In the software development industry, technical debt is regarded as a critical issue in term of the negative consequences such as increased software development cost, low product quality, decreased maintainability, and slowed progress to the long-term success of developing software. However, despite the vast research contributions in technical debt management for software engineering, the idea of technical debt fails to provide a holistic consideration to include both IT and business aspects. Further, implementing an enterprise architecture (EA) project might not always be a success due to uncertainty and unavailability of resources. Therefore, we relate the consequences of EA implementation failure with a new metaphor --Enterprise Architecture Debt (EA Debt). We anticipate that the accumulation of EA Debt will negatively influence EA quality, also expose the business into risk.
Since many aspects influence the system's properties a unified meta model is difficult to create. However, @cite_15 show that even different enterprise orientations with divergent focuses prefer certain qualities over others. In general, they show that the striven qualities differ across enterprises, nevertheless, there are some general qualities that are desired in most situations. Thus, a specialized prioritization and adaptation is needed for the EA and its IT business alignment.
@cite_15
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Enterprise Architecture models can be used to support IT business alignment. However, existing approaches do not distinguish between different IT business alignment situations. Since companies face diverse challenges in achieving a high degree of IT business alignment, a universal ‘one size fits all’ approach does not seem appropriate. This paper proposes to decompose the IT business alignment problem into tangible qualities for business, IT systems, and IT governance. An explorative study among 162 professionals is used to distinguish four IT business alignment situations, i.e. four clusters of IT business alignment problems. These situations each represent the current state according to certain qualities and also the priorities for future development. In order to increase IT business alignment, enterprise architecture meta models are proposed for each identified situation. One core meta model (to reflect common priorities) as well as situation specific extensions are presented. This article presents a model for projects that have to adhere to Enterprise Architecture (EA) in order for their results to be aligned with the broader organization. The model features project artifacts (i.e. deliverables such as Software Architecture Documents), their mutual relationships, their relationship with EA, and the processes in which they are created and tested on conformance. We start with applying Activity Theory to show the crucial mediating role that artifacts have in projects and to identify and justify the new EA-related artifacts we introduce. We subsequently incorporate these findings and existing best practices in a standard systems development approach in order to create a practical model that projects can apply for EA conformance. This model features both new, dedicated EA artifacts, and well-known existing artifacts of which we describe the way they should conform to EA. Finally, two action research studies are used to empirically support the model. Customer preferences for products are drifting over time. Product perception and popularity are constantly changing as new selection emerges. Similarly, customer inclinations are evolving, leading them to ever redefine their taste. Thus, modeling temporal dynamics should be a key when designing recommender systems or general customer preference models. However, this raises unique challenges. Within the eco-system intersecting multiple products and customers, many different characteristics are shifting simultaneously, while many of them influence each other and often those shifts are delicate and associated with a few data instances. This distinguishes the problem from concept drift explorations, where mostly a single concept is tracked. Classical time-window or instance-decay approaches cannot work, as they lose too much signal when discarding data instances. A more sensitive approach is required, which can make better distinctions between transient effects and long term patterns. The paradigm we offer is creating a model tracking the time changing behavior throughout the life span of the data. This allows us to exploit the relevant components of all data instances, while discarding only what is modeled as being irrelevant. Accordingly, we revamp two leading collaborative filtering recommendation approaches. Evaluation is made on a large movie rating dataset by Netflix. Results are encouraging and better than those previously reported on this dataset. Customer preferences for products are drifting over time. Product perception and popularity are constantly changing as new selection emerges. Similarly, customer inclinations are evolving, leading them to ever redefine their taste. Thus, modeling temporal dynamics is essential for designing recommender systems or general customer preference models. However, this raises unique challenges. Within the ecosystem intersecting multiple products and customers, many different characteristics are shifting simultaneously, while many of them influence each other and often those shifts are delicate and associated with a few data instances. This distinguishes the problem from concept drift explorations, where mostly a single concept is tracked. Classical time-window or instance decay approaches cannot work, as they lose too many signals when discarding data instances. A more sensitive approach is required, which can make better distinctions between transient effects and long-term patterns. We show how to model the time changing behavior throughout the life span of the data. Such a model allows us to exploit the relevant components of all data instances, while discarding only what is modeled as being irrelevant. Accordingly, we revamp two leading collaborative filtering recommendation approaches. Evaluation is made on a large movie-rating dataset underlying the Netflix Prize contest. Results are encouraging and better than those previously reported on this dataset. In particular, methods described in this paper play a significant role in the solution that won the Netflix contest.