This paper describes the convergence of some of the most influential technologies in the last few years, namely data warehousing (DW), on-line analytical processing (OLAP), and the Semantic Web (SW). OLAP is used by enterprises to derive important business-critical knowledge from data inside the company. However, the most interesting OLAP queries can no longer be answered on internal data alone, external data must also be discovered (most often on the web), acquired, integrated, and (analytically) queried, resulting in a new type of OLAP, exploratory OLAP. When using external data, an important issue is knowing the precise semantics of the data. Here, SW technologies come to the rescue, as they allow semantics (ranging from very simple to very complex) to be specified for web-available resources. SW technologies do not only support capturing the “passive” semantics, but also support active inference and reasoning on the data. The paper first presents a characterization of DW/OLAP environments, followed by an introduction to the relevant SW foundation concepts. Then, it describes the relationship of multidimensional (MD) models and SW technologies, including the relationship between MD models and SW formalisms. Next, the paper goes on to survey the use of SW technologies for data modeling and data provisioning, including semantic data annotation and semantic-aware extract, transform, and load (ETL) processes. Finally, all the findings are discussed and a number of directions for future research are outlined, including SW support for intelligent MD querying, using SW technologies for providing context to data warehouses, and scalability issues.

This paper surveys the most relevant research on combining Data Warehouse (DW) and Web data. It studies the XML technologies that are currently being used to integrate, store, query and retrieve web data, and their application to DWs. The paper reviews different DW distributed architectures and the use of XML languages as an integration tool in these systems. It also introduces the problem of dealing with semi-structured data in a DW. It studies Web data repositories, the design of multidimensional databases for XML data sources and the XML extensions of On-Line Analytical Processing techniques. The paper addresses the application of information retrieval technology in a DW to exploit text-rich documents collections. The authors hope that the paper will help to discover the main limitations and opportunities that offer the combination of the DW and the Web fields, as well as, to identify open research lines.

Current data warehouse and OLAP (Kimball and Ross, 2002) technologies can be efficiently applied to analyze the huge amounts of structured data that companies produce. These organizations also produce many text documents and use the Web as their largest source of external information. Although these documents include highly valuable information that should also be exploited by companies, they cannot be analyzed by current OLAP technologies because they are unstructured and mainly contain text. The current trend is to find these documents available in XML-like formats. Our proposal is to build XML document warehouses that can be used by companies to store unstructured information coming from their internal and external sources. In (Perez et al., 2005) we proposed an architecture for the integration of a corporate warehouse of structured data with a warehouse of text-rich XML documents. We call the resulting warehouse a contextualized warehouse. Since the XML document warehouse may contain documents about many different topics, we apply well-known information retrieval (IR) (Baeza-Yates and Ribeiro-Neto, 1999) techniques to select the context of analysis from the document warehouse. First, the user specifies an analysis context by supplying a sequence of keywords (e.g., an IR condition like "financial crisis"). Then, the analysis is performed on a so-called R-cube (Relevance cube), which is materialized by retrieving the documents and facts related to the selected context. Each fact in the R-cube will be linked to the set of documents that describe its context, and will have assigned a numerical value representing its relevance with respect to the specified context (e.g., how important the fact is for a "financial crisis"). In (Perez et al., 2005) we provided R-cubes with a data model and an algebra. This paper presents a prototype R-cube system, and explains how to use it.

Current data warehouse and OLAP technologies can be applied to analyze the structured data that companies store in their databases. The circumstances that describe the context associated with these data can be found in other internal and external sources of documents. In this paper we propose to combine the traditional corporate data warehouse with a document warehouse, resulting in a contextualized warehouse. Thus, contextualized warehouses keep a historical record of the fact and their contexts as described by the documents. In this framework, the user selects an analysis context which is represented as a novel type of OLAP cube, here called R-cube. R-cubes are characterized by two special dimensions, namely: the relevance and the context dimensions. The first dimension measures the relevance of each fact in the selected analysis context, whereas the second one relates each fact with the documents that explain their circumstances. In this work we extend an existing multi-dimensional data model and algebra for representing the R-cubes.

A new adaptive segmentation strategy is proposed to segment gymnasts in sport sequences accurately. It is based on a Markov random fields (MRF) change detection analysis operating on a multiresolution combination of static and dynamic image differences. After a morphological analysis of the segmented masks, estimated motion information in the area of interest is incorporated to improve the efficiency of the segmentation process. Although presented in the particular context of gymnastic exercises, the new segmentation strategy could be applied to other applications where moving objects on a quasi-static background need to be segmented.

Abstract This paper presents a model for the representation and retrieval of structured documents considering their temporal properties. The purpose of this model is to serve as a platform for the development of digital library applications. Thus, it consists of both a new data model and a query language that are specially adapted to the requirements of these applications. The main elements of the data model are a flexible type system for structured documents, and two temporal dimensions that represent the temporal properties of documents and the evolution of the database schema. As for its query language, it allows the retrieval of documents by specifying conditions on their structure, contents and temporal properties. This query language has been designed for exploiting the temporal information stored into a large digital library, making possible to relate document contents in time, as well as to analyse the evolution of topics. The paper also includes some guidelines for the efficient implementation of databases of structured documents by adopting the proposed data and query models. Copyright © 2001 John Wiley & Sons, Ltd.