Maintaining data cubes under slowly changing dimensions

In the multi-dimensional model of data warehouses, data drawn from multiple sources is organized into fact tables and dimension tables. Data cubes pre-compute the aggregations of measurements along combinations of dimensions to speed up the processing of Online Analytical Processing (OLAP) queries. When the underlying fact tables or dimension tables change over time, data cubes need to be incrementally maintained to reflect these changes. In this thesis, we study the problem of incrementally maintaining data cubes under slowly changing dimensions. We argue that temporal features are essential in a data warehouse to avoid loss of historical information, and to help maintain data cubes to be consistent with the state of the underlying tables. We first characterize view maintenance problems based on changing dimensions. We then introduce our temporal multi-dimensional model that can describe changing dimension data, and we discuss strategies for incrementally maintaining data cubes based on that temporal model. A maintenance algorithm is developed, and its performance is evaluated experimentally. The results show that our approach is highly effective and is more efficient than re-materialization in some change cases. The results also show that our approach significantly outperforms existing incremental maintenance approaches

Cooperative Wool Marketing Pools and Warehouses: Industry Update, Issues and Options

In 1981, there were 158 cooperative wool marketing pools and 9 cooperative warehouses. Pools operate a few days each year to assemble and sell wool. Warehouses operate daily and also grade, store, and blend wool to buyer specifications. Pools frequently sell without knowledge of grade and clean fiber content. Producer bargaining power is also limited by declining wool production, large variation in pool membership and volume, and overlapping marketing territories among warehouses. Processing, consolidating pool and warehouse marketing, and changing pool pricing to reflect clean fiber content are options to lower marketing costs and better market power.Wool, cooperative, pool, Agribusiness,

Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses

A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses

Using cross-functional, cross-firm teams to co-create value: The role of financial measures

Increasingly, the involvement of representatives from all major business functions in cross-functional, crossfirmteams is being viewed as a means to develop and maintain profitable business-to-business relationships.However, if the measurements of the value co-created in these relationships with customers and suppliers donot incorporate the financial outcomes of joint cross-functional initiatives, managers can be led to makedecisions that jeopardize the long-term profitability of the two firms. In this paper, the authors explore thedifferences in value co-creation when a company is linked to key customers and key suppliers through crossfunctionalteams and when it is not. Using a case study approach, the authors measured value co-creation infinancial terms and describe how managers changed their behaviors toward customers and suppliers whenthey were able to compare the value that was being co-created in each relationship. In each pair ofrelationships, one involved cross-functional teams and the other did not. The results indicate that crossfunctional,cross-firm involvement leads to increased value co-creation. The research suggests that marketingscholars and managers should emphasize the use of cross-functional teams that involve all major functions tomanage relationships with key customers, and should incorporate financial measures in the evaluation ofrelationship performance

Building a Data Warehouse and Data Mining for a Strategic Advantage

Technology is fundamentally changing the way companies do business. Consolidations, globalization, and deregulation have put increased pressure on managers to better understand their businesses and take them to the next level. Given the fast-paced business environment today, decision-making cycles have been shortened and managers need accurate information in a timely manner in order to make quality decisions. A properly designed and populated data warehouse can provide the relevant data necessary to make good decisions. Significant advances in computer hardware and end user software have made it easy to access, analyze, and display information at the desktop. The data companies continue to collect from their current information system provides a great source of information about its customers and processes. Data mining software programs are powerful tools that can be used to interrogate the massive amounts of data contained in the data warehouse in order to uncover relationships. To help business leaders and decision makers manage their companies effectively, companies need to make as much information as possible available and give decision-makers the tools they need to explore it according to Kapstone (1995). By implementing a data warehouse and using data mining tools companies can uncover relationships that can be used to achieve strategic advantages. First, I will explain data warehouses, why they are built, and how to build them. Second, I will cover data mining tools and the benefits companies are experiencing by using them. Finally, I will focus on the strategic advantages of building a data warehouse and extracting valuable data using sophisticated data mining tools

GUARDIANS final report

Emergencies in industrial warehouses are a major concern for firefghters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist fire fighters in searching a large warehouse. In this report we discuss the technology developed for a swarm of robots searching and assisting fire fighters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also one of the means to locate the robots and humans. Thus the robot swarm is able to locate itself and provide guidance information to the humans. Together with the re ghters we explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings