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Rooted in electronic publishing, XML is now widely used for modelling and storing structured text documents. Especially in the WWW, retrieval of XML documents is most useful in combination with a relevance-based ranking of the query result. Index structures with ranking support are therefore needed for fast access to relevant parts of large document collections. This paper proposes a classification scheme for both XML ranking models and index structures, allowing to determine which index suits which ranking model. An analysis reveals that ranking parameters related to both the content and structure of the data are poorly supported by most known XML indices. The IR-CADG index, owing to its tight integration of content and structure, supports various XML ranking models in a very efficient retrieval process. Experiments show that it outperforms separate content/structure indexing by more than two orders of magnitude for large corpora of several hundred MB.

F1 Query is a stand-alone, federated query processing platform that executes SQL queries against data stored in different filebased formats as well as different storage systems at Google (e.g., Bigtable, Spanner, Google Spreadsheets, etc.). F1 Query eliminates the need to maintain the traditional distinction between different types of data processing workloads by simultaneously supporting: (i) OLTP-style point queries that affect only a few records; (ii) low-latency OLAP querying of large amounts of data; and (iii) large ETL pipelines. F1 Query has also significantly reduced the need for developing hard-coded data processing pipelines by enabling declarative queries integrated with custom business logic. F1 Query satisfies key requirements that are highly desirable within Google: (i) it provides a unified view over data that is fragmented and distributed over multiple data sources; (ii) it leverages datacenter resources for performant query processing with high throughput and low latency; (iii) it provides high scalability for large data sizes by increasing computational parallelism; and (iv) it is extensible and uses innovative approaches to integrate complex business logic in declarative query processing. This paper presents the end-to-end design of F1 Query. Evolved out of F1, the distributed database originally built to manage Google's advertising data, F1 Query has been in production for multiple years at Google and serves the querying needs of a large number of users and systems.

Abstract. This article reports on the XML retrieval system X 2 which has been developed at the University of Munich over the last five years. In a typical session with X 2 , the user first browses a structural summary of the XML database in order to select interesting elements and keywords occurring in documents. Using this intermediate result, queries combining structure and textual references are composed semiautomatically. After query evaluation, the full set of answers is presented in a visual and structured way. X 2 largely exploits the structure found in documents, queries and answers to enable new interactive visualization and exploration techniques that support mixed IR and database-oriented querying, thus bridging the gap between these three views on the data to be retrieved. Another salient characteristic of X 2 which distinguishes it from other visual query systems for XML is that it supports various degrees of detailedness in the presentation of answers, as well as techniques for dynamically reordering and grouping retrieved elements once the complete answer set has been computed.

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In XML retrieval, two distinct approaches have been established and pursued without much cross-fertilization taking place so far. On the one hand, native XML databases tailored to the semistructured data model have received considerable attention, and a wealth of index structures, join algorithms, tree encodings and query rewriting techniques for XML have been proposed. On the other hand, the question how to make XML fit the relational data model has been studied in great detail, giving rise to a multitude of storage schemes for XML in relational database systems (RDBSs). In this paper we examine how native XML indexing techniques can boost the retrieval of XML stored in an RDBS. We present the Relational CADG (RCADG), an adaptation of several native indexing approaches to the relational model, and show how it supports the evaluation of a clean formal language of conjunctive XML queries. Unlike relational storage schemes for XML, the RCADG largely preserves the underlying tree structure of the data in the RDBS, thus addressing several open problems known from the literature. Experiments show that the RCADG accelerates retrieval by up to two or even three orders of magnitude compared to both native and relational approaches.

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