Both RDBMS and NoSQL database vendors have added varying degrees of support for storing and processing JSON data. Some vendors store JSON directly as text while others add new JSON type systems backed by binary encoding formats. The latter option is increasingly popular as it enables richer type systems and efficient query processing. In this paper, we present our new native JSON datatype and how it is fully integrated with the Oracle Database ecosystem to transform Oracle Database into a mature platform for serving both SQL and NoSQL style access paradigms. We show how our uniquely designed Oracle Binary JSON format (OSON) is able to speed up both OLAP and OLTP workloads over JSON documents.

The AquaLogic Data Services Platform (ALDSP) is a middleware platform developed at BEA Systems for building services, referred to as data services, that integrate, access, and manipulate information coming from multiple heterogeneous sources of data (including databases, files, and other services). ALDSP uses functions that produce and consume XML to model heterogeneous information sources, so the integration logic for data access services in ALDSP is specified declaratively using the XQuery language. A challenge that we faced in developing ALDSP was providing effective graphical tooling to help data service developers to develop information integration queries. In this paper, we describe the graphical XQuery Editor (XQE) that resulted from our attempt to tackle this challenge. XQE handles the full XQuery language and provides a robust two-way editing experience involving both graphical and source views of each query. XQE is novel in being the first commercial graphical XQuery editor to support both of these features.

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.

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.

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.

This paper introduces the Tuple Graph (TUG) synopses, a new class of data summaries that enable accurate selectivity estimates 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 estimate the selectivity of 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 TUGs within a specific storage budget. We validate the performance of TUGs with an extensive experimental study on real-life and synthetic data sets. Our results verify the effectiveness of TUGs in generating accurate selectivity estimates for complex join queries, and demonstrate their benefits over existing summarization techniques.