My Name is Pramod Koneru, I am a Master's Thesis student

I am a Masters student at Kno.e.sis center, College of Engineering and Computer Science, Wright State University. Kno.e.sis Center is recognized as Ohio Center of Excellence in Knowledge Enabled Computing, is ranked significantly by Microsoft in the World Wide Web Domain. I am advised by     Dr. Amit P. Sheth, Lexis Nexis Ohio Eminent Scholar. Just as Sir. Tim Berners Lee believed in the fact that 'Data is precious thing and will last longer that systems themselves' so do I. I enjoy working with real-time and scalability aspects of Big Data. I am part of social semantics group, here at Kno.e.sis.

My research interests include Real-time social data analysis, Using No-SQL databases for scalability, cloud computing and Semantic Web.

I got my bachelors degree in Information and Communication Technology (ICT) from DA-IICT, one of the prestigious university in India.


Twitris+: 360 degree Social Media Analytics platform

A Semantic Web Application ,that facilitates understanding of social perceptions by Semantics-based processing of massive amounts of event-centric data.

My Contrubution

1. Made the application real-time and scalable by integrating it with Twitter Storm ( It is a distributed, fault-tolerant, real-time computational system, released on October 2011 ) and maintaining it. Also I am running this in a 4-Node cluster which can be extended to any number.
2. Search & Explore Tab: Extracting entities from the tweets, using the back-ground knowledge (DBpedia), and loading the schema into RDF store. SPARQL 1.1 is used to Query the RDF Store.

Real-time Events on Linked Open Data

Linked Open Data (LOD) describes a method of publishing structured data so that it can be interlinked and become more useful (Example:DBPedia). Transforming unstructured social data (Tweets) to structured and publishing it on LOD, will enrich its value and enables one to issue expressive queries. In this project we published social data related to on-going events on LOD in real-time. We have published the structured data as RDF and provided SPARQL endpoint for richer querying.

The Search & Explore tab in Twitris (Semantic Social Media analysis application), will demonstrate this.

UMLS To ICD10 Mapping

This project involves mapping a given set of UMLS codes to an ICD10 code. Mapping these codes is a difficult task owing to the following reasons:

1. Each ICD10 code may be a combination of more than one UMLS codes .
2. There is no one to one mapping of UMLS code disease/condition to the diseases mentioned in the ICD10 codes because of the hierarchical structure of the disease.

We proposed a novel method ,where in which, using semantic web technologies like ontology (for encoding ICD10 codes) and SPARQL inferencing, will automatically annotate the given EMR document against billable ICD10 codes.


Graduate Research Assistant

Kno.e.sis, The Ohio Center of Excellence in Knowledge-Enabled Computing

At Kno.e.sis, I worked on a state-of-art project named Twitris+:360, its a Semantic Web Application. In this we provide social perceptions by semantic based processing of massive amounts of data from social-networks. Twitris mines twitter data for spatio-temporal and thematic analysis, summarization of events. We are using Twitter Storm (a distributed, fault-tolerant, real-time computation system) for crawling twitter data and for doing semantic analysis on the data real-time. Also I have migrated the backend database from RDBMS to No-SQL esp., MongoDB making it scalable and agile for development.

Coding during this period included Java, PHP and Semantic Web Technologies such as RDF and SPARQL.

Research Intern

ezDi, Easy Data-Intelligence

My work at ezDi includes migrating the current database ,which is MySQL, to MongoDB thereby making the application more agile, dynamic and also using Hadoop on top of MongoDB to make it more scalable and performant. MongoDB is the leading No-SQL, document oriented and scalable database which is being widely adapted by the industry in place of conventional DBMS.

Coding during this internship included Java, using Map-Reduce paradigm for Hadoop and familiarity with MongoDB.



  • Programming Languages :
  • Programming Paradigms :
  • Semantic Web Technologies :
  • Databases :
  • Operating Systems :
  • Web Technologies :
  • Distributed Technologies :

  • Java, Scheme, C, Pig (Hadoop).
  • OOP (Object-Oriented Programming), Map-Reduce
  • MySQL, No-SQL (MongoDB).
  • Unix (Mac), Linux, Windows.
  • HTML, XML, PHP (Basics).
  • Apache Hadoop, Twitter Storm.


  • Web Information Systems
  • Cloud Computing
  • Information Retrieval
  • Database Systems & Design
  • Comparative Languages
  • Programming Languages
  • Distributed Computing Principles
  • Data Structures and Algorithms
  • Object-Oriented Programming and Design


Scalable and Real time Processing of Big Social Data

Comming soon