67 research outputs found
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
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