Thanh T. L. Tran

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Recent innovations in RFID technology are enabling large-scale cost-effective deployments in retail, healthcare, pharmaceuticals and supply chain management. The advent of mobile or handheld readers adds significant new challenges to RFID stream processing due to the inherent reader mobility, increased noise, and incomplete data. In this paper, we address(More)
The problem of privately releasing data is to provide a version of a dataset without revealing sensitive information about the individuals who contribute to the data. The model of differential privacy allows such private release while providing strong guarantees on the output. A basic mechanism achieves differential privacy by adding noise to the frequency(More)
Uncertain data streams, where data is <i>incomplete, imprecise</i>, and even <i>misleading</i>, have been observed in many environments. Feeding such data streams to existing stream systems produces results of unknown quality, which is of paramount concern to monitoring applications. In this paper, we present the PODS system that supports stream processing(More)
Differential privacy is fast becoming the method of choice for releasing data under strong privacy guarantees. A standard mechanism is to add noise to the counts in contingency tables derived from the dataset. However, when the dataset is sparse in its underlying domain, this vastly increases the size of the published data, to the point of making the(More)
Uncertain data streams, where data are incomplete and imprecise, have been observed in many environments. Feeding such data streams to existing stream systems produces results of unknown quality, which is of paramount concern to monitoring applications. In this paper, we present the claro system that supports stream processing for uncertain data naturally(More)
We present the design and development of a data stream system that captures data uncertainty from data collection to query processing to final result generation. Our system focuses on data that is naturally modeled as continuous random variables such as many types of sensor data. To provide an end-to-end solution, our system employs probabilistic modeling(More)
Despite its promise, RFID technology presents numerous challenges, including incomplete data, lack of location and containment information, and very high volumes. In this work, we present a novel data interpretation and compression substrate over RFID streams to address these challenges in enterprise supply-chain environments. Our results show that our(More)
Uncertain data streams are increasingly common in real-world deployments and monitoring applications require the evaluation of complex queries on such streams. In this paper, we consider complex queries involving conditioning (e.g., selections and group by’s) and aggregation operations on uncertain data streams. To characterize the uncertainty of answers to(More)
Uncertain data management has become crucial in many sensing and scientific applications. As user-defined functions (UDFs) become widely used in these applications, an important task is to capture result uncertainty for queries that evaluate UDFs on uncertain data. In this work, we provide a general framework for supporting UDFs on uncertain data.(More)
HIGH-PERFORMANCE PROCESSING OF CONTINUOUS UNCERTAIN DATA MAY 2013 THANH T. L. TRAN B.E., UNIVERSITY OF MELBOURNE M.S., UNIVERSITY OF MASSACHUSETTS AMHERST Ph.D., UNIVERSITY OF MASSACHUSETTS AMHERST Directed by: Professor Yanlei Diao Uncertain data has arisen in a growing number of applications such as sensor networks, RFID systems, weather radar networks,(More)