International Journal of Information Technology & Decision Making

This paper first explores the decision-making process in agile teams using scrum practices and second identifies factors that influence the decision-making process during the Sprint Planning and Daily Scrum Meetings. We conducted 34 semi-structured interviews and 18 observations across four agile teams. Our findings show that a rational decision-making process is sometimes followed in the Sprint Planning and Daily Scrum Meetings and that three factors can influence the rational decision-making process: sprint duration, experience and resource availability. Additionally, decisions are not always made in a collaborative manner by team members. This research contributes to the decision-making literature and project management literature by highlighting difficulties pertinent to decision making in agile teams.

Risk management in global information technology (IT) projects is becoming a critical area of concern for practitioners. Global IT projects usually span multiple locations involving various culturally diverse groups that use multiple standards and technologies. These multiplicities cause dynamic risks through interactions among internal (i.e., people, process, and technology) and external elements (i.e., business and natural environments) of global IT projects. This study proposes an agile risk-management framework for global IT project settings. By analyzing the dynamic interactions among multiplicities (e.g., multi-locations, multi-cultures, multi-groups, and multi-interests) embedded in the project elements, we identify the dynamic risks threatening the success of a global IT project. Adopting the principles of service-oriented architecture (SOA), we further propose a set of agile management strategies for mitigating the dynamic risks. The mitigation strategies are conceptually validated. The proposed framework will help practitioners understand the potential risks in their global IT projects and resolve their complex situations when certain types of dynamic risks arise.

With the increased interest among organizations in managing Information Technology (IT) projects as a portfolio, there has been a growing need for research on factors influencing adoption of IT Project Portfolio Management (ITPPM). ITPPM allows organizations to improve the alignment of IT projects with organizational strategy, by balancing the risks and returns from inter-related projects. To contribute to our understanding of ITPPM, this study examines the adoption of ITPPM through the theoretical lens of diffusion of innovation. Using the technology, organization, and environment (TOE) framework, we model ITPPM adoption and test our model using survey data from 302 project managers. Our results show that an organization's external pressure, ITPPM costs, organizational support, quality of data on existing IT projects, number of IT projects within the organization, and business resistance are significantly related to the adoption decision. We also find interesting results in how expected benefits of ITPPM and compatibility are perceived as equally important by both adopters and nonadopters. In light of the increasingly complex IT project management environment faced by organizations, our paper helps managers to understand and focus on key enablers of ITPPM adoption and better manage ITPPM inhibitors.

A multi-class support vector machine (M-SVM) is developed, its dual is derived, its dual is mapped to high dimensional feature spaces using inner product kernels, and its performance is tested. The M-SVM is formulated as a quadratic programming model. Its dual, also a quadratic programming model, is very elegant and is easier to solve than the primal. The discriminant functions can be directly constructed from the dual solution. By using inner product kernels, the M-SVM can be built and nonlinear discriminant functions can be constructed in high dimensional feature spaces without carrying out the mappings from the input space to the feature spaces. The size of the dual, measured by the number of variables and constraints, is independent of the dimension of the input space and stays the same whether the M-SVM is built in the input space or in a feature space. Compared to other models published in the literature, this M-SVM is equally or more effective. An example is presented to demonstrate the dual formulation and solution in feature spaces. Very good results were obtained on benchmark test problems from the literature.

Based on the leximin and leximax preferences, we consider two threshold preference relations on the set X of alternatives, each of which is characterized by an n-dimensional vector (n ≥ 2) with integer components varying between 1 and m(m ≥ 2). We determine explicitly in terms of binomial coefficients the unique utility function for each of the two relations, which in addition maps X onto the natural 'interval' , where is the quotient set of X with respect to the indifference relation I on X induced by the threshold preference. This permits us to evaluate all equivalence classes and indifference classes of the threshold order on X, present an algorithm of ordering the monotone representatives of indifference classes, and restore the indifference class of an alternative via its ordinal number with respect to the threshold preference order.

