Podcasts about xquery

Michael Kay is the editor of the W3C XSLT 2.0 and 3.0 language specifications for performing XML transformations and the developer of the Saxon XSLT and XQuery processing software. The video is here: https://youtu.be/2Zt9oJtFKGw

Find out more on our website: https://bit.ly/3Jzlmz9 Functional programmers have long claimed that languages such as Haskell and O'Caml allow one to write code more quickly and more reliably. Widespread uptake of functional programming among the financial community supports that claim, and this talk will examine a few cases. Speaker: Philip Wadler is Professor of Theoretical Computer Science at the University of Edinburgh and Senior Research Fellow at IOHK. He is an ACM Fellow, a Fellow of the Royal Society of Edinburgh, and editor-in-chief of Proceedings of the ACM for Programming Languages. He is past chair of ACM SIGPLAN, past holder of a Royal Society-Wolfson Research Merit Fellowship, winner of the SIGPLAN Distinguished Service Award, and a winner of the POPL Most Influential Paper Award. Previously, he worked or studied at Stanford, Xerox Parc, CMU, Oxford, Chalmers, Glasgow, Bell Labs, and Avaya Labs, and visited as a guest professor in Copenhagen, Sydney, and Paris. He has an h-index of over 70 with more than 25,000 citations to his work, according to Google Scholar. He contributed to the designs of Haskell, Java, and XQuery, and is co-author of Introduction to Functional Programming (Prentice Hall, 1988), XQuery from the Experts (Addison Wesley, 2004), Generics and Collections in Java (O'Reilly, 2006), and Programming Language Foundations in Agda (2018). He has delivered invited talks in locations ranging from Aizu to Zurich.

Michael has been doing data processing and query languages since the 1980s. Among other things he has been representing Microsoft on the XQuery and SQL design committees and has taken SQL Server beyond relational with XML, Geospatial and Semantic Search. Currently he is working on Big Data query languages such as SCOPE and U-SQL when he is not enjoying time with his family under water, on the ski slopes, or at autocross.This talk has taken place during PASS Summit in Seattle, WA, on 2nd November 2017 (Thursday).Do you ever wonder how many people work on the USQL or Azure Data Lake? What animals does Michael see from his window?Listen to this podcast to get to know what two career choices Michael had had when he finished PhD in Switzerland.

Michael has been doing data processing and query languages since the 1980s. Among other things he has been representing Microsoft on the XQuery and SQL design committees and has taken SQL Server beyond relational with XML, Geospatial and Semantic Search. Currently he is working on Big Data query languages such as SCOPE and U-SQL when he is not enjoying time with his family under water, on the ski slopes, or at autocross.This talk has taken place during PASS Summit in Seattle, WA, on 2nd November 2017 (Thursday).Do you ever wonder how many people work on the USQL or Azure Data Lake? What animals does Michael see from his window?Listen to this podcast to get to know what two career choices Michael had had when he finished PhD in Switzerland.

Don Chamberlin holds a B.S. degree from Harvey Mudd College and a Ph.D. from Stanford University. For many years, he worked at Almaden Research Center, researching database languages and systems. He was a member of the System R research team that developed much of today’s relational database technology and, together with Ray Boyce, he designed the original SQL database language. More recently, he was a member of the W3C Working Group on XML Query Languages and an editor of the XPath 2.0 and XQuery language specifications, which became W3C Recommendations in 2007. With Jonathan Robie and Dana Florescu, he designed the Quilt language, which became the basis for the design of XQuery. He likes to teach and recently taught a Java programming class at University of California, Santa Cruz. For the last several years he has been a judge and problem contributor to the ACM International Collegiate Programming Contest.

John Swanson updates us on the hole in his backyard and then we do a quick rundown on new feature and highlights on the site for the week of Feb 15-23.

Visions of the next generation Web such as the "Semantic Web" or the "Web 2.0" have triggered the emergence of a multitude of data formats. These formats have different characteristics as far as the shape of data is concerned (for example tree- vs. graph-shaped). They are accompanied by a puzzlingly large number of query languages each limited to one data format. Thus, a key feature of the Web, namely to make it possible to access anything published by anyone, is compromised. This thesis is devoted to versatile query languages capable of accessing data in a variety of Web formats. The issue is addressed from three angles: language design, common, yet uniform semantics, and common, yet uniform evaluation. % Thus it is divided in three parts: First, we consider the query language Xcerpt as an example of the advocated class of versatile Web query languages. Using this concrete exemplar allows us to clarify and discuss the vision of versatility in detail. Second, a number of query languages, XPath, XQuery, SPARQL, and Xcerpt, are translated into a common intermediary language, CIQLog. This language has a purely logical semantics, which makes it easily amenable to optimizations. As a side effect, this provides the, to the best of our knowledge, first logical semantics for XQuery and SPARQL. It is a very useful tool for understanding the commonalities and differences of the considered languages. Third, the intermediate logical language is translated into a query algebra, CIQCAG. The core feature of CIQCAG is that it scales from tree- to graph-shaped data and queries without efficiency losses when tree-data and -queries are considered: it is shown that, in these cases, optimal complexities are achieved. CIQCAG is also shown to evaluate each of the aforementioned query languages with a complexity at least as good as the best known evaluation methods so far. For example, navigational XPath is evaluated with space complexity O(q d) and time complexity O(q n) where q is the query size, n the data size, and d the depth of the (tree-shaped) data. CIQCAG is further shown to provide linear time and space evaluation of tree-shaped queries for a larger class of graph-shaped data than any method previously proposed. This larger class of graph-shaped data, called continuous-image graphs, short CIGs, is introduced for the first time in this thesis. A (directed) graph is a CIG if its nodes can be totally ordered in such a manner that, for this order, the children of any node form a continuous interval. CIQCAG achieves these properties by employing a novel data structure, called sequence map, that allows an efficient evaluation of tree-shaped queries, or of tree-shaped cores of graph-shaped queries on any graph-shaped data. While being ideally suited to trees and CIGs, the data structure gracefully degrades to unrestricted graphs. It yields a remarkably efficient evaluation on graph-shaped data that only a few edges prevent from being trees or CIGs.

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