@article {593, title = {AI for Traffic Analytics}, journal = {The IEEE Intelligent Informatics Bulletin}, volume = {17}, year = {2016}, chapter = {21}, author = {Raghava Mutharaju and Freddy L{\'e}cu{\'e} and Jeff Z. Pan and Jiewen Wu and Pascal Hitzler} } @conference {570, title = {Reasoning with Large Scale OWL 2 EL Ontologies Based on MapReduce}, booktitle = {Web Technologies and Applications - 18th Asia-Pacific Web Conference, APWeb 2016, Suzhou, China, September 23-25, 2016}, year = {2016}, publisher = {Springer}, organization = {Springer}, address = {Heidelberg}, author = {Zhangquan Zhou and Guilin Qi and Chang Liu and Raghava Mutharaju and Pascal Hitzler} } @conference {157, title = {Are We Really Standing on the Shoulders of Giants?}, booktitle = {Proceedings of the 1st Workshop on Negative or Inconclusive Results in Semantic Web (NoISE 2015) co-located with 12th Extended Semantic Web Conference (ESWC 2015) }, year = {2015}, address = {Portoroz, Slovenia}, author = {Raghava Mutharaju and Pavan Kapanipathi} } @conference {156, title = {Distributed and Scalable OWL EL Reasoning}, booktitle = {Proceedings of the 12th Extended Semantic Web Conference (ESWC 2015) }, year = {2015}, publisher = {Springer}, organization = {Springer}, address = {Portoroz, Slovenia}, abstract = {

OWL 2 EL is one of the tractable proles of the Web Ontology\ Language (OWL) which is a W3C-recommended standard. OWL 2\ EL provides sucient expressivity to model large biomedical ontologies\ as well as streaming data such as trac, while at the same time allows\ for ecient reasoning services. Existing reasoners for OWL 2 EL, however,\ use only a single machine and are thus constrained by memory and\ computational power. At the same time, the automated generation of\ ontological information from streaming data and text can lead to very\ large ontologies which can exceed the capacities of these reasoners. We\ thus describe a distributed reasoning system that scales well using a cluster\ of commodity machines. We also apply our system to a use case on\ city trac data and show that it can handle volumes which cannot be\ handled by current single machine reasoners.

}, keywords = {DistEL, Distributed Reasoning, Ontology Classification, OWL EL}, author = {Raghava Mutharaju and Pascal Hitzler and Prabhaker Mateti and Freddy L{\'e}cu{\'e}} } @conference {421, title = {Distributed Reasoning over Ontology Streams and Large Knowledge Base}, booktitle = {NSF Data Science Workshop 2015}, year = {2015}, address = {Seattle, USA}, author = {Raghava Mutharaju} } @conference {390, title = {An Ontology For Specifying Spatiotemporal Scopes in Life Cycle Assessment}, booktitle = {Proceedings of the 1st International Diversity++ Workshop co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pennsylvania, USA, October 12, 2015}, volume = {1501}, year = {2015}, month = {10/2015}, pages = {25-30}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, url = {http://ceur-ws.org/Vol-1501/Diversity2015-paper_4.pdf}, author = {Bo Yan and Yingjie Hu and Brandon Kuczenski and Krzsyztof Janowicz and Andrea Ballatore and Adila Krisnadhi and Pascal Hitzler and Sangwon Suh and Wesley Ingwersen}, editor = {Claudia d{\textquoteright}Amato and Freddy L{\'e}cu{\'e} and Raghava Mutharaju and Thomas Narock and Fabian Wirth} } @proceedings {489, title = {Proceedings of the 1st International Diversity++ Workshop co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pennsylvania, USA, October 12, 2015}, journal = {CEUR Workshop Proceedings}, volume = {1501}, year = {2015}, publisher = {CEUR-WS.org}, address = {Bethlehem, PA, USA}, url = {http://ceur-ws.org/Vol-1501}, editor = {Claudia d{\textquoteright}Amato and Freddy L{\'e}cu{\'e} and Raghava Mutharaju and Tom Narock and Fabian Wirth} } @conference {391, title = {{R2R+BCO-DMO} {\textendash} Linked Oceanographic Datasets}, booktitle = {Proceedings of the 1st International Diversity++ Workshop co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pennsylvania, USA, October 12, 2015}, volume = {1501}, year = {2015}, month = {10/2015}, pages = {15-24}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, abstract = {The Biological and Chemical Oceanography Data Management Office (BCO-DMO) and the Rolling Deck to Repository (R2R) program are two key data repositories for oceanographic research, supported by the U.S. National Science Foundation (NSF). R2R curates digital data and documentation generated by environmental sensor systems installed on vessels from the U.S. academic research fleet, with support from the NSF Oceanographic Technical Services and Arctic Research Logistics Programs. BCO-DMO human-curates and maintains data and metadata including biological, chemical, and physical measurements and results from projects funded by the NSF Biological Oceanography, Chemical Oceanography, and Antarctic Organisms \& Ecosystems Programs. These two repositories have a strong connection, and document several thousand U.S. oceanographic research expeditions since the 1970{\textquoteright}s. Recently, R2R and BCO-DMO have made their metadata collections available as Linked Data, accessible via public SPARQL endpoints. In this paper, we report on these datasets.}, author = {Adila Krisnadhi and Robert Arko and Suzanne Carbotte and Cynthia Chandler and Michelle Cheatham and Pascal Hitzler and Yingjie Hu and Krzysztof Janowicz and Peng Ji and Nazifa Karima and Adam Shepherd and Peter Wiebe}, editor = {Claudia d{\textquoteright}Amato and Freddy L{\'e}cu{\'e} and Raghava Mutharaju and Thomas Narock and Fabian Wirth} } @conference {420, title = {Social Signal Processing for Real-time Situational Understanding: a Vision and Approach}, booktitle = {First International Workshop on Social Sensing (SocialSens 2015)}, year = {2015}, address = {Dallas, Texas, USA}, abstract = {

