隨著感測器技術的成熟與普及。在可想見的未來，智慧型感測器系統將融入人們的生活環境，提供多樣的感測器資料，因此如何管理與應用感測器所蒐集的資料，即成為極具價值的研究課題。本研究計畫將研發一以智慧型商店經營為應用之感測器系統，該系統以感測器系統為平台，利用感測器資料為基礎，進行經營決策協助與購物推薦服務，並以此應用所需技術為主題，以三年為期進行研究。在本年度計畫執行過程中，我們已完成預定完成之研究項目，分別為感測器感測頻率管理技術、多重事件串流之段落規則探勘技術及感測器交易串流之高頻樣型探勘技術，並已發表於國際一流相關期刊及會議。本期中報告茲就本年度所完成的研究成果進行報告。 As the advance of wireless sensor network technologies, sensor network applications have received considerable attention in recent years. In near future, sensor network systems will gradually and seamlessly weave into hu-man’s living space and provide mass and streaming sensor data in various types. In this project, we will build an intelligent store management system, which considers busi-ness decision supports and personalized rec-ommendation based on sensor data, and de-velop the core techniques needed in this ap-plication. In this progress report, we report three research results we achieve in this year. They are (1) efficient sensor data acquisition technique, (2) episode rule mining over multi-ple event streams, and (3) frequent pattern mining over transaction streams.
