A Horizontal Fragmentation Algorithm for the Fact Relation in a Distributed Data Warehouse

Data warehousing is one of the major research topics of appliedside database investigators. Most of the work to date has focused on building large centralized systems that are integrated repositories founded on pre-existing systems upon which all corporate-wide data are based. Unfortunately, this approach is very expensive and tends to ignore the advantages realized during the past decade in the area of distribution and support for data localization in a geographically dispersed corporate structure. This research investigates building distributed data warehouses with particular emphasis placed on distribution design for the data warehouse environment. The article provides an architectural model for a distributed data warehouse, the formal definition of the relational data model for data warehouse and a methodology for distributed data warehouse design along with a "horizontal" fragmentation algorithm for the fact relation. Most of the work to date has focused on building large centralized systems that are integrated repositories founded on pre-existing systems upon which all corporate-wide data is based. The centralized data warehouse is very expensive and tends to ignore the advantages realized during the past decade in the areas of distribution and support for data localization in a geographically dispersed corporate structure. Further, it would be unwise to enforce a centralized data warehouse when the operational systems exist over a widely distributed geographical area. The distributed data warehouse supports the decision makers by providing a single view of data even though that data are physically distributed across multiple data warehouses in multiple systems at different branches. Currently, the field of distributed data warehouse in terms of architecture and design is considered an important research problem that needs investigation. This research contributes to the problem of distributed data warehouse architecture and design by: Keywords distributed data warehouse architecture, distributed data warehouse design, horizontal fragmentation. Extending the preliminary architecture model that has been presented in [8] by proposing a distributed data warehouse system architecture and describing the functionality of its components

Data Mining-based Fragmentation of XML Data Warehouses

With the multiplication of XML data sources, many XML data warehouse models have been proposed to handle data heterogeneity and complexity in a way relational data warehouses fail to achieve. However, XML-native database systems currently suffer from limited performances, both in terms of manageable data volume and response time. Fragmentation helps address both these issues. Derived horizontal fragmentation is typically used in relational data warehouses and can definitely be adapted to the XML context. However, the number of fragments produced by classical algorithms is difficult to control. In this paper, we propose the use of a k-means-based fragmentation approach that allows to master the number of fragments through its $k$ parameter. We experimentally compare its efficiency to classical derived horizontal fragmentation algorithms adapted to XML data warehouses and show its superiority

Comparative Analysis of Decision Tree Algorithms for Data Warehouse Fragmentation

One of the main problems faced by Data Warehouse designers is fragmentation.Several studies have proposed data mining-based horizontal fragmentation methods.However, not exists a horizontal fragmentation technique that uses a decision tree. This paper presents the analysis of different decision tree algorithms to select the best one to implement the fragmentation method. Such analysis was performed under version 3.9.4 of Weka, considering four evaluation metrics (Precision, ROC Area, Recall and F-measure) for different selected data sets using the Star Schema Benchmark. The results showed that the two best algorithms were J48 and Random Forest in most cases; nevertheless, J48 was selected because it is more efficient in building the model.One of the main problems faced by Data Warehouse designers is fragmentation.Several studies have proposed data mining-based horizontal fragmentation methods.However, not exists a horizontal fragmentation technique that uses a decision tree. This paper presents the analysis of different decision tree algorithms to select the best one to implement the fragmentation method. Such analysis was performed under version 3.9.4 of Weka, considering four evaluation metrics (Precision, ROC Area, Recall and F-measure) for different selected data sets using the Star Schema Benchmark. The results showed that the two best algorithms were J48 and Random Forest in most cases; nevertheless, J48 was selected because it is more efficient in building the model

Development of new data partitioning and allocation algorithms for query optimization of distributed data warehouse systems

Distributed databases and in particular distributed data warehousing are becoming an increasingly important technology for information integration and data analysis. Data Warehouse (DW) systems are used by decision makers for performance measurement and decision support. However, although data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, the OLAP query response time is strongly affected by the volume of data need to be accessed from storage disks. Data partitioning is one of the physical design techniques that may be used to optimize query processing cost in DWs. It is a non redundant optimization technique because it does not replicate data, contrary to redundant techniques like materialized views and indexes. The warehouse partitioning problem is concerned with determining the set of dimension tables to be partitioned and using them to generate the fact table fragments. In this work an enhanced grouping algorithm that avoids the limitations of some existing vertical partitioning algorithms is proposed. Furthermore, a static partitioning algorithm that allows fragmentation at early stages of schema design is presented. The thesis also, investigates the performance of the data warehouse after implementing a combination of Genetic Algorithm (GA) and Simulated Annealing (SA) techniques to horizontally partition the data warehouse star schema. It, then presents the experimentation and implementation results of the proposed algorithm. This research presented different approaches to optimize data fragments allocation cost using a greedy mathematical model and a combination of simulated annealing and genetic algorithm to determine the site by site allocation leading to optimal solutions for fragments distribution. Throughout this thesis, the term fragmentation and partitioning will be used interchangeably

