=Paper=
{{Paper
|id=Vol-2786/Paper3
|storemode=property
|title=Real Application of Machine Learning (REALM): Situation Knowledge on Demand (SKOD); - Abstract
|pdfUrl=https://ceur-ws.org/Vol-2786/Paper3.pdf
|volume=Vol-2786
|authors=Bharat Bhargava
|dblpUrl=https://dblp.org/rec/conf/isic2/Bhargava21
}}
==Real Application of Machine Learning (REALM): Situation Knowledge on Demand (SKOD); - Abstract==
<pdf width="1500px">https://ceur-ws.org/Vol-2786/Paper3.pdf</pdf>
<pre>
                                                                                                                    27




Real Application of Machine Learning (REALM): Situation
Knowledge on Demand (SKOD)
Bharat ​Bhargava​a
a​
     Purdue University, Indiana, United States




                        Abstract:​ ​Extracting relevant patterns from heterogeneous data streams poses significant
                        computational and analytical challenges. Identifying such patterns and pushing corresponding
                        content to interested users according to mission needs in real-time is the challenge. This
                        research utilizes the best in Database systems, Knowledge representation, Machine Learning
                        to get the right data to the right user at the right time with completeness and low noise. If a
                        user's need is unmet, queries evolve and get modified to come close to satisfy mission needs
                        which may themselves be unclear. If need is partially met, when new streaming data streams
                        in, our research connects relevant data to queries. The knowledge for further processing is
                        kept in the form of queries (megabytes) vs database (giga bytes). The project deals with
                        multimedia data at peta and zeta scale. The research leads to a scalable, real-time,
                        fault-tolerant, privacy preserving architecture that consumes streams of multimodal data (e.g.,
                        video, text, sound) utilizing publish/subscribe stream engines and RDBMS microservices. We
                        utilize neural networks to extract relevant objects from video and latent semantic indexing
                        techniques to model topics for unstructured text. We present a unique Situational Knowledge
                        Query Engine that continuously builds a multimodal relational knowledge base constructed
                        using SQL queries and pushes dynamic content to relevant users through triggers based on
                        modeling of users’ interests. We analyze an extensive collection of Cambridge data (millions
                        of Twitter tweets, 35+ structured datasets, and 100+ hours of video traffic, and needs for
                        police, public works and citizens). At present data from West Lafayette police is being
                        analyzed to provide identifying suspicious activity and deal with disasters such as school
                        shootings. We will continue to learn from NG researchers to demonstrate the feasibility of the
                        proof-of-concept. Research has resulted in Darpa proposals, collaborations with Sandia, JPL,
                        and multiple NGC IRADS and many research papers and Ph.D thesis.

________________________________
ISIC’21:International Semantic Intelligence Conference, February
25–27, 2021, New Delhi, India
✉​ : ​bbshail@purdue.edu​ (Bharat Bhargava)

                 Copyright © 2021 for this paper by the authors. Use permitted
                 under​ Creative Commons License Attribution 4.0 International ​(CC BY 4.0)​.
                        ​


                 CEUR Workshop Proceedings ​(​CEUR-WS.org​)
                                                                                                       28




    1. Acknowledgements                               It includes cognitive autonomy, reflexivity, deep
                                                      learning and knowledge discovery. His earlier
         We thank Jim Macdonald for continuous        work on Waxed Prune with MIT and NGC built a
guidance and his participation in research ideas on   prototype    for     privacy     preserving  data
a daily basis. Thanks to NG leaders Jeff, Hong,
                                                      dissemination in cross-domains. Currently he is
Eric for initiating REALM. Thanks for continued
interactions among brilliant research team            leading the NGC REALM consortium.
members in finding solutions for NG clients.               He has graduated the largest number of Ph.D
Thanks to over ten students at MIT, CMU,              students in the CS department at Purdue and is
Stanford, and Purdue who are collaborating and        active in supporting/mentoring minority students.
interacting and contributing to data, system and      In 2003, he was inducted in the Purdue's Book of
use cases.                                            Great Teachers. In 2017, he received the Helen
                                                      Schleman Gold Medallion Award for supporting
    2. Bio                                            women at Purdue and Focus award for advancing
    Bharat Bhargava is a professor of the             technology for differently abled students.
Department of Computer Science with a courtesy        __________________________________
appointment in the School of Electrical &             urls - ​https://www.cs.purdue.edu/homes/bb/
Computer Engineering at Purdue University. His        https://www.cs.purdue.edu/news/articles/2019/bharga
recent research is on Intelligent Autonomous          va-realm-ng.html
Systems and data analytics and machine learning.

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