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This article introduces the Tuple Graph (TuG) synopses, a new class of data summaries that enable accurate approximate answers for complex relational queries. The proposed summarization framework adopts a “semi-structured” view of the relational database, modeling a relational data set as a graph of tuples and join queries as graph traversals, respectively. The key idea is to approximate the structure of the induced data graph in a concise synopsis, and to approximate the answer to a query by performing the corresponding traversal over the summarized graph. We detail the (TuG) synopsis model that is based on this novel approach, and we describe an efficient and scalable construction algorithm for building accurate (TuG) within a specific storage budget. We validate the performance of (TuG) with an extensive experimental study on real-life and synthetic datasets. Our results verify the effectiveness of (TuG) in generating accurate approximate answers for complex join queries, and demonstrate their benefits over existing summarization techniques.

The AquaLogic Data Services Platform (ALDSP) is a middleware platform for building data services that integrate and provide operations over data drawn from spanning multiple heterogeneous information sources. A data service consists of an XML Schema instance, describing its information content, and a collection of XQuery functions and procedures that comprise its set of operations. This paper describes access control in ALDSP. We describe ALDSP's securable resource hierarchy, its fine-grained access control capabilities for securing portions of data service schemas, how XQuery can be used to specify data-driven security policies, and how user identity mapping is supported. We then provide an in-depth overview of how ALDSP works, including implementation techniques to keep access control checking from interacting badly with view rewriting, query optimization, and caching.

The BEA AquaLogic Data Services Platform (ALDSP) is a middleware platform for creating services that integrate and manipulate information from disparate enterprise data sources. This paper provides a technical overview of the all-new update support in ALDSP 3.0, released in January 2008. It describes the update side of data services, our unique model for making update automation transparent and flexible, and the use of the XQuery Scripting Extension (XQSE) for further customizing the system's default handling of updates. It also gives an overview of the ALDSP update processing machinery, including the automatic generation of update maps from read functions, translation of update maps into Update Virtual Machine (UVM) programs, the UVM instruction interpreter, and SQL generation for updates to data drawn from relational data sources.