PROVISIONING LARGE-SCALED DATA WITH PARAMETERIZED QUERY PLANS: A CASE STUDY

In a general scenario, the approaches linked to the innovation of large-scaled data seem ordinary; the informational measures of such aspects can differ based on the applications as these are associated with different attributes that may support high data volumes high data quality. Accordingly, the challenges can be identified with an assurance of high-level protection and data transformation with enhanced operation quality. Based on large-scale data applications in different virtual servers, it is clear that the information can be measured by enlisting the sources linked to sensors networked and provisioned by the analysts. Therefore, it is very much essential to track the relevance and issues with enormous information. While aiming towards knowledge extraction, applying large-scaled data may involve the analytical aspects to predict future events. Accordingly, the soft computing approach can be implemented in such cases to carry out the analysis. During the analysis of large-scale data, it is essential to abide by the rules associated with security measures because preserving sensitive information is the biggest challenge while dealing with large-scale data. As high risk is observed in such data analysis, security measures can be enhanced by having provisioned with authentication and authorization. Indeed, the major obstacles linked to the techniques while analyzing the data are prohibited during security and scalability. The integral methods towards application on data possess a better impact on scalability. It is observed that the faster scaling factor of data on the processor embeds some processing elements to the system. Therefore, it is required to address the challenges linked to processors correlating with process visualization and scalability.

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STANDARDS-BASED SERVICES FOR BIG SPATIO-TEMPORAL DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b4-691-2016 ◽

2016 ◽

Vol XLI-B4 ◽

pp. 691-699

Author(s):

P. Baumann ◽

V. Merticariu ◽

A. Dumitru ◽

D. Misev

Keyword(s):

Data Model ◽

Large Scale ◽

Point Clouds ◽

Current Status ◽

Temporal Data ◽

Large Scale Data ◽

Spatio Temporal ◽

High Level ◽

Immense Potential ◽

Scale Data

With the unprecedented availability of continuously updated measured and generated data there is an immense potential for getting new and timely insights – yet, the value is not fully leveraged as of today. The quest is up for high-level service interfaces for dissecting datasets and rejoining them with other datasets – ultimately, to allow users to ask "any question, anytime, on any size" enabling them to "build their own product on the go". <br><br> With OGC Coverages, a concrete, interoperable data model has been established which unifies n-D spatio-temporal regular and irregular grids, point clouds, and meshes. The Web Coverage Service (WCS) suite provides versatile streamlined coverage functionality ranging from simple access to flexible spatio-temporal analytics. Flexibility and scalability of the WCS suite has been demonstrated in practice through massive services run by large-scale data centers. We present the current status in OGC Coverage data and service models, contrast them to related work, and describe a scalable implementation based on the rasdaman array engine.

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Concussion in soccer: a comprehensive review of the literature

Concussion ◽

10.2217/cnc-2020-0004 ◽

2020 ◽

Vol 5 (3) ◽

pp. CNC76 ◽

Cited By ~ 1

Author(s):

James Mooney ◽

Mitchell Self ◽

Karim ReFaey ◽

Galal Elsayed ◽

Gustavo Chagoya ◽

...

Keyword(s):

Lower Risk ◽

Large Scale ◽

Female Athletes ◽

Data Gathering ◽

Review Of The Literature ◽

Comprehensive Review ◽

Large Scale Data ◽

Injury Mechanisms ◽

High Level ◽

Scale Data

Sports-related concussion has been examined extensively in collision sports such as football and hockey. However, historically, lower-risk contact sports such as soccer have only more recently garnered increased attention. Here, we review articles examining the epidemiology, injury mechanisms, sex differences, as well as the neurochemical, neurostructural and neurocognitive changes associated with soccer-related concussion. From 436 titles and abstracts, 121 full texts were reviewed with a total of 64 articles identified for inclusion. Concussion rates are higher during competitions and in female athletes with purposeful heading rarely resulting in concussion. Given a lack of high-level studies examining sports-related concussion in soccer, clinicians and scientists must focus research efforts on large-scale data gathering and development of improved technologies to better detect and understand concussion.

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Security

Computers and Society ◽

10.1093/oso/9780198827085.003.0013 ◽

2019 ◽

Author(s):

Ronald M. Baecker

Keyword(s):

Mobile Phones ◽

Large Scale ◽

Impact Damage ◽

Computer Systems ◽

Personal Data ◽

Sensitive Information ◽

Loss Of Life ◽

Large Scale Data ◽

Financial Losses ◽

Scale Data

Throughout history, humanity has invented valuable technologies and ways to organize society. These innovations are typically accompanied by risks. Fire cooks food, and also provides heat on cold nights. Yet, when left unchecked, fire can cause huge damage as well as loss of life. Cities enabled new forms of community and commerce. However, they brought us more thievery, and made it easier for epidemics to spread. The automobile allowed a separation of locales for work and residence; trucks allowed goods to be shipped long distances. But vehicular accidents have caused far greater injury and loss of life than did mishaps with horses and mules. Information technology, like other technologies, has potential for good and for harm. In the first six chapters, we introduced aspects of human activity, such as education, medicine, and government, in which IT has been transformative and mostly positive. The next three chapters examine areas in which the negatives of IT are dominant, in which risks seem everywhere. This chapter focuses on security. IT security flaws are exploited by outsiders for personal or political gain. In Chapter 8 we shall look at safety, where the risks are often injury or loss of life. In Chapter 9, we shall look at privacy, where the risks are exposure of private, confidential, and even sensitive information. Security is the attribute of a computer system that ensures that it can continue to function properly after an attack. Attacks against computer systems happen routinely now, are in the news almost every week, and are accelerating in numbers and in impact. Damage to both individuals and organizations—financial losses, chaos, and deteriorating morale— is severe. We shall provide a primer on the multitude of ways computer systems, from large networks to mobile phones, can be ‘hacked’ so that they no longer function properly. We shall define the most common kinds of destructive software, often called malware. We will discuss large-scale data breaches, which now happen frequently and expose the personal data of millions to billions of people. The word hackers refers to individuals who disrupt digital technologies and thereby damage the functioning of an institution or a society.

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Multiview Active Learning for Scene Classification with High-Level Semantic-Based Hypothesis Generation

Scientific Programming ◽

10.1155/2020/3878153 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Tuozhong Yao ◽

Wenfeng Wang ◽

Yuhong Gu ◽

Qiuguo Zhu

Keyword(s):

Active Learning ◽

Large Scale ◽

Autonomous Driving ◽

Complex Environments ◽

Scene Classification ◽

Large Scale Data ◽

Potential Applications ◽

Modeling Strategy ◽

High Level ◽

Scale Data

Multiview active learning (MVAL) is a technique which can result in a large decrease in the size of the version space than traditional active learning and has great potential applications in large-scale data analysis. This paper made research on MVAL-based scene classification for helping the computer accurately understand diverse and complex environments macroscopically, which has been widely used in many fields such as image retrieval and autonomous driving. The main contribution of this paper is that different high-level image semantics are used for replacing the traditional low-level features to generate more independent and diverse hypotheses in MVAL. First, our algorithm uses different object detectors to achieve local object responses in the scenes. Furthermore, we design a cascaded online LDA model for mining the theme semantic of an image. The experimental results demonstrate that our proposed theme modeling strategy fits the large-scale data learning, and our MVAL algorithm with both high-level semantic views can achieve significant improvement in the scene classification than traditional active learning-based algorithms.

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