Indexing Open Schemas

Significant work has been done towards achieving the goal of placing semistructured data on an equal footing with relational data. While much attention has been paid to performance issues, far less work has been done to address one of the fundamental issues of semistructured data: schema evolution

Indexing Open Schemas

Abstract. Significant work has been done towards achieving the goal of placing semistructured data on an equal footing with relational data. While much attention has been paid to performance issues, far less work has been done to address one of the fundamental issues of semistructured data: schema evolution. Semistructured indexing and storage solutions tend to end where schema evolution begins. In practice, a real promise of semistructured data management will be realized where schemas evolve and change. In contrast to fixed schemas, we refer to schemas that grow and change as open schemas. This paper addresses the central complications associated with indexing open and evolving schemas: we specify the features and functionality that should be supported in order to handle evolving semistructured data. Specific contributions include a map of the steps for handling open schemas and an index for open schemas.

Middle-Tier Extensible Data Management

this paper, we discuss how extensible, middle-tier data management can address the twin challenges of flexibility and efficiency for today's e-commerce applications. Specifically, we make several contributions: . We present an architecture for deploying eXtensible Data Management in the middle tier of an ecommerce applicatio

Extensible Data Management in the Middle-Tier

Current data management solutions are largely optimized for intra-enterprise, client-server applications. They depend on predictability, predefined structure, and universal administrative control, and cannot easily cope with change and lack of structure. However, modern ecommerce applications are dynamic, unpredictable, organic, and decentralized, and require adaptability. eXtensible Data Management (XDM) is a new approach that enables rapid development and deployment of networked, data-intensive services by providing semantically-rich, high-performance middle-tier data management, and allows heterogeneous data from different sources to be accessed in a uniform manner. Here, we discuss how middle tier extensible data management can benefit an enterprise, and present technical details and examples from the Index Fabric, an XDM engine we have implemented. 1

A fast index for semistructured data

Queries navigate semistructured data via path expressions, and can be accelerated using an index. Our solution encodes paths as strings, and inserts those strings into a special index that is highly optimized for long and complex keys. We describe the Index Fabric, an indexing structure that provides the efficiency and flexibility we need. We discuss how "raw paths " are used to optimize ad hoc queries over semistructured data, and how "refined paths " optimize specific access paths. Although we can use knowledge about the queries and structure of the data to create refined paths, no such knowledge is needed for raw paths. A performance study shows that our techniques, when implemented on top of a commercial relational database system, outperform the more traditional approach of using the commercial system’s indexing mechanisms to query the XML. 1