An architecture for automatically developing secure OLAP applications from models

ContextDecision makers query enterprise information stored in Data Warehouses (DW) by using tools (such as On-Line Analytical Processing (OLAP) tools) which use specific views or cubes from the corporate DW or Data Marts, based on the multidimensional modeling. Since the information managed is critical, security constraints have to be correctly established in order to avoid unauthorized accesses.ObjectiveIn previous work we have defined a Model-Driven based approach for developing a secure DWs repository by following a relational approach. Nevertheless, is also important to define security constraints in the metadata layer that connects the DWs repository with the OLAP tools, that is, over the same multidimensional structures that final users manage. This paper defines a proposal to develop secure OLAP applications and incorporates it into our previous approach.MethodOur proposal is composed of models and transformations. Our models have been defined using the extension capabilities from UML (conceptual model) and extending the OLAP package of CWM with security (logical model). Transformations have been defined by using a graphical notation and implemented into QVT and MOFScript. Finally, this proposal has been evaluated through case studies.ResultsA complete MDA architecture for developing secure OLAP applications. The main contributions of this paper are: improvement of a UML profile for conceptual modeling; definition of a logical metamodel for OLAP applications; and definition and implementation of transformations from conceptual to logical models, and from logical models to the secure implementation into a specific OLAP tool (SSAS).ConclusionOur proposal allows us to develop secure OLAP applications, providing a complete MDA architecture composed of several security models and automatic transformations towards the final secure implementation. Security aspects are early identified and fitted into a most robust solution that provides us a better information assurance and a saving of time in maintenance.     

OLAP在企业中的多维数据分析研究

分析了OLAP的特点,进行数据仓库存储方式的构建和多维数据分析功能的实现。引入动态的OLAP多维视图分析,决策者可以从各种维度对销售数据进行审视,进行切片、切块、旋转和钻取等OLAP基本分析操作。文章介绍了组织用于OLAP的多维数据结构。用OLAP进行销售数据多维数据分析。     

Modelling and querying geographical data warehouses

A number of proposals for integrating geographical (Geographical Information Systems—GIS) and multidimensional (data warehouse—DW and online analytical processing—OLAP) processing are found in the database literature. However, most of the current approaches do not take into account the use of a GDW (geographical data warehouse) metamodel or query language to make available the simultaneous specification of multidimensional and spatial operators. To address this, this paper discusses the UML class diagram of a GDW metamodel and proposes its formal specifications. We then present a formal metamodel for a geographical data cube and propose the Geographical Multidimensional Query Language (GeoMDQL) as well. GeoMDQL is based on well-known standards such as the MultiDimensional eXpressions (MDX) language and OGC simple features specification for SQL and has been specifically defined for spatial OLAP environments based on a GDW. We also present the GeoMDQL syntax and a discussion regarding the taxonomy of GeoMDQL query types. Additionally, aspects related to the GeoMDQL architecture implementation are described, along with a case study involving the Brazilian public healthcare system in order to illustrate the proposed query language.     

Combining objects with rules to represent aggregation knowledge in data warehouse and OLAP systems

Data warehouses are based on multidimensional modeling. Using On-Line Analytical Processing (OLAP) tools, decision makers navigate through and analyze multidimensional data. Typically, users need to analyze data at different aggregation levels (using roll-up and drill-down functions). Therefore, aggregation knowledge should be adequately represented in conceptual multidimensional models, and mapped in subsequent logical and physical models. However, current conceptual multidimensional models poorly represent aggregation knowledge, which (1) has a complex structure and dynamics and (2) is highly contextual. In order to account for the characteristics of this knowledge, we propose to represent it with objects (UML class diagrams) and rules in the Production Rule Representation language (PRR). Static aggregation knowledge is represented in the class diagrams, while rules represent the dynamics (i.e. how aggregation may be performed depending on context). We present the class diagrams, and a typology and examples of associated rules. We argue that this representation of aggregation knowledge enables an early modeling of user requirements in a data warehouse project. A prototype has been developed based on the Java Expert System Shell (Jess).     

