Structural characterizations of the semantics of XPath as navigation tool on a document
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
On the Expressive Power of the Relational Algebra on Finite Sets of Relation Pairs
IEEE Transactions on Knowledge and Data Engineering
Towards a theory of search queries
ACM Transactions on Database Systems (TODS)
Relative expressive power of navigational querying on graphs
Proceedings of the 14th International Conference on Database Theory
Efficient external-memory bisimulation on DAGs
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
FoIKS'12 Proceedings of the 7th international conference on Foundations of Information and Knowledge Systems
A structural approach to indexing triples
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
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An old idea from the humanistic sciences has it that the language we use not only restricts the manner in which we view the world, but also, in a very real sense, shapes the world around us. This view has deep roots across fields as diverse as anthropology, linguistics, and philosophy. Recently, my colleagues and I have been exploring the interesting ways in which this idea manifests itself in data management. In particular, we have been studying the expressive power of graph query languages at the instance level, where the focus is on characterizing the ability of languages to restrict and shape concrete graph instances, purely in terms of the structure of the instances. In this talk, I will begin with a brief recap of such structural characterizations of query languages for structured and semi-structured data [4, 7, 8]. I will then introduce the theoretical framework we have been developing for reasoning over graph structured data [1, 2, 3, 6]. Following this, I will discuss how we put the framework to work, with the design of structural indexes for (RDF) graphs [5, 12]. I will also give an overview of our recent results on effectively computing the characterizations on which these index data structures are built [9, 10, 11]. Finally, I will conclude with a discussion of broader applications of the framework in data management and indications for further research.