Welcome to the Graph-Oriented AppLications Research Lab in CS@RIT. Our research focuses on graphs and their multiple applications: from integrating graph databases to program comprehension or from finding subgraphs efficiently to the Web of Data.
We attended FSE'17 in Paderborn, Germany to present our paper: ARCC: Assistant for Repetitive Code Comprehension.
New paper in FSE
A new paper was accepted in the ACM SIGSOFT Symposium on the Foundations of Software Engineering: ARCC: Assistant for Repetitive Code Comprehension. We used subgraph pattern matching against program dependence graphs to assist in program comprehension. Watch our screencast.
We have created a new prototype to deliver personalized feedback.
We attended ICDE'17 in San Diego, USA to present our paper: Automated Personalized Feedback in Introductory Java Programming MOOCs. Carlos R. Rivero was the chair of the Social networks session.
Seminar by Victor J. Marin
Victor J. Marin delivered a seminar at RIT on providing personalized feedback using subgraph pattern matching in programming MOOCs.
New paper in ICDE
A new paper was accepted in the IEEE International Conference on Data Engineering: Automated Personalized Feedback in Introductory Java Programming MOOCs. We developed a new framework to provide personalized feedback in introductory programming courses.
Seminars by Carlos R. Rivero
Carlos R. Rivero delivered two seminars at RIT. He presented his work on providing personalized feedback using subgraph pattern matching in introductory programming courses. These talks were held in the context of the GCCIS PhD Colloquium Series and the Theory Canal: The Rochester Theory Seminar.
New paper in KAIS
A new paper was accepted in the Knowledge and Information Systems journal: Efficient and scalable labeled subgraph matching using SGMatch. It presents a new exact subgraph matching technique based on graphlets and minimum hub covers.
Atendding Summer School
Andrea Cimmino attended the Second ScaDS Summer School on Big Data in Leipzig (Picture). He learned about Big Data Storage/NoSQL, Distributed Data Processing (HPC/MapReduce/Streaming/Spark/Flink), Graph Analytics/Management and Big Data Integration.
Seminar by Andrea Cimmino
Andrea Cimmino delivered a seminar at RIT entitled: Using context to improve integration in the Web of Data. He presented an overview of his PhD dissertation on improving link discovery using context information in the Web of Data.
We attended WWW'16 in Montreal, Canada to present our paper: Improving Link Specifications using Context-Aware Information.
New paper in LDOW (WWW workshops)
A new paper was accepted in the Workshop on Linked Data on the Web (LDOW) co-located with the International World Wide Web Conference (WWW): Improving Link Specifications using Context-Aware Information. We developed a new framework to model data collected from a crowd-sensing platform using semantic-web technologies.
New paper in CoMoRea (PerCom workshops)
A new paper was accepted in the Workshop on Context and Activity Modeling and Recognition (CoMoRea)co-located with the IEEE International Conference on Pervasive Computing and Communication (PerCom): PLOMaR: An ontology framework for context modeling and reasoning on crowd-sensing platform. It presents a new approach to improve link discovery in the Web of Data by exploiting context information.
Many software-related activities require comprehending programs that other programmers wrote. In the majority of such scenarios, program comprehension is a manual process since current approaches cannot adequately handle program variability and, in particular, interleaved tasks, i.e., sets of non-contiguous program statements with specific semantic purposes. We apply recent advances in graph databases to programs modeled as system dependence graphs. We rely on
subgraph patterns to model tasks and subgraph matching to compute pattern occurrences.
Additional info: System Dependence Graph builder, ARCC.
Subgraph matching is a very hard problem and it has many important applications in graph databases to retrieve data represented as graphs, such as biological networks, chemical compounds, geographic maps, social networks and more. There exist several algorithms that exploit different heuristics and indexing structures to perform subgraph matching efficiently in practice. In general, it is not clear how these algorithms compare in terms of performance and/or accuracy. We aim to build a framework on top of Neo4j to fairly compare side by side current and future subgraph matching algorithms. This framework will allow to build intelligent query engines able to estimate, given a subgraph query and a data graphs, which algorithm will have the best performance.
In the context of the Web of Data, there are datasets that usually store entities that refer to the same real-world concept. Link discovery focuses on the generation of high-level rules that indicate how such entities relate. Existing techniques exploit genetic programming algorithms to compute the rules based on positive and negative examples of related entities. In general, genetic programming algorithms are very sensible to configuration parameters and different parameter combinations may result in the computation of completely different rules. We aim to perform in-depth evaluations of existing algorithms regarding these combinations, and to develop genetic programming operators that take sets of entities into account.
We are in the third floor of the Golisano College of Computing and Information Sciences. If you visit us, you need to get a temporary parking permit at the Welcome Center. The closest lots are J and S.
Graph-Oriented AppLications Research Lab
Computer Science Department
Rochester Institute of Technology
Rochester, NY 14623