A set of research papers on materialized views and data warehouse. Chaudhuri and Dayal (CD97) provide a general overview of data warehousing and OLAP technology. There are a good number of introductory-level textbooks on data warehousing and OLAP technology, including Kimball and Ross (KR02), Imhofi, Galemmo and Geiger (IGG03), Inmon (Inm96), Berson and Smith (BS97), and Thomsen (Tho97). 1 Introduction Data warehouse (DW) equips users with more effective decision support tools by integrating enterprise-wide corporate data into a single repository from which business end-users can run reports and perform ad hoc data analysis. Finally, by a cost model, we analyze the effectiveness of our approach visa -vis the unpartitioned approach. Given a set of queries, we use primary and derived partitioning algorithms to select (near) optimal AHCPs, thereby embedding query semantics into the partitioned framework. Partitioning (AHCP) technique on the ORDW schema. In this paper, we show the efficacy in building semantic-rich hybrid class partitions by incorporating the Associated Horizontal Class. In an Object Relational Data Warehousing (ORDW) environment, the semantics of data and queries can be explicitly captured, represented, and utilized based on is-a and class composition hierarchies, thereby resulting in more efficient OLAP query processing. Key implications for theory and practice are discussed. Utilizing information from six organizations, the empirical evidence presented indicates that the organizations with higher levels of centralized IT authority are likely to implement a more centralized data warehousing approach. A replicated case study approach coupled with a research survey was used to provide a comprehensive understanding of the relationship between modes of IT governance and DW topology. Three primary modes of IT governance-centralized, decentralized, and hybrid - were used to arrange key IT activities. This article examines the relationship between modes of IT governance and DW topology to determine whether or not the implementation differences in DW topology can be described by differences in IT governance arrangements. The primary question is whether to start DW projects with enterprise-wide data warehouses (EDWs) or with smaller-scale data marts (DMs). Information systems (IS) strategic planners debate what is the most appropriate data warehouse (DW) topology for an organization. This paper refers as case study to a Mexican Public R&D Center, which has re-engineered its operating model with a systems approach. This paper presents a proposal to integrate new elements into the IT strategy, considering the interactions with other organizational functions, defining an implementation and transition plan that takes into account the organization dynamics, which has limited resources and, therefore, requires a gradual and long term transition plan. The strategy of an organization must take into account the integration of this aspect with other organizational functions. The decision-making process requires the use of information technology tools, since the information amount is large and requires reliable methods for collecting, accessing, storing, processing, distributing, and evaluating, in order to provide reliable information to decision makers. To achieve this goal, the proposed framework captures core business activities in a comprehensive process map and assesses their relative importance and possible data support with multi-criteria decision analysis.ĭecision making in new technologies is a crucial activity to raise competitiveness, especially for technology organizations. The present research describes a prescriptive framework to prioritise data items for business analytics and applies it to human resources. While the literature acknowledges this decision problem, no model-based approach to inform this decision has hitherto been proposed. Thus, potential additional insights resulting from a new data item in the BI system need to be balanced with the often high costs of data creation.
Expanding conventional BI systems often leads to high costs of internally generating, cleansing and maintaining new data items whilst the additional data storage costs are in many cases of minor concern - what is a conceptual difference to big data systems. The determination of data items that should be stored in the BI system is vital to ensure the success of an organisation's business analytic strategy. The popularity of business intelligence (BI) systems to support business analytics has tremendously increased in the last decade.
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