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Graphflow	Distributed BSP Systems
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Current Projects

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Kùzu is a new embeddable graph database management system (GDBMS) that is designed for high scalability and very fast querying. Kùzu is actively being developed and is acquring more and more features. Kùzu`s core architecture is informed by our insights from our previous GraphflowDB project but Kùzu is disk-based and more importantly aims to be a fully functional user facing system. Our research on Kùzu focuses on techniques for making GDBMSs competent on knowledge graph management and in graph data science pipelines. Specifically, we are studying how to integrate GDBMSs better into Python graph data science ecosystem and performant on highly heterogeneous and string/URI-heavy knowledge graphs.

Previous Projects

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GraphflowDB is a graph database management system (GDBMS) we are building from scratch. The system is implemented in Java and supports the openCypher language. Our research focuses on rethinking each core database component for contemporary GDBMSs, including core query optimization and processing techniques such as join optimization, indexes, and cardinality estimation.

Graphsurge is a new system built on top of Timely Dataflow and Differential Dataflow for performing analytical computations on multiple snapshots or views of large-scale static property graphs. Graphsurge allows users to create view collections, a set of related views of a graph created by applying filter predicates on node and edge properties, and run analytical computations on all the views of a collection efficiently. The system is designed to support contingency, perturbation, or temporal analysis applications that require running computations on thousands of but similar graph snapshots at a time.

GraphWrangler is a system that is designed to streamline the manual and often tedious ETL pipeline of extracting tabular data into a graph format for processing in a graph-specific software. GraphWrangler allows users to connect to an RDBMS, MySQL in our current implementation, and within a few clicks extract graphs out of their tabular data, visualize and explore these graphs, and automatically generate scripts for their ETL pipelines. Watch our demonstration video here!.

GPS, A Graph Processing System, is an open-source system for scalable, fault-tolerant, and easy-to-program execution of algorithms on extremely large graphs. GPS is similar to Google’s proprietary Pregel system, and Apache Giraph. GPS is a distributed system designed to run on a cluster of machines, such as Amazon`s EC2.

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Students

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Ph.D. Students:

• Anurag Chakraborty
• Jiamin Hou (Visiting PhD Student)

M.Math. Students:

• Rui F. David
• Gaurav Sehgal

Alumni:

• Siddhartha Sahu, PhD
  Thesis: Optimizing Differential Computation for Large-Scale Graph Processing
  Last known position: Software Engieer at Materialize Inc.
• Ziyi Chen, Research Associate Last known position: Co-founder and Software Engineer at Kùzu Inc., Waterloo, Canada
• Guodong Jin, Postdoc
Last known position: Co-founder and Software Engineer at Kùzu Inc., Waterloo, Canada
• Arif Usta, Postdoc
  Last known position: Senior AI Engineer at St.Jude Children`s Research Hospital, Memphis, USA
• Amine Mhedhbi, PhD
  Thesis: GraphflowDB: Scalable Query Processing on Graph-Structured Relations (Cheriton SCS Dissertation Award)
  Last known position: Assistant Professor at Polytechnique Montréal
• Khaled Ammar, PhD (co-advised with Tamer Özsu)
  Thesis: Systems and Algorithms for Dynamic Graph Processing
  Last known position: Head of Data at Borealis AI
• Chang Liu, MMath
  Last known position: Co-founder and DevOps Engineer at Kùzu Inc., Waterloo, Canada
• Xiyang Feng (MMath), 2019-2021
  Last known position: Co-founder and Software Engineer at Kùzu Inc., Waterloo, Canada
• Tamal Adhikary (co-advised with Khuzaima Daudjee) (MMath), 2019-2022
  Last known position: Engineer at Huawei, Markham, Canada
• Jeremy Chen (MMath), 2019-2020
  Thesis: Join Cardinality Estimation Graphs: Analyzing Pessimistic and Optimistic Estimators Through a Common Lens
  Last known position: Software Engineer at Snowflake, USA.
  
