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.

Multidimensional data are commonly utilized in many application areas like electronic shopping, cartography and many others. These data structures support various types of queries, e.g. point or range query. The range query retrieves all tuples of a multidimensional space matched by a query rectangle. Processing range queries in a multidimensional data structure has some performance issues, especially in the case of a higher space dimension or a lower query selectivity. As result, these data are often stored in an array or one-dimensional index like B-tree and range queries are processed with a sequence scan. Many real world queries can be transformed to a multiple range query: the query including more than one query rectangle. In this article, we aim our effort to processing of this type of the range query. First, we show an algorithm processing a sequence of range queries. Second, we introduce a special type of the multiple range query, the Cartesian range query. We show optimality of these algorithms from the IO and CPU costs point of view and we compare their performance with current methods. Although we introduce these algorithms for the R-tree, we show that these algorithms are appropriate for all multidimensional data structures with nested regions.

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.

Multi-dimensional data structures are applied in many real index applications, i.e. data mining, indexing multimedia data, indexing of text documents and so on. Many index structures and algorithms have been proposed. There are two major approaches to multi-dimensional indexing: data structures to indexing metric and vector spaces. R-trees, R*-trees and (B)UB-trees are representatives of the vector data structures. These data structures provide efficient processing of many types of queries, i.e. point queries, range queries and so on. As far as the vector data structures are concerned, the range query retrieves all points in defined hyper box in an n-dimensional space. The narrow range query is an important type of the range query. Its processing is inefficient in vector data structures. Moreover, the efficiency decreases as the dimension of the indexed space increases. We depict an application of the signature for more efficient processing of narrow range queries. The approach puts the signature into the multi-dimensional data structures like R-tree or UB-tree but original functionalities are preserved, i.e. the range query algorithm for general range query. The novel data structure is called the signature data structure, e.g., Signature R-tree or Signature UB-tree.

We describe an application of information extraction from company Web sites focusing on product offers. A statistical approach to text analysis is used in conjunction with different ways of image classification. Ontological knowledge is used to group the extracted items into structured objects. The results are stored in an RDF repository and made available for structured search.