Enabling Quality Control for Entity Resolution: A Human and Machine Cooperation Framework

Even though many machine algorithms have been proposed for entity resolution, it remains very challenging to find a solution with quality guarantees. In this paper, we propose a novel HUman and Machine cOoperation (HUMO) framework for entity resolution (ER), which divides an ER workload between the machine and the human. HUMO enables a mechanism for quality control that can flexibly enforce both precision and recall levels. We introduce the optimization problem of HUMO, minimizing human cost given a quality requirement, and then present three optimization approaches: a conservative baseline one purely based on the monotonicity assumption of precision, a more aggressive one based on sampling and a hybrid one that can take advantage of the strengths of both previous approaches. Finally, we demonstrate by extensive experiments on real and synthetic datasets that HUMO can achieve high-quality results with reasonable return on investment (ROI) in terms of human cost, and it performs considerably better than the state-of-the-art alternatives in quality control.Comment: 12 pages, 11 figures. Camera-ready version of the paper submitted to ICDE 2018, In Proceedings of the 34th IEEE International Conference on Data Engineering (ICDE 2018

Security and privacy protection in RFID-enabled supply chain management

Radio frequency identification-enabled supply chain systems are in an open system environment, where different organisations have different business workflows and operate on different standards and protocols. This supply-chain environment can only be effective if the partners can trust each other and be collaborative. However, Radio Frequency Identification (RFID) involves growing privacy and security concerns in part because humans cannot sense the radio frequency radiation used to read tags and the tags themselves maintain no history of past readings. Counterfeiting in the form of cloned or fraudulent RFID tags is a consequence of a lack of security measures and trust among the partners when RFID technology is used to automate their business transactions. This paper discusses the ways in which privacy and security protection can be maintained in an open-loop RFID supply chain. A cost-based detection of counterfeit tags using different classifiers is presented