Multidimensional Expressions Research Articles

SSF-CDW: achieving scalable, secure, and fast OLAP query for encrypted cloud data warehouse

Implementing a cloud-based data warehouse to store sensitive or critical strategic data presents challenges primarily related to the security of the stored information and the exchange of OLAP queries between the cloud server and users. Although encryption is a viable solution for safeguarding outsourced data, applying it to OLAP queries involving multidimensional data, measures, and Multidimensional Expressions (MDX) operations on encrypted data poses difficulties. Existing searchable encryption solutions are inadequate for handling such complex queries, which complicates the use of business intelligence tools that rely on efficient and secure data processing and analysis.This paper proposes a new privacy-preserving cloud data warehouse scheme called SSF-CDW which facilitates a secure and scalable solution for an encrypted cloud data warehouse. Our SSF-CDW improves the OLAP queries accessible only to authorized users who can decrypt the query results with improved query performance compared to traditional OLAP tools. The approach involves utilizing symmetric encryption and Ciphertext Policy Attribute-Based Encryption (CP-ABE) to protect the privacy of the dimension and fact data modeled in Multidimensional OLAP (MOLAP). To support efficient OLAP query execution, we proposed a new data cube retrieval mechanism using a Redis schema which is an in-memory database. This technique dynamically compiles queries by disassembling them down into multiple levels and consolidates the results mapped to the corresponding encrypted data cube. The caching of dimensional and fact data associated with the encrypted cube is also implemented to improve the speed of frequently queried data. Experimental comparisons between our proposed indexed search strategy and other indexing schemes demonstrate that our approach surpasses alternative techniques in terms of search speed for both ad-hoc and repeated OLAP queries, all while preserving the privacy of the query results.

A spatial data warehouse recommendation approach: conceptual framework and experimental evaluation

AbstractSpatial data warehouses store a large amount of historized and aggregated data. They are usually exploited by Spatial OLAP (SOLAP) systems to extract relevant information. Extracting such information may be complex and difficult. The user might ignore what part of the warehouse contains the relevant information and what the next query should be. On the other hand, recommendation systems aim to help users to retrieve relevant information according to their preferences and analytical objectives. Hence, developing a SOLAP recommendation system would enhance spatial data warehouses exploitation. This paper proposes a SOLAP recommendation approach that aims to help users better exploit spatial data warehouses and retrieve relevant information by recommending personalized spatial MDX (Multidimensional Expressions) queries. The approach detects implicitly the preferences and needs of SOLAP users using a spatio-semantic similarity measure. The approach is described theoretically and validated by experiments.

Overview of OLAP cubes, importance, build Considerations and querying with MDX.

An OLAP cube is a method of storing data in a multidimensional form, generally for reporting purposes. The purpose of an OLAP cube is to store data in such a way that an end user can slice and dice through the data and the storage structure itself (multidimensional) while allowing for very fast access to the data. Cubes are characterized by dimensions and measures. Dimensions refer to how the data is organized and are usually made up of character fields which tell us about the data. Measures, on the other hand, are those elements in the data which quantify something (a number) and can usually be additive . The business intelligence industry uses the word "cube" because it best describes the resulting data. The query language used to interact and perform tasks with OLAP cubes is multidimensional expressions (MDX).In this paper an attempt is made to give the overview, importance and build considerations of Olap cubes. Also querying of OLAP cube with MDX is discussed.

OLAP 상에서 데이터 접근 제어 메커니즘 설계 및 구현

OLAP(On-Line Analytical Processing) is a tool to satisfy the requirements of managing overflowing data analysis. OLAP can provide an interactive analytical processing environment to every end-user. Security policy is necessary to secure sensitive data of organization according to users direct access database. But earlier studies only handled the subject in its functional aspects such as MDX(Multidimensional Expressions) and XMLA(XML for Analysis). This research work is purported for solving such problems by designing and implementing an efficient data access control mechanism for the information security on OLAP. Experimental evaluation result is proposed and its efficiency and accuracy are verified through it.

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.

