Nenad Jukic

Modeling strategies and alternatives for data warehousing projects

Choosing the appropriate modeling approach is often the critical factor in the success or failure of a data warehousing implementation.

Comprehensive data warehouse exploration with qualified association-rule mining

Data warehouses store data that explicitly and implicitly reflect customer patterns and trends, financial and business practices, strategies, know-how, and other valuable managerial information. In this paper, we suggest a novel way of acquiring more knowledge from corporate data warehouses. Association-rule mining, which captures co-occurrence patterns within data, has attracted considerable efforts from data warehousing researchers and practitioners alike. In this paper, we present a new data-mining method called qualified association rules. Qualified association rules capture correlations across the entire data warehouse, not just over an extracted and transformed portion of the data that is required when a standard data-mining tool is used.

Ad-hoc association-rule mining within the data warehouse

Many organizations often underutilize their existing data warehouses. In this paper, we suggest a way of acquiring more information from corporate data warehouses without the complications and drawbacks of deploying additional software systems. Association-rule mining, which captures co-occurrence patterns within data, has attracted considerable efforts from data warehousing researchers and practitioners alike. Unfortunately, most data mining tools are loosely coupled, at best, with the data warehouse repository. Furthermore, these tools can often find association rules only within the main fact table of the data warehouse (thus ignoring the information-rich dimensions of the star schema) and are not easily applied on non-transaction level data often found in data warehouses. In this paper, we present a new data-mining framework that is tightly integrated with the data warehousing technology. Our framework has several advantages over the use of separate data mining tools. First, the data stays at the data warehouse, and thus the management of security and privacy issues is greatly reduced. Second, we utilize the query processing power of a data warehouse itself, without using a separate data-mining tool. In addition, this framework allows ad-hoc data mining queries over the whole data warehouse, not just over a transformed portion of the data that is required when a standard data-mining tool is used. Finally, this framework also expands the domain of association-rule mining from transaction-level data to aggregated data as well.

A belief-consistent multilevel secure relational data model☆

Abstract Multilevel relations, based on the current multilevel secure (MLS) relational data models, can present a user with information that is difficult to interpret and may display an inconsistent outlook about the views of other users. Such ambiguity is due to the lack of a comprehensive method for asserting and interpreting beliefs about information at lower security levels. In this paper we present a belief-consistent MLS relational database model which provides an unambiguous interpretation of all visible information and gives the user access to the beliefs of users at lower security levels, neither of which was possible in any of the existing models. We identify different beliefs that can be held by users at higher security levels about information at lower security levels, and introduce a mechanism for asserting beliefs about all accessible tuples. This mechanism provides every user with an unambiguous interpretation of all viewable information and presents a consistent account of the views at all levels visible to the user. In order to implement this assertion mechanism, new database operations, such as verify true and verify false, are presented. We specify the constraints for the write operations, such as update and delete, that maintain belief consistency and redefine the relational algebra operations, such as select, project, union, difference and join.

Asserting beliefs in MLS relational models

Multilevel relations, based on the current multilevel secure (MLS) relational data models, can present a user with information that is difficult to interpret and may display an inconsistent outlook about the views of other users. Such ambiguity is due to the lack of a comprehensive method for asserting and interpreting beliefs about lower level information. In this paper we identify different beliefs that can be held by higher level users about lower level information, and we introduce the new concept of a mirage tuple. We present a mechanism for asserting beliefs about all accessible tuples, including lower level tuples. This mechanism provides every user of an MLS database with an unambiguous interpretation of all viewable information and presents a consistent account of the views at all levels below the users level.

Augmenting Data Warehouses with Big Data

In the past decade, corporations are increasingly engaging in efforts whose aim is the analysis and wide-ranging use of big data. The majority of academic big data articles have been focused on methods, approaches, opportunities, and organizational impact of big data analytics. In this article, the focus is on the ability of big data (while acting as a direct source for impactful analysis) to also augment and enrich the analytical power of data warehouses.

Using real data to invigorate student learning

Students need hands-on-experience with industrial strength databases and large data sets so they can deal with them when once employed. Many schools find it too expensive because of the cost and faculty resources required. That excuse is no longer valid. Colleges and Universities throughout the world can avail themselves of an extraordinary gift from Teradata Corporation that allows introducing large, complex problems into computer science classes without cost. The gift includes software and enormous databases available through the Internet as Software as a Service. This editorial describes the Teradata University Network, and how faculty can obtain this resource for their classes.

Information System Planning and Decision Making Framework: A Case Study

Although numerous academic and industry studies have identified a variety of connections among strategies, processes, and systems, the phenomenon of information system adoption and post-implementation failure is still common. One factor in system adoption that has not yet been thoroughly considered is the conceptualization of processes and systems at multiple levels of complexity. This paper uses a case study to illustrate a framework that outlines the flow of conceptualization efforts during system adoption and implementation.

Improving e-business customer relationship management systems with multilevel secure data models

Customer relationship management (CRM) leverages technology to coordinate business-customer interactions with the objective of building long-term loyalty. Multilevel secure (MLS) data models were originally developed as database models for the management of information in environments with a strict hierarchy of security levels, such as military institutions and government security agencies. The paper demonstrates how an MLS model can be used to address the issue of sharing and managing customer-related information in e-businesses with diverse constituencies. The potential for exploiting the advantages offered by the MLS models in the e-business CRM context is examined. Several examples are used to show how organizing a database management system based on MLS principles can be used to help e-businesses to provide consistent and appropriate content to various customers and partners in a more efficient manner.

Data Models for Information Sharing in E-Partnerships: Analysis, Improvements, and Relevance

In this article we address the issue of sharing information in organizational forms involving parties with various levels of mutual trust and need for cooperation. We evaluate existing data access models and emphasize their failure to deliver a feasible method for providing consistent views of corporate information shared with other partners in an ad hoc relationship. A new data access model is proposed as a solution to this problem. This model combines the ease of maintenance with the ability to present semantically consistent views of the world to all constituents of a virtual enterprise. We discuss the relevance of information sharing across multiple-access levels for achieving competitive advantage in the electronic marketplace.