The US Army Research Laboratory (ARL) and\ the Air Force Research Laboratory (AFRL) have established a\ collaborative research enterprise referred to as the Situational\ Understanding Research Institute (SURI). The goal is to develop\ an information processing framework to help the military obtain\ real-time situational awareness of physical events by harnessing\ the combined power of multiple sensing sources to obtain insights\ about events and their evolution. It is envisioned that one could\ use such information to predict behaviors of groups, be they local\ transient groups (e.g., protests) or widespread, networked groups,\ and thus enable proactive prevention of nefarious activities. This\ paper presents a vision of how social media sources can be\ exploited in the above context to obtain insights about events,\ groups, and their evolution.

}, author = {Kasthuri Jayarajah and Shuochao Yao and Raghava Mutharaju and Archan Misra and Geeth De Mel and Julie Skipper and Tarek Abdelzaher and Michael Kolodny} } @conference {488, title = {Towards a Rule Based Distributed OWL Reasoning Framework}, booktitle = {12th OWL Experiences and Directions Workshop (OWLED 2015) co-located with the 14th International Semantic Web Conference (ISWC 2015)}, year = {2015}, publisher = {Springer}, organization = {Springer}, address = {Bethlehem, PA, USA}, abstract = {The amount of data exposed in the form of RDF and OWL continues to increase exponentially. Some approaches have already been proposed for the scalable reasoning over several language profiles such as RDFS, OWL Horst, OWL 2 EL, OWL 2 RL etc. But all those approaches are limited to the particular ruleset that the reasoner supports. In this work, we propose the idea for a rule-based distributed reasoning framework that can support any given ruleset and highlight some of the challenges that needs to be solved in order to implement such a framework.}, author = {Raghava Mutharaju and Prabhaker Mateti and Pascal Hitzler} } @conference {158, title = {Developing a Distributed Reasoner for the Semantic Web}, booktitle = {Proceedings of the ISWC Developers Workshop 2014, co-located with the 13th International Semantic Web Conference (ISWC 2014)}, volume = {1268}, year = {2014}, pages = {108{\textendash}112}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Riva del Garda, Italy}, abstract = {

OWL 2 EL is one of the tractable profiles of the Web Ontology Language (OWL) which has been standardized by the W3C. OWL 2\ EL provides suficient expressivity to model large biomedical ontologies\ as well streaming traffic data. Automated generation of ontologies from\ streaming data and text can lead to very large ontologies. There is a\ need to develop scalable reasoning approaches which scale with the size\ of the ontologies. We briefly describe our distributed reasoner, DistEL\ along with our experience and lessons learned during its development.

