XWeB: the XML Warehouse Benchmark

With the emergence of XML as a standard for representing business data, new decision support applications are being developed. These XML data warehouses aim at supporting On-Line Analytical Processing (OLAP) operations that manipulate irregular XML data. To ensure feasibility of these new tools, important performance issues must be addressed. Performance is customarily assessed with the help of benchmarks. However, decision support benchmarks do not currently support XML features. In this paper, we introduce the XML Warehouse Benchmark (XWeB), which aims at filling this gap. XWeB derives from the relational decision support benchmark TPC-H. It is mainly composed of a test data warehouse that is based on a unified reference model for XML warehouses and that features XML-specific structures, and its associate XQuery decision support workload. XWeB's usage is illustrated by experiments on several XML database management systems

Benchmarking Summarizability Processing in XML Warehouses with Complex Hierarchies

Business Intelligence plays an important role in decision making. Based on data warehouses and Online Analytical Processing, a business intelligence tool can be used to analyze complex data. Still, summarizability issues in data warehouses cause ineffective analyses that may become critical problems to businesses. To settle this issue, many researchers have studied and proposed various solutions, both in relational and XML data warehouses. However, they find difficulty in evaluating the performance of their proposals since the available benchmarks lack complex hierarchies. In order to contribute to summarizability analysis, this paper proposes an extension to the XML warehouse benchmark (XWeB) with complex hierarchies. The benchmark enables us to generate XML data warehouses with scalable complex hierarchies as well as summarizability processing. We experimentally demonstrated that complex hierarchies can definitely be included into a benchmark dataset, and that our benchmark is able to compare two alternative approaches dealing with summarizability issues.Comment: 15th International Workshop on Data Warehousing and OLAP (DOLAP 2012), Maui : United States (2012

Business Intelligence Approach In A Business Performance Context

Subordinated to performance management, Business Intelligence approaches help firms to optimize business performance. Key performance indicators will be added to the multidimensional model grounding the performance perspectives. With respect to the Business Intelligence value chain, a theoretical approach was introduced and a practice example, based on Microsoft SQL Server specific services, for the customer perspective was implemented.business intelligence, performance management, key performance indicators

Solutions for decision support in university management

The paper proposes an overview of decision support systems in order to define the role of a system to assist decision in university management. The authors present new technologies and the basic concepts of multidimensional data analysis using models of business processes within the universities. Based on information provided by scientific literature and on the authors’ experience, the study aims to define selection criteria in choosing a development environment for designing a support system dedicated to university management. The contributions consist in designing a data warehouse model and models of OLAP analysis to assist decision in university management.university management, decision support, multidimensional analysis, data warehouse, OLAP

Benchmarking Big Data OLAP NoSQL Databases

With the advent of Big Data, new challenges have emerged regarding the evaluation of decision support systems (DSS). Existing evaluation benchmarks are not configured to handle a massive data volume and wide data diversity. In this paper, we introduce a new DSS benchmark that supports multiple data storage systems, such as relational and Not Only SQL (NoSQL) systems. Our scheme recognizes numerous data models (snowflake, star and flat topologies) and several data formats (CSV, JSON, TBL, XML, etc.). It entails complex data generation characterized within “volume, variety, and velocity” framework (3 V). Next, our scheme enables distributed and parallel data generation. Furthermore, we exhibit some experimental results with KoalaBench

A model of enterprise systems capabilities

This study has developed a model of ES capabilities to analyze the extent and quality of the use of ES in organizational contexts. The model consists of six general ES capabilities that can be used and deployed by organizations: 1) transaction automation, 2) decision-making process support, 3) monitoring performance, 4) customer service, 5) coordination, and 6) process management automation. The model itself was initially formulated from concepts in IS and ES literature. Then, the model was applied, validated and tuned through an in-depth case study.Enterprise systems, ES capabilities, ES use

The use of alternative data models in data warehousing environments

Data Warehouses are increasing their data volume at an accelerated rate; high disk space consumption; slow query response time and complex database administration are common problems in these environments. The lack of a proper data model and an adequate architecture specifically targeted towards these environments are the root causes of these problems. Inefficient management of stored data includes duplicate values at column level and poor management of data sparsity which derives from a low data density, and affects the final size of Data Warehouses. It has been demonstrated that the Relational Model and Relational technology are not the best techniques for managing duplicates and data sparsity. The novelty of this research is to compare some data models considering their data density and their data sparsity management to optimise Data Warehouse environments. The Binary-Relational, the Associative/Triple Store and the Transrelational models have been investigated and based on the research results a novel Alternative Data Warehouse Reference architectural configuration has been defined. For the Transrelational model, no database implementation existed. Therefore it was necessary to develop an instantiation of it’s storage mechanism, and as far as could be determined this is the first public domain instantiation available of the storage mechanism for the Transrelational model

Driving continuous improvement

The quality of improvement depends on the quality of leading and lagging performance indicators. For this reason, several tools, such as process mapping, cause and effect analysis and FMEA, need to be used in an integrated way with performance measurement models, such as balanced scorecard, integrated performance measurement system, performance prism and so on. However, in our experience, this alone is not quite enough due to the amount of effort required to monitor performance indicators at operational levels. The authors find that IT support is key to the successful implementation of performance measurement-driven continuous improvement schemes