The goal of a data warehouse is to integrate applications at the data level. Data warehouse architectures are developing in response to our increasing data and information requirements. The traditional notion of data warehouses is evolving into a federated warehouse augmented by a set of processes and services to support integrated and consistent access to heterogeneous, decentralized warehouse...

New age telecommunication sector (GSM) has been based on new customer acquisition plans and tactics. For this purpose, evaluation of the call traffic data from the data warehouses and making some predictions by data mining algorithms are so important. Data mining is a process for filtering massive amounts of data to find useful information to optimize decision making in the firms. Data mining m...

Many efficient algorithms to compute multidimensional aggregation and Cube for relational OLAP have been developed. However, to our knowledge, there is nothing to date in the literature on aggregation algorithms on compressed data warehouses for multidimensional OLAP. This paper presents a set of aggregation algorithms on very large compressed data warehouses for multidimensional OLAP. These al...

With the emergence of XML as a new standard for representing business data, new decision-support applications (namely, XML data warehouses) are being developed. To ensure their feasibility, the issue of performance must be addressed. Performance in general, and the efficiency of performance optimization techniques in particular, is usually assessed with the help of benchmarks. However, there ar...

It is a persistent challenge to achieve a high quality of data in data warehouses. Data cleaning is a crucial task for such a challenge. To deal with this challenge, a set of methods and tools has been developed. However, there are still at least two questions needed to be answered: How to improve the efficiency while performing data cleaning? How to improve the degree of automation when perfor...

Data warehouses contain valuable information for decisionmaking purposes, they can be queried and visualised with Online Analytical Processing (OLAP) tools. They contain time-related information and thus representing and reasoning on temporal data is important both to guarantee the efficacy and the quality of decision-making processes, and to detect any emergency situation as soon as possible. ...

For extended enterprises, value-chain integration means that enterprise business systems can no longer be confined to internal processes, applications, and data repositories. What this means for data warehouses, is that their interoperation is a prerequisite in collaborative environments such as Business-to-Business exchanges where they are physically scattered along the value chain. Adopting s...

All of the existing (iceberg) cube computation algorithms assume that the data is stored in a single base table, however, in practice, a data warehouse is often organized in a schema of multiple tables, such as star schema and snowflake schema. In terms of both computation time and space, materializing a universal base table by joining multiple tables is often very expensive or even unaffordabl...

Recently, the integration of data warehouses and data mining has been recognized as the primary platform for facilitating knowledge discovery. Effective data mining from data warehouses, however, needs exploratory data analysis. The users often need to investigate the warehousing data from various perspectives and analyze them at different levels of abstraction. To this end, comprehensive infor...

Tribolium castaneum (Herbst) is a cosmopolitan pest of stored products, grain processing, and foodwarehouses. This studywas initiated tomore fully characterize the role of structure, sanitation, and trap location on capture of T. castaneum in pheromone-baited pitfall traps commonly used in insect monitoring programs. Food patches, spatial structure, and trap position were manipulated in pilot s...