}, url = {http://ceur-ws.org/Vol-1268/paper18.pdf}, author = {Raghava Mutharaju and Prabhaker Mateti and Pascal Hitzler}, editor = {Ruben Verborgh and Erik Mannens} } @conference {11, title = {Distributed OWL EL Reasoning: The Story So Far}, booktitle = {Proceedings of the 10th International Workshop on Scalable Semantic Web Knowledge Base Systems, Riva Del Garda, Italy}, volume = {1261}, year = {2014}, month = {10/2014}, pages = {61-76}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Riva del Garda, Italy}, abstract = {

Automated generation of axioms from streaming data, such as traffic and text, can result in very large ontologies that single machine reasoners cannot handle. Reasoning with large ontologies requires distributed solutions. Scalable reasoning techniques for RDFS, OWL Horst and OWL 2 RL now exist. For OWL 2 EL, several distributed reasoning approaches have been tried, but are all perceived to be inefficient. We analyze this perception. We analyze completion rule based distributed approaches, using different characteristics, such as dependency among the rules, implementation optimizations, how axioms and rules are distributed. We also present a distributed queue approach for the classification of ontologies in description logic EL+ (fragment of OWL 2 EL).

}, keywords = {Distributed Reasoning, OWL EL, Scalability}, author = {Raghava Mutharaju and Pascal Hitzler and Prabhaker Mateti}, editor = {Thorsten Liebig and Achille Fokoue} } @conference {10, title = {DistEL: A Distributed EL+ Ontology Classifier}, booktitle = {Proceedings of the 9th International Workshop on Scalable Semantic Web Knowledge Base Systems, co-located with the International Semantic Web Conference (ISWC 2013)}, volume = {1046}, year = {2013}, month = {10/2013}, pages = {17-32}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Sydney, Australia}, abstract = {OWL 2 EL ontologies are used to model and reason over data from diverse domains such as biomedicine, geography and road traffic. Data in these domains is increasing at a rate quicker than the increase in main memory and computation power of a single machine. Recent efforts in OWL reasoning algorithms lead to the decrease in classification time from several hours to a few seconds even for large ontologies like SNOMED CT. This is especially true for ontologies in the description logic EL+ (a fragment of the OWL 2 EL profile). Reasoners such as Pellet, Hermit, ELK etc. make an assumption that the ontology would fit in the main memory, which is unreasonable given projected increase in data volumes. Increase in the data volume also necessitates an increase in the computation power. This lead us to the use of a distributed system, so that memory and computation requirements can be spread across machines. We present a distributed system for the classification of EL+ ontologies along with some results on its scalability and performance.}, keywords = {Classification, DistEL, Distributed Reasoning, EL+, OWL, Scalability}, author = {Raghava Mutharaju and Pascal Hitzler and Prabhaker Mateti}, editor = {Thorsten Liebig and Achille Fokoue} } @conference {159, title = {D-SPARQ: Distributed, Scalable and Efficient RDF Query Engine}, booktitle = {Proceedings of the ISWC 2013 Posters \& Demonstrations Track}, volume = {1035}, year = {2013}, pages = {261{\textendash}264}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Sydney, Australia}, abstract = {

We present D-SPARQ, a distributed RDF query engine that combines the MapReduce processing framework with a NoSQL distributed data store, MongoDB. The performance of processing SPARQL queries mainly depends on the efficiency of handling the join operations between the RDF triple patterns. Our system features two unique characteristics that enable efficiently tackling this challenge: 1) Identifying specific patterns of the input queries that enable improving the performance by running different parts of the query in a parallel mode. 2) Using the triple selectivity information for reordering the individual triples of the input query within the identified query patterns. The preliminary results demonstrate the scalability and efficiency of our distributed RDF query engine.