MaxPart: An Efficient Search-Space Pruning Approach to Vertical Partitioning

Vertical partitioning is the process of subdividing the attributes of a relation into groups, creating fragments. It represents an effective way of improving performance in the database systems where a significant percentage of query processing time is spent on the full scans of tables. Most of proposed approaches for vertical partitioning in databases use a pairwise affinity to cluster the attributes of a given relation. The affinity measures the frequency of accessing simultaneously a pair of attributes. The attributes having high affinity are clustered together so as to create fragments containing a maximum of attributes with a strong connectivity. However, such fragments can directly and efficiently be achieved by the use of maximal frequent itemsets. This technique of knowledge engineering reflects better the closeness or affinity when more than two attributes are involved. The partitioning process can be done faster and more accurately with the help of such knowledge discovery technique of data mining. In this paper, an approach based on maximal frequent itemsets to vertical partitioning is proposed to efficiently search for an optimized solution by judiciously pruning the potential search space. Moreover, we propose an analytical cost model to evaluate the produced partitions. Experimental studies show that the cost of the partitioning process can be substantially reduced using only a limited set of potential fragments. They also demonstrate the effectiveness of our approach in partitioning small and large tables

Partout: A Distributed Engine for Efficient RDF Processing

The increasing interest in Semantic Web technologies has led not only to a rapid growth of semantic data on the Web but also to an increasing number of backend applications with already more than a trillion triples in some cases. Confronted with such huge amounts of data and the future growth, existing state-of-the-art systems for storing RDF and processing SPARQL queries are no longer sufficient. In this paper, we introduce Partout, a distributed engine for efficient RDF processing in a cluster of machines. We propose an effective approach for fragmenting RDF data sets based on a query log, allocating the fragments to nodes in a cluster, and finding the optimal configuration. Partout can efficiently handle updates and its query optimizer produces efficient query execution plans for ad-hoc SPARQL queries. Our experiments show the superiority of our approach to state-of-the-art approaches for partitioning and distributed SPARQL query processing

Formal design of data warehouse and OLAP systems : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems at Massey University, Palmerston North, New Zealand

A data warehouse is a single data store, where data from multiple data sources is integrated for online business analytical processing (OLAP) of an entire organisation. The rationale being single and integrated is to ensure a consistent view of the organisational business performance independent from different angels of business perspectives. Due to its wide coverage of subjects, data warehouse design is a highly complex, lengthy and error-prone process. Furthermore, the business analytical tasks change over time, which results in changes in the requirements for the OLAP systems. Thus, data warehouse and OLAP systems are rather dynamic and the design process is continuous. In this thesis, we propose a method that is integrated, formal and application-tailored to overcome the complexity problem, deal with the system dynamics, improve the quality of the system and the chance of success. Our method comprises three important parts: the general ASMs method with types, the application tailored design framework for data warehouse and OLAP, and the schema integration method with a set of provably correct refinement rules. By using the ASM method, we are able to model both data and operations in a uniform conceptual framework, which enables us to design an integrated approach for data warehouse and OLAP design. The freedom given by the ASM method allows us to model the system at an abstract level that is easy to understand for both users and designers. More specifically, the language allows us to use the terms from the user domain not biased by the terms used in computer systems. The pseudo-code like transition rules, which gives the simplest form of operational semantics in ASMs, give the closeness to programming languages for designers to understand. Furthermore, these rules are rooted in mathematics to assist in improving the quality of the system design. By extending the ASMs with types, the modelling language is tailored for data warehouse with the terms that are well developed for data-intensive applications, which makes it easy to model the schema evolution as refinements in the dynamic data warehouse design. By providing the application-tailored design framework, we break down the design complexity by business processes (also called subjects in data warehousing) and design concerns. By designing the data warehouse by subjects, our method resembles Kimball's "bottom-up" approach. However, with the schema integration method, our method resolves the stovepipe issue of the approach. By building up a data warehouse iteratively in an integrated framework, our method not only results in an integrated data warehouse, but also resolves the issues of complexity and delayed ROI (Return On Investment) in Inmon's "top-down" approach. By dealing with the user change requests in the same way as new subjects, and modelling data and operations explicitly in a three-tier architecture, namely the data sources, the data warehouse and the OLAP (online Analytical Processing), our method facilitates dynamic design with system integrity. By introducing a notion of refinement specific to schema evolution, namely schema refinement, for capturing the notion of schema dominance in schema integration, we are able to build a set of correctness-proven refinement rules. By providing the set of refinement rules, we simplify the designers's work in correctness design verification. Nevertheless, we do not aim for a complete set due to the fact that there are many different ways for schema integration, and neither a prescribed way of integration to allow designer favored design. Furthermore, given its °exibility in the process, our method can be extended for new emerging design issues easily