Handling multiple points of view in a multimedia data warehouse

Problems and Solutions, 2003

Journal of Computing Science and Engineering

Data Warehousing a nd OLAP (On-Line Ana lytical Processing) have t urned into t he key technology for comprehensive dat a a nalysis. Origina lly developed for the needs of d ecision support in business, dat a warehouses have proven to be an adequat e solution for a var iety of non-business a pplications a nd doma ins, s uch as government, research, a nd medicine. Ana lytical power of the OLAP technology comes from its underlying multidimensiona l d at a model, which a llows users to see dat a from di fferent perspectives. However, t his model displays a number of defici encies when a pplied to non-convent iona l scenarios and a na lysis t asks. This paper presents a n at tempt to syst ema tically summarize va rious ext ensions of the origina l multidimensiona l d ata model that have been proposed by researchers a nd practit ioners in the recent years. Presented concepts are arra nged into a forma l classification consisting of fact types, factua l and fact-dimensiona l relationships, a nd dimension types, supplied wit h expla natory examples fro m real-world usage scena rios. Both t he st atic elements of the model, such as types of fact a nd dimension hierarchy schemes, and dynamic features, such as support for adva nced operat ors a nd derived elements. We a lso propose a semant ically rich gra phical notat ion called X-DFM that extends the p opular Dimensiona l Fact Model by refinin g and modifying t he set of constructs as to make it coherent wit h the for mal model. An eva luat ion of our framework against a set of co mmon modeling requirements summarizes the contribution .

2008

This paper introduces a Conceptual Data Model for Data Warehouse including multidimensional aggregation. It is based on Entity-Relationships data model. The conceptual data model gracefully extends standard Entity-Relationship data model with multidimensional aggregated entities. The model has a clear mathematical theoretic semantics grounded on standard ER semantics and the GMD logic-based multidimensional data model. The aim of this work is not to propose yet another conceptual data model, but to find the most general and precise formalism considering all the proposals for a conceptual data model in the data warehouse field, making therefore a possible formal comparison of the differences of the models in the literature, and to study the formal properties or extensions of such data models.

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

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.

Information Systems, 2001

On-line analytical processing (OLAP) systems considerably improve data analysis and are finding wide-spread use. OLAP systems typically employ multidimensional data models to structure their data. This paper identifies 11 modeling requirements for multidimensional data models. These requirements are derived from an assessment of complex data found in real-world applications. A survey of 14 multidimensional data models reveals shortcomings in meeting some of the requirements. Existing models do not support many-to-many relationships between facts and dimensions, lack built-in mechanisms for handling change and time, lack support for imprecision, and are generally unable to insert data with varying granularities. This paper defines an extended multidimensional data model and algebraic query language that address all 11 requirements. The model reuses the common multidimensional concepts of dimension hierarchies and granularities to capture imprecise data. For queries that cannot be answered precisely due to the imprecise data, techniques are proposed that take into account the imprecision in the grouping of the data, in the subsequent aggregate computation, and in the presentation of the imprecise result to the user. In addition, alternative queries unaffected by imprecision are offered. The data model and query evaluation techniques discussed in this paper can be implemented using relational database technology. The approach is also capable of exploiting multidimensional query processing techniques like pre-aggregation. This yields a practical solution with low computational overhead. r

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.

2005

Current data warehouse and OLAP technologies can be applied to analyze the structured data that companies store in their databases. The circumstances that describe the context associated with these data can be found in other internal and external sources of documents. In this paper we propose to combine the traditional corporate data warehouse with a document warehouse, resulting in a contextualized warehouse. Thus, contextualized warehouses keep a historical record of the facts and their contexts as described by the documents. In this framework, the user selects an analysis context which is represented as a novel type of OLAP cube, here called R-cube. R-cubes are characterized by two special dimensions, namely: the relevance and the context dimensions. The first dimension measures the relevance of each fact in the selected analysis context, whereas the second one relates each fact with the documents that explain their circumstances. In this work we extend an existing multi-dimensional data model and algebra for representing the R-cubes.

Emirates Research Publishing July 8-9, 2015 Singapore, 2015

Starting from the fundamental concepts of Data Warehousing and their importance in Business Intelligence applications, we may speak of the necessity of multideminsionalizing data and their relations, in the sense that the transaction processing system, through its complexity, attracts an exponential development of techniques involved in extracting information from data, a supporting process in decision making. In the virtue of rapid development of data warehousing techniques in extracting information from data and abstracting existing relations between data structures, the current paper aims to: present an introduction in approaching data warehousing, and to present two specific approaches in current data warehousing and presenting the conclusions.