}, keywords = {D-SPARQ, Distributed Querying, Scalable RDF querying, SPARQL}, url = {http://ceur-ws.org/Vol-1035/iswc2013_poster_21.pdf}, author = {Raghava Mutharaju and Sherif Sakr and Alessandra Sala and Pascal Hitzler}, editor = {Eva Blomqvist and Tudor Groza} } @conference {164, title = {Scale reasoning with fuzzy-EL+ ontologies based on MapReduce}, booktitle = {Proceedings of the IJCAI-2013 Workshop on Weighted Logics for Artificial Intelligence (WL4AI 2013)}, year = {2013}, pages = {87-93}, address = {Beijing, China}, abstract = {

Fuzzy extension of Description Logics (DLs) allows the formal representation and handling of fuzzy or vague knowledge. In this paper, we consider the problem of reasoning with fuzzy-EL+, which is a fuzzy extension of EL+. We first identify the challenges and present revised completion classification rules for fuzzy-EL+ that can be handled by MapReduce programs. We then propose an algorithm for scale reasoning with fuzzy-EL+ ontologies using MapReduce. Some preliminary experimental results are provided to show the scalability of our algorithm.

}, author = {Zhangquan Zhou and Guilin Qi and Chang Liu and Pascal Hitzler and Raghava Mutharaju}, editor = {Lluis Godo and Henri Prade and Guilin Qi} } @conference {166, title = {How I Would Like Semantic Web To Be, For My Children}, booktitle = {What will the Semantic Web look like 10 years from now? co-located with the 11th International Semantic Web Conference 2012 (ISWC 2012)}, year = {2012}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Boston, MA, USA}, abstract = {

Semantic Web, since its inception, has gone through lot of developments in its relatively nascent existence; right from people{\textquoteright}s perception, to the standards and to its adoption by the industry and more importantly by the scientific community. This impressive growth only seems to increase. In this paper, we project this growth to the next 10 years and highlight some of the facets on which Semantic Web could have a major impact on. We also present the challenges that Semantic Web and its community has to deal with in order to get there.

}, author = {Raghava Mutharaju} } @conference {170, title = {Konf Connect}, booktitle = {Metadata Challenge at the 21st International Conference on World Wide Web (WWW 2012)}, year = {2012}, address = {Lyon, France}, abstract = {

We present an application called Konf-Connect to improve\ the conference attending experience of the people who attend a conference. This tool provides search facilities to nd people with similar\ interests. The application makes use of Semantic Web dog food dataset\ to gather information regarding the conference at hand. This is helpful for people attending the conference who are looking for networking\ opportunities with people having expertise in the specic areas of interest. The application can also be extended to be used as general purpose\ expert search system.

}, author = {David Carral and Joshi, Amit Krishna and Adila Krisnadhi and Raghava Mutharaju and Kunal Sengupta and Cong Wang} } @conference {96, title = {Reasoning with Fuzzy-EL+ Ontologies Using MapReduce}, booktitle = {ECAI 2012 - 20th European Conference on Artificial Intelligence. Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstrations Track}, volume = {242}, year = {2012}, pages = {933{\textendash}934}, publisher = {IOS Press}, organization = {IOS Press}, address = {Montpellier, France}, abstract = {

Fuzzy extension of Description Logics (DLs) allows the formal representation and handling of fuzzy knowledge. In this paper, we consider fuzzy-EL+, which is a fuzzy extension of EL+. We first present revised completion rules for fuzzy-EL+ that can be handled by MapReduce programs. We then propose an algorithm for scale reasoning with fuzzy-EL+ ontologies based on MapReduce.

}, doi = {10.3233/978-1-61499-098-7-933}, url = {http://dx.doi.org/10.3233/978-1-61499-098-7-933}, author = {Zhangquan Zhou and Guilin Qi and Chang Liu and Pascal Hitzler and Raghava Mutharaju}, editor = {Luc De Raedt and Christian Bessi{\`e}re and Didier Dubois and Patrick Doherty and Paolo Frasconi and Fredrik Heintz and Peter J. F. Lucas} } @conference {160, title = {Very Large Scale OWL Reasoning through Distributed Computation}, booktitle = {11th International Semantic Web Conference (ISWC 2012), Proceedings, Part II}, volume = {7650}, year = {2012}, pages = {407{\textendash}414}, publisher = {Springer}, organization = {Springer}, address = {Boston, MA, USA}, abstract = {

Due to recent developments in reasoning algorithms of the\ various OWL profiles, the classification time for an ontology has come\ down drastically. For all of the popular reasoners, in order to process\ an ontology, an implicit assumption is that the ontology should fit in\ primary memory. The memory requirements for a reasoner are already\ quite high, and considering the ever increasing size of the data to be\ processed and the goal of making reasoning Web scale, this assumption\ becomes overly restrictive. In our work, we study several distributed\ classification approaches for the description logic EL+ (a fragment of OWL 2 EL profile). We present the lessons learned from each approach, our current results, and plans for future work.

