Recent ones

1. BDTA2023

   BigText-QA: Question Answering over a Large-Scale Hybrid Knowledge Graph

   Xu, Jingjing, Biryukov, Maria, Theobald, Martin, and Venugopal, Vinu Ellampallil

   2023
2. ICSC23

   Ontology Mediated Document Retrieval for Exploratory Big Data Analytics

   Kulkarni, Apurva, Ramanathan, Chandrashekar, and Venugopal, Vinu E.

   In 17th IEEE International Conference on Semantic Computing, ICSC 2023, Laguna Hills, CA, USA, February 1-3, 2023 2023
3. CoRR23

   TensAIR: Online Learning from Data Streams via Asynchronous Iterative Routing

   Tosi, Mauro Dalle Lucca, Venugopal, Vinu E., and Theobald, Martin

   CoRR 2022
4. BDA22

   Ontology Augmented Data Lake System for Policy Support

   Kulkarni, Apurva, Bassin, Pooja, Parasa, Niharika Sri, Venugopal, Vinu E., Srinivasa, Srinath, and Ramanathan, Chandrashekar

   In Big Data Analytics in Astronomy, Science, and Engineering - 10th International Conference on Big Data Analytics, BDA 2022, Aizu, Japan, December 5-7, 2022, Proceedings 2022
5. JPDC22

   Targeting a light-weight and multi-channel approach for distributed stream processing

   Venugopal, Vinu Ellampallil, Theobald, Martin, Tassetti, Damien, Chaychi, Samira, and Tawakuli, Amal

   Journal of Parallel and Distributed Computing 2022

   Abs

   Processing high-throughput data-streams has become a major challenge in areas such as real-time event monitoring, complex dataflow processing, and big data analytics. While there has been tremendous progress in distributed stream processing systems in the past few years, the high-throughput and low-latency (a.k.a. high sustainable-throughput) requirement of modern applications is pushing the limits of traditional data processing infrastructures. This paper introduces a new distributed stream processing engine (DSPE), called Asynchronous Iterative Routing (or simply “AIR”), which implements a light-weight, dynamic sharding protocol. AIR expedites direct and asynchronous communication among all the worker nodes via a channel-like communication protocol on top of the Message Passing Interface (MPI), thereby completely avoiding the need for a dedicated driver node. The system adopts a new progress-tracking protocol, called hew-meld, which has been experimentally observed to show a low processing latency on our asynchronous master-less architecture when compared to the conventional low-watermark technique. The current version of AIR is also equipped with two fault tolerance and recovery strategies namely checkpointing & rollback and replication. With its unique design, AIR scales out particularly well to multi-core HPC architectures; specifically, we deployed it on clusters with up to 16 nodes and 448 cores (thus reaching a peak of 435.3 million events and 55.14 GB of data processed per second), which we found to significantly outperform existing DSPEs.

6. CODS-COMAD22

   Convergence time analysis of Asynchronous Distributed Artificial Neural Networks

   Tosi, Mauro Dalle Lucca, Venugopal, Vinu Ellampallil, and Theobald, Martin

   In International Conference on Data Science & Management of Data 2022
7. BNAIC21

   Verbalizing but Not Just Verbatim Translations of Ontology Axioms

   Venugopal, Vinu Ellampallil, and Kumar, P. Sreenivasa

   In 33rd Benelux Conference on Artificial Intelligence 2021