Data-intensive applications, challenges, techniques and technologies
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摘要It is already true that Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises.大數據在資訊科技、政策研究人員,以及政府和企業的決策上已經引起極大注意已經是事實。
As the speed of information growth exceeds Moore’s Law at the beginning of this new century, excessive data is making great troubles to human beings.作為資訊擴大的速度超過摩爾定律法則,在這個新世紀的開始,過度數據已經對人類生存造成很重的麻煩However, there are so much potential and highly useful values hidden in the huge volume of data.不過,這是具有潛力的和很高的使用價值隱藏在數據的巨大容量A new scientific paradigm is born as data-intensive scientific discovery (DISD), also known as Big Data problems.1.IntroductionB ig Data has been one of the current and future research frontiers.大數據已經在目前和未來的一個研究新領域In this year, Gartner listed the “Top 10 Strategic Technology Trends For 2013”[158]and “Top 10 Critical Tech Trends For The Next Five Years”[157], and Big Data is listed in the both two.在今年,Gartner列出2013年10大戰略技術和接下來五年十大關鍵的技術發展趨勢,那大數據在這兩個表單都位居第二。
It is right to say that Big Data will revolutionize many fields, including business, the scientific research, public administration, and so on.更確切的說,大數據將在多個領域產生革命,包含商業、科學研究、公共管理等等。
For the definition of the Big Data, there are various different explanations from 3Vs to 4Vs. Doug Laney used volume,velocity and variety, known as 3Vs[96], to characterize the concept of Big Data.對於大數據的定義,有很多不同的解釋從3v到4v。
Doug Laney使用大量、快速和多變被稱為3V,以表示大數據概念。
The term volume is the size of the data set, velocity indicates the speed of data in and out, and variety describes the range of data types and sources. Sometimes, people extend another V according to their special requirements.術語”大量”是數據集的大小,速度只的是數據輸入和輸出的速度,和多變是形容數據類型和來源的範圍。
Sometimes, people extend another V according to their special requirements.有時候,根據特別的要求人們也會延伸另一個V。
The fourth V can be value,variability, or virtual[207].第四個V可以是價值、異變或虛擬More commonly, Big Data is a collection of very huge data sets with a great diversity of types s o that it becomes difficult to process by using state-of-the-art data processing approaches or traditional data processing platforms.通常的情況下,大數據是非常巨大數據集的收集,隨著類型的極大多樣性,所以他通過使用最先進數據處理方式或傳統數據處理平台去處理就顯得困難。
In 2012, Gartner retrieved and gave a more detailed definition as: “Big Data are high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization”.在2012年,Gartner重新找到和給予更多詳細的定義為;大數據更大量、更快速和更多變的資訊資產,需要處理的新形式,來增強決策、洞察發現和流程優化。
More generally, a data set can be called Big Data if it is formidable to perform capture, curation, analysis and visualization on it at the current technologies.更普遍的說,一個數據集如果在目前的技術上,在執行採集、綜合處理、分析和可視化是很強大的,也可以稱為大數據With diversified data provisions, such as sensor networks, telescopes, scientific experiments, and high throughput instruments, the datasets increase at exponential rate[178]and[110]as demonstrated in Fig. 1(source from[67]).隨著多元化數據提供,例如傳感器網路、望遠鏡、科學實驗、和高吞吐量的儀器,數據加指數率和證明在圖1。
The off-the-shelf techniques and technologies that we ready used to store and analyse data cannot work efficiently and satisfactorily.現成的技術和方法,我們準備好儲存和分析數據的使用,不能有效和令人滿意的工作。
The challenges arise from data capture and data curation to data analysis and data visualization.挑戰的出現是從數據取得和數據綜合處理到數據分析和數據可視化。
I n many instances, science is legging behind the real world in the capabilities of discovering the valuable knowledge from massive volume of data.在許多情況下,科學在發掘數據的海量有價值知識的能力是落後於真實的世界。
Based on precious knowledge, we need to develop and create new techniques and technologies to excavate Big Data and benefit our specified purposes.基於寶貴得知是,我們需要去發展和創造新技術和方法去挖掘大數據和有利於我們指定的用途。
Big Data has changed the way that we adopt in doing businesses, managements and researches.大數據已經改變了我們採用做生意、管理、和研究方式。
Data-intensive science especially in data-intensive computing is coming into the world that aims to provide the tools that we need to handle the Big Data problems.數據密集科學,尤其是在數據密集運算是未來成為提供大量我們需求處理大數據問題的工具。
Data-intensive science[18]is emerging as the fourth scientific paradigm in terms of the previous three, namely empirical science, theoretical science and computational science.數據密集科學正在成為第四種科學範例,那前三種的項目,即是經驗科學,理論科學和計算科學。
Thousand years ago, scientists describing the natural phenomenon only based on human empirical evidences, so we call the science at that time as empirical science.一千年前,科學家說明自然現象只有基於人類經驗證明,所以那時候我門科學上稱為經驗科學。
It is also the beginning of science and classified as the first paradigm.他也是科學的開頭和最初範例的類別。