}, keywords = {Distributed Reasoning, Ontology Classification, OWL EL}, doi = {10.1007/978-3-642-35173-0_30}, url = {http://dx.doi.org/10.1007/978-3-642-35173-0_30}, author = {Raghava Mutharaju}, editor = {Philippe Cudr{\'e}-Mauroux and Jeff Heflin and Evren Sirin and Tania Tudorache and J{\'e}r{\^o}me Euzenat and Manfred Hauswirth and Josiane Xavier Parreira and James A. Hendler and Guus Schreiber and Abraham Bernstein and Eva Blomqvist} } @article {167, title = {Distributed Reasoning with EL++ Using MapReduce}, year = {2010}, institution = {Wright State University}, address = {Dayton, OH, USA}, abstract = {

It has recently been shown that the MapReduce framework for distributed computation can be used effectively for large-scale RDF Schema reasoning, computing the deductive closure of over a billion RDF triples within a reasonable time [23]. Later work has carried this approach over to OWL Horst [22]. In this paper, we provide a MapReduce algorithm for classifying knowledge bases in the description logic EL++, which is essentially the OWL 2 profile OWL 2 EL. The traditional EL++ classification algorithm is recast into a form compatible with MapReduce, and it is shown how the revised algorithm can be realized within the MapReduce framework. An analysis of the circumstances under which the algorithm can be effectively used is also provided.

}, author = {Frederick Maier and Raghava Mutharaju and Pascal Hitzler} } @conference {161, title = {A MapReduce Algorithm for EL+}, booktitle = {Proceedings of the 23rd International Workshop on Description Logics (DL 2010)}, volume = {573}, year = {2010}, pages = {464-474}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Waterloo, Ontario, Canada}, abstract = {

Recently, the use of the MapReduce framework for distributed RDF Schema reasoning has shown that it is possible to compute the deductive closure of sets of over a billion RDF triples within a reasonable time span [22], and that it is also possible to carry the approach over to OWL Horst [21]. Following this lead, in this paper we provide a MapReduce algorithm for the description logic EL+, more precisely for the classification of EL+ ontologies. To do this, we first modify the algorithm usually used for EL+ classification. The modified algorithm can then be converted into a MapReduce algorithm along the same key ideas as used for RDF schema.

}, url = {http://ceur-ws.org/Vol-573/paper_35.pdf}, author = {Raghava Mutharaju and Frederick Maier and Pascal Hitzler}, editor = {Volker Haarslev and David Toman and Grant E. Weddell} } @conference {168, title = {Ontology Driven Integration of Biology Experiment Data}, booktitle = {Ohio Collaborative Conference on BioInformatics (OCCBIO 2009), Posters \& Demos}, year = {2009}, address = {Cleveland, OH, USA}, author = {Raghava Mutharaju and Satya S. Sahoo and D. Brent Weatherly and Pramod Anantharam and Flora Logan and Amit Sheth and Rick Tarleton} } @conference {162, title = {Ontology-Driven Provenance Management in eScience: An Application in Parasite Research}, booktitle = {On the Move to Meaningful Internet Systems: OTM 2009, Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009, Proceedings, Part II}, volume = {5871}, year = {2009}, pages = {992{\textendash}1009}, publisher = {Springer}, organization = {Springer}, address = {Vilamoura, Portugal}, abstract = {

