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A company, majorly company that active in commercial (profit orientation) need to analyze their sales performance. By analyzing sales performance, company can increase their sales performance. One of method to analyze sales performance is by collecting historical data that relates to sales and then process that data so that produce information that show company sales performance. A data warehouse is a set of data that has characteristic subject oriented, time variant, integrated, and nonvolatile that help company management in processing of decision making. Design of data warehouse is started from collecting data that relate to sales such as product, customer, sales area, sales transaction, etc. After collecting the data, next is data extraction and transformation. Data extraction is a process f or selecting data that will be loaded into data warehouse. Data transformation is making some change to the data afte r extracted to be more consistent. After transformation processing, data are loaded into data warehouse. Data in data warehouse is processed by OLAP (On Line Analytical Processing) to produce information. Information that are produced from data processing by OLAP are chart and query reporting. Chart reporting are sales chart based on cement type, sales chart based on sales area, sales chart based on plant, monthly and year ly sales chart, and chart based on customer feedback. Query reporting are sales based on cement type, sales area, plant and customer. Keywords: Data warehouse; OLAP; Sales performance analysis; Ready mix market

As an analysis tool either of on-line analytical processing (OLAP) or data mining has its own strong points and weaknesses. It is an effective way to enhance the power and flexibility of data mining in data warehouses and large databases by integrating data mining with OLAP to offset their weaknesses. In the paper, a proposal of integrating data mining and OLAP is put forward. The mechanism of the on-line analytical data mining in data warehouses is described. Aspects which are necessary for developing a successful on-line analytical data mining system is also discussed.

Cloud computing helps reduce costs, increase business agility and deploy solutions with a high return on investment for many types of applications, including data warehouses and on-line analytical processing. However, storing and transferring sensitive data into the cloud raises legitimate security concerns. In this paper, the authors propose a new multi-secret sharing approach for deploying data warehouses in the cloud and allowing on-line analysis processing, while enforcing data privacy, integrity and availability. The authors first validate the relevance of their approach theoretically and then experimentally with both a simple random dataset and the Star Schema Benchmark. The authors also demonstrate its superiority to related methods.

SummaryThe Model‐Driven Architecture (MDA) is an approach that aligns modeling and automation for software development. By applying such an approach to data warehouse (DW) projects, we can minimize a great deal of time and cost. Furthermore, most of OnLine Analytical Processing (OLAP) platforms seem to be like black boxes that provide wizards only to business intelligence developers to create and manipulate OLAP objects without allowing their sustainability and migration from a platform to another. That is why many works in the literature have proposed using the MDA approach in DW projects. However, most of them have mainly focused on the generation of the DW relational model from the conceptual one, and they overlooked the OLAP model and the cube implementation.To deal with this problem, we propose in this paper an MDA solution to automate the process of getting OLAP cube and its implementation through a set of metamodels and automatic transformations among them. In fact, the proposal generates the OLAP and DW relational models (PSMs) from the conceptual one, using also a PDM model that describes the target business intelligence platform. After that, the source code to create the cube is got from both PSM models. For this aim, we define a set of transformation rules implemented using the Atlas transformation language. Finally, a case study will be provided to validate our approach.

