Abstract. XML query languages need to provide some mechanism to inspect and manipulate nodes at all levels of an input tree. In this paper we investigate the expressive power provided in this regard by structural recursion. We show that the combination of vertical recursion down a tree combined with horizontal recursion across a list of trees gives rise to a robust class of transformations: it captures the class of all primitive recursive queries. Since queries are expected to be computable in at most polynomial time for all practical purposes, we next identify a restriction of structural recursion that captures the polynomial time queries. Although this restriction is semantical in nature, and therefore undecidable, we provide an effective syntax. We also give corresponding results for list-based complex objects. 1

Computation with large-scale heterogeneous data typically requires universal traversal to search forall occurrences of a substructure that matches a possibly complex search pattern, whose context may be different in different places within the data. Both aspects cause difficulty for existing general-purpose programming languages, because these languages are designed for homogeneous data and have problems typing the different substructures in heterogeneous data, and the complex patterns to match with the substructures. Programmers either have to hard-code the structures and search patterns, preventing programs from being reusable and scalable, or have to use low-level untyped programming or programming with special-purpose query languages, opening the door to type mismatches that cause a high risk of program correctness and security problems. This thesis invents the concept of pattern structures, and proposes a general solution to the above problems -- a programming technique using pattern structures. In this solution, well-typed pattern structures are defined to represent complex search patterns, and pattern searching over heterogeneous data is programmed with pattern parameters, in a statically-typed language that supports first-class typing of structures and patterns. The resulting programs are statically-typed, highly reusable for different data structures and different patterns, and highly scalable in terms of the complexity of data structures and patterns. Adding new kinds of patterns for an application no longer requires changing the language in use or creating new ones, but is only a programming task. The thesis demonstrates the application of this approach to, and its advantages in, two important examples of computation with heterogeneous data, i.e., XML data processing and Java bytecode analysis.

We demonstrate XRPC, a minimal XQuery extension that enables distributed querying between heterogeneous XQuery engines. The XRPC language extension enhances the existing concept of XQuery functions with the Remote Procedure Call (RPC) paradigm. XRPC is orthogonal to all XQuery features, including the XQuery Update Facility (XQUF). Note that executing xquf updating functions over XRPC leads to the phenomenon of distributed transactions. XRPC achieves heterogeneity by an open SOAP-based network protocol, that can be implemented by any engine, and an XRPC Wrapper that allows even XRPC-oblivious XQuery engines to handle XRPC requests efficiently. XRPC is fully implemented in the open-source MonetDB/XQuery engine, and is demonstrated here to co-operate with Saxon, Galax and X-Hive through the XRPC wrapper. This demonstration will focus on the following features of XRPC: (i) glue-less interaction between AJAX style webbased applications with XQuery databases thanks to the SOAP-based nature of the XRPC network protocol, (ii) the efficiency of XRPC communication also for voluminous interserver communication thanks to the Bulk RPC feature that optimizes network communication and exposes set-at-a-time opportunities to the underlying XQuery engines, (iii) the interoperability between different XQuery engines that can handle both distributed transactions (both read-only requests and updates) (iv) support and performance trade-offs of two different isolation levels for distributed transactions among different XQuery engines.

Effective support for XML query languages is becoming increasingly important with the emergence of new applications that access large volumes of XML data. All existing proposals for querying XML (e.g., XQuery) rely on a pattern-specification language that allows (1) path navigation and branching through the label structure of the XML data graph, and (2) predicates on the values of specific path/branch nodes, in order to reach the desired data elements. Clearly, optimizing such queries requires approximating the result cardinality of the referenced paths and hence hinges on the existence of concise synopsis structures that enable accurate compile-time selectivity estimates for complex path expressions over the base XML data. In this article, we introduce a novel approach to building and using statistical summaries of large XML data graphs for effective path-expression selectivity estimation. Our proposed graph-synopsis model (termed XSKETCH) exploits localized graph stability and value-distribution summaries (e.g., histograms) to accurately approximate (in limited space) the path and branching distribution, as well as the complex correlation patterns that can exist between and across path structure and element values in the data graph. To the best of our knowledge, ours is the first work to address this timely problem in the most general setting of graph-structured XML data with values, and complex (branching) path expressions.

Current search engines such as Google and Yahoo! are prevalent for searching the Web. Search in dynamic pages, however, is either inexistent or far from perfect. AJAX and Rich Internet Application are such applications. They are increasingly frequent on the Web (in YouTube, Amazon, GMail, Yahoo!Mail) or mobile devices and are offering a high degree of interactivity to the user, by seamlessly loading content from the server without the need to refresh the page. Current search engines cannot correctly index AJAX applications. This produces false positives and false negatives, because search engines do not understand the application logic that loads content dynamically. Crawling an AJAX application is a difficult problem. Since the user invokes events on the page, crawling must identify the different application states generated by the client-side logic. This demo sets the stage for this new type of search and shows that a search engine for AJAX can be built. Among others, the challenges, as opposed to traditional search engines, are: automatically identifying states by triggering events, efficiently crawling application states, avoiding the invocation of potentially very numerous events, scalability in the number of events, duplicate elimination of states, result presentation and aggregation, ranking. The demo presents the AJAX search engine: crawler, indexer and query processor, applied on a real application and showcases challenges and solutions. 1.

To ease the development of data-intensive P2P applications, we envision a P2P XML Database Management System (P2P XDBMS) that acts as a database middle-ware, providing a uniform database abstraction on top of a dynamic set of distributed data sources. In this PhD work, we research which features such a database abstraction should offer and how it can be realised efficiently by extending and combining existing XML databases with P2P technologies. The first step in this research is a distributed database extension called XRPC. Our planned future work builds upon this, adding P2P abstractions to all main database functionalities (query processing, transactions and data storage). 1.

Effective support for XML query languages is becoming increasingly important with the emergence of new applications that access large volumes of XML data. All existing proposals for querying XML (e.g., XQuery) rely on a pattern-specification language that allows (1) path navigation and branching through the label structure of the XML data graph, and (2) predicates on the values of specific path/branch nodes, in order to reach the desired data elements. Clearly, optimizing such queries requires approximating the result cardinality of the referenced paths and hence hinges on the existence of concise synopsis structures that enable accurate compile-time selectivity estimates for complex path expressions over the base XML data. In this article, we introduce a novel approach to building and using statistical summaries of large XML data graphs for effective path-expression selectivity estimation. Our proposed graph-synopsis model (termed XSKETCH) exploits localized graph stability and value-distribution summaries (e.g., histograms) to accurately approximate (in limited space) the path and branching distribution, as well as the complex correlation patterns that can exist between and across path structure and element values in the data graph. To the best of our knowledge, ours is the first work to address this timely problem in the most general setting of graph-structured XML data with values, and complex (branching) path expressions.