are often successful in easing application development. In this paper, we report our experiences in compiling a recently developed XML Query Language, XQuery for applications that process scientific datasets.

The modern XML query language called XQuery includes advanced facilities both to query and to transform XML data. An XQuery query optimizer should be able to optimize any query. For “querying ” queries almost all techniques inherited from SQL-oriented DBMS may be applied. The XQuery transformation facilities are XML-specific and have no counterparts in other languages. That is why XQuery transformational queries need to be optimized with novel techniques. In this paper two kinds of such techniques (namely push predicates down XML element constructors and projection of transformation) are proposed and formally proved. 1

XPath 2.0 path expressions can observe and preserve the document order and identity of XML values in a document. In particular, their semantics requires that the complete result and the result of each individual step in a path expression be in document order and duplicate-free. Implementations of this semantics often guarantee correctness by inserting explicit operations that sort and remove duplicates after each step. Such operations, however, can be redundant, because an intermediate result may already be sorted and/or duplicate-free. This work presents a sound and complete set of inference rules that decide whether each step in a path expression always yields a result in document order and with no duplicates. The inference rules are implemented by an efficient, automaton-based algorithm. Experimental results show that the algorithm detects and eliminates all redundant sorting and duplicate elimination operators, and is effective on most

We take a closer look at the optimization problems that are associated with the XQuery language. We discuss the research that has been done and some open problems along with potential solutions.

To evaluate a query it is necessary to split it to subqueries. It can be done in several di#erent ways resulted in various granularity.

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Abstract. Selectivity estimation of path expressions in querying XML data plays an important role in query optimization. A path expression may contain multiple branches with predicates, each of which having its impact on the selectivity of the entire query. In this paper, we propose a novel method based on 2-dimensional value histograms to estimate the selectivity of path expressions embedded with predicates. The value histograms capture the correlation between the structures and the values in the XML data. We define a set of operations on the value histograms as well as on the traditional histograms that capture nodes positional distribution. We then construct a cost tree based on such operations. The selectivity of any node (or branch) in a path expression can be estimated by executing the cost tree. Compared with previous methods (which ignore value distribution) our method offers much better estimation accuracy. 1

Development of applications that process large scientific datasets is often complicated by complex and specialized data storage formats. We show how XML Schemas can be used to give a high-level abstraction of a dataset to an application developer. We also show how the use of XQuery with such high-level Schemas can simplify application development. The nature of the computations in scientific data processing applications and use of high-level Schemas poses a number of challenges in XQuery compilation, which are being addressed in our current work. 1.

We present a sound and complete rule set for determining whether sorting by document order and duplicate removal operations in the query plan of XPath expressions are unnecessary.

Several requirements for algebra suitable for e cient cost-based optimization are presented. It is shown that known XML algebras do not fully satisfy this requirements. A new algebra to satisfy better the requirements is introduced. 1