Graph Summarization

The continuous and rapid growth of highly interconnected datasets, which are both voluminous and complex, calls for the development of adequate processing and analytical techniques. One method for condensing and simplifying such datasets is graph summarization. It denotes a series of application-specific algorithms designed to transform graphs into more compact representations while preserving structural patterns, query answers, or specific property distributions. As this problem is common to several areas studying graph topologies, different approaches, such as clustering, compression, sampling, or influence detection, have been proposed, primarily based on statistical and optimization methods. The focus of our chapter is to pinpoint the main graph summarization methods, but especially to focus on the most recent approaches and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie

The relationship between IR and multimedia databases

Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud \ud Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud \ud Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud \ud First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud \ud Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud \ud Third, we add the functionality to process the users' relevance feedback.\ud \ud We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud \ud We conclude with an outline for implementation of miRRor on top of the Monet extensible database system

On Achieving Diversity in the Presence of Outliers in Participatory Camera Sensor Networks

This paper addresses the problem of collection and delivery of a representative subset of pictures, in participatory camera networks, to maximize coverage when a significant portion of the pictures may be redundant or irrelevant. Consider, for example, a rescue mission where volunteers and survivors of a large-scale disaster scout a wide area to capture pictures of damage in distressed neighborhoods, using handheld cameras, and report them to a rescue station. In this participatory camera network, a significant amount of pictures may be redundant (i.e., similar pictures may be reported by many) or irrelevant (i.e., may not document an event of interest). Given this pool of pictures, we aim to build a protocol to store and deliver a smaller subset of pictures, among all those taken, that minimizes redundancy and eliminates irrelevant objects and outliers. While previous work addressed removal of redundancy alone, doing so in the presence of outliers is tricky, because outliers, by their very nature, are different from other objects, causing redundancy minimizing algorithms to favor their inclusion, which is at odds with the goal of finding a representative subset. To eliminate both outliers and redundancy at the same time, two seemingly opposite objectives must be met together. The contribution of this paper lies in a new prioritization technique (and its in-network implementation) that minimizes redundancy among delivered pictures, while also reducing outliers.unpublishedis peer reviewe