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

SOFOS: Demonstrating the Challenges of Materialized View Selection on Knowledge Graphs

Analytical queries over RDF data are becoming prominent as a result of the proliferation of knowledge graphs. Yet, RDF databases are not optimized to perform such queries efficiently, leading to long processing times. A well known technique to improve the performance of analytical queries is to exploit materialized views. Although popular in relational databases, view materialization for RDF and SPARQL has not yet transitioned into practice, due to the non-trivial application to the RDF graph model. Motivated by a lack of understanding of the impact of view materialization alternatives for RDF data, we demonstrate SOFOS, a system that implements and compares several cost models for view materialization. SOFOS is, to the best of our knowledge, the first attempt to adapt cost models, initially studied in relational data, to the generic RDF setting, and to propose new ones, analyzing their pitfalls and merits. SOFOS takes an RDF dataset and an analytical query for some facet in the data, and compares and evaluates alternative cost models, displaying statistics and insights about time, memory consumption, and query characteristics

SHARE : A Framework for Personalized and Healthy Recipe Recommendations

This paper presents a personalized recommendation system that suggests recipes to users based on their health history and similar users' preferences. Specifically, the system utilizes collaborative filtering to determine other users with similar dietary preferences and exploits this information to identify suitable recipes for an individual. The system is able to handle a wide range of health constraints, preferences, and specific diet plans, such as low-carb or vegetarian. We demonstrate the usability of the system through a series of experiments on a large real-world data set of recipes. The results indicate that our system is able to provide highly personalized and accurate recommendations.publishedVersionPeer reviewe