Journal: IEEE Transactions on Services Computing

Abbreviation

Publisher

IEEE

Journal Volumes

ISSN

1939-1374

Description

Filters

2010 - 201912020 - 20232
Reset filters

Search Results

Publications 1 - 3 of 3
  • A Transformational Approach to Managing Data Model Evolution of Web Services
    Item type: Journal Article
    Beurer-Kellner, Luca; von Pilgrim, Jens; Tsigkanos, Christos; et al. (2023)
    IEEE Transactions on Services Computing
    The communication of web services is typically organized through public APIs, which rely on a common data model shared among all system components. Over time, this data model must be changed in order to accommodate new or changing requirements, and the system components including the data they are operating on must be migrated. In practice, however, not all the affected components can be migrated instantly and at the same time. A common approach is to plan data model changes in a backward compatible fashion, which eventually causes serious maintenance problems and is a common cause of technical debt. In this paper, we propose an alternative solution to this problem by using a translation layer serving as a round-trip migration service which is responsible for the lossless forth-and-back translation of object-oriented data model instances of different versions. We present a framework which offers a version-aware interface definition language (IDL) for APIs, a typed JavaScript-based language for defining migration functions using the IDL definition, and a run-time environment for executing migrations. This is bundled into an integrated development environment assisting developers in implementing migration functions. From a methodological point of view, the development of round-trip migrations is supported by a catalog which comprises a set of typical data model evolution scenarios along with corresponding suitable round-trip migration strategies. We validate our framework by carrying out an extensive evaluation including a systematic assessment of expressiveness using our catalog, micro-benchmarking the performance of round-trip migrations, as well as a practical application in a case study of a real-world e-commerce web application obtained from an industrial partner.
  • Interacting with the SOA-Based Internet of Things
    Item type: Journal Article
    Guinard, Dominique; Trifa, Vlad; Karnouskos, Stamatis; et al. (2010)
    IEEE Transactions on Services Computing
  • Gradient Boosted Neural Decision Forest
    Item type: Journal Article
    Dong, Manqing; Yao, Lina; Wang, Xianzhi; et al. (2023)
    IEEE Transactions on Services Computing
    Tree-based models and deep neural networks are two schools of effective classification methods in machine learning. While tree-based models are robust irrespective of data domain, deep neural networks have advantages in handling high-dimensional data. Adding a differentiable neural decision forest to the neural network can generally help exploit the benefits of both models. Therefore, traditional decision trees diverge into a bagging version (i.e., random forest) and a boosting version (i.e., gradient boost decision tree). In this work, we aim to harness the advantages of both bagging and boosting by applying gradient boost to a neural decision forest. We propose a gradient boost that can learn the residual using neural decision forest, considering the residual as a part for the final prediction. Besides, we design a structure for learning the parameters of neural decision forest and gradient boost module in contiguous steps, which is extendable to incorporate multiple gradient-boosting modules in an end-to-end manner. Our extensive experiments on several public datasets demonstrate the competitive performance and efficiency of our model against a series of baseline methods in solving various machine learning tasks.
Publications 1 - 3 of 3