Using Ontologies for the Design of Data Warehouses

Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure

Regional Data Archiving and Management for Northeast Illinois

This project studies the feasibility and implementation options for establishing a regional data archiving system to help monitor and manage traffic operations and planning for the northeastern Illinois region. It aims to provide a clear guidance to the regional transportation agencies, from both technical and business perspectives, about building such a comprehensive transportation information system. Several implementation alternatives are identified and analyzed. This research is carried out in three phases. In the first phase, existing documents related to ITS deployments in the broader Chicago area are summarized, and a thorough review is conducted of similar systems across the country. Various stakeholders are interviewed to collect information on all data elements that they store, including the format, system, and granularity. Their perception of a data archive system, such as potential benefits and costs, is also surveyed. In the second phase, a conceptual design of the database is developed. This conceptual design includes system architecture, functional modules, user interfaces, and examples of usage. In the last phase, the possible business models for the archive system to sustain itself are reviewed. We estimate initial capital and recurring operational/maintenance costs for the system based on realistic information on the hardware, software, labor, and resource requirements. We also identify possible revenue opportunities. A few implementation options for the archive system are summarized in this report; namely: 1. System hosted by a partnering agency 2. System contracted to a university 3. System contracted to a national laboratory 4. System outsourced to a service provider The costs, advantages and disadvantages for each of these recommended options are also provided.ICT-R27-22published or submitted for publicationis peer reviewe

Value-driven Security Agreements in Extended Enterprises

Today organizations are highly interconnected in business networks called extended enterprises. This is mostly facilitated by outsourcing and by new economic models based on pay-as-you-go billing; all supported by IT-as-a-service. Although outsourcing has been around for some time, what is now new is the fact that organizations are increasingly outsourcing critical business processes, engaging on complex service bundles, and moving infrastructure and their management to the custody of third parties. Although this gives competitive advantage by reducing cost and increasing flexibility, it increases security risks by eroding security perimeters that used to separate insiders with security privileges from outsiders without security privileges. The classical security distinction between insiders and outsiders is supplemented with a third category of threat agents, namely external insiders, who are not subject to the internal control of an organization but yet have some access privileges to its resources that normal outsiders do not have. Protection against external insiders requires security agreements between organizations in an extended enterprise. Currently, there is no practical method that allows security officers to specify such requirements. In this paper we provide a method for modeling an extended enterprise architecture, identifying external insider roles, and for specifying security requirements that mitigate security threats posed by these roles. We illustrate our method with a realistic example

A HOLISTIC APPROACH FOR MANAGING REQUIREMENTS OF DATA WAREHOUSE SYSTEMS

Quantitive evaluations show that many data warehouse projects fail at an alarming rate, wasting all the time, money, and effort spent on them. Cost overruns and schedule delays are attributed in large part, to problems associated with requirements. Therefore, a crucial success factor for the implementation of data warehouse systems is efficient requirements management. In this paper we introduce the easyREMOTE (easy Requirements Modeling Technique for Data WareHouses) requirements management approach, which facilitates an incremental and iterative development of complete, correct, feasible, necessary, prioritized, unambiguous, and verifiable data warehouse requirements. As a foundation we present a requirements specification template with consideration of different stakeholder perspectives and the typical characteristics of data warehouse systems. We discuss traditional requirements management activities (change control, version control, status tracking, requirements tracing) in the context of data warehouse environments and extend these traditional activities to support more efficient requirements documentation and visibility. Keywords: Data warehousing, requirements engineering, requirements managemen

Towards a framework for developing visual analytics in supply chain environments

Visual Analytics (VA) has shown to be of significant importance for Supply Chain (SC) analytics. However, SC partners still have challenges incorporating it into their data-driven decision-making activities. A conceptual framework for the development and deployment of a VA system provides an abstract, platform-independent model for the whole process of VA, covering requirement specification, data collection and pre-processing, visualization recommendation, visualization specification and implementation, and evaluations. In this paper, we propose such a framework based on three main aspects: 1) Business view, 2) Asset view, and 3) Technology view. Each of these views covers a set of steps to facilitate the development and maintenance of the system in its context. The framework follows a consistent process structure that comprises activities, tasks, and people. The final output of the whole process is the VA as a deliverable. This facilitates the alignment of VA activities with business processes and decision-making activities. We presented the framework's applicability using an actual usage scenario and left the implementation of the system for future work

Towards a framework for developing visual analytics in supply chain environments

Visual Analytics (VA) has shown to be of significant importance for Supply Chain (SC) analytics. However, SC partners still have challenges incorporating it into their data-driven decision-making activities. A conceptual framework for the development and deployment of a VA system provides an abstract, platform-independent model for the whole process of VA, covering requirement specification, data collection and pre-processing, visualization recommendation, visualization specification and implementation, and evaluations. In this paper, we propose such a framework based on three main aspects: 1) Business view, 2) Asset view, and 3) Technology view. Each of these views covers a set of steps to facilitate the development and maintenance of the system in its context. The framework follows a consistent process structure that comprises activities, tasks, and people. The final output of the whole process is the VA as a deliverable. This facilitates the alignment of VA activities with business processes and decision-making activities. We presented the framework\u27s applicability using an actual usage scenario and left the implementation of the system for future work

Towards Design Principles for Data-Driven Decision Making: An Action Design Research Project in the Maritime Industry

Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level. Yet, the path from data to value is not always an easy one and various organizational and psychological factors mediate and moderate the translation of data-driven insights into better decisions and, subsequently, effective business actions. The current body of academic literature on DDD lacks prescriptive knowledge on how to successfully employ DDD in complex organizational settings. Against this background, this paper reports on an action design research study aimed at designing and implementing IT artifacts for DDD at one of the largest ship engine manufacturers in the world. Our main contribution is a set of design principles highlighting, besides decision quality, the importance of model comprehensibility, domain knowledge, and actionability of results