• Nafisa Anzum, 2018-2020
  Thesis: Systems for Graph Extraction from Tabular Data
  Last known position: Software Engineer, Presto Inc., Waterloo, CA.
  
• Pranjal Gupta (MMath), 2018-2020
  Thesis: Integrating Column-Oriented Storage and Query Processing Techniques into Graph Database Management Systems
  Last known position: Software Engineer, Apple Inc., London, UK.
  
• Chathura Kankanamge (MMath), 2016-2018
  Thesis: Multiple Continuous Subgraph Query Optimization Using Delta Subgraph Queries
  Last known position: Software Engineer at Amazon AWS, Vancouver, Canada.
  
• Kaleb Alway (co-supervised with Eric Blais) (MMath), 2017-2019
  Thesis: Domain Ordering and Box Cover Problems for Beyond Worst-Case Join Processing
  Last known position: Software Engineer at SAP, Waterloo, Canada.
  
• Shahid Khaliq (MMath), 2017-2019
  Thesis: Highly Flexible Adjacency Lists in Graph Database Management Systems
  Last known position: Software Engineer at Top Hat, Toronto, Canada.
  
• Arman Naeimian (co-supervised with Mei Nagappan) (MMath) 2017-2019
  Thesis: Parallel Paths Analysis Using Function Call Graphs
  Last known position: Software Engineer at Sandvine, Waterloo, Canada.
  
• Azin Nazari (co-supervised with Ian Munro) (MMath) 2017-2019
  Thesis: Majority in the Three-Way Comparison Model
  

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Teaching

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CS 848: Beyond Relational Systems (Fall 2024)

This seminar covers three important classes of database management systems that either offer different data model and/or query and computational capabilities than relational systems. When possible we focus on applications of these systems to AI.

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CS 848: Knowledge Graphs (Fall 2022)

This seminar covers seminal work in the space of knowledge graph representation, querying, management, and past and primarily modern applications that are powered by knowledge graphs. Topics include knowledge models, ontologies, query languages, graph data management systems, public knowledge graphs, knowledge graph embeddings, popular successful past and present applications.

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CS 348: Introduction to Database Systems (Winter 2022, Fall 2021)

An introduction to database management systems. The course covers topics in three areas: (1) fundamental features of relational database management systems: the relational data model and its query languages, integrity constraints, indexes and views, and transactions; (2) database design methodology; and (3) core topics about the internals and architectures of DBMSs, such as physical record design, query planning and optimization, indexes, and transaction protocols.

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CS 341: Algorithms (Summer 2016, Winter 2017, Winter 2018, Winter 2019, Spring 2020)

The study of efficient algorithms and effective algorithm design techniques. Topics include divide and conquer algorithms, recurrences, greedy algorithms, dynamic programming, graph search and backtrack, theory of NP-completeness and its implications.

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CS 848: Graph Analytics and Data Management (Winter 2020)

The seminar is an updated version of a previous seminar that focused primarily on graph data management in Fall 2018. The current offering covers fewer database topics and instead surveys a wider range of topics in graph analytics. The goal of this offering is to showcase the specific interests of a very wide range of scientific communities that work on graphs including communities within computer science, such as databases, semantic web, hpc and computer architecture, data mining, machine learning, and hci as well as communities outside of computer science, such as physics and neuro-science. The seminar's reading list is particularly tailored for students who have interest in doing graudate studies in graph processing and analytics, as it tries to cover some of the seminal work on graph processing from multiple communities.

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CS 848: Graph Data Management (Fall 2018)

The seminar covered the historical waves that made graph data models popular, such as the web and semantic web. The seminar also covered recent topics popular in the database research community e.g. modern graph databases, graph data processing software based on Hadoop and Spark-like software, knowledge graphs, and example machine learning applications on graphs.

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CS 848/858: Modern Data Processing Systems (Fall 2016)

The seminar surveyed the models that underly modern large-scale data processing systems, e.g. MapReduce, Spark, Pregel, Flink, Storm, Timely, and others. The goal is to identify the fundamental advantages and limitations of different models and demonstrate the systems and applications that are built on each model.

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