数据仓库中的一种立方体数据模型

数据仓库和联机分析处理(OLAP)是商业数据处理领域中的两个最重大的新技术。OLAP应用要求对数据仓库中存储的大量数据进行分析,用标准关系数据库技术来实现非常复杂的查询是相当困难的。所以,在数据仓库中,数据被组织成立方体数据模型。该文提出了一种简单、直观的数据立方体模型以及在这个立方体上支持OLAP操作的代数。为复杂的查询提供了简要的表述方法。     

餐饮决策支持系统中OLAP数据仓库的设计与实现

餐饮联机事务处理系统（OLTP）能规范企业业务流程和提高业务运转效率，但对企业决策的支持能力差，因此建立基于餐饮企业业务数据集的后台决策支持系统（DSS）是有必要的。提出了采用数据仓库，OLAP和数据挖掘技术建立餐饮决策支持系统的思路，重点讨论系统的体系结构设计，基于原型法，从开始的数据仓库模型设计，数据的抽取、转换和加载及多维数据集的设计，到最后的OLAP分析，对系统的实施进行了探讨。     

一种OLAP应用系统的设计和实现

通过对数据仓库和OLAP概念及体系结构的分析,描述了一种OLAP应用系统的设计方案,并介绍了它的具体实现方法。基于数据仓库的查询,一般都是及时特定查询,要在严格的响应时间内执行复杂的查询,遍历百万上亿的记录,同时进行可能很复杂的搜索、连接和汇总的操作。查询的数据吞吐量和响应时间是判断数据仓库性能的重点。CUBE的计算是OLAP及时查询的基础,提高查询的速度需要对OLAP进行预先的计算。文中系统比较了一些计算立方体的算法,并运用到具体的系统当中。     

Development of Secure XML Data Warehouses with QVT

ContextData warehouses are systems which integrate heterogeneous sources to support the decision making process. Data from the Web is becoming increasingly more important as sources for these systems, which has motivated the extensive use of XML to facilitate data and metadata interchange among heterogeneous data sources from the Web and the data warehouse. However, the business information that data warehouses manage is highly sensitive and must, therefore, be carefully protected. Security is thus a key issue in the design of data warehouses, regardless of the implementation technology. It is important to note that the idiosyncrasy of the unstructured and semi-structured data requires particular security rules that have been specifically tailored to these systems in order to permit their particularities to be captured correctly. Unfortunately, although security issues have been considered in the development of traditional data warehouses, current research lacks approaches with which to consider security when the target platform is based on XML technology.ObjectiveWe shall focus on defining transformations to obtain a secure XML Schema from the conceptual multidimensional model of a data warehouse.MethodWe have first defined the rationale behind the transformation rules and how they have been developed in natural language, and we have then established them clearly and formally by using the QVT language. Finally, in order to validate our proposal we have carried out a case study.ResultsWe have proposed an approach for the model driven development of Secure XML Data Warehouses, defining a set of QVT transformation rules.ConclusionThe main benefit of our proposal is that it is possible to model security requirements together with the conceptual model of the data warehouse during the early stages of a project, and automatically obtain the corresponding implementation for XML.     

The automatic creation of OLAP cube using an MDA approach

The Model‐Driven Architecture (MDA) is an approach that aligns modeling and automation for software development. By applying such an approach to data warehouse (DW) projects, we can minimize a great deal of time and cost. Furthermore, most of OnLine Analytical Processing (OLAP) platforms seem to be like black boxes that provide wizards only to business intelligence developers to create and manipulate OLAP objects without allowing their sustainability and migration from a platform to another. That is why many works in the literature have proposed using the MDA approach in DW projects. However, most of them have mainly focused on the generation of the DW relational model from the conceptual one, and they overlooked the OLAP model and the cube implementation. To deal with this problem, we propose in this paper an MDA solution to automate the process of getting OLAP cube and its implementation through a set of metamodels and automatic transformations among them. In fact, the proposal generates the OLAP and DW relational models (PSMs) from the conceptual one, using also a PDM model that describes the target business intelligence platform. After that, the source code to create the cube is got from both PSM models. For this aim, we define a set of transformation rules implemented using the Atlas transformation language. Finally, a case study will be provided to validate our approach.     

