Business intelligence gap analysis: a user, supplier and academic perspective

Business intelligence (BI) takes many different forms, as indicated by the varying definitions of BI that can be found in industry and academia. These different definitions help us understand of what BI issues are important to the main players in the field of BI; users, suppliers and academics. The goal of this research is to discover gaps and trends from the standpoints of BI users, BI suppliers and academics, and to examine their effects on business and academia. Consultants also play an important role since they can be seen as the link between users and suppliers. Two research methods are combined to accomplish this goal. We examine the BI focus of users and suppliers through a survey, and we gain insight to the BI focus of academics, vendor-neutral consultants (typical representatives like Forrester, Gartner and IDC) and vendor- specific consultants (typical representatives like IBM, Information builders, Microsoft, Oracle and SAP) through their publications. Previous studies indicate that similar article analyses often focus on academic research methods only. That means that the results so far often reveal the academic perspective. Unlike these previous studies, the perspective of this research is not limited to academics. Our results provide insight of the BI trends and BI issue ranking of BI users, suppliers, academics, vendors neutral consultants and vendor specific consultant

On-line analytical processing

On-line analytical processing (OLAP) describes an approach to decision support, which aims to extract knowledge from a data warehouse, or more specifically, from data marts. Its main idea is providing navigation through data to non-expert users, so that they are able to interactively generate ad hoc queries without the intervention of IT professionals. This name was introduced in contrast to on-line transactional processing (OLTP), so that it reflected the different requirements and characteristics between these classes of uses. The concept falls in the area of business intelligence.Peer ReviewedPostprint (author's final draft

Personnalisation de Systèmes OLAP Annotés

National audienceThis paper deals with personalization of annotated OLAP systems. Data constellation is extended to support annotations and user preferences. Annotations reflect the decision-maker experience whereas user preferences enable users to focus on the most interesting data. User preferences allow annotated contextual recommendations helping the decision-maker during his/her multidimensional navigations

MULTIDIMENSIONAL DATABASES IN INFORMATION SYSTEMS OF UNIVERSITIES

The article is devoted to the description of the method of multidimensional database, which is an effective method of data storage, which allows analyzing data qualitatively, and most importantly in a short time. The article discusses the capabilities of multidimensional databases, in particular, multidimensional OLAP (On-Line Analytical Processing) cubes when analyzing large amounts of data. Provides an overview and features of a multidimensional database and discusses the steps you need to take with a multidimensional database to understand the structure and capabilities of an OLAP cube. To create a knowledge base, it describes the steps you can take to create and execute a multidimensional database that you can collect from various sources, save to a database, and then prepare a report using OLAP analysis. Various information system data processing technologies such as OLTP and OLAP were considered. The algorithm of the data storage process for analysis purposes was studied. A model of a multidimensional database in the form of a three-dimensional cube was presented. Examples of analysis and ways of obtaining information from the data cube were also given. The use of a multidimensional database in higher education institutions as a simple and effective method of data storage is considered. There are also illustrations of the structure of a higher educational institution to see the bulkiness of information, and what kind of information database operates in the educational institution

QueRIE: Collaborative Database Exploration

Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user’s querying behavior and finds matching patterns in the system’s query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where the active user’s session is represented by a set of query fragments. The recorded fragments are used to identify similar query fragments in the previously recorded sessions, which are in turn assembled in potentially interesting queries for the active user. We show through experimentation that the proposed method generates meaningful recommendations on real-life traces from the SkyServer database and propose a scalable design that enables the incremental update of similarities, making real-time computations on large amounts of data feasible. Finally, we compare this fragment-based instantiation with our previously proposed tuple-based instantiation discussing the advantages and disadvantages of each approach