• Data Search Systems for Emerging Trends - Jianguo Wang


  • ABSTRACT:
    Building scalable data systems is not only important for computer science, but also for modern society. Historically, data was primarily managed by databases. Now, we see a proliferation of modern data systems such as search systems, key-value stores, and cloud systems. This is largely due to the emerging trends that make people rethink whether existing designs are still valid. Among which, three driving forces protrude: modern hardware, emerging applications, and cloud computing.

    In this talk, I will focus on a widespread class of data systems, namely data search systems, and show how modern hardware and emerging applications alter the design tradeoffs. First, I will introduce a memory-centric compression that enables fast query processing directly over compressed data by leveraging the properties of modern hardware. Second, I will present a protein data search system that supports billion-scale interactive data analytics by fully exploring domain knowledge and application-specific data characteristics.  Finally, I will outline future research plans on developing scalable data systems for emerging trends.

    BIO:
    Jianguo Wang is currently working at Amazon AWS on cloud-native databases. He received his Ph.D. in Computer Science from the University of California San Diego in December 2018, under the supervision of Professors Yannis Papakonstantinou and Steven Swanson. His research interests span database systems, data search systems, modern hardware, emerging applications, and cloud computing, with a focus on building efficient data management systems for emerging trends. He interned at Microsoft Research, Oracle, and Samsung on data-intensive systems. His work won the bests of ICDT 2019.