一種簡單的存儲和可視化物聯(lián)網(wǎng)傳感器數(shù)據(jù)的方法

　　物聯(lián)網(wǎng)（IOT）設(shè)計(jì)的應(yīng)用有望產(chǎn)生數(shù)據(jù)填補(bǔ)云提供可操作的信息，令。然而，對于大多數(shù)物聯(lián)網(wǎng)開發(fā)商，流數(shù)據(jù)的云和意義，它需要專家級的數(shù)據(jù)交換和分析技術(shù)的理解。

　　沒有簡單的方法來深入分析數(shù)據(jù)流的實(shí)時(shí)從多個(gè)來源，物聯(lián)網(wǎng)開發(fā)人員可以發(fā)現(xiàn)自己很難理解其應(yīng)用程序的整體功能性能的數(shù)據(jù)質(zhì)量問題的影響。

　　為了幫助這一點(diǎn)，初始狀態(tài)技術(shù)提供了一個(gè)分析服務(wù)，開發(fā)人員可以使用輕松獲得深入了解物聯(lián)網(wǎng)設(shè)備數(shù)據(jù)。然后，他們可以轉(zhuǎn)換成有用的信息，可訪問桌面或移動(dòng)設(shè)備。

　　The need for embedded analytics

　　For any IoT application， the product of interest to its users is the actionable information derived from data streams generated by sensor nodes attached to individuals， equipment， or structures. Among the challenges facing IoT developers， the verification of these data streams requires the ability to visualize and measure the data itself.

　　For this， IoT application developers can take advantage of a growing array of cloud-based services to transform raw data into the required result. Within these data processing workflows， data analytics provides a critical capability for humans to understand the nature of the data to ensure its quality and identify useful events or anomalies that cannot be anticipated in the workflow.

　　For developers， IoT data analytics brings an entirely new set of requirements beyond those familiar to Web developers. While the latter typically requires offline batch processing of large log files， IoT data analytics combines large data repositories （“big data”） with “fast data.” To be effective， IoT applications must process large streams of data in real time to react to real-world events.