[Colloquium] Chunwei Liu Candidacy Exam/Feb 7, 2022

[Megan Woodward]

This is an announcement of Chunwei Liu's Candidacy Exam.
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Candidate: Chunwei Liu

Date: Monday, February 07, 2022

Time: 11 am CST

Remote Location:  https://uchicago.zoom.us/j/96617537629?pwd=UHVNRExhZEUxNC9nYXJKNndFb3dvdz09 Meeting ID: 966 1753 7629 Passcode: 192356

Title: Fast and Effective Compression for IoT Systems

Abstract: Nowadays, the Internet of Things (IoT) is compelling as it enables connections of trillions of sensors and data collection for connectivity and analytics. The amount of IoT-generated data has exploded due to the rapid growth of interconnected IoT devices from a wide range of IoT applications. IoT systems need an effective solution to ingest, store, and analyze the exploding IoT data to make the best of the limited resources in IoT systems, such as network, storage, energy, and computation power. Due to the extensive usage of IoT across diverse applications, IoT data includes dynamic data streaming with various data types and changing data statistics. Given the special features of IoT data: endless, heterogeneous, and dynamic, we introduce new compression techniques to handle those special data features: MOP handles the endlessness of IoT data by pre-allocating encoding space for the dynamic incoming data and enables in-situ queries in the encoded domain for fast query execution. BUFF addresses the heterogeneity of IoT data by applying decomposed but compact encoding space for the IoT numeric data with different statistics and achieves efficient query execution support according to the host's hardware. In addition, the dynamic IoT data imposes challenges to the traditional compression selection strategies as there is no one-size-fits-all solution for IoT systems with varying data statistics, different workloads, and constrained hardware resources. We, therefore, propose AdaEdge as a hardware-conscious encoding selection framework for resource-constrained devices.

Advisors: Aaron Elmore

Committee Members: Aaron Elmore, Sanjay Krishnan, and Raul Castro Fernandez



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