Media B.V.
Secure big data storage and sharing scheme for cloud tenants Some full text available
The Cloud is increasingly being used to store and process big data for its tenants and classical security mechanisms using encryption are neither sufficiently efficient nor suited to the task of protecting big data in the Cloud. In this paper, we present an alternative approach which divides big data into sequenced parts and stores them among multiple Cloud storage service providers. Instead of protecting the big data itself, the proposed scheme protects the mapping of the various data elements to each provider using a trapdoor function. Analysis, comparison and simulation prove that the proposed scheme is efficient and secure for the big data of Cloud tenants.
Identifier: ISSN: 1673-5447 ; DOI: 10.1109/CC.2015.7122469
Source: IEEE Journals & Magazines
 
The E uropean U nion's Proposed Equality and Data Protection Rules: An Existential Problem for Insurers? Some full text available
Insurance companies use personal data to price personal insurance risks. Innovative data‐collection and processing strategies, including big data, offer the potential for better analysis of traditional risks and for markets in new types of insurance. This paper examines the potential for data protection and anti‐discrimination legislation – both existing and proposed – to threaten not only this potential but also the traditional personal insurance business. It offers a strategy based upon codes of practice and technological innovation that would allow insurers to protect their business and to innovate while meeting the concerns of legislators about discrimination and data protection.
Identifier: ISSN: 0265-0665 ; E-ISSN: 1468-0270 ; DOI: 10.1111/ecaf.12127
Source: John Wiley & Sons, Inc.

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