Meeting new challenges in higher education: Two educational activities and an interdisciplinary competency framework

This article explores how educational institutions are faced with changes in the modern global business environment, and how this leads to a need for changes in curricula for business schools and information systems schools. Most of academia still uses a strict disciplinary model of education resulting in a high degree of specialization within each discipline while the modern business environments require knowledge workers who can address problems that cut across disciplines on an increasingly global scale. Research papers and governmental reports call for more emphasis on particularly three interdisciplinary topics; 1) competencies in globalization issues, 2) communication/working in team, and 3) information literacy. The academic disciplines of business education and information systems education in particular have received much attention in this respect with several calls for change. How to bring about such a change is, however, still an open question. Currently many universities are looking into their educational offerings in order to adapt to the new situation. This paper proposes to address this issue in two ways; by suggesting two new educational activities and by proposing a new educational interdisciplinary competency framework to guide curriculum development when including interdisciplinary topics

Roles of multidimensionality and granularity in warehousing Australian resources data

Granularity of data modeled in multidimensional data structures is an important factor for a data warehouse. Grain sizes and number of dimensions participating in the model are critical in ascertaining the quality of analytical queries that are run on such data warehouses. In this paper, exploration and production data of Australian resources industry, pertinent to oil and gas, over the past five decades have been examined for multidimensionality and grain size. This research shows how using an ER approach combined with multidimensional data modeling helps in considerable reduction in the size of the data warehouse, making it more effective and efficient

Educational Activities and a Competency Framework for Meeting New Challenges in Higher Education

In his article we explore how educational institutions are faced with changes in the modern global business environment, and how this leads to need for changes in curricula for business schools and information systems schools. Most of academia still uses a strict disciplinary model of education resulting in a high degree of specialization within each discipline while the modern business environments require knowledge workers who can address problems that cut across disciplines on an increasingly global scale. Research papers and governmental reports call for more emphasis on particularly three interdisciplinary topics; 1) competencies in globalization issues, 2) communication/working in team, and 3) information literacy. The disciplines of business and in particular information systems education have received much attention in this respect with several calls for change. How to bring about such a change is, however, still an open question. This paper proposes to address this issue in two ways; by suggesting two new educational activities and by proposing a new educational interdisciplinary competency framework to guide curriculum development when including interdisciplinary topics

Mapping of oil and gas exploration business data entities for effective operational management

Spatio-temporal data of petroleum resources businesses are heterogeneous in nature with multiple relationships among various entities and attributes. Object oriented (OO) systems provide alternative solutions for handling the complex exploration business data entities, where traditional database systems pose serious limitations. Exploration, which is a key business object class in any petroleum business environment, is composed of several sub classes, such as navigation, seismic, vertical seismic profiling (VSP), well-log and reservoir. Authors classify these typical spatio-temporal data items as classes and sub class objects in the OO modelling. In the present paper, logical entity relationship (ER) models have been re-written in multidimensional and object-oriented models. Syntax of typical exploration data object classes, attributes, operations and their relationships has been described for implementation purposes. This work demonstrates how object class logical data models are flexible and interoperable for fast changing petroleum business situations. Models presented in this paper, guide exploration data managers for effectively managing their operations. An OLAP model discussed in this paper is a pursuit of cost saving detailed exploration for oil and gas prospect investigation in any basin

Warehousing of object oriented petroleum data for knowledge mapping

Australia produces a-third of world?s natural resources. Enormous amounts of energy and financial resources are expended in order to tap these natural reserves from the earth?s surface. Vast amounts of these resources, however, remain unexplored and under exploited. Data pertaining natural resources, such as mineral and petroleum, are, in general, heterogeneous and complex in nature. Volumes of these types of data are geographically distributed among many companies in Australia and abroad. The existing historical resources data are logically and physically organized using warehousing techniques. Entity relationship (ER) and object oriented (OO) data mapping techniques are used for analyzing the data entities, dimensions and objects. In this paper object oriented data and warehousing of object class data models have been described. Data mining techniques can be employed to explore many more resources hidden, under great depths of the earth?s crust, without additional efforts of exploration and development. Warehoused object oriented resources data can significantly reduce the complexity of the resources data structuring and enhance the data integration and information sharing among various operational units of the resources industry. Large amount of financial inputs can be saved if these technologies are successfully implemented in the resources industry

Selecting adequate samples for approximate decision support queries

For highly selective queries, a simple random sample of records drawn from a large data warehouse may not contain sufficient number of records that satisfy the query conditions. Efficient sampling schemes for such queries require innovative techniques that can access records that are relevant to each specific query. In drawing the sample, it is advantageous to know what would be an adequate sample size for a given query. This paper proposes methods for picking adequate samples that ensure approximate query results with a desired level of accuracy. A special index based on a structure known as the k-MDI Tree is used to draw samples. An unbiased estimator named inverse simple random sampling without replacement is adapted to estimate adequate sample sizes for queries. The methods are evaluated experimentally on a large real life data set. The results of evaluation show that adequate sample sizes can be determined such that errors in outputs of most queries are wtihin the acceptable limit of 5%

Mining optimal item packages using mixed integer programming

Traditional methods for discovering frequent patterns from large databases are based on attributing equal weights to all items of the database. In the real world, managerial decisions are based on economic values attached to the item sets. In this paper, we introduce the concept of the value based frequent item packages problems. Furthermore, we provide a mixed integer linear programming (MILP) model for value based optimization problem in the context of transaction data. The problem discussed in this paper is to find an optimal set of item packages (or item sets making up the whole transaction) that returns maximum profit to the organization under some limited resources. The specification of this problem opens the way for applying existing and new MILP solution techniques to deal with a number of practical decision problems. The model has been implemented and tested with real life retail data. The test results are reported in the paper

An efficient sampling scheme for approximate processing of decision support queries

Decision support queries usually involve accessing enormous amount of data requiring significant retrieval time. Faster retrieval of query results can often save precious time for the decision maker. Pre-computation of materialised views and sampling are two ways of achieving significant speed up. However, drawing random samples for queries on range restricted attributes has two problems: small random samples may miss relevant records and drawing larger samples from disk can be inefficient due to the large number of disk accesses required. In this paper, we propose an efficient indexing scheme for quickly drawing relevant samples for data warehouse queries as well as propose the concepts of database and sample relevancy ratios. We describe a method for estimating query results for range restricted queries using this index and experimentally evaluate the scheme using a relatively large real dataset. Further, we compute the confidence intervals for the estimates to investigate whether the results can be guaranteed to be within the desired level of confidence. Our experiments on data from a retail data warehouse show promising results. We also report the levels of accuracy achieved for various types of aggregate queries and relate them to the database relevancy ratios of the queries

Data warehouse structuring methodologies for efficient mining of Western Australian petroleum data sources

Representing the knowledge domain of a petroleum system is a complex problem. In the present study, logical modelling of shared attributes of resources industry entities (dimensions or objects) has been used for construction of a dynamic and time-variant metadata model. This work demonstrates effectiveness of multidimensional data modelling for petroleum industry, which will be further investigated for fine-grain data presentation and interpretation for quality knowledge discovery