Integrating heterogeneous data warehouses using XML technologies

Data warehousing has been widely adopted by contemporary enterprises. For inter-organizational information sharing, the need cannot be over-emphasized to conduct researches on the integration of heterogeneous data warehouses to overcome the challenging situations today. That makes it urgent to establish a systematic integration methodology for integrating heterogeneous data warehouses via the Internet or proprietary extranets. Traditionally, researchers usually employed a canonical format as the integration medium for logical data integrations among heterogeneous systems. In this paper, to fully utilize the power of the Internet, we propose a framework and develop a prototype to integrate heterogeneous data warehouses by XML technologies. We first formally define the elements in data warehousing and discuss various semantic conflicts occurring among heterogeneous data cubes. Then, we propose the system architecture and related resolution procedures for all kinds of semantic conflicts. For local data cubes with different schemas, we define a global XML Schema to integrate the local cube structures, and transform each local cube respectively into an XML document conforming to the global XML Schema. These transformed XML documents obtained from local cubes will be manipulated by pre-defined XQuery commands to form a unified XML document, which can be regarded as the global cube. The integrated global cube can be easily stored and manipulated in native XML databases. The proposed methodology enables global users to browse or pose multi-dimensional expressions (MDX) on the global cube to obtain a result in the same way as they perform locally.

Design of a multi-dimensional query expression for document warehouses

During the past decade, data warehousing has been widely adopted in the business community. It provides multi-dimensional analyses on cumulated historical business data for helping contemporary administrative decision-makings. However, many data warehousing query language in present only provides on-line analytical processing (OLAP) for numeric data. For example, MDX (Multi-Dimensional eXpressions) has been proposed as a query language to allow describing multi-dimensional queries over databases with OLAP capabilities. Nevertheless, it is believed there is only about 20% information can be extracted from data warehouses concerning numeric data only, the other 80% information is hidden in non-numeric data or even in documents. Therefore, many researchers now advocate it is time to conduct research works on document warehousing to capture complete business intelligence. Document warehouses, unlike traditional document management systems, include extensive semantic information about documents, cross-document feature relations, and document grouping or clustering to provide a more accurate and more efficient access to text-oriented business intelligence. In this paper, we extend the structure of MDX into a new one containing complete constructs for querying document warehouses. The traditional MDX only contains SELECT, FROM, and WHERE clauses, which is not rich enough for document warehousing. In this paper, we present how to extend the language constructs to include GROUP BY, HAVING, and ORDER BY to design an SQL-like query language for document warehousing. The work is essential for establishing an infrastructure to help combining text processing with numeric OLAP processing technologies. Hopefully, the combination of data warehousing and document warehousing will be one of the most important kernels of knowledge management and customer relationship management applications.

Multi-query optimization for on-line analytical processing

Multi-dimensional expressions (MDX) provide an interface for asking several related OLAP queries simultaneously. An interesting problem is how to optimize the execution of an MDX query, given that most data warehouses maintain a set of redundant materialized views to accelerate OLAP operations. A number of greedy and approximation algorithms have been proposed for different versions of the problem. In this paper we evaluate experimentally their performance, concluding that they do not scale well for realistic workloads. Motivated by this fact, we develop two novel greedy algorithms. Our algorithms construct the execution plan in a top–down manner by identifying in each step the most beneficial view, instead of finding the most promising query. We show by extensive experimentation that our methods outperform the existing ones in most cases.

Simultaneous optimization and evaluation of multiple dimensional queries

Database researchers have made significant progress on several research issues related to multidimensional data analysis, including the development of fast cubing algorithms, efficient schemes for creating and maintaining precomputed group-bys, and the design of efficient storage structures for multidimensional data. However, to date there has been little or no work on multidimensional query optimization. Recently, Microsoft has proposed “OLE DB for OLAP” as a standard multidimensional interface for databases. OLE DB for OLAP defines Multi-Dimensional Expressions (MDX), which have the interesting and challenging feature of allowing clients to ask several related dimensional queries in a single MDX expression. In this paper, we present three algorithms to optimize multiple related dimensional queries. Two of the algorithms focus on how to generate a global plan from several related local plans. The third algorithm focuses on generating a good global plan without first generating local plans. We also present three new query evaluation primitives that allow related query plans to share portions of their evaluation. Our initial performance results suggest that the exploitation of common subtask evaluation and global optimization can yield substantial performance improvements when relational database systems are used as data sources for multidimensional analysis.