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nuptsww

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[求助] 幫翻譯一段論文

（Google 或 baidu 的翻譯就不要回了）
T
HE practices of data-driven management and decision making have been pervasive and widely used in today’s industrial, business and governmental applications after initial successes of big data techniques in internet business. The data quality is regarded as a significant issue of industrial process, market success and decision-making activities .
However, more than 41% of the relevant projects would fail if only the original data were used due to the poor or insufficient quality of raw data according to a study by the Meta Group. Missing data which means that electronic data during some period is lost or hidden by uncontrollable factors is one of the major potential flaws in raw data and could result in severe failure. Therefore, the engineers have to sacrifice much time to retrieve this kind of data for further analysis. As a consequence, (semi-)automatic missing data prediction methods have been proposed.
A large collection of data mining and statistical methods have been proposed to improve data quality due to missing data. For example, Ma’s team proposed a good method for missing data prediction. The algorithm focused on recommender systems using improved collaborative filtering method which outperformed the traditional collaborative filtering method. Nogueira et alsolved a practical problem based on the Fast Fuzzy Clustering Algorithm in real world: the prediction of bankruptcy, in which the used data set has missing values. Lei and Wang presents a method for pre-processing the missing observed data by adopting the multiple imputation technique for Macau air pollution index (API) prediction using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The API forecasting performance after missing data pre-processing is better than the conventional case without pre-processing.
In power grid systems, data missing happens so frequently due to the harsh working condition of sensors that classic methods often fail to handle. Expensive critical equipment such as main power transformers are monitored by multiple sensors. Unfortunately, these sensors are not as reliable as the equipment in the harsh open air working condition under the workload of 7*24 hours. Moreover, sensors in remote rural areas such as mountains are usually maintained at an even worse level by workers who received less training than workers in city. Thus, it is normal and inevitable for the sensor system to produce flaw data sets, which lost or hidden some necessary information . These losses affect the data quality so badly that classic data mining and statistical methods alone cannot process these data properly.

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1樓 2015-06-12 16:47:53

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baoshanqiu

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【答案】應(yīng)助回帖

數(shù)據(jù)驅(qū)動管理和決策的運(yùn)用在互聯(lián)網(wǎng)業(yè)務(wù)的大數(shù)據(jù)技術(shù)中取得最初成功以后，已經(jīng)得到普及，現(xiàn)在廣泛用于工業(yè)、商業(yè)和政府部門。數(shù)據(jù)的質(zhì)量被認(rèn)為是工業(yè)生產(chǎn)過程、市場成功以及決策活動的關(guān)鍵。
然而根據(jù)Meta集團(tuán)的研究，如果直接使用原始數(shù)據(jù)，有超過41%的相關(guān)項(xiàng)目會因原始數(shù)據(jù)質(zhì)量差或不足而失敗。缺失數(shù)據(jù)是指因某一時期電子數(shù)據(jù)丟失或因無法控制的因素造成隱匿，是原始數(shù)據(jù)最主要的潛在缺陷之一，可造成嚴(yán)重失效。因此工程師必須花大量時間去恢復(fù)這樣的數(shù)據(jù)作進(jìn)一步分析。結(jié)果就提出了（半）自動化的缺失數(shù)據(jù)預(yù)測方法。
缺失數(shù)據(jù)的存在產(chǎn)生了大量的數(shù)據(jù)挖掘和統(tǒng)計學(xué)方法以改善數(shù)據(jù)質(zhì)量。如Ma的團(tuán)隊(duì)提出了一種預(yù)測缺失數(shù)據(jù)的好方法。其算法集中于采納一種比傳統(tǒng)協(xié)同篩選法優(yōu)越的改良協(xié)同篩選法的推薦系統(tǒng)。Nogueira等利用快速模糊聚類算法解決了現(xiàn)實(shí)生活中的一個實(shí)際問題：預(yù)測破產(chǎn)，其中使用的數(shù)據(jù)組有缺失值。Lei 和 Wang報告了一種采用多重填補(bǔ)技術(shù)預(yù)處理缺失觀察數(shù)據(jù)的方法，利用自適應(yīng)神經(jīng)模糊推理系統(tǒng)(ANFIS)對澳門空氣污染指數(shù)(API)進(jìn)行預(yù)測。經(jīng)缺失數(shù)據(jù)預(yù)處理后API預(yù)測性能比未經(jīng)預(yù)處理的常規(guī)方法更好。
在電網(wǎng)中，由于傳感器處于惡劣的工作條件而造成數(shù)據(jù)缺失屢見不鮮，慣常的處理方法難以奏效。像電力變壓器這類昂貴的關(guān)鍵設(shè)備是由多個傳感器監(jiān)控的。不幸的是，在惡劣的露天工作條件和周7天24小時工作負(fù)荷下，這些傳感器并沒有該設(shè)備那么可靠。此外，在偏遠(yuǎn)的農(nóng)村地區(qū)比如山區(qū)這些傳感器得到的維護(hù)甚至更差。那里參與維護(hù)的工人接受的訓(xùn)練比城里的工人少。因此這樣的傳感器系統(tǒng)產(chǎn)生丟失或隱藏了一些必要信息的缺陷數(shù)據(jù)組是正常的、不可避免的。這些缺失嚴(yán)重影響數(shù)據(jù)的質(zhì)量，單純用傳統(tǒng)的數(shù)據(jù)挖掘和統(tǒng)計方法不能恰當(dāng)?shù)靥幚磉@些數(shù)據(jù)。

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2樓2015-06-24 04:58:06

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