对不确定数据的实用驱动数据分析
Utility-driven Data Analytics on Uncertain Data
摘要：
现代物联网（IoT）应用程序生成大量数据，其中大部分以读数、事件和日志条目的对象/项目的形式存在。具体而言，这些物联网数据中的大多数对象都包含丰富的嵌入式信息（例如，频率和不确定性）和不同的重要性级别（例如，项目的单位效用、兴趣度、成本、风险或权重）。数据采掘和分析中的许多现有方法具有局限性，诸如在事务内仅考虑二进制属性，以及具有相等权重或重要性的所有对象/项。为了解决这些问题，提出了一种效用驱动的数据分析算法HUPNU，该算法从不确定数据中同时考虑正效用和负效用来提取高效用模式。该方法可以有效地发现合格的高效用模式，用于风险预测、生产管理、决策等。通过使用开发的垂直概率-效用表与正负效用结构，以及几个有效的修剪策略。实验结果表明，改进的HUPNU方法能够高效地挖掘出合格模式。
英文摘要：
Modern Internet of Things (IoT) applications generate massive amounts of data, much of it in the form of objects/items of readings, events, and log entries. Specifically, most of the objects in these IoT data contain rich embedded information (e.g., frequency and uncertainty) and different level of importance (e.g., unit utility of items, interestingness, cost, risk, or weight). Many existing approaches in data mining and analytics have limitations such as only the binary attribute is considered within a transaction, as well as all the objects/items having equal weights or importance. To solve these drawbacks, a novel utility-driven data analytics algorithm named HUPNU is presented, to extract High-Utility patterns by considering both Positive and Negative unit utilities from Uncertain data. The qualified high-utility patterns can be effectively discovered for risk prediction, manufacturing management, decision-making, among others. By using the developed vertical Probability-Utility list with the Positive-and-Negative utilities structure, as well as several effective pruning strategies. Experiments showed that the developed HUPNU approach performed great in mining the qualified patterns efficiently and effectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