以数据仓库和OLAP技术为基础的税务决策支持系统的研究和应用

本文介绍了数据仓库和ＯＬＡＰ技术的有关概念和内容，为读者了解这些技术提供了一个较全面的视图，同时，我们提供了一个才气仓库和ＯＬＡＰ技术应用实例的概貌和结构，以期读者对这些技术有进一步理解。     

SCDDWS中的数据操纵以及查询优化

研究和开发概念设计的CAD工具已经成为当今的热点,由于概念设计是一个不断决策的过程,而数据仓库和OLAP(OnLineAnalyticalProcessing)技术是开发决策支持系统的重要技术,因而笔者建立了一个数据仓库系统用以辅助概念设计决策,称该数据仓库系统为SCDDWS。文章首先介绍该仓库系统中所提供的数据操作命令。其中,除已有的5种OLAP典型操作外,又提出了两个新的数据操作命令,使得用户能够方便和高效地获得所需的数据。然后介绍一种改进的优化算法,它用来建立和保存一些多维联合的链接以便得到较高的查询效率。     

P2P OLAP: Data model,implementation and case study

It is a common situation nowadays that business groups own different companies that operate in an autonomous way. Nevertheless, these companies must be requested to provide the headquarters with summarized information for decision-making. An architecture for cooperative interchange of decision-making information seems to be a natural solution for this problem. We propose the use of a peer-to-peer (P2P) architecture for addressing the problem of processing OLAP data in a distributed environment, in a way that all companies involved can maintain full autonomy over the use of its own data resources. In a scenario like this, data exchange between peers occurs when one of them, in the role of a local peer, receives a query and, for answering it, requests data available in other nodes, denoted acquaintances. No global schema is assumed to exist for any data under this computing paradigm. Henceforth, data provided by an acquaintance of a local peer must be adapted, in a manner that answers to queries posed by local peer users conform the view those users have of their data. Because multidimensional data normally consist of a collection of views of aggregated data, a careful translation process is needed in this case, in order to transform any summary concept that appears in a peer acquaintance into a summary concept meaningful to the requesting peer. We first present a model for multidimensional data distributed in a P2P network, and a query rewriting technique, that allows a local peer to propagate OLAP queries among its acquaintances, obtaining a meaningful and correct answer. Mappings are performed using a novel technique called revise and map, based on belief revision concepts. Revising a dimension instance allows to produce consistent aggregations when an OLAP query is answered at more than one node. We then describe an implementation of a P2P system for answering OLAP queries over a network of data warehouses. We apply our proposal to a real-world case study of an insurance group. Finally, we report the results of an experimental evaluation of our implementation, and discuss the issues that must be accounted for in this setting.     

时态多维模型设计

传统的数据仓库基于维度时不变的观点,只考虑事实随时间的变化.实际上,数据仓库的多维模式是随时间缓慢变化的.数据仓库多维模式的变化会影响OLAP查询结果的正确性,有必要对多维模式的历史变化进行维护.在分析多维模型结构的基础上,对多维模式的元素进行时态扩展,给出了一种能解决复杂多维结构的时态多维模型的形式定义,并对时态模型更新操作的实现进行了分析,最后对时态模型各元素应满足的时间一致性约束进行了分析.     

Doing more with more information: Changing healthcare planning with OLAP tools

On-line analytical processing (OLAP) is an example of a new breed of tools for decision support that give decision makers the flexibility to customize the selection, aggregation, and presentation of data. To understand the impact of this type of tool, we study an implementation of an OLAP interface on the CATCH data warehouse used by knowledge workers at a regional health planning agency in the State of Florida. The results of a qualitative field study show that after the OLAP implementation, these workers made use of the additional capabilities of OLAP (e.g., aggregation levels and intuitive data manipulation), thereby leveraging their individual abilities to enhance and expand on the tasks they performed for their community. Consequently, they were able to perform in more of a consultative role to their clients, and improved their reputation in the community they serve. This research adds a new dimension to prior research in data warehousing by focusing on the decision support capabilities of OLAP.     

