Join us for our upcoming meetup on Tuesday, August 22nd! Thank you to PowerReviews for hosting us and sponsoring food & beverages.
Here is our agenda for the evening:
6:00 pm - Doors open (food and beverages will be provided)
6:30 pm - Talks start
1. Elasticsearch and Linked Data by Wolters Kluwer's Quentin Reul
2. Elastic Stack - Machine Learning by Elastic's Adis Cesir
3. PowerReviews Elasticsearch Use Case by Mike Kalimuthu, Senior Software Engineer and Mario Harvey, Sr. Site Reliability Engineer
8:00-8:30 pm - time for Q&A + time to chat
Elasticsearch and Linked Data
Linked data is a method to expose, share and connect pieces of (structured) data, information and knowledge based on URIs and the Resource Description Framework. Traditionally, this type of data would be stored to a triplestore optimized for running semantic queries. However, triple stores generally suffer from performance issues when performing search and retrieving a large quantity of data. As such, we have investigated a range of alternative for storing data. Based on the JSON-LD serialization of RDF and on ElasticSearch, we were able to develop (performant) tools for managing change events to legal and regulatory content as well as maintaining tax return data to identify accountants’ clients impacted by these changes.
Quentin Reul works as a Content Integration Manager for Wolters Kluwer. In his role, he is responsible for maintaining the Wolters Kluwer semantic model as well as the development of new solutions leveraging data expressed according to this model. For instance, he was a lead architect on the development of set of tools to identify changes in legal and regulatory content and to identify accountants’ clients impacted by these changes.
Quentin has earned his Bachelor of Science in Computing Science and his Ph.D. on ontology management from the University of Aberdeen (Scotland). Over the years, he has been involved in several W3C groups including the Semantic Web Deployment Working Group that developed the Simple Knowledge Organization System (SKOS) specification and the RDF & XML interoperability community group.
Elastic Stack - Machine Learning
Data sets keep growing in size and complexity. Spotting infrastructure problems, cyber attacks, or business issues using only dashboards or rules become increasingly difficult as your data grows. Learn how the X-Pack Machine Learning feature can model the typical behavior of your time series data in real time to identify anomalies, streamline root cause analysis, and reduce false positives using an unsupervised approach.
Adis Cesir is a Solutions Architect at Elastic. Prior to coming to Elastic he has spent 15 years in the Data World working with in Data Warehousing, MDM with various RDBMS, MPP systems. He has spent the last 5 years working with Big Data and various Distributed computing platforms primarily in the Open Source.