Challenges and opportunities with big data

Labrinidis, Alexandros; Jagadish, H. V.

doi:10.14778/2367502.2367572

Cited by 724 publications

(319 citation statements)

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“…Although the emergency of the Solid Disk and Phase Change Memory has alleviated this problem, this gap has not been merged fundamentally. In other words, a newly designed storage system is urgently demanded for targeting at the data processing procedure [8].…”

Section: Stream Computingmentioning

confidence: 99%

A Stream Tilling Approach to Surface Area Estimation for Large Scale Spatial Data in a Shared Memory System

et al. 2017

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Surface area estimation is a widely used tool for resource evaluation in the physical world. When processing large scale spatial data, the input/output (I/O) can easily become the bottleneck in parallelizing the algorithm due to the limited physical memory resources and the very slow disk transfer rate. In this paper, we proposed a stream tilling approach to surface area estimation that first decomposed a spatial data set into tiles with topological expansions. With these tiles, the one-to-one mapping relationship between the input and the computing process was broken. Then, we realized a streaming framework towards the scheduling of the I/O processes and computing units. Herein, each computing unit encapsulated a same copy of the estimation algorithm, and multiple asynchronous computing units could work individually in parallel. Finally, the performed experiment demonstrated that our stream tilling estimation can efficiently alleviate the heavy pressures from the I/O-bound work, and the measured speedup after being optimized have greatly outperformed the directly parallel versions in shared memory systems with multi-core processors.

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Section: Stream Computingmentioning

confidence: 99%

A Stream Tilling Approach to Surface Area Estimation for Large Scale Spatial Data in a Shared Memory System

et al. 2017

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“…Besides, it is the basic of effective technology application that being sufficiently appreciated by users. Therefore, techniques related to big data based on the exact sources must be easy to understand, by explaining the reason of each result [35]. In addition, guaranteeing the quality of data, like veracity, possesses a very high position in the process of data manipulation [4].…”

Section: Technologymentioning

confidence: 99%

“…That might need them to re-establish their culture to promote employees to working on account of data more often [1]. Formulating relevant data standards [34] Grasping and analyzing data constantly [29] Communicating about data privacy and so on [3] Data technology Data techniques must be understood sufficiently [35] Preprocessing the types of data [31] Integrating data [4,6,10,27] People Individual ability Comprehensive talent [31] Individual attitude Top managers must support to big data application [6] Transformational leadership is more suitable for big data firms [36] Task Strategic management…”

Section: Structurementioning

confidence: 99%

Influence of Big Data on Manufacturing Industry and Strategies of Enterprises: A Literature Review

Zhao

Yang

2017

MATEC Web Conf.

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Abstract. Along with the rapid development of information technologies, such as cloud computing, mobile internet and internet of things, and the promotion of IT application, all kinds of data are generated and accumulated rapidly in various ways, big data era is coming, in which enterprises are faced with both opportunities and unprecedented challenges. Various processes, from decision making to operation and from designing to marketing, are being influenced by big data in manufacturing industry. This paper, according to the nature and features of big data, analyzes and extends a classical model of organizational change, Leavitt's model of organizational change, in order to explore the ways for enterprises to cope with challenges and seize chances of development in big data era. Then, using the extended Leavitt's model, opportunities and challenges derive from big data are combed, and approaches to making use of big data and coping with big data are generalized from five perspectives, including task, structure, people, technology and environment.

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“…With the rapid development of IT, communication technology, as well as the digitization in various fields, large amounts of data are being generated every day in different fields (Labrinidis and Jagadish 2012), such as hospitals, factories, mines, government agencies, schools, social networking sites, e-commerce and so on. Compared with traditional data, big data often contains large amounts of unstructured data requiring real-time analysis.…”

Section: Introductionmentioning

confidence: 99%

Granular computing: from granularity optimization to multi-granularity joint problem solving

2016

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Human beings solve problems in different granularity worlds and shift from one granularity world to another quickly. It reflects human beings' intelligence in problem solving to some extent. In the era of big data, some new problems are emerging in real life. For example, traditional big data processing models always compute from raw data, failing to consider the granularity feature of human. Thus, they are hard to solve the 3 V characteristics of big data. Granular computing (GrC) combines the multigranularity thinking pattern of human intelligence with problem solving mode to deal with big data. Based on the related notions and characteristics of GrC, this paper reviews the previous studies of GrC in three progressive levels: granularity optimization, granularity conversion and multi-granularity joint problem solving. Then we proposed the diagram for relationship among three basic modes of GrC. Furthermore, the feasibility of GrC for big data processing is analyzed. Some research prospects of granular computing are given.

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Challenges and opportunities with big data

Cited by 724 publications

References 0 publications

A Stream Tilling Approach to Surface Area Estimation for Large Scale Spatial Data in a Shared Memory System

A Stream Tilling Approach to Surface Area Estimation for Large Scale Spatial Data in a Shared Memory System

Influence of Big Data on Manufacturing Industry and Strategies of Enterprises: A Literature Review

Granular computing: from granularity optimization to multi-granularity joint problem solving

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