Vincent Oria


Contact Info



Associate Professor



GITC 4302




Computer Science

Web page 


Academic Interests: Multimedia Databases, Spatio-Temporal Databases and Recommender Systems.






Current Research Projects

·         Relevant set correlation clustering: Application to cashing and recommender systems

(Research conducted in collaboration Michael Houle, NII, Tokyo, Japan)

In this research we are interested in active caching mechanisms and multi-attribute recommendations:

Active Caching: In several novel applications (such as search engines, recommender systems and multimedia databases), the result of a query is a ranked list obtained by applying a similarity measure to features of database objects. Generating ranked lists is typically an expensive operation that often results in access latency. Caching of frequently-accessed data has been shown to have many useful applications for reducing stress on limited resources and improving response time. However, traditional caching techniques defined for exact match queries cannot be applied to ranked list queries. In this paper, we propose an `active caching' technique for ranked list queries that not only returns cached results, but also actively processes queries whose results are not present in the cache, by aggregating those ranked list results stored in the cache for related queries. The solution is based on concepts from the relevant set correlation (RSC) clustering model, which measures the similarity between two objects in terms of the number of other objects in the common intersection of their neighborhoods.

 Multi-attribute Recommendations: This research investigates the application of clustering to multi-criteria ratings as a method of improving the precision of top-N recommendations. With the advent of ecommerce sites that allow multi-criteria rating of items, there is an opportunity for recommender systems to use the additional information to gain a better understanding of user preference. This research proposes the use of the relevant set correlation model for a clustering-based collaborative filtering system. It is anticipated this novel system will handle large numbers of users and items without sacrificing the relevance of recommended items.

·         Moving Object Trajectory Management

(Research conducted in collaboration with Karine Zeitouni an Iulian Sandu Popa, University of Versailles St Quentin, France)

We proposed to work on a new access method for objects moving in trajectories. Although this subjected has been investigated most indexes proposed make the assumption that the objects move freely. We proposed PARINET, a new access method to efficiently retrieve the trajectories of objects moving in networks. The structure of PARINET is based on a combination of data partitioning and composite B+-tree local indexes. Unlike the existing approaches, the new approach relies on the distribution of the data to be indexed. For historical data, the data distribution can be known in advance. The partitioning of the data is based on graph partitioning theory, and can be tuned for a given query load.  We studied different types of queries, and provided an optimal configuration for several scenarios. PARINET can easily be integrated in any RDBMS, which is an essential aspect particularly for industrial or commercial applications.


The PARINET index is suitable to index past trajectories and is neither adapted to present trajectories nor future ones. It cannot work efficiently for real time trajectory indexing because the index structure is based on graph partitioning and the continuous flow of trajectories can lead dynamic partitioning of the trajectories which is expensive. Luckily in several real life applications, moving objects follow routines with similar trajectories. For example, people that have to start work at the same time every week day leave home around the same time everyday and most of the time, follow the same itinerary. This information can be mined for past trajectories and used to initialize the index.  The other part of this research consists in adapting the initialized index to the current flow in order to have an index that can work for present and future trajectories.


  • Semi-Automatic Image Annotation: Knowledge Propagation in Large Image Databases Using Neighborhood Information

This is join research between NJIT and NII that involves Prof. Michael Houle and Prof. Shin’ichi Satoh from NII, Tokyo and Jichao Sun, PhD student at NJIT.

 Propagating semantic information associated to some objects of interest to an entire image database has several applications ranging from home photo album management to security. Existing solutions are labor intensive and not always accurate. The aim of this research is to reduce to a minimum the human intervention in semantic annotations. Ideally, we would like a sample of each object of interest to be labeled once and have the label propagated to the occurrences of the object in the entire image database. To that end, we proposed a neighborhood-based approach called KProp (Knowledge Propagation) which builds a voting model and effectively propagates the knowledge associated to some objects to related objects in the database. Each object iteratively collects opinions from neighbors, makes a decision on its \status" and provides this information to the others. We show that this procedure can perform efficiently through matrix computations. KnowledgeProp is applicable as long as pair-wise similarities of objects are available and requires no human interactions besides the original labeling. We applied KnowledgeProp to simple object and face classifications. The experimental results show that our approach is more stable and achieves better results with fewer labeled examples per object.

  • A Steorological Approach to Sub-Query Result Integration

This is join research between NJIT and NII that involves Prof. Michael Houle from NII, Tokyo and Xiguo Ma, PhD student at NJIT.

In several applications such as multimedia and recommender systems, complex queries aiming to retrieve from large databases those objects that best match the query specification are usually processed by splitting the queries into a set of m simpler sub-queries, each dealing with only some of the query features. To determine which the overall best-matching objects are, a rule is then needed to integrate the results of such sub-queries, i.e., how to globally rank the m-dimensional vectors of matching degrees, or partial scores, that objects obtain on the m sub-queries. It is a fact that state-of-the-art approaches all adopt as integration rule a scoring function, such as weighted average, that aggregates the m partial scores into an overall (numerical) similarity score, so that objects can be linearly ordered and only the highest scored ones returned to the user. This choice however forces the system to compromise between the different sub-queries and can easily lead to miss relevant results. In this research we propose a steorological approach to sub-query result integration. In the past, measures of intrinsic dimension (such as the expansion dimension) have been used strictly for the analysis of similarity search methods. This research aims at demonstrating that tests of stereological dimension can be used dynamically to guide the decisions made by search algorithms.

Previous Research

  • 2006-2009 Event-Based Fusion of Distributed Multimedia Data Sources, funded by DoD-Army Research Laboratories (ARL) as part of the KIMCOE center of excellence, Morgan State University
  • 2004-2008 General Recommendation Engine, funded by NSF
  • DISIMA (Distributed Image Management System), Project led by Tamer Ozsu at the University of Alberta, Canada, funded by NSERC