Information retrieval, global visualisation, 3D representation, multidimensional visualisation and analysis This paper presents a novel user interface that provides global visualisations of large document sets in order to help users to formulate the query that corresponds to their information needs and to access the corresponding documents. An important element of the approach we introduce is the use of concept hierarchies (CHs) in order to structure the document collection. Each CH corresponds to a facet of the documents users can be interested in. Users browse these CHs in order to specify and refine their information needs. Additionally the interface is based on OLAP principles and multi-dimensional analysis operators are provided to users in order to allow them to explore a document collection. 1 KEY-WORDS DocCube: multi-dimensional analysis of documents Information retrieval, global visualisation, 3D representation, multidimensional analysis 1.

Materialised views are important in data warehouses where they are used to speed up query processing in decision support activities. There are many applications, such as those involving financial or stock market data for example, where the base data is volatile and the updates to the materialised views need to be computed efficiently. In such cases, incremental update techniques are often used for performance reasons. In this paper we propose an incremental technique for efficiently maintaining materialised views in these high performance applications by materialising additional relations which are derived from the intermediate results of the view computation. We present an algorithm which uses the operator tree for the view to determine which additional relations need to be materialised in order to maintain the view. We then give an incremental algorithm for maintaining both the view and the additional relations which has several desirable features. Firstly, it is an 'exact change' al...

Data Warehouse (DWH) information is accessed by business processes, and sometimes may also initiate changes of the control flow of business process instances. Today, there are no conceptual models available that make the relationship between the DWH and the business processes transparent. In this paper, we extend the Event-Driven Process Chain, a business process modeling language, with an additional perspective to make this relationship explicit in a conceptual model. The model is tested with example business processes. 1 1.

A holistic approach, in the form of a process, is proposed in order to discover marketing intelligence from Internet data. The Internet-enabled knowledge discovery process contains the steps human resource identification, problem specification, data prospecting, domain knowledge elicitation, methodology identification, data pre-processing, pattern discovery, and knowledge post-processing. It also involves the three types of expertise required during a project, namely a web administrator, a marketing expert, and a data mining specialist. To show the validity and applicability of the proposed approach, the electronic commerce marketing scenarios of customer attraction, customer retention, cross-sales, and churn are tackled with the outlined process, respectively. 1. Introduction Knowledge discovery of Internet data (also known as web mining), has been an area of recent cross-disciplinary research interest. For the discovery of patterns that are actionable in electronic commerce environ...

OLAP (Online Analysis Processing) applications have very special requirements to the underlying multidimensional data that differs significantly from other areas of application (e.g. the existence of highly structured dimensions). In addition, providing access and search among multiple, heterogeneous, distributed and autonomous data warehouses, especially web warehouses, has become one of the leading issues in data warehouse research and industry. This paper proposes MetaCube-X to provide interoperability search among Web data warehouses.

Abstract—A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, “whether a total sales amount 1,000 items indicates a good or bad sales performance ” is still unclear. From the decision makers ’ point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses. Index Terms—Data warehouse, semantic model, algebraic operator, extended SQL, fuzzy set, membership function. 1

In this paper, we propose a conceptual multidimensional data model that facilitates a precise rigorous conceptualization for OLAP. First, our approach has strong relation with mathematics by applying a new defined concept, i.e. H-set. Afterwards, the mathematic soundness provides a foundation to handle natural hierarchical relationships among data elements within dimensions with many levels of complexity in their structures. Hereafter, the multidimensional data model organizes data in the form of metacubes, the concept of which is a generalization of other cube models. In addition, a metacube is associated with a set of groups each of which contains a subset of the metacube domain, which is a H-set of data cells. Furthermore, metacube operators (e.g. jumping, rollingUp and drillingDown) are defined in a very elegant manner.

In this paper, we propose a conceptual multidimensional data model that facilitates a precise rigorous conceptualization for OLAP. First, our approach has strong relation with mathematics by applying a new defined concept, i.e. H-set. Afterwards, the mathematic soundness provides a foundation to handle natural hierarchical relationships among data elements within dimensions with many levels of complexity in their structures. Hereafter, the multidimensional data model organizes data in the form of metacubes, the concept of which is a generalization of other cube models. In addition, a metacube is associated with a set of groups each of which contains a subset of the metacube domain, which is a H-set of data cells. Furthermore, metacube operators (e.g. jumping, rollingUp and drillingDown) are defined in a very elegant manner.

Association rule mining is an important data mining problem. It is found to be useful for conventional relational data. However, previous work has mostly targeted on mining a single table. In real life, a database is typically made up of multiple tables and one important case is where some of the tables form a star schema. The tables typically correspond to entity sets and joining the tables in a star schema gives relationships among entity sets which can be very interesting information. Hence mining on the join result is an important problem. Based on characteristics of the star schema we propose an efficient algorithm for mining association rules on the join result but without actually performing the join operation. We show that this approach can significantly out-perform the join-then-mine approach even when the latter adopts a fastest known mining algorithm.

Data warehousing and electronic-commerce are two of the most rapidly expanding fields in recent information technologies. In this paper, we discuss the design of data warehouses for e-commerce environment. We discuss requirement analysis, logical design, and physical design issues in e-commerce environments. We have collected an extensive set of interesting OLAP queries for e-commerce environments, and classified them into categories. Based on these OLAP queries, we illustrate our design with data warehouse bus architecture, dimension table structures, a base star schema, and an aggregation star schema. We finally present various physical design considerations for implementing the dimensional models. We believe that our collection of OLAP queries and dimensional models would be very useful in developing any real-world data warehouses in e-commerce environments.