Database Theory - ICDT 2003 [electronic resource] : 9th International Conference, Siena, Italy, January 8-10, 2003, Proceedings /

Invited Papers -- Open Problems in Data-Sharing Peer-to-Peer Systems -- Approximations in Database Systems -- Bioinformatics Adventures in Database Research -- Reasoning about XML Schemas and Queries -- Incremental Validation of XML Documents -- Typechecking Top-Down Uniform Unranked Tree Transducers -- Structural Properties of XPath Fragments -- On Reasoning about Structural Equality in XML: A Description Logic Approach -- Aggregate Queries -- Containment of Aggregate Queries -- Auditing Sum Queries -- CRB-Tree: An Efficient Indexing Scheme for Range-Aggregate Queries -- Optimal Range Max Datacube for Fixed Dimensions -- Query Evaluation -- Processing XML Streams with Deterministic Automata -- Deciding Termination of Query Evaluation in Transitive-Closure Logics for Constraint Databases -- Query Rewriting and Reformulation -- Data Exchange: Semantics and Query Answering -- Reformulation of XML Queries and Constraints -- New Rewritings and Optimizations for Regular Path Queries -- Database Interrogation Using Conjunctive Queries -- Semistructured versus Structured Data -- On the Difficulty of Finding Optimal Relational Decompositions for XML Workloads: A Complexity Theoretic Perspective -- Generating Relations from XML Documents -- Query Containment -- Containment for XPath Fragments under DTD Constraints -- XPath Containment in the Presence of Disjunction, DTDs, and Variables -- Decidable Containment of Recursive Queries -- Containment of Conjunctive Queries with Safe Negation -- Consistency and Incompleteness -- Probabilistic Interval XML -- Condensed Representation of Database Repairs for Consistent Query Answering -- Typing Graph-Manipulation Operations -- Characterizing the Temporal and Semantic Coherency of Broadcast-Based Data Dissemination -- Data Structures -- An Efficient Indexing Scheme for Multi-dimensional Moving Objects -- Nearest Neighbors Can Be Found Efficiently If the Dimension Is Small Relative to the Input Size.