Provenance, from the French word {\textquotedblleft}provenir{\textquotedblright}, describes the lineage or history of a data entity. Provenance is critical information in scientific applications to verify experiment process, validate data quality and associate trust values with scientific results. Current industrial scale eScience projects require an end-to-end provenance management infrastructure. This infrastructure needs to be underpinned by formal semantics to enable analysis of large scale provenance information by software applications. Further, effective analysis of provenance information requires well-defined query mechanisms to support complex queries over large datasets. This paper introduces an ontology-driven provenance management infrastructure for biology experiment data, as part of the Semantic Problem Solving Environment (SPSE) for Trypanosoma cruzi (T.cruzi). This provenance infrastructure, called T.cruzi Provenance Management System (PMS), is underpinned by (a) a domain-specific provenance ontology called Parasite Experiment ontology, (b) specialized query operators for provenance analysis, and (c) a provenance query engine. The query engine uses a novel optimization technique based on materialized views called materialized provenance views (MPV) to scale with increasing data size and query complexity. This comprehensive ontology-driven provenance infrastructure not only allows effective tracking and management of ongoing experiments in the Tarleton Research Group at the Center for Tropical and Emerging Global Diseases (CTEGD), but also enables researchers to retrieve the complete provenance information of scientific results for publication in literature.

}, doi = {10.1007/978-3-642-05151-7_18}, url = {http://dx.doi.org/10.1007/978-3-642-05151-7_18}, author = {Satya S. Sahoo and D. Brent Weatherly and Raghava Mutharaju and Pramod Anantharam and Amit Sheth and Rick Tarleton}, editor = {Robert Meersman and Tharam S. Dillon and Pilar Herrero} } @conference {163, title = {Spatio-Temporal-Thematic Analysis of Citizen Sensor Data: Challenges and Experiences}, booktitle = {Web Information Systems Engineering - WISE 2009, 10th International Conference}, volume = {5802}, year = {2009}, pages = {539{\textendash}553}, publisher = {Springer}, organization = {Springer}, address = {Poznan, Poland}, abstract = {

We present work in the spatio-temporal-thematic analysis of citizen-sensor observations pertaining to real-world events. Using Twitter as a platform for obtaining crowd-sourced observations, we explore the interplay between these 3 dimensions in extracting insightful summaries of social perceptions behind events. We present our experiences in building a web mashup application, Twitris [1] that extracts and facilitates the spatio-temporal-thematic exploration of event descriptor summaries.

}, doi = {10.1007/978-3-642-04409-0_52}, url = {http://dx.doi.org/10.1007/978-3-642-04409-0_52}, author = {Meenakshi Nagarajan and Karthik Gomadam and Amit Sheth and Ajith Ranabahu and Raghava Mutharaju and Ashutosh Jadhav}, editor = {Gottfried Vossen and Darrell D. E. Long and Jeffrey Xu Yu} } @conference {169, title = {Trykipedia: Collaborative Bio-Ontology Development using Wiki Environment}, booktitle = {Ohio Collaborative Conference on BioInformatics (OCCBIO 2009), Posters \& Demos}, year = {2009}, address = {Cleveland, OH, USA}, author = {Pramod Anantharam and Satya S. Sahoo and D. Brent Weatherly and Flora Logan and Raghava Mutharaju and Amit Sheth and Rick Tarleton} } @conference {165, title = {Twitris: Socially Influenced Browsing}, booktitle = {Semantic Web Challenge at the 8th International Semantic Web Conference (ISWC 2009)}, year = {2009}, address = {Washington DC, USA}, abstract = {

In this paper, we present Twitris, a semantic Web application that facilitates browsing for news and information, using social perceptions as the fulcrum. In doing so we address challenges in large scale crawling, processing of real time information, and preserving spatiotemporal-thematic properties central to observations pertaining to realtime events. We extract metadata about events from Twitter and bring related news and Wikipedia articles to the user. In developing Twitris, we have used the DBPedia ontology.

}, author = {Ashutosh Jadhav and Wenbo Wang and Raghava Mutharaju and Pramod Anantharam and Vinh Nguyen and Amit Sheth and Karthik Gomadam and Meenakshi Nagarajan and Ajith Ranabahu} } @article {171, title = {Traceability from Use Case to .NET Assembly via Design Patterns}, year = {2006}, institution = {Motilal Nehru National Institute of Technology (MNNIT)}, address = {Allahabad, India}, author = {Raghava Mutharaju and Banshi D. Chaudhary} }