Models of multi-dimensional analysis for qualitative data and its application

There are ambiguities and vagueness in solar radiation records during a day. As a consequence, the development and adaptation of automatic knowledge acquisition techniques under uncertainty is entirely advisable. Among them fuzzy sets theory formulizes and analyses the situations in which the uncertainty is due to non-precise environment. Any good decision is based on information. Decision makers should reduce their uncertainty by obtaining as much reliable and consistent information as possible. In this paper, we introduce a novel decision support system for handling uncertain climate data consolidated with SAS application system. This novel approach is based on the membership function and multidimensional analysis. From our findings, we conclude that data warehousing and data mining are essentials for an effective decision support system. Because organizations frequently process uncertain information, decision makers should reduce their uncertainty by obtaining as much reliable and consistent information as possible. Due to the potential benefits of a data warehouse as well as internal and external pressure for creating a competitive advantage, many organizations have launched data warehouse projects with the expectations of acquiring a consistent and reliable source of data for their DSS. Therefore, a DSS with data warehouse should improve the performance of users by improving information accessibility which positively affects the quality of decision making [1]. At present there is a great need to provide decision makers from middle management upward with information at the correct level of detail to support decision making. Data warehousing, on-line analytical processing (OLAP), and data mining provide this functionality [2]. Although there are researchers in fields of certainty, such as the natural science fields of physics, mathematics, biology, and in the social science fields of philosophy, economy, society, psychology, cognition, almost nobody places suspicion on the uncertain essence of the world. An increasing number of scientists believe that uncertainty is the charm of the world and only uncertainty itself is certain! For a long time, humans thought uncertainty was equal to randomness. Probability theory is the main mathematical tool to solve the problem of randomness. With more in-depth studies, people found a kind of uncertainties that could not be described with randomness. That was “fuzziness”. Fuzziness is a characteristic feature of modern science to describe quantitative relationships and space formation by using precise definitions and rigidly proven theorems, and to explore the laws of the objective world by using precisely controlled experimental methods, accurate measurements, and calculation so as to establish a rigorous theoretic system [3]. There are ambiguities and vagueness in solar radiation records during a day. There is a desired need for providing a simple technique whereby uncertainties in the process of solar radiation measurements being handled. In this paper, we introduce a novel decision support system for handling uncertain climate data consolidated with SAS application system. This novel approach is based on the membership function and multidimensional analysis. The rest of the paper is organized as follows. The next section reviews related work. In Section 3 we will describe the characteristics of decision support systems. In Section 4 we demonstrate the basic data warehousing concepts. Section 5 provides the definition of a general framework for fuzzy sets in the presence of uncertainty. Section 6 introduces climate case study. In Section 7 we present our approach for solving this problem. Section 8 presents the results analysis. Section 9 shows International Journal for Information Security Research (IJISR), Volume 1, Issue 2, June 2011 Copyright © 2011, Infonomics Society 35

Decision makers query enterprise information stored in Data Warehouses (DW) by using tools (such as On-Line Analytical Processing (OLAP) tools) which employ specific views or cubes from the corporate DW or Data Marts, based on multidimensional modelling. Since the information managed is critical, security constraints have to be correctly established in order to avoid unauthorized access. In previous work we defined a Model-Driven based approach for developing a secure DW repository by following a relational approach. Nevertheless, it is also important to define security constraints in the metadata layer that connects the DW repository with the OLAP tools; that is, over the same multidimensional structures that end users manage. This paper incorporates a proposal for developing secure OLAP applications within our previous approach: it improves a UML profile for conceptual modelling; it defines a logical metamodel for OLAP applications; and it defines and implements transformations from conceptual to logical models, as well as from logical models to secure implementation in a specific OLAP tool (SQL Server Analysis Services).

Data cube has been playing an essential role in fast OLAP (online analytical processing) in many data warehouses. The pre-computation of data cubes is critical for improving the OLAP response time of in large high-dimensional data warehouses. However, as the sizes of data warehouses grow, the time it takes to perform this pre-computation becomes a significant performance bottleneck. In a high dimensional data warehouse, it might not be practical to build all these cuboids and their indices. In this paper, we propose a hierarchical cubing algorithm to partition the high dimensional data cube into low dimensional cube segments. It permits a significant reduction of CPU and I/O overhead for many queries by restricting the number of cube segments to be processed for both the fact table and bitmap indices. Experimental results show that the proposed method is significantly more efficient than other existing cubing methods.

In today's knowledge-, service-, and cloud-based economy, businesses accumulate massive amounts of data from a variety of sources. In order to understand businesses one may need to perform considerable analytics over large hybrid collections of heterogeneous and partially unstructured data that is captured related to the process execution. This data, usually modeled as graphs, increasingly come to show all the typical properties of big data: wide physical distribution, diversity of formats, non-standard data models, independently-managed and heterogeneous semantics. We use the term big process graph to refer to such large hybrid collections of heterogeneous and partially unstructured process related execution data. Online analytical processing (OLAP) of big process graph is challenging as the extension of existing OLAP techniques to analysis of graphs is not straightforward. Moreover, process data analysis methods should be capable of processing and querying large amount of data effectively and efficiently, and therefore have to be able to scale well with the infrastructure's scale. While traditional analytics solutions (relational DBs, data warehouses and OLAP), do a great job in collecting data and providing answers on known questions, key business insights remain hidden in the interactions among objects: it will be hard to discover concept hierarchies for entities based on both data objects and their interactions in process graphs. In this paper, we introduce a framework and a set of methods to support scalable graph-based OLAP analytics over process execution data. The goal is to facilitate the analytics over big process graph through summarizing the process graph and providing multiple views at different granularity. To achieve this goal, we present a model for process OLAP (P-OLAP) and define OLAP specific abstractions in process context such as process cubes, dimensions, and cells. We present a MapReduce-based graph processing engine, to support big data analytics over process graphs. We have implemented the P-OLAP framework and integrated it into our existing process data analytics platform, ProcessAtlas, which introduces a scalable architecture for querying, exploration and analysis of large process data. We report on experiments performed on both synthetic and real-world datasets that show the viability and efficiency of the approach.

