Ananth Gundabattula

Senior Architect, Commonwealth Bank of Australia

Ananth Gundabattula is a senior application architect on the decisioning and advanced analytics engineering team for the Commonwealth Bank of Australia (CBA). His core contributions include building ML stacks using auto feature engineering, autoML and providing architectural guidance to the fraud and big data platforms. Previously, he was an architect at ThreatMetrix, a member of the core team that scaled ThreatMetrix architecture to 100 million transactions per day—which runs at very low latencies using Cassandra, Zookeeper, and Kafka besides building their next gen data warehouse stack. Ananth is a committer for Apache Apex.Ananth has presented at a number of conferences including Strata San Jose, YOW! Data and the Dataworks summit conference in Australia. Ananth holds a PhD in computer science security. He's interested in all things data, including low-latency distributed processing systems, machine learning, and data engineering domains. He holds three patents and has one application pending.

 

Talks on Wurreka:

Time series patterns are pretty common in everyday life. Extracting patterns on these data sets and providing interactive search responses has immense value across many software systems. This talk presents an overview of how such a search system can be built for very large time series data points.

This talk focuses on the STAMP algorithm (Scalable Time Series Anytime Matrix profile) to showcase how time series patterns can be extracted for big data sets. The talk covers concepts of Matrix profile and how motifs/patterns are detected. Once the patterns are analyzed, the results are converted into a document format that is amenable for indexing via a search engine. The session then proceeds to extend the idea to detecting time series across multiple dimensions. A part of the session also covers the distributed approach to extract these time series patterns to tackle big data sets. Further, you will also learn about mechanisms to calculate time series segmentations, time series chains and discords or anomalies thereby enabling ideas for users to search for anomalies and segments of time series.

Dask, Stumpy and Elasticsearch stack is used to describe a reference implementation.

Searching for documents in a collection is typically implemented via a TF/IDF principle in open source document search engines. However recent developments in the field of NLP has shown positive results in representing text into more concise vector representations as opposed to a bag of words construct. In addition to this, these approaches also add richness to the information models like taking care of analogies and semantics of the words. This talk would walk through an end to end data workflow to enable such a construct.

The first part of the session would describe the typical flow of how a search query is processed by default in any of the lucene powered search engines today. The concept of TF/IDF is also introduced in this part of the session.

The session then proceeds to describe the concept of word embeddings using a library like Facebooks fasttext.

Subsequently, a representative data pipeline is discussed as to how an incoming stream of data can be turned into vector representations and made amenable for searching with a few seconds of turn around time.

The session would close with a few references to the more recent developments in this space.

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