Window function expressions (WFEs) became part of the SQL:2003 standard, and since then, they have often been implemented in database systems (DBS). They are especially essential to OLAP DBSs, and people use them daily. Even though WFEs are a heavily used part of the SQL language, the amount of research done on their optimization in the last two decades is not significant. WFE does not extend the expressive power of the SQL language, but it makes writing SQL queries easier and more transparent. DBSs always compile SQL queries with WFE using a sequence of partition-sort-compute operators, which we call a linear strategy. Plans resulting from the linear strategy are robust and, in many cases, efficient. This article introduces an alternative strategy using a self-join, which is not considered in the current DBSs. We call it the self-join strategy, and it is based on an SQL query transformation where the result query uses a self-join query plan to compute WFE. One output of this work is a tool that can automatically perform such SQL query transformations. We created a microbenchmark showing that the self-join strategy is more effective than the linear strategy in many cases. We also performed a cost-based experiment to evaluate the query optimizers' ability to select an appropriate strategy. The article's main aim is to show that usage of the self-join strategy for queries with WFE is beneficial if selected in a cost-based manner.

XML queries can be modeled by twig pattern queries (TPQs) specifying predicates on XML nodes and XPath relationships satisfied between them. A lot of TPQ types have been proposed; this paper takes into account a TPQ model extended by a specification of output and non-output query nodes since it complies with the XQuery semantics and, in many cases, it leads to a more efficient query processing. In general, there are two types of approaches to process a TPQ: holistic joins and binary joins. Whereas the binary join approach builds a query plan as a tree of interconnected binary operators, the holistic join approach evaluates a whole query using one operator (i.e., using one complex algorithm). Surprisingly, a thorough analytical and experimental comparison is still missing despite an enormous research effort in this area. In this paper, we try to fill this gap; we analytically and experimentally show that the binary joins used in a fully-pipelined plan (i.e., the plan where each join operation does not wait for the complete result of the previous operation and no explicit sorting is used) can often outperform the holistic joins, especially for TPQs with a higher ratio of non-output query nodes. The main contributions of this paper can be summarized as follows: (i) we introduce several improvements of existing binary join approaches allowing to build a fully-pipelined plan for a TPQ considering non-output query nodes, (ii) we prove that for a certain class of TPQs such a plan has the linear time complexity with respect to the size of the input and output as well as the linear space complexity with respect to the XML document depth (i.e., the same complexity as the holistic join approaches), (iii) we show that our improved binary join approach outperforms the holistic join approaches in many situations, and (iv) we propose a simple combined approach that utilizes advantages of both types of approaches.

Structural XQuery and XPath queries are often modeled by twig pattern queries (TPQs) specifying predicates on XML nodes and structural relationships to be satisfied between them. This paper considers a TPQ model extended by a specification of output and non-output query nodes since it complies with the XQuery and XPath semantics. There are two types of TPQ processing approaches: binary joins and holistic joins. The binary joins utilize a query plan of interconnected binary operators, whereas the holistic joins are based on one complex operator to process the whole query. In the recent years, the holistic joins have been considered as the state-of-the-art TPQ processing method. However, a thorough analytical and experimental comparison of binary and holistic joins has been missing despite an enormous research effort in this area. In this paper, we try to fill this gap. We introduce several improvements of the binary join operators which enable us to build a so-called fully-pipelined (FP) query plan for any TPQ with the specification of output and non-output query nodes. We analytically show that, for a class of queries, the proposed approach has the same time and space complexity as holistic joins, and we experimentally demonstrate that the proposed approach outperforms holistic joins in many cases.

XML (Extensible Mark-up Language) is a well established format which is often used for modeling of semi-structured data. XPath and XQuery are de facto standards among XML query languages and searching for occurrences of a twig pattern query (TPQ) in an XML document is one of their core tasks.

XML (Extensible Mark-up Language) has been embraced as a new approach to data modeling. Nowadays, more and more information is formatted as semi-structured data, e.g., articles in a digital library, documents on the web, and so on. Implementation of an efficient system enabling storage and querying of XML documents requires development of new techniques. Many different techniques of XML indexing have been proposed in recent years.