Personalization and OLAP Databases

2014

Abstract. In this paper we have highlighted five existing approaches for introducing personalization in OLAP: preference constructors, dynamic personalization, visual OLAP, recommendations with user session analysis and recommendations with user profile analysis and have analyzed research papers within these directions. We have pointed out applicability of personalization to OLAP schema elements in these approaches. The comparative analysis has been made in order to highlight a certain personalization approach. A new method has been proposed, which provides exhaustive description of interaction between user and data warehouse, using the concept of Zachman Framework [1, 2], according to which a set of user-describing profiles (user, preference, temporal, spatial, preferential and recommendational) has been developed. Methods of profile data gathering and processing are described in this paper.

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, 2011

OLAP systems facilitate analysis by providing a multidimensional data space which decision makers explore interactively by a succession of OLAP operations. However, these systems are developed for a group of decision makers or topic analysis "subject-oriented", which are presumed, have identical needs. It makes them unsuitable for a particular use. Personalization aims to better take into account the user; first this paper presents a summary of all work undertaken in this direction with a comparative study. Secondly we developed a search algorithm for class association rules between query type and user (s) to deduce the profile of a particular user or a user set in the same category. These will be extracted from the log data file of OLAP server. For this we use a variant of prediction and explanation algorithms. These profiles then form a knowledge base. This knowledge base will be used to generate automatically a rule base (ACE), for assigning weights to the attributes of data warehouses by type of query and user preferences. More it will deduce the best contextual sequence of requests for eventual use in a recommended system.

Multidimensional databases are a great asset for decision making. Their users express complex OLAP (On-Line Analytical Processing) queries, often returning huge volumes of facts, sometimes providing little or no information. Furthermore, due to the huge volume of historical data stored in DWs, the OLAP applications may return a big amount of irrelevant information that could make the data exploration process not efficient and tardy. OLAP personalization systems play a major role in reducing the effort of decision-makers to find the most interesting information. Several works dealing with OLAP personalization were presented in the last few years. This paper aims to provide a comprehensive review of literature on OLAP personalization approaches. A benchmarking study of OLAP personalization methods is proposed. Several evaluation criteria are used to identify the existence of trends as well as potential needs for further investigations.

Lecture Notes in Business Information Processing, 2009

OLAP systems offering multidimensional and large information space cannot solely rely on standard navigation but need to apply recommendations to make the analysis process easy and to help users quickly find relevant data for decision-making. In this paper, we propose a recommendation methodology for assisting the user during his decision-support analysis. The system helps the user in querying multidimensional data and exposes him to the most interesting patterns, i.e. it provides to the user anticipatory as well as alternative decisionsupport data. We provide a preference-based approach to apply such methodology.

Very Large Data Bases, 1997

We present a multi-dimensional database model, which we believe can serve as a con- ceptual model for On-Line Analytical Pro- cessing (OLAP)-based applications. Apart from providing the functionalities necessary for OLAP-based applications, the main fea- ture of the model we propose is a clear sepa- ration between structural aspects and the con- tents. This separation of concerns allows us to

Lecture Notes in Computer Science, 2009

This paper presents a framework for integrating OLAP and recommendations. We focus on the anticipatory recommendation process that assists the user during his OLAP analysis by proposing to him the forthcoming analysis step. We present a context-aware preference model that matches decisionmakers intuition, and we discuss a preference-based approach for generating personalized recommendations.

IBM Systems Journal, 2002

Lecture Notes in Business Information Processing, 2012

This paper presents an OLAP reporting tool and an approach for determining and processing user OLAP preferences, which are useful for generating recommendations on potentially interesting reports. We discuss the metadata layers of the reporting tool including our proposed OLAP preferences metamodel, which supports various scenarios of formulating preferences of two different types: schema-specific and report-specific. The process of semantic metadata usage at the stage of formulating user preferences is also considered. The methods for processing schema-specific and report-specific OLAP preferences are outlined.

