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

SM4MQ: a semantic model for multidimensional queries

On-Line Analytical Processing (OLAP) is a data analysis approach to support decision-making. On top of that, Exploratory OLAP is a novel initiative for the convergence of OLAP and the Semantic Web (SW) that enables the use of OLAP techniques on SW data. Moreover, OLAP approaches exploit different metadata artifacts (e.g., queries) to assist users with the analysis. However, modeling and sharing of most of these artifacts are typically overlooked. Thus, in this paper we focus on the query metadata artifact in the Exploratory OLAP context and propose an RDF-based vocabulary for its representation, sharing, and reuse on the SW. As OLAP is based on the underlying multidimensional (MD) data model we denote such queries as MD queries and define SM4MQ: A Semantic Model for Multidimensional Queries. Furthermore, we propose a method to automate the exploitation of queries by means of SPARQL. We apply the method to a use case of transforming queries from SM4MQ to a vector representation. For the use case, we developed the prototype and performed an evaluation that shows how our approach can significantly ease and support user assistance such as query recommendation.Peer ReviewedPostprint (author's final draft

Semantic mappings in description logics for spatio-temporal database schema integration

Abstract. The interoperability problem arises in heterogeneous systems where different data sources coexist and there is a need for meaningful information sharing. One of the most representive realms of diversity of data representation is the spatio-temporal domain. Spatio-temporal data are most often described according to multiple and greatly diverse perceptions or viewpoints, using different terms and with heterogeneous levels of detail. Reconciling this heterogeneity to build a fully integrated database is known to be a complex and currently unresolved problem, and few formal approaches exist for the integration of spatio-temporal databases. The paper discusses the interoperation issue in the context of conceptual schema integration. Our proposal relies on two well-known formalisms: conceptual models and description logics. The MADS conceptual model with its multiple representation capabilities allows to fully describe semantics of the initial and integrated spatio-temporal schemas. Description logics are used to express the set of inter-schema mappings. Inference mechanisms of description logics allow us to check the compatibility of the semantic mappings and to propose different structural solutions for the integrated schema.

An Envisioned Approach for Modeling and Supporting User-Centric Query Activities on Data Warehouses

In this vision paper, the authors discuss models and techniques for integrating, processing and querying data, information and knowledge within data warehouses in a user-centric manner. The user-centric emphasis allows us to achieve a number of clear advantages with respect to classical data warehouse architectures, whose most relevant ones are the following: (i) a unified and meaningful representation of multidimensional data and knowledge patterns throughout the data warehouse layers (i.e., loading, storage, metadata, etc); (ii) advanced query mechanisms and guidance that are capable of extracting targeted information and knowledge by means of innovative information retrieval and data mining techniques. Following this main framework, the authors first outline the importance of knowledge representation and management in data warehouses, where knowledge is expressed by existing ontology or patterns discovered from data. Then, the authors propose a user-centric architecture for OLAP query processing, which is the typical applicative interface to data warehouse systems. Finally, the authors propose insights towards cooperative query answering that make use of knowledge management principles and exploit the peculiarities of data warehouses (e.g., multidimensionality, multi-resolution, and so forth)

CubeLoad: a Parametric Generator of Realistic OLAP Workloads

Differently from OLTP workloads, OLAP workloads are hardly predictable due to their inherently extemporary nature. Besides, obtaining real OLAP workloads by monitoring the queries actually issued in companies and organizations is quite hard. On the other hand, hardware and software benchmarking in the industrial world, as well as comparative evaluation of novel approaches in the research community, both need reference databases and workloads. In this paper we present CubeLoad, a parametric generator of workloads in the form of OLAP sessions, based on a realistic profile-based model. After describing the main features of CubeLoad, we discuss the results of some tests that show how workloads with very different features can be generated

Query-By-Trace: Visual Predicate Specification In Spatio-Temporal Databases

In this paper we propose a visual interface for the specification of predicates to be used in queries on spatio-temporal databases. The approach is based on a visual specification method for temporally changing spatial situations. This extends existing concepts for visual spatial query languages, which are only capable of querying static spatial situations. We outline a preliminary user interface that supports the specification on an intuitive and easily manageable level, and we describe the design of the underlying visual language. The visual notation can be used directly as a visual query interface to spatio-temporal databases, or it can provide predicate specifications that can be integrated into textual query languages leading to heterogeneous languages. Key Words Spatio-Temporal Queries, Visual Predicate Specification, Visual Database Interface 1. INTRODUCTION Spatio-temporal databases deal with spatial objects that change over time (for example, they move or they grow): cars, ..

OLAP Personalization and Recommendation

International audienceDEFINITION Personalizing or recommending OLAP queries aims at making the OLAP user experience less disorientating when navigating huge amounts of multidimensional data (also called cubes). Such approaches allow coping with too many or too few query results, or suggesting new queries to pursue the navigation. Personalization allows adding preferences to a query for filtering out irrelevant results or ranking the results to focus on the most relevant first. It also allows turning selection predicates (hard constraints) into preferences (soft constraints) to favor non-empty answers. On the other end, recommendation allows to leverage the cube instance and/or past navigations on it to complement the current query result. The general problem can be formally defined by: given a sequence of queries S= (a session from now on) over an instance I of a cube schema C, a user profile P (consisting of ordered multidimensional objects), a set of past sessions L (a log from now on), generate a set of one or more queries Q={q p 1 , … q p n } such that, typically: The queries in Q are sub-queries of q c (personalization), in the classical sense of query inclusion, or none of the queries of Q are sub-queries of the queries of S (recommendation), The queries in Q maximize an interestingness score, In this definition, S represents the current session, with q c the last query of this session (the current query). HISTORICAL BACKGROUND OLAP Personalization and recommendation approaches are distant descendants of cooperative database [11] techniques aiming at enhancing database management systems with a cooperative behavior. Cooperation can be introduced at the different stages of the retrieval process, which is typically iterative. The purpose of the cooperation includes: helping the user to formulate a query corresponding to an objective and acceptable by the database system, dealing with empty answers or too few results, or suggesting additional information and explaining the query result. This retrieval process perfectly reflects the activity of OLAP users, who interactively analyze multidimensional data, often without exactly knowing what they are looking for. OLAP queries are normally formulated in the form of sequences called OLAP sessions, by using basic operations to transform one OLAP query into another, so that the new query gives a better understanding of the information retrieved so far. The huge number of possible aggregations and selections that can be operated on data may make the user experience disorientating, and OLA