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.

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Integrating Data Warehouses with Web Data: A Survey

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.

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Using Semantic Web Technologies for Exploratory OLAP: A Survey

Self-service business intelligence is about enabling non-expert users to make well-informed decisions by enriching the decision process with situational data, i.e., data that have a narrow focus on a specific business problem and, typically, a short lifespan for a small group of users. Often, these data are not owned and controlled by the decision maker; their search, extraction, integration, and storage for reuse or sharing should be accomplished by decision makers without any intervention by designers or programmers. The goal of this paper is to present the framework we envision to support self-service business intelligence and the related research challenges; the underlying core idea is the notion of fusion cubes, i.e., multidimensional cubes that can be dynamically extended both in their schema and their instances, and in which situational data and metadata are associated with quality and provenance annotations.

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Fusion Cubes: Towards Self-Service Business Intelligence

research. Given this unique “generational” vantage point, we present the changes in and impact of DSS research as well as future considerations for decision support in the IS field. We argue that the DSS area has remained vital as technology has evolved and our understanding of decision-making processes has deepened. DSS work over the last several years has contributed both breadth and depth to decision-making research; the challenge now is to make sense of it all by placing it in an understandable context and by applying our analysis to the relevant issues looming in the future. One major outcome of this commentary is the identification of future trends in DSS research and what the users of these new DSS outlets can learn from the past. Trends include the increasing impact of social and mobile computing on DSS research, as well as knowledge management DSS and negotiation support systems that shift the focus to delivering more customer-centric and marketplace support.

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A Look Toward the Future: Decision Support Systems Research is Alive and Well

In this paper we examine the audio source separation problem using the general framework of Independent Component Analysis (ICA). For the greatest part of the analysis, it has been assumed that equal number of sensors and sound objects. Firstly, it explores the case that the auditory scene is modeled as instantaneous mixtures of the auditory objects, to establish the basic tools for the analysis. The case of real room recordings, modeled as convolutive mixtures of the auditory objects, is then introduced. A novel Fast ICA framework is introduced, using two possible implementations. A great number of audio source separation problems can be addressed successfully using Independent Component Analysis. And concludes by highlighting some of the as yet unsolved problems to tackle the actual audio source separation problem in full.

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Preparation of Papers for International Journal of Scientific & Engineering Research

The Semantic Web enables organizations to attach semantic annotations taken from domain and application ontologies to the information they generate. The concepts in these ontologies could describe the facts, dimensions and categories implied in the analysis subjects of a data warehouse. In this paper we propose the Semantic Data Warehouse to be a repository of ontologies and semantically annotated data resources. We also propose an ontology-driven framework to design multidimensional analysis models for Semantic Data Warehouses. This framework provides means for building a Multidimensional Integrated Ontology (MIO) including the classes, relationships and instances that represent interesting analysis dimensions, and it can be also used to check the properties required by current multidimensional databases (e.g., dimension orthogonality, category satisfiability, etc.) In this paper we also sketch how the instance data of a MIO can be translated into OLAP cubes for analysis purposes. Finally, some implementation issues of the overall framework are discussed.

/pdf/multidimensional-integrated-ontologies-a-framework-for-27yrhrf06y.pdf

Multidimensional integrated ontologies: a framework for designing semantic data warehouses

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.

http://www.cis.drexel.edu/faculty/song/dolap/dolap05/paper/p19-perez.pdf

A relevance-extended multi-dimensional model for a data warehouse contextualized with documents

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.

R-Cubes: OLAP Cubes Contextualized with Documents

This paper presents a relevance model to rank the facts of a data warehouse that are described in a set of documents retrieved with an information retrieval (IR) query The model is based in language modeling and relevance modeling techniques We estimate the relevance of the facts by the probability of finding their dimensions values and the query keywords in the documents that are relevant to the query The model is the core of the so-called contextualized warehouse, which is a new kind of decision support system that combines structured data sources and document collections The paper evaluates the relevance model with the Wall Street Journal (WSJ) TREC test subcollection and a self-constructed fact database

Juan Manuel Pérez

Papers

Integrating Data Warehouses with Web Data: A Survey

Multidimensional integrated ontologies: a framework for designing semantic data warehouses

A relevance-extended multi-dimensional model for a data warehouse contextualized with documents

R-Cubes: OLAP Cubes Contextualized with Documents

A relevance model for a data warehouse contextualized with documents