Hawaii International Conference on System Sciences, 2010

Multidimensional data are the foundation for OLAP applications. They can be provided in several ways: relational OLAP, multidimensional OLAP, or hybrid OLAP. The usage of the underlying technology, which is well understood and in most cases formally defined, does not resolve the issue of a missing vocabulary for multidimensional data on a conceptual level. Some basic definitions are broadly used;

Proceedings of the 5th ACM international workshop on Data Warehousing and OLAP - DOLAP '02, 2002

When changes occur on data organization, conventional multidimensional structures are not adapted because dimensions are supposed to be static. In many cases, especially when time covered by the data warehouse is large, dimensions of the hypercube must be redesigned in order to integrate evolutions. We propose an approach allowing to track history but also to compare data, mapped into static structures. We define a conceptual model building a Mutiversion Fact the Temporal Multidimensional Schema and we introduce the notion of temporal modes of representation corresponding to different ways to analyze data and their evolution.

2001

On-Line Analytical Processing (OLAP) is a trend in database technology, based on the multidimensional view of data. The aim of this paper is twofold: (a) to list general problems and solutions applicable to the design of any OLAP system and (b) to present the specific design decisions that we made for a prototype under development at NTUA, which we call ERATOSTHENES. The paper addresses requirements and design issues for all three models involved in an OLAP system: the presentational, logical and physical model. It also discusses in detail the architecture and the major components of ERATOSTHENES. * This research has been partially funded by the European Union's Information Society Technologies Programme (IST) under project EDITH (IST-1999-20722).

Lecture Notes in Computer Science, 2000

It is commonly agreed that multidimensional data cubes form the basic logical data model for OLAP applications. Still, there seems to be no agreement on a common model for cubes. In this paper we propose a logical model for cubes based on the key observation that a cube is not a self-existing entity, but rather a view over an underlying data set. We accompany our model with syntactic characterisations for the problem of cube usability. To this end, we have developed algorithms to check whether (a) the marginal conditions of two cubes are appropriate for a rewriting, in the presence of aggregation hierarchies and (b) an implication exists between two selection conditions that involve different levels of aggregation of the same dimension hierarchy. Finally, we present a rewriting algorithm for the cube usability problem.

Lecture Notes in Computer Science, 2014

As the amount of textual information grows explosively in various kinds of business systems, it becomes more and more essential to analyze both structured data and unstructured textual data simultaneously. However information contained in non structured data (documents and so on) is only partially used in business intelligence (BI). Indeed On-Line Analytical Processing (OLAP) cubes which are the main support of BI analysis in decision support systems have focused on structured data. This is the reason why OLAP is being extended to unstructured textual data. In this paper we introduce the innovative "Diamond" multidimensional model that will serve as a basis for semantic OLAP on XML documents and then we describe the meta modeling, generation and implementation of a the Diamond multidimensional model.

The relational data model, which was introduced by Codd in 1970 and earned him the Turing Award a decade later, was the foundation of today’s multi-billion-dollar database industry. During the 1990s, a new type of data model, the multidimensional data model, has emerged, which has taken over from the relational model where the objective is to analyze data, rather than to perform on-line transactions. Multidimensional data models are designed expressly to support data analyses. A number of such models have been proposed by researchers from academia and industry. In academia, formal mathematical models have been proposed, while the industrial proposals have typically been specified more or less implicitly by the concrete software tools that implement them. Briefly, multidimensional models categorize data as being either facts with associated numerical measures, or as being dimensions that characterize the facts and are mostly textual. For example, in a retail business, products are sold to customers at certain times in certain amounts and at certain prices. A typical fact would be a purchase. Typical measures would be the amount and price of the purchase. Typical dimensions would be the location of the purchase, the type of product being purchased, and the time of the purchase. Queries then aggregate measure values over ranges of dimension values to produce results such as the total sales per month and product type........

2014

Data Warehouses (DW) resources are shared by users’ from different backgrounds (e.g., domain, culture, education, profession). Those resources (e.g., OLAP queries, Excel files) are interpreted differently from a user to another. Unfortunately, misinterpreting data could induce serious problems and conflicts. To guarantee relevant interpretation of resources, additional semantic description of resources concepts is necessary. In this context, we present an Ontology-driven Personalization System (OPS) based on three connected ontologies: domain ontology, DW ontology and resources ontology. OPS return a set of personalized resources search based on users’ domain and his recurring interests. In addition, resources are enhanced with a semantic description provided by the ontologies. This paper focuses on the methodology used to develop connected ontologies used by OPS. Keywords-data warehouse, ontology, personalization, decision support systems, decision making, healthcare institution ma...