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

Enabling instant- and interval-based semantics in multidimensional data models: the T+MultiDim Model

Time is a vital facet of every human activity. Data warehouses, which are huge repositories of historical information, must provide analysts with rich mechanisms for managing the temporal aspects of information. In this paper, we (i) propose T+MultiDim, a multidimensional conceptual data model enabling both instant- and interval-based semantics over temporal dimensions, and (ii) provide suitable OLAP (On-Line Analytical Processing) operators for querying temporal information. T+MultiDim allows one to design typical concepts of a data warehouse including temporal dimensions, and provides one with the new possibility of conceptually connecting different temporal dimensions for exploiting temporally aggregated data. The proposed approach allows one to specify and to evaluate powerful OLAP queries over information from data warehouses. In particular, we define a set of OLAP operators to deal with interval-based temporal data. Such operators allow the user to derive new measure values associated to different intervals/instants, according to different temporal semantics. Moreover, we propose and discuss through examples from the healthcare domain the SQL specification of all the temporal OLAP operators we define. (C) 2019 Elsevier Inc. All rights reserved

Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation

Increasingly, business projects are ephemeral. New Business Intelligence tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds are a popular community-driven visualization technique. Hence, we investigate tag-cloud views with support for OLAP operations such as roll-ups, slices, dices, clustering, and drill-downs. As a case study, we implemented an application where users can upload data and immediately navigate through its ad hoc dimensions. To support social networking, views can be easily shared and embedded in other Web sites. Algorithmically, our tag-cloud views are approximate range top-k queries over spontaneous data cubes. We present experimental evidence that iceberg cuboids provide adequate online approximations. We benchmark several browser-oblivious tag-cloud layout optimizations.Comment: Software at https://github.com/lemire/OLAPTagClou

Using Ontologies for the Design of Data Warehouses

Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure