Approximate Data Analytics Systems

Datum

22.01.2018

Zeit

10:45 - 11:45

Sprecher

M. Sc. Do Le Quoc

Zugehörigkeit

Institut für Systemarchitektur, Professur für Systems Engineering

Sprache

en

Hauptthema

Informatik

Andere Themen

Informatik

Beschreibung

Today, more and more modern online services make use of big data analytics systems to extract useful information from the publicly available digital data. The data normally arrives as a continuous data stream at a high speed and in huge volumes. The cost of handling this massive data can be significant. Providing interactive latency in processing the data is often impractical due to the fact that the data is growing exponentially and even faster than Moore's law predictions. To overcome this problem, approximate computing has recently emerged as a promising solution. Approximate computing is based on the observation that many modern applications are amenable to an approximate, rather than the exact output. Unlike traditional computing, approximate computing tolerates lower accuracy to achieve lower latency by computing over a partial subset instead of the entire input data. In this thesis, we design and implement approximate computing techniques for processing and interacting with high-speed and large-scale data with low latency and efficient utilization of resources. To achieve these goals, we have designed and built the following approximate data analytics systems: (1) StreamApprox - a data stream analytics system for approximate computing. (2) IncApprox - a data analytics system for incremental approximate computing. (3) PrivApprox - a data stream analytics system for privacy-preserving and approximate computing. (4) ApproxJoin - an approximate distributed joins system. Our evaluation based on micro-benchmarks and real world case studies shows that these systems can achieve significant performance compared to state-of-the-art systems by tolerating negligible accuracy loss of the analytics output. In addition, our systems allow users to systematically make a trade-off between accuracy and throughput/latency and require no/minor modifications to the existing applications.

Letztmalig verändert: 22.01.2018, 09:02:08