An infinite adaptive online learning model for segmentation and classification of streaming data

© 2014 IEEE. In recent years, the desire and need to understand streaming data has been increasing. Along with the constant flow of data, it is critical to classify and segment the observations on-the-fly without being limited to a rigid number of classes. In other words, the system needs to be adaptive to the streaming data and capable of updating its parameters to comply with natural changes. This interesting problem, however, is poorly addressed in the literature, as many of the common studies focus on offline classification over a pre-defined class set. In this paper, we propose a novel adaptive online system based on Markov switching models with hierarchical Dirichlet process priors. This infinite adaptive online approach is capable of segmenting and classifying the streaming data over infinite classes, while meeting the memory and delay constraints of streaming contexts. The model is further enhanced by a 'predictive batching' mechanism, that is able to divide the flowing data into batches of variable size, imitating the ground-truth segments. Experiments on two video datasets show significant performance of the proposed approach in frame-level accuracy, segmentation recall and precision, while determining the accurate number of classes in acceptable computational time

AdOn HDP-HMM: An Adaptive Online Model for Segmentation and Classification of Sequential Data

© 2012 IEEE. Recent years have witnessed an increasing need for the automated classification of sequential data, such as activities of daily living, social media interactions, financial series, and others. With the continuous flow of new data, it is critical to classify the observations on-the-fly and without being limited by a predetermined number of classes. In addition, a model should be able to update its parameters in response to a possible evolution in the distributions of the classes. This compelling problem, however, does not seem to have been adequately addressed in the literature, since most studies focus on offline classification over predefined class sets. In this paper, we present a principled solution for this problem based on an adaptive online system leveraging Markov switching models and hierarchical Dirichlet process priors. This adaptive online approach is capable of classifying the sequential data over an unlimited number of classes while meeting the memory and delay constraints typical of streaming contexts. In this paper, we introduce an adaptive 'learning rate' that is responsible for balancing the extent to which the model retains its previous parameters or adapts to new observations. Experimental results on stationary and evolving synthetic data and two video data sets, TUM Assistive Kitchen and collated Weizmann, show a remarkable performance in terms of segmentation and classification, particularly for sequences from evolutionary distributions and/or those containing previously unseen classes

Software versus IT service: A comparative study from requirements engineering perspective

Services are ubiquitous and are a critical element of the modern world economies. They play an increasingly important role in most industry sectors including Information Technology (IT). The essential differences between a software product and an IT Service are unclear. IT services are seldom offered in isolation and are often packaged and provided with software products. More so than before, IT service providers need to conduct a rigorous and systematic elicitation, and analysis of the needs and requirements of the intended users so that the resulting IT services are closely aligned with the enterprise business needs. This paper presents a comparative study of software and IT services from the Requirements Engineering (RE) perspective; both from process and product viewpoints. The overall aim of this compare and contrast is to investigate key similarities and differences between software and IT services in order to ascertain the applicability and relevance of RE research and practice results for eliciting, analyzing, documenting and managing IT services. © 2011 IEEE