A Multi-Scan Labeled Random Finite Set Model for Multi-object State Estimation

State space models in which the system state is a finite set--called the multi-object state--have generated considerable interest in recent years. Smoothing for state space models provides better estimation performance than filtering by using the full posterior rather than the filtering density. In multi-object state estimation, the Bayes multi-object filtering recursion admits an analytic solution known as the Generalized Labeled Multi-Bernoulli (GLMB) filter. In this work, we extend the analytic GLMB recursion to propagate the multi-object posterior. We also propose an implementation of this so-called multi-scan GLMB posterior recursion using a similar approach to the GLMB filter implementation

CRM packaged software: a study of organisational experiences

Customer Relationship Management (CRM) packaged software has become a key contributor to attempts at aligning business and IT strategies in recent years. Throughout the 1990s there was, in many organisations strategies, a shift from the need to manage transactions and toward relationship management. Where Enterprise Resource Planning packages dominated the management of transactions era, CRM packages lead in regard to relationships. At present, balanced views of CRM packages are scantly presented instead relying on vendor rhetoric. This paper uses case study research to analyse some of the issues associated with CRM packages. These issues include the limitations of CRM packages, the need for a relationship orientation and the problems of a dominant management perspective of CRM. It is suggested that these issues could be more readily accommodated by organisational detachment from beliefs in IT as utopia, consideration of prior IS theory and practice and a more informed approach to CRM package selection