基于医药连锁销售的决策支持系统设计

在原企业管理信息系统基础上,为充分利用日益增长的海量数据,针对其在决策支持方面的不足,提出了一种基于数据仓库、联机分析处理和数据挖掘三项技术的销售决策支持系统。讨论了系统总体结构,对系统数据仓库、多维数据集和数据挖掘模型进行了设计分析。     

A UML profile for multidimensional modeling in data warehouses

The multidimensional (MD) modeling, which is the foundation of data warehouses (DWs), MD databases, and On-Line Analytical Processing (OLAP) applications, is based on several properties different from those in traditional database modeling. In the past few years, there have been some proposals, providing their own formal and graphical notations, for representing the main MD properties at the conceptual level. However, unfortunately none of them has been accepted as a standard for conceptual MD modeling.

In this paper, we present an extension of the Unified Modeling Language (UML) using a UML profile. This profile is defined by a set of stereotypes, constraints and tagged values to elegantly represent main MD properties at the conceptual level. We make use of the Object Constraint Language (OCL) to specify the constraints attached to the defined stereotypes, thereby avoiding an arbitrary use of these stereotypes. We have based our proposal in UML for two main reasons: (i) UML is a well known standard modeling language known by most database designers, thereby designers can avoid learning a new notation, and (ii) UML can be easily extended so that it can be tailored for a specific domain with concrete peculiarities such as the multidimensional modeling for data warehouses. Moreover, our proposal is Model Driven Architecture (MDA) compliant and we use the Query View Transformation (QVT) approach for an automatic generation of the implementation in a target platform. Throughout the paper, we will describe how to easily accomplish the MD modeling of DWs at the conceptual level. Finally, we show how to use our extension in Rational Rose for MD modeling.     

基于医药连锁销售的决策支持系统设计

在原企业管理信息系统基础上,为充分利用日益增长的海量数据,针对其在决策支持方面的不足,提出了一种基于数据仓库、联机分析处理和数据挖掘三项技术的销售决策支持系统。讨论了系统总体结构,对系统数据仓库、多维数据集和数据挖掘模型进行了设计分析。     

数据仓库及联机分析处理技术

数据仓库和联机分析处理（Ｏｎｉｎｅ Ａｎａｌｙｔｉｃａｌ Ｐｒｏｃｅｓｓｉｎｇ,简称ＯＬＡＰ）是决策支持系统的有机组成部分。本语文介绍了数据仓库、数据集市、联机分析处理的概念,分析并比较了建立数据仓库的三种策略及联机分析处理系统的三种结构,在文章结尾我们给出了联机分析处理系统的两个新的发展方向－ＷＥＢ
ＯＬＡＰＯＬＡＰ＋数据挖掘。     

基于数据仓库的法院信息决策支持系统的设计

为了加快法院信息化建设,为法院决策层提供决策信息支持,提出了一种基于数据仓库的法院信息决策支持系统的总体框架。在DSS系统设计过程中,采用了OLAP+DM+Web相结合的方式,重点介绍分析了多维数据模型的设计,数据仓库的构建,以及以可视化交互的决策支持,最后通过实例展示决策支持系统的设计分析过程。     

EXODuS: Exploratory OLAP over Document Stores

OLAP has been extensively used for a couple of decades as a data analysis approach to support decision making on enterprise structured data. Now, with the wide diffusion of NoSQL databases holding semi-structured data, there is a growing need for enabling OLAP on document stores as well, to allow non-expert users to get new insights and make better decisions. Unfortunately, due to their schemaless nature, document stores are hardly accessible via direct OLAP querying. In this paper we propose EXODuS, an interactive, schema-on-read approach to enable OLAP querying of document stores in the context of self-service BI and exploratory OLAP. To discover multidimensional hierarchies in document stores we adopt a data-driven approach based on the mining of approximate functional dependencies; to ensure good performances, we incrementally build local portions of hierarchies for the levels involved in the current user query. Users execute an analysis session by expressing well-formed multidimensional queries related by OLAP operations; these queries are then translated into the native query language of MongoDB, one of the most popular document-based DBMS. An experimental evaluation on real-world datasets shows the efficiency of our approach and its compatibility with a real-time setting.