Refinement of SDBC Business Process Models Using ISDL

Aiming at aligning business process modeling and software specification, the SDBC approach considers a multi-viewpoint modeling where static, dynamic, and data business process aspect models have to be mapped adequately to corresponding static, dynamic, and data software specification aspect models. Next to that, the approach considers also a business process modeling viewpoint which concerns real-life communication and coordination issues, such as meanings, intentions, negotiations, commitments, and obligations. Hence, in order to adequately align communication and dynamic aspect models, SDBC should use at least two modeling techniques. However, the transformation between two techniques unnecessarily complicates the modeling process. Next to that, different techniques use different modeling formalisms whose reflection sometimes causes limitations. For this reason, we explore in the current paper the value which the (modeling) language ISDL could bring to SDBC in the alignment of communication and behavioral (dynamic) business process aspect models; ISDL can usefully refine dynamic process models. Thus, it is feasible to expect that ISDL can complement the SDBC approach, allowing refinement of dynamic business process aspect models, by adding communication and coordination actions. Furthermore, SDBC could benefit from ISDL-related methods assessing whether a realized refinement conforms to the original process model. Our studies in the paper are supported by an illustrative example

Fuzzy Interval-Valued Multi Criteria Based Decision Making for Ranking Features in Multi-Modal 3D Face Recognition

Soodamani Ramalingam, 'Fuzzy interval-valued multi criteria based decision making for ranking features in multi-modal 3D face recognition', Fuzzy Sets and Systems, In Press version available online 13 June 2017. This is an Open Access paper, made available under the Creative Commons license CC BY 4.0 https://creativecommons.org/licenses/by/4.0/This paper describes an application of multi-criteria decision making (MCDM) for multi-modal fusion of features in a 3D face recognition system. A decision making process is outlined that is based on the performance of multi-modal features in a face recognition task involving a set of 3D face databases. In particular, the fuzzy interval valued MCDM technique called TOPSIS is applied for ranking and deciding on the best choice of multi-modal features at the decision stage. It provides a formal mechanism of benchmarking their performances against a set of criteria. The technique demonstrates its ability in scaling up the multi-modal features.Peer reviewedProo

Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales

Predicting the class of a customer profile is a key task in marketing, which enables businesses to approach the right customer with the right product at the right time through the right channel to satisfy the customer's evolving needs. However, due to costs, privacy and/or data protection, only the business' owned transactional data is typically available for constructing customer profiles. Predicting the class of customer profiles based on such data is challenging, as the data tends to be very large, heavily sparse and highly skewed. We present a new approach that is designed to efficiently and accurately handle the multi-class classification of customer profiles built using sparse and skewed transactional data. Our approach first bins the customer profiles on the basis of the number of items transacted. The discovered bins are then partitioned and prototypes within each of the discovered bins selected to build the multi-class classifier models. The results obtained from using four multi-class classifiers on real-world transactional data from the food sales domain consistently show the critical numbers of items at which the predictive performance of customer profiles can be substantially improved

COSMOS-7: Video-oriented MPEG-7 scheme for modelling and filtering of semantic content

MPEG-7 prescribes a format for semantic content models for multimedia to ensure interoperability across a multitude of platforms and application domains. However, the standard leaves it open as to how the models should be used and how their content should be filtered. Filtering is a technique used to retrieve only content relevant to user requirements, thereby reducing the necessary content-sifting effort of the user. This paper proposes an MPEG-7 scheme that can be deployed for semantic content modelling and filtering of digital video. The proposed scheme, COSMOS-7, produces rich and multi-faceted semantic content models and supports a content-based filtering approach that only analyses content relating directly to the preferred content requirements of the user