Business data collection is growing exponentially in recent years. A variety of industries and businesses have adopted new technologies of data storages such as data warehouses. On Line Analytical Processing (OLAP) has become an important tool for executives, managers, and analysts to explore, analyze, and extract interesting patterns from enormous amount of data stored in data warehouses and multidimensional databases. However, it is difficult for human analysts to interpret and extract meaningful information from large amount of data if the data is presented in textual form as relational tables. Visualization and interactive tools employ graphical display formats that help analysts to understand and extract useful information fast from huge data sets. This paper presents a new visual interactive exploration technique for an analysis of multidimensional databases. Users can gain both overviews and refine views on any particular region of interest of data cubes through the combination of interactive tools and navigational functions such as drilling down, rolling up, and slicing. Our technique allows users who are not experts in OLAP technology to explore and analyze OLAP data cubes and data warehouses without generating sophisticated queries. Furthermore, the visualization in our technique displays the exploration path enhancing the user's understanding of the exploration.

Chapter 64 - Exploiting Versions for On-line Data Warehouse Maintenance in MOLAP Servers

Radio Frequency Identification (RFID) technology is used in many applications for monitoring object movement. The use of RFID in supply chain management systems enables to track the movement of products from suppliers to warehouses, store backrooms, and eventually points of sale. The vast amount of data resulting from the proliferation of RFID readers and tags poses challenges for data management and analytics. RFID data warehousing can enhance data quality and consistency, and give great potential benefits for Online Analytical Processing (OLAP) applications. Traditional data warehouses are built primarily on relational database management systems. However, the size of RFID data being collected and analyzed in the industry for business intelligence is growing rapidly, making traditional warehousing solutions prohibitively expensive. Hive is an open-source data warehousing solution built on top of Hadoop which is a popular Big Data computing framework. This paper presents alternative RFID data warehouse designs which can handle a large amount of RFID data and support a variety of OLAP queries. The proposed approaches are implemented on Hive and evaluated for query performance in cloud computing environment.

Traditional data warehousing technologies and On-Line Analytical Processing (OLAP) are unable to analyze textual data. Moreover, as OLAP queries of a decision-maker are generally related to a context, contextual information must be taken into account during the exploitation of data warehouses. Thus, we propose a contextual text cube model denoted CXT-Cube which considers several contextual factors during the OLAP analysis in order to better consider the contextual information associated with textual data. CXT-Cube is characterized by several contextual dimensions, each one related to a contextual factor. In addition, we extend our aggregation OLAP operator for textual data ORank (OLAP-Rank) to consider all the contextual factors defined in our CXT-Cube model. To validate our model, we perform an experimental study and the preliminary results show the importance of our approach for integrating textual data into a data warehouse and improving the decision-making.

Defining and structuring management views is a fundamental task of the specification of data marts and reporting systems. Today such systems are based on data warehouses. A data warehouse stores materialized views on business processes to support the management’s information requirements (cf. Inmon 1996; Inmon, Hackathorn 1994; Inmon et al. 1997). It is located on a central layer of an idealized layer oriented architecture connecting online transaction processing (OLTP) systems and components enabling online analytical processing (OLAP) (cf. Becker, Holten 1998; Chaudhuri, Dayal 1997). The latter components are intended to support navigations adequate for management users through so called multi dimensional information spaces. OLTP systems directly support the business processes and are the sources of data used by OLAP systems. Typically, the integration of OLTP systems and the data warehouse is based on tools performing so called extraction, transformation and loading tasks (ETL) (cf. Inmon 1996; Widom 1995)

Multidimensional aggregation is a dominant operation on data warehouses for on-line analytical processing (OLAP). Many efficient algorithms to compute multidimensional aggregation on relational database based data warehouses have been developed. However, to our knowledge, there is nothing to date in the literature about aggregation algorithms on multidimensional data warehouses that store datasets in multidimensional arrays rather than in tables. This paper presents a set of multidimensional aggregation algorithms on very large and compressed multidimensional data warehouses. These algorithms operate directly on compressed datasets in multidimensional data warehouses without the need to first decompress them. They are applicable to a variety of data compression methods. The algorithms have different performance behavior as a function of dataset parameters, sizes of outputs and main memory availability. The algorithms are described and analyzed with respect to the I/O and CPU costs. A decision procedure to select the most efficient algorithm, given an aggregation request, is also proposed. The analytical and experimental results show that the algorithms are more efficient than the traditional aggregation algorithms.