A data warehouse (DW) is a large data repository system designed for decision-making purposes. Its design relies on a specific model called multidimensional. This multidimensional model supports analyses of huge volumes of data that trace the enterprise's activities over time. Several design methods were proposed to build multidimensional schemas from either the relational data model or the entity-relationship data model. Almost all proposals that treated the object-oriented data model assume the presence of the data source UML class-diagram. However, in practice, either such a diagram does not exist or is obsolete due to multiple changes/evolutions of the information system. Furthermore, these few proposals require an intense manual intervention of the designer, which requires a high expertise both in the DW domain and in the object database domain. To overcome these disadvantages, this work proposes an automatic DW schema design method starting from an object database (schema and its instances). This method applies a set of extraction rules to identify multidimensional concepts and to generate star schemas. It is defined for the standard ODMG model and, thus, can be adapted with slight changes for other object database models. In addition, its extraction rules have the merit of being independent of the domain semantics. Furthermore, they automatically generate schemas classified according to their analytical potential; this classification helps the DW designer in selecting the most relevant schemas among the generated ones. Finally, being automatic, our method is supported by a toolset that also prepares for the automatic generation of the Extract Transform and Load procedures used to load the DW.

We introduce a new method for stock keeping unit (SKU)-store level sales prediction in the presence of promotions to support order quantity and promotion planning decisions for retail managers. The method leverages the marketing literature to generate features, and data mining techniques to train a model that provides accurate sales predictions for existing and new SKUs, as well as consistent, actionable insights into category, store and promotion dynamics. The proposed "Driver Moderator" method uses basic SKU-store information and historical sales and promotion data to generate many features. It simultaneously selects few relevant features and estimates their parameters by using an L1-norm regularized epsilon insensitive regression that is formulated to pool information across SKUs and stores. Evaluations on two grocery store databases from Turkey and the USA show that out-of-sample predictions for existing and new SKUs are as good as, or more accurate than benchmark methods. Using the method's predictions for inventory decisions doubles the inventory turn ratio versus using individual regressions by lowering lost sales and inventory levels at the same time.

In ubiquitous data stream mining, different devices often aim to learn concepts that are similar to some extent. In many applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy. Coll-Stream integrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluate Coll-Stream classification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show that Coll-Stream resultant model achieves stability and accuracy in a variety of situations using both synthetic and real-world datasets.

This paper presents a novel model for scoring web pages, entitled SCOPAS (Semantic COmputation of PAge Score). With the prolific growth in the number of users of World Wide Web and the heterogeneity of their information needs, it becomes mandatory to evaluate the relevance of a web page in terms of user specific requirements. SCOPAS is aimed at modeling the web pages to facilitate efficient evaluation by harnessing the inherent features of the page in terms of its content and structure. The proposed model further enriches the scoring procedure by fine-graining the evaluation to a micro level through segmentation of the page. A variable magnitude, multi-dimensional approach is proposed for evaluating each of the segments by incorporating the relevance of intra-segment level components. The user-interest is captured with the help of FOAF (Friend Of A Friend) Ontology to achieve personalized page scoring. The generic SCOPAS model is extended to SCOPAS-Rank, which explores utilization of the model in improving the web search engine's result ordering. A prototype implementation of the proposed SCOPAS-Rank model is developed and experiments were conducted on it. The results of the experiments validate the effectiveness of the proposed model.

Product Development Process (PDP) has been recognized as a source of competitive profits, and thus, it has received increasing attention. The existing methods for monitoring PDP performance, however, are either cumbersome or fail to connect the competitive priorities to the performance management process. Accordingly, in this paper, a model to evaluate PDP performance is considered as a multi-criteria decision-making problem; and a solution method, based on DEMATEL and ANP, is proposed. The evaluation criteria set was developed using the concept of competitive priorities, and the overall model was applied to an actual data set from a group of machine manufacturers in Turkey. The use